WorldWideScience

Sample records for automated content-based image

  1. Automated and effective content-based image retrieval for digital mammography.

    Science.gov (United States)

    Singh, Vibhav Prakash; Srivastava, Subodh; Srivastava, Rajeev

    2018-01-01

    Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

  2. Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs.

    Science.gov (United States)

    Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Bekri, Lynda; Daccache, Wissam; Roux, Christian; Cochener, Béatrice

    2011-10-21

    Recent studies on diabetic retinopathy (DR) screening in fundus photographs suggest that disagreements between algorithms and clinicians are now comparable to disagreements among clinicians. The purpose of this study is to (1) determine whether this observation also holds for automated DR severity assessment algorithms, and (2) show the interest of such algorithms in clinical practice. A dataset of 85 consecutive DR examinations (168 eyes, 1176 multimodal eye fundus photographs) was collected at Brest University Hospital (Brest, France). Two clinicians with different experience levels determined DR severity in each eye, according to the International Clinical Diabetic Retinopathy Disease Severity (ICDRS) scale. Based on Cohen's kappa (κ) measurements, the performance of clinicians at assessing DR severity was compared to the performance of state-of-the-art content-based image retrieval (CBIR) algorithms from our group. At assessing DR severity in each patient, intraobserver agreement was κ = 0.769 for the most experienced clinician. Interobserver agreement between clinicians was κ = 0.526. Interobserver agreement between the most experienced clinicians and the most advanced algorithm was κ = 0.592. Besides, the most advanced algorithm was often able to predict agreements and disagreements between clinicians. Automated DR severity assessment algorithms, trained to imitate experienced clinicians, can be used to predict when young clinicians would agree or disagree with their more experienced fellow members. Such algorithms may thus be used in clinical practice to help validate or invalidate their diagnoses. CBIR algorithms, in particular, may also be used for pooling diagnostic knowledge among peers, with applications in training and coordination of clinicians' prescriptions.

  3. Metadata for Content-Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Adrian Sterca

    2010-12-01

    Full Text Available This paper presents an image retrieval technique that combines content based image retrieval with pre-computed metadata-based image retrieval. The resulting system will have the advantages of both approaches: the speed/efficiency of metadata-based image retrieval and the accuracy/power of content-based image retrieval.

  4. Material Recognition for Content Based Image Retrieval

    NARCIS (Netherlands)

    Geusebroek, J.M.

    2002-01-01

    One of the open problems in content-based Image Retrieval is the recognition of material present in an image. Knowledge about the set of materials present gives important semantic information about the scene under consideration. For example, detecting sand, sky, and water certainly classifies the

  5. Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.

    Science.gov (United States)

    Sklan, Judah E S; Plassard, Andrew J; Fabbri, Daniel; Landman, Bennett A

    2015-03-19

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  6. Enhancing Image Retrieval System Using Content Based Search ...

    African Journals Online (AJOL)

    ... performing the search on the entire image database, the image category option directs the retrieval engine to the specified category. Also, there is provision to update or modify the different image categories in the image database as need arise. Keywords: Content-based, Multimedia, Search Engine, Image-based, Texture ...

  7. Teleconsultations using content-based retrieval of parametric images.

    Science.gov (United States)

    Ruminski, J

    2004-01-01

    The problem of medical teleconsultations with intelligent computer system rather than with a human expert is analyzed. System for content-based retrieval of images is described and presented as a use case of a passive teleconsultation. Selected features, crucial for retrieval quality, are introduced including: synthesis of parametric images, regions of interest detection and extraction, definition of content-based features, generation of descriptors, query algebra, system architecture and performance. Additionally, electronic business pattern is proposed to generalize teleconsultation services like content-based retrieval systems.

  8. Human-Centered Content-Based Image Retrieval

    NARCIS (Netherlands)

    van den Broek, Egon

    2005-01-01

    Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image

  9. IMAGE RETIEVAL COLOR, SHAPE AND TEXTURE FEATURES USING CONTENT BASED

    OpenAIRE

    K. NARESH BABU,; SAKE. POTHALAIAH; Dr.K ASHOK BABU

    2010-01-01

    Content-based image retrieval (CBIR) is an important research area for manipulating large amount of image databases and archives. Extraction of invariant features is the basis of CBIR. This paper focuses on the problem of texture, color& shape feature extractions. Using just one feature information for comparing images may cause inaccuracy than compared with using more than one features. Therefore many image retrieval system use many feature information like color, shape and other features. W...

  10. AN INTELLIGENT CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR MAMMOGRAM IMAGE ANALYSIS

    Directory of Open Access Journals (Sweden)

    K. VAIDEHI

    2015-11-01

    Full Text Available An automated segmentation method which dynamically selects the parenchymal region of interest (ROI based on the patients breast size is proposed from which, statistical features are derived. SVM classifier is used to model the derived features to classify the breast tissue as dense, glandular and fatty. Then K-nn with different distance metrics namely city-block, Euclidean and Chebchev is used to retrieve the first k similar images closest to the given query image. The proposed method was tested with MIAS database and achieves an average precision of 86.15%. The results reveals that the proposed method could be employed for effective content based mammograms retrieval.

  11. Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

    Science.gov (United States)

    Gururaj, C.; Jayadevappa, D.; Tunga, Satish

    2018-02-01

    Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

  12. Human-Centered Content-Based Image Retrieval

    NARCIS (Netherlands)

    van den Broek, Egon; Kok, Thijs; Schouten, Theo E.; Vuurpijl, Louis G.; Rogowitz, Bernice E.; Pappas, Thrasyvoulos N.

    2008-01-01

    A breakthrough is needed in order to achieve a substantial progress in the field of Content-Based Image Retrieval (CBIR). This breakthrough can be enforced by: 1) optimizing user-system interaction, 2) combining the wealth of techniques from text-based Information Retrieval with CBIR techniques, 3)

  13. Content-Based Image Retrial Based on Hadoop

    Directory of Open Access Journals (Sweden)

    DongSheng Yin

    2013-01-01

    Full Text Available Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

  14. Retrieval Architecture with Classified Query for Content Based Image Recognition

    Directory of Open Access Journals (Sweden)

    Rik Das

    2016-01-01

    Full Text Available The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

  15. The Use of QBIC Content-Based Image Retrieval System

    Directory of Open Access Journals (Sweden)

    Ching-Yi Wu

    2004-03-01

    Full Text Available The fast increase in digital images has caught increasing attention on the development of image retrieval technologies. Content-based image retrieval (CBIR has become an important approach in retrieving image data from a large collection. This article reports our results on the use and users study of a CBIR system. Thirty-eight students majored in art and design were invited to use the IBM’s OBIC (Query by Image Content system through the Internet. Data from their information needs, behaviors, and retrieval strategies were collected through an in-depth interview, observation, and self-described think-aloud process. Important conclusions are:(1)There are four types of information needs for image data: implicit, inspirational, ever-changing, and purposive. The types of needs may change during the retrieval process. (2)CBIR is suitable for the example-type query, text retrieval is suitable for the scenario-type query, and image browsing is suitable for the symbolic query. (3)Different from text retrieval, detailed description of the query condition may lead to retrieval failure more easily. (4)CBIR is suitable for the domain-specific image collection, not for the images on the Word-Wide Web.[Article content in Chinese

  16. Content based image retrieval based on wavelet transform coefficients distribution.

    Science.gov (United States)

    Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

    2007-01-01

    In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process.

  17. Content Based Image Retrieval based on Wavelet Transform coefficients distribution

    Science.gov (United States)

    Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

    2007-01-01

    In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

  18. Content-Based Image Retrieval for Semiconductor Process Characterization

    Directory of Open Access Journals (Sweden)

    Kenneth W. Tobin

    2002-07-01

    Full Text Available Image data management in the semiconductor manufacturing environment is becoming more problematic as the size of silicon wafers continues to increase, while the dimension of critical features continues to shrink. Fabricators rely on a growing host of image-generating inspection tools to monitor complex device manufacturing processes. These inspection tools include optical and laser scattering microscopy, confocal microscopy, scanning electron microscopy, and atomic force microscopy. The number of images that are being generated are on the order of 20,000 to 30,000 each week in some fabrication facilities today. Manufacturers currently maintain on the order of 500,000 images in their data management systems for extended periods of time. Gleaning the historical value from these large image repositories for yield improvement is difficult to accomplish using the standard database methods currently associated with these data sets (e.g., performing queries based on time and date, lot numbers, wafer identification numbers, etc.. Researchers at the Oak Ridge National Laboratory have developed and tested a content-based image retrieval technology that is specific to manufacturing environments. In this paper, we describe the feature representation of semiconductor defect images along with methods of indexing and retrieval, and results from initial field-testing in the semiconductor manufacturing environment.

  19. Content-based histopathology image retrieval using CometCloud.

    Science.gov (United States)

    Qi, Xin; Wang, Daihou; Rodero, Ivan; Diaz-Montes, Javier; Gensure, Rebekah H; Xing, Fuyong; Zhong, Hua; Goodell, Lauri; Parashar, Manish; Foran, David J; Yang, Lin

    2014-08-26

    The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two

  20. Biased discriminant euclidean embedding for content-based image retrieval.

    Science.gov (United States)

    Bian, Wei; Tao, Dacheng

    2010-02-01

    With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.

  1. Using deep learning for content-based medical image retrieval

    Science.gov (United States)

    Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

    2017-03-01

    Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

  2. Automating the construction of scene classifiers for content-based video retrieval

    NARCIS (Netherlands)

    Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

    2004-01-01

    This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

  3. A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

    Science.gov (United States)

    Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

    2015-03-01

    This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

  4. Toward content-based image retrieval with deep convolutional neural networks

    Science.gov (United States)

    Sklan, Judah E. S.; Plassard, Andrew J.; Fabbri, Daniel; Landman, Bennett A.

    2015-03-01

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128x128 to an output encoded layer of 4x384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This preliminary effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  5. Indexing, learning and content-based retrieval for special purpose image databases

    NARCIS (Netherlands)

    M.J. Huiskes (Mark); E.J. Pauwels (Eric)

    2005-01-01

    textabstractThis chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current

  6. Design Guidelines for a Content-Based Image Retrieval Color-Selection Interface

    NARCIS (Netherlands)

    Eggen, Berry; van den Broek, Egon; van der Veer, Gerrit C.; Kisters, Peter M.F.; Willems, Rob; Vuurpijl, Louis G.

    2004-01-01

    In Content-Based Image Retrieval (CBIR) two query-methods exist: query-by-example and query-by-memory. The user either selects an example image or selects image features retrieved from memory (such as color, texture, spatial attributes, and shape) to define his query. Hitherto, research on CBIR

  7. Adapting content-based image retrieval techniques for the semantic annotation of medical images.

    Science.gov (United States)

    Kumar, Ashnil; Dyer, Shane; Kim, Jinman; Li, Changyang; Leong, Philip H W; Fulham, Michael; Feng, Dagan

    2016-04-01

    The automatic annotation of medical images is a prerequisite for building comprehensive semantic archives that can be used to enhance evidence-based diagnosis, physician education, and biomedical research. Annotation also has important applications in the automatic generation of structured radiology reports. Much of the prior research work has focused on annotating images with properties such as the modality of the image, or the biological system or body region being imaged. However, many challenges remain for the annotation of high-level semantic content in medical images (e.g., presence of calcification, vessel obstruction, etc.) due to the difficulty in discovering relationships and associations between low-level image features and high-level semantic concepts. This difficulty is further compounded by the lack of labelled training data. In this paper, we present a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR). CBIR is a well-established image search technology that uses quantifiable low-level image features to represent the high-level semantic content depicted in those images. Our method extends CBIR techniques to identify or retrieve a collection of labelled images that have similar low-level features and then uses this collection to determine the best high-level semantic annotations. We demonstrate our annotation method using retrieval via weighted nearest-neighbour retrieval and multi-class classification to show that our approach is viable regardless of the underlying retrieval strategy. We experimentally compared our method with several well-established baseline techniques (classification and regression) and showed that our method achieved the highest accuracy in the annotation of liver computed tomography (CT) images. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Dhanoa Jaspreet Singh

    2016-01-01

    Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

  9. Use of a JPEG-2000 Wavelet Compression Scheme for Content-Based Ophtalmologic Retinal Images Retrieval.

    Science.gov (United States)

    Lamard, Mathieu; Daccache, Wissam; Cazuguel, Guy; Roux, Christian; Cochener, Beatrice

    2005-01-01

    In this paper we propose a content based image retrieval method for diagnosis aid in diabetic retinopathy. We characterize images without extracting significant features, and use histograms obtained from the compressed images in JPEG-2000 wavelet scheme to build signatures. The research is carried out by calculating signature distances between the query and database images. A weighted distance between histograms is used. Retrieval efficiency is given for different standard types of JPEG-2000 wavelets, and for different values of histogram weights. A classified diabetic retinopathy image database is built allowing algorithms tests. On this image database, results are promising: the retrieval efficiency is higher than 70% for some lesion types.

  10. Content-Based Image Retrieval Benchmarking: Utilizing color categories and color distributions

    NARCIS (Netherlands)

    van den Broek, Egon; Kisters, Peter M.F.; Vuurpijl, Louis G.

    From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color

  11. Content based image retrieval using local binary pattern operator and data mining techniques.

    Science.gov (United States)

    Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

    2015-01-01

    Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

  12. Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

    International Nuclear Information System (INIS)

    Xu Jiajing; Napel, Sandy; Greenspan, Hayit; Beaulieu, Christopher F.; Agrawal, Neeraj; Rubin, Daniel

    2012-01-01

    . Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests. Results: In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors’ proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors’ feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets. Conclusions: The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

  13. Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

    Science.gov (United States)

    Jiexian, Zeng; Xiupeng, Liu

    2014-01-01

    Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise. PMID:24883416

  14. Automated ship image acquisition

    Science.gov (United States)

    Hammond, T. R.

    2008-04-01

    The experimental Automated Ship Image Acquisition System (ASIA) collects high-resolution ship photographs at a shore-based laboratory, with minimal human intervention. The system uses Automatic Identification System (AIS) data to direct a high-resolution SLR digital camera to ship targets and to identify the ships in the resulting photographs. The photo database is then searchable using the rich data fields from AIS, which include the name, type, call sign and various vessel identification numbers. The high-resolution images from ASIA are intended to provide information that can corroborate AIS reports (e.g., extract identification from the name on the hull) or provide information that has been omitted from the AIS reports (e.g., missing or incorrect hull dimensions, cargo, etc). Once assembled into a searchable image database, the images can be used for a wide variety of marine safety and security applications. This paper documents the author's experience with the practicality of composing photographs based on AIS reports alone, describing a number of ways in which this can go wrong, from errors in the AIS reports, to fixed and mobile obstructions and multiple ships in the shot. The frequency with which various errors occurred in automatically-composed photographs collected in Halifax harbour in winter time were determined by manual examination of the images. 45% of the images examined were considered of a quality sufficient to read identification markings, numbers and text off the entire ship. One of the main technical challenges for ASIA lies in automatically differentiating good and bad photographs, so that few bad ones would be shown to human users. Initial attempts at automatic photo rating showed 75% agreement with manual assessments.

  15. Evaluation of shape indexing methods for content-based retrieval of x-ray images

    Science.gov (United States)

    Antani, Sameer; Long, L. Rodney; Thoma, George R.; Lee, Dah-Jye

    2003-01-01

    Efficient content-based image retrieval of biomedical images is a challenging problem of growing research interest. Feature representation algorithms used in indexing medical images on the pathology of interest have to address conflicting goals of reducing feature dimensionality while retaining important and often subtle biomedical features. At the Lister Hill National Center for Biomedical Communications, a R&D division of the National Library of Medicine, we are developing a content-based image retrieval system for digitized images of a collection of 17,000 cervical and lumbar x-rays taken as a part of the second National Health and Nutrition Examination Survey (NHANES II). Shape is the only feature that effectively describes various pathologies identified by medical experts as being consistently and reliably found in the image collection. In order to determine if the state of the art in shape representation methods is suitable for this application, we have evaluated representative algorithms selected from the literature. The algorithms were tested on a subset of 250 vertebral shapes. In this paper we present the requirements of an ideal algorithm, define the evaluation criteria, and present the results and our analysis of the evaluation. We observe that while the shape methods perform well on visual inspection of the overall shape boundaries, they fall short in meeting the needs of determining similarity between the vertebral shapes based on the pathology.

  16. Efficient content-based low-altitude images correlated network and strips reconstruction

    Science.gov (United States)

    He, Haiqing; You, Qi; Chen, Xiaoyong

    2017-01-01

    The manual intervention method is widely used to reconstruct strips for further aerial triangulation in low-altitude photogrammetry. Clearly the method for fully automatic photogrammetric data processing is not an expected way. In this paper, we explore a content-based approach without manual intervention or external information for strips reconstruction. Feature descriptors in the local spatial patterns are extracted by SIFT to construct vocabulary tree, in which these features are encoded in terms of TF-IDF numerical statistical algorithm to generate new representation for each low-altitude image. Then images correlated network is reconstructed by similarity measure, image matching and geometric graph theory. Finally, strips are reconstructed automatically by tracing straight lines and growing adjacent images gradually. Experimental results show that the proposed approach is highly effective in automatically rearranging strips of lowaltitude images and can provide rough relative orientation for further aerial triangulation.

  17. A Novel Optimization-Based Approach for Content-Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Manyu Xiao

    2013-01-01

    Full Text Available Content-based image retrieval is nowadays one of the possible and promising solutions to manage image databases effectively. However, with the large number of images, there still exists a great discrepancy between the users’ expectations (accuracy and efficiency and the real performance in image retrieval. In this work, new optimization strategies are proposed on vocabulary tree building, retrieval, and matching methods. More precisely, a new clustering strategy combining classification and conventional K-Means method is firstly redefined. Then a new matching technique is built to eliminate the error caused by large-scaled scale-invariant feature transform (SIFT. Additionally, a new unit mechanism is proposed to reduce the cost of indexing time. Finally, the numerical results show that excellent performances are obtained in both accuracy and efficiency based on the proposed improvements for image retrieval.

  18. Content Based medical image retrieval based on BEMD: optimization of a similarity metric.

    Science.gov (United States)

    Jai-Andaloussi, Said; Lamard, Mathieu; Cazuguel, Guy; Tairi, Hamid; Meknassi, Mohamed; Cochener, Beatrice; Roux, Christian

    2010-01-01

    Most medical images are now digitized and stored in patients files databases. The challenge is how to use them for acquiring knowledge or/and for aid to diagnosis. In this paper, we address the challenge of diagnosis aid by Content Based Image Retrieval (CBIR). We propose to characterize images by using the Bidimensional Empirical Mode Decomposition (BEMD). Images are decomposed into a set of functions named Bidimensional Intrinsic Mode Functions (BIMF). Two methods are used to characterize BIMFs information content: the Generalized Gaussian density functions (GGD) and the Huang-Hilbert transform (HHT). In order to enhance results, we introduce a similarity metric optimization process: weighted distances between BIMFs are adapted for each image in the database. Retrieval efficiency is given for different databases (DB), including a diabetic retinopathy DB, a mammography DB and a faces DB. Results are promising: the retrieval efficiency is higher than 95% for some cases.

  19. Parallel content-based sub-image retrieval using hierarchical searching.

    Science.gov (United States)

    Yang, Lin; Qi, Xin; Xing, Fuyong; Kurc, Tahsin; Saltz, Joel; Foran, David J

    2014-04-01

    The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

  20. System for accessing a collection of histology images using content-based strategies

    International Nuclear Information System (INIS)

    Gonzalez F; Caicedo J C; Cruz Roa A; Camargo, J; Spinel, C

    2010-01-01

    Histology images are an important resource for research, education and medical practice. The availability of image collections with reference purposes is limited to printed formats such as books and specialized journals. When histology image sets are published in digital formats, they are composed of some tens of images that do not represent the wide diversity of biological structures that can be found in fundamental tissues; making a complete histology image collection available to the general public having a great impact on research and education in different areas such as medicine, biology and natural sciences. This work presents the acquisition process of a histology image collection with 20,000 samples in digital format, from tissue processing to digital image capturing. The main purpose of collecting these images is to make them available as reference material to the academic community. In addition, this paper presents the design and architecture of a system to query and explore the image collection, using content-based image retrieval tools and text-based search on the annotations provided by experts. The system also offers novel image visualization methods to allow easy identification of interesting images among hundreds of possible pictures. The system has been developed using a service-oriented architecture and allows web-based access in http://www.informed.unal.edu.co

  1. A SYSTEM FOR ACCESSING A COLLECTION OF HISTOLOGY IMAGES USING CONTENT-BASED STRATEGIES

    Directory of Open Access Journals (Sweden)

    F González

    2010-09-01

    Full Text Available Histology images are an important resource for research, education and medical practice. The availability of image collections with reference purposes is limited to printed formats such as books and specialized journals. When histology image sets are published in digital formats, they are composed of some tens of images that do not represent the wide diversity of biological structures that can be found in fundamental tissues. Making a complete histology image collection available to the general public having a great impact on research and education in different areas such as medicine, biology and natural sciences. This work presents the acquisition process of a histology image collection with 20,000 samples in digital format, from tissue processing to digital image capturing. The main purpose of collecting these images is to make them available as reference material to the academic comunity. In addition, this paper presents the design and architecture of a system to query and explore the image collection, using content-based image retrieval tools and text-based search on the annotations provided by experts. The system also offers novel image visualization methods to allow easy identification of interesting images among hundreds of possible pictures. The system has been developed using a service-oriented architecture and allows web-based access in http://www.informed.unal.edu.co

  2. Implementation and evaluation of a medical image management system with content-based retrieval support

    International Nuclear Information System (INIS)

    Carita, Edilson Carlos; Seraphim, Enzo; Honda, Marcelo Ossamu; Azevedo-Marques, Paulo Mazzoncini de

    2008-01-01

    Objective: the present paper describes the implementation and evaluation of a medical images management system with content-based retrieval support (PACS-CBIR) integrating modules focused on images acquisition, storage and distribution, and text retrieval by keyword and images retrieval by similarity. Materials and methods: internet-compatible technologies were utilized for the system implementation with free ware, and C ++ , PHP and Java languages on a Linux platform. There is a DICOM-compatible image management module and two query modules, one of them based on text and the other on similarity of image texture attributes. Results: results demonstrate an appropriate images management and storage, and that the images retrieval time, always < 15 sec, was found to be good by users. The evaluation of retrieval by similarity has demonstrated that the selected images extractor allowed the sorting of images according to anatomical areas. Conclusion: based on these results, one can conclude that the PACS-CBIR implementation is feasible. The system has demonstrated to be DICOM-compatible, and that it can be integrated with the local information system. The similar images retrieval functionality can be enhanced by the introduction of further descriptors. (author)

  3. Content Based Radiographic Images Indexing and Retrieval Using Pattern Orientation Histogram

    Directory of Open Access Journals (Sweden)

    Abolfazl Lakdashti

    2008-06-01

    Full Text Available Introduction: Content Based Image Retrieval (CBIR is a method of image searching and retrieval in a  database. In medical applications, CBIR is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. As the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly  becoming a necessity.  Materials and Methods: This paper presents a new content based radiographic image retrieval approach  based on histogram of pattern orientations, namely pattern orientation histogram (POH. POH represents  the  spatial  distribution  of  five  different  pattern  orientations:  vertical,  horizontal,  diagonal  down/left,  diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks  and  the  frequency  of  each  type  of  pattern  is  determined  in  each  image-block.  Then,  local  pattern  histograms for each of these image-blocks are computed.   Results: The method was compared to two well known texture-based image retrieval methods: Tamura  and  Edge  Histogram  Descriptors  (EHD  in  MPEG-7  standard.  Experimental  results  based  on  10000  IRMA  radiography  image  dataset,  demonstrate  that  POH  provides  better  precision  and  recall  rates  compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are,  respectively, 48% and 18% better than the best of the two above mentioned methods.    Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as  its global composition, the proposed matching method can retrieve semantically similar medical images.

  4. Improving performance of content based image retrieval system with color features

    Directory of Open Access Journals (Sweden)

    Aleš Hladnik

    2017-04-01

    Full Text Available Content based image retrieval (CBIR encompasses a variety of techniques with a goal to solve the problem of searching for digital images in a large database by their visual content. Applications where the retrieval of similar images plays a crucial role include personal photo and art collections, medical imaging, multimedia publications and video surveillance. Main objective of our study was to try to improve the performance of the query-by-example image retrieval system based on texture features – Gabor wavelet and wavelet transform – by augmenting it with color information about the images, in particular color histogram, color autocorrelogram and color moments. Wang image database comprising 1000 natural color images grouped into 10 categories with 100 images was used for testing individual algorithms. Each image in the database served as a query image and the retrieval performance was evaluated by means of the precision and recall. e number of retrieved images ranged from 10 to 80. e best CBIR performance was obtained when implementing a combination of all 190 texture- and color features. Only slightly worse were the average precision and recall for the texture- and color histogram-based system. is result was somewhat surprising, since color histogram features provide no color spatial informa- tion. We observed a 23% increase in average precision when comparing the system containing a combination of texture- and all color features with the one consisting of exclusively texture descriptors when using Euclidean distance measure and 20 retrieved images. Addition of the color autocorrelogram features to the texture de- scriptors had virtually no e ect on the performance, while only minor improvement was detected when adding rst two color moments – the mean and the standard deviation. Similar to what was found in the previous studies with the same image database, average precision was very high in case of dinosaurs and owers and very low

  5. Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

    Science.gov (United States)

    Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

    2017-10-01

    Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

  6. Wavelet optimization for content-based image retrieval in medical databases.

    Science.gov (United States)

    Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

    2010-04-01

    We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

  7. An efficient similarity measure for content based image retrieval using memetic algorithm

    Directory of Open Access Journals (Sweden)

    Mutasem K. Alsmadi

    2017-06-01

    Full Text Available Content based image retrieval (CBIR systems work by retrieving images which are related to the query image (QI from huge databases. The available CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. Where, features are extracted from the given QI in the similar fashion. Consequently, a novel similarity evaluation using a meta-heuristic algorithm called a memetic algorithm (genetic algorithm with great deluge is achieved between the features of the QI and the features of the database images. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision.

  8. Automating Shallow Seismic Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Steeples, Don W.

    2004-12-09

    This seven-year, shallow-seismic reflection research project had the aim of improving geophysical imaging of possible contaminant flow paths. Thousands of chemically contaminated sites exist in the United States, including at least 3,700 at Department of Energy (DOE) facilities. Imaging technologies such as shallow seismic reflection (SSR) and ground-penetrating radar (GPR) sometimes are capable of identifying geologic conditions that might indicate preferential contaminant-flow paths. Historically, SSR has been used very little at depths shallower than 30 m, and even more rarely at depths of 10 m or less. Conversely, GPR is rarely useful at depths greater than 10 m, especially in areas where clay or other electrically conductive materials are present near the surface. Efforts to image the cone of depression around a pumping well using seismic methods were only partially successful (for complete references of all research results, see the full Final Technical Report, DOE/ER/14826-F), but peripheral results included development of SSR methods for depths shallower than one meter, a depth range that had not been achieved before. Imaging at such shallow depths, however, requires geophone intervals of the order of 10 cm or less, which makes such surveys very expensive in terms of human time and effort. We also showed that SSR and GPR could be used in a complementary fashion to image the same volume of earth at very shallow depths. The primary research focus of the second three-year period of funding was to develop and demonstrate an automated method of conducting two-dimensional (2D) shallow-seismic surveys with the goal of saving time, effort, and money. Tests involving the second generation of the hydraulic geophone-planting device dubbed the ''Autojuggie'' showed that large numbers of geophones can be placed quickly and automatically and can acquire high-quality data, although not under rough topographic conditions. In some easy

  9. Comparing features sets for content-based image retrieval in a medical-case database

    Science.gov (United States)

    Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

    2004-04-01

    Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the

  10. Optimizing top precision performance measure of content-based image retrieval by learning similarity function

    KAUST Repository

    Liang, Ru-Ze

    2017-04-24

    In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

  11. TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

    Science.gov (United States)

    Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

    2010-03-01

    Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

  12. A content-based digital image watermarking scheme resistant to local geometric distortions

    International Nuclear Information System (INIS)

    Yang, Hong-ying; Chen, Li-li; Wang, Xiang-yang

    2011-01-01

    Geometric distortion is known as one of the most difficult attacks to resist, as it can desynchronize the location of the watermark and hence cause incorrect watermark detection. Geometric distortion can be decomposed into two classes: global affine transforms and local geometric distortions. Most countermeasures proposed in the literature only address the problem of global affine transforms. It is a challenging problem to design a robust image watermarking scheme against local geometric distortions. In this paper, we propose a new content-based digital image watermarking scheme with good visual quality and reasonable resistance against local geometric distortions. Firstly, the robust feature points, which can survive various common image processing and global affine transforms, are extracted by using a multi-scale SIFT (scale invariant feature transform) detector. Then, the affine covariant local feature regions (LFRs) are constructed adaptively according to the feature scale and local invariant centroid. Finally, the digital watermark is embedded into the affine covariant LFRs by modulating the magnitudes of discrete Fourier transform (DFT) coefficients. By binding the watermark with the affine covariant LFRs, the watermark detection can be done without synchronization error. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as sharpening, noise addition, and JPEG compression, etc, but also robust against global affine transforms and local geometric distortions

  13. Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching

    Directory of Open Access Journals (Sweden)

    Tianyang Cao

    2017-01-01

    Full Text Available Self-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects. Therefore, keyframes global map establishing method for robot localization in multiple rooms and corridors is needed. Content-based image matching is the core of this method. It is designed for the situation, by establishing keyframes containing both floor and distorted wall images. Image distortion, caused by robot view angle and movement, is analyzed and deduced. And an image matching solution is presented, consisting of extraction of overlap regions of keyframes extraction and overlap region rebuild through subblocks matching. For improving accuracy, ceiling points detecting and mismatching subblocks checking methods are incorporated. This matching method can process environment video effectively. In experiments, less than 5% frames are extracted as keyframes to build global map, which have large space distance and overlap each other. Through this method, robot can localize itself by matching its real-time vision frames with our keyframes map. Even with many similar objects/background in the environment or kidnapping robot, robot localization is achieved with position RMSE <0.5 m.

  14. Content-Based Image Retrieval by Metric Learning From Radiology Reports: Application to Interstitial Lung Diseases.

    Science.gov (United States)

    Ramos, José; Kockelkorn, Thessa T J P; Ramos, Isabel; Ramos, Rui; Grutters, Jan; Viergever, Max A; van Ginneken, Bram; Campilho, Aurélio

    2016-01-01

    Content-based image retrieval (CBIR) is a search technology that could aid medical diagnosis by retrieving and presenting earlier reported cases that are related to the one being diagnosed. To retrieve relevant cases, CBIR systems depend on supervised learning to map low-level image contents to high-level diagnostic concepts. However, the annotation by medical doctors for training and evaluation purposes is a difficult and time-consuming task, which restricts the supervised learning phase to specific CBIR problems of well-defined clinical applications. This paper proposes a new technique that automatically learns the similarity between the several exams from textual distances extracted from radiology reports, thereby successfully reducing the number of annotations needed. Our method first infers the relation between patients by using information retrieval techniques to determine the textual distances between patient radiology reports. These distances are subsequently used to supervise a metric learning algorithm, that transforms the image space accordingly to textual distances. CBIR systems with different image descriptions and different levels of medical annotations were evaluated, with and without supervision from textual distances, using a database of computer tomography scans of patients with interstitial lung diseases. The proposed method consistently improves CBIR mean average precision, with improvements that can reach 38%, and more marked gains for small annotation sets. Given the overall availability of radiology reports in picture archiving and communication systems, the proposed approach can be broadly applied to CBIR systems in different medical problems, and may facilitate the introduction of CBIR in clinical practice.

  15. PROTOTYPE CONTENT BASED IMAGE RETRIEVAL UNTUK DETEKSI PEN YAKIT KULIT DENGAN METODE EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    Erick Fernando

    2016-05-01

    Full Text Available Dokter spesialis kulit melakukan pemeriksa secara visual objek mata, capture objek dengan kamera digital dan menanyakan riwayat perjalanan penyakit pasien, tanpa melakukan perbandingan terhadap gejala dan tanda yang ada sebelummnya. Sehingga pemeriksaan dan perkiraan jenis penyakit kulit. Pengolahan data citra dalam bentuk digital khususnya citra medis sudah sangat dibutuhkan dengan pra-processing. Banyak pasien yang dilayani di rumah sakit masih menggunakan data citra analog. Data analog ini membutuhkan ruangan khusus untuk menyimpan guna menghindarkan kerusakan mekanis. Uraian mengatasi permasalahan ini, citra medis dibuat dalam bentuk digital dan disimpan dalam sistem database dan dapat melihat kesamaan citra kulit yang baru. Citra akan dapat ditampilkan dengan pra- processing dengan identifikasi kesamaan dengan Content Based Image Retrieval (CBIR bekerja dengan cara mengukur kemiripan citra query dengan semua citra yang ada dalam database sehingga query cost berbanding lurus dengan jumlah citra dalam database.

  16. Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.

    Science.gov (United States)

    Bedo, Marcos Vinicius Naves; Pereira Dos Santos, Davi; Ponciano-Silva, Marcelo; de Azevedo-Marques, Paulo Mazzoncini; Ferreira de Carvalho, André Ponce de León; Traina, Caetano

    2016-02-01

    Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

  17. Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces.

    Science.gov (United States)

    Sridhar, Akshay; Doyle, Scott; Madabhushi, Anant

    2015-01-01

    Content-based image retrieval (CBIR) systems allow for retrieval of images from within a database that are similar in visual content to a query image. This is useful for digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction methods have become popular for embedding high-dimensional data into a reduced-dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced-dimensional space. However, most dimensionality reduction methods implicitly assume, in computing the reduced-dimensional representation, that all features are equally important. In this paper we present boosted spectral embedding(BoSE), which utilizes a boosted distance metric to selectively weight individual features (based on training data) to subsequently map the data into a reduced-dimensional space. BoSE is evaluated against spectral embedding (SE) (which employs equal feature weighting) in the context of CBIR of digitized prostate and breast cancer histopathology images. The following datasets, which were comprised of a total of 154 hematoxylin and eosin stained histopathology images, were used: (1) Prostate cancer histopathology (benign vs. malignant), (2) estrogen receptor (ER) + breast cancer histopathology (low vs. high grade), and (3) HER2+ breast cancer histopathology (low vs. high levels of lymphocytic infiltration). We plotted and calculated the area under precision-recall curves (AUPRC) and calculated classification accuracy using the Random Forest classifier. BoSE outperformed SE both in terms of CBIR-based (area under the precision-recall curve) and classifier-based (classification accuracy

  18. Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces

    Directory of Open Access Journals (Sweden)

    Akshay Sridhar

    2015-01-01

    Full Text Available Context : Content-based image retrieval (CBIR systems allow for retrieval of images from within a database that are similar in visual content to a query image. This is useful for digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction methods have become popular for embedding high-dimensional data into a reduced-dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced-dimensional space. However, most dimensionality reduction methods implicitly assume, in computing the reduced-dimensional representation, that all features are equally important. Aims : In this paper we present boosted spectral embedding (BoSE, which utilizes a boosted distance metric to selectively weight individual features (based on training data to subsequently map the data into a reduced-dimensional space. Settings and Design : BoSE is evaluated against spectral embedding (SE (which employs equal feature weighting in the context of CBIR of digitized prostate and breast cancer histopathology images. Materials and Methods : The following datasets, which were comprised of a total of 154 hematoxylin and eosin stained histopathology images, were used: (1 Prostate cancer histopathology (benign vs. malignant, (2 estrogen receptor (ER + breast cancer histopathology (low vs. high grade, and (3 HER2+ breast cancer histopathology (low vs. high levels of lymphocytic infiltration. Statistical Analysis Used : We plotted and calculated the area under precision-recall curves (AUPRC and calculated classification accuracy using the Random Forest classifier. Results : BoSE outperformed SE both

  19. Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval.

    Science.gov (United States)

    Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian

    2010-01-01

    We present in this paper a novel way to adapt a multidimensional wavelet filter bank, based on the nonseparable lifting scheme framework, to any specific problem. It allows the design of filter banks with a desired number of degrees of freedom, while controlling the number of vanishing moments of the primal wavelet ((~)N moments) and of the dual wavelet ( N moments). The prediction and update filters, in the lifting scheme based filter banks, are defined as Neville filters of order (~)N and N, respectively. However, in order to introduce some degrees of freedom in the design, these filters are not defined as the simplest Neville filters. The proposed method is convenient: the same algorithm is used whatever the dimensionality of the signal, and whatever the lattice used. The method is applied to content-based image retrieval (CBIR): an image signature is derived from this new adaptive nonseparable wavelet transform. The method is evaluated on four image databases and compared to a similar CBIR system, based on an adaptive separable wavelet transform. The mean precision at five of the nonseparable wavelet based system is notably higher on three out of the four databases, and comparable on the other one. The proposed method also compares favorably with the dual-tree complex wavelet transform, an overcomplete nonseparable wavelet transform.

  20. Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

    Science.gov (United States)

    Mustapha, Aouache; Hussain, Aini; Samad, Salina Abdul; Zulkifley, Mohd Asyraf; Diyana Wan Zaki, Wan Mimi; Hamid, Hamzaini Abdul

    2015-01-16

    Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities. In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures. Experimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets. The proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.

  1. Automated medical image segmentation techniques

    Directory of Open Access Journals (Sweden)

    Sharma Neeraj

    2010-01-01

    Full Text Available Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT and Magnetic resonance (MR imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

  2. Optimization of reference library used in content-based medical image retrieval scheme

    International Nuclear Information System (INIS)

    Park, Sang Cheol; Sukthankar, Rahul; Mummert, Lily; Satyanarayanan, Mahadev; Zheng Bin

    2007-01-01

    Building an optimal image reference library is a critical step in developing the interactive computer-aided detection and diagnosis (I-CAD) systems of medical images using content-based image retrieval (CBIR) schemes. In this study, the authors conducted two experiments to investigate (1) the relationship between I-CAD performance and size of reference library and (2) a new reference selection strategy to optimize the library and improve I-CAD performance. The authors assembled a reference library that includes 3153 regions of interest (ROI) depicting either malignant masses (1592) or CAD-cued false-positive regions (1561) and an independent testing data set including 200 masses and 200 false-positive regions. A CBIR scheme using a distance-weighted K-nearest neighbor algorithm is applied to retrieve references that are considered similar to the testing sample from the library. The area under receiver operating characteristic curve (A z ) is used as an index to evaluate the I-CAD performance. In the first experiment, the authors systematically increased reference library size and tested I-CAD performance. The result indicates that scheme performance improves initially from A z =0.715 to 0.874 and then plateaus when the library size reaches approximately half of its maximum capacity. In the second experiment, based on the hypothesis that a ROI should be removed if it performs poorly compared to a group of similar ROIs in a large and diverse reference library, the authors applied a new strategy to identify 'poorly effective' references. By removing 174 identified ROIs from the reference library, I-CAD performance significantly increases to A z =0.914 (p<0.01). The study demonstrates that increasing reference library size and removing poorly effective references can significantly improve I-CAD performance

  3. Using an image-extended relational database to support content-based image retrieval in a PACS.

    Science.gov (United States)

    Traina, Caetano; Traina, Agma J M; Araújo, Myrian R B; Bueno, Josiane M; Chino, Fabio J T; Razente, Humberto; Azevedo-Marques, Paulo M

    2005-12-01

    This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

  4. AUTOMATION OF IMAGE DATA PROCESSING

    Directory of Open Access Journals (Sweden)

    Preuss Ryszard

    2014-12-01

    Full Text Available This article discusses the current capabilities of automate processing of the image data on the example of using PhotoScan software by Agisoft . At present, image data obtained by various registration systems (metric and non - metric cameras placed on airplanes , satellites , or more often on UAVs is used to create photogrammetric products. Multiple registrations of object or land area (large groups of photos are captured are usually performed in order to eliminate obscured area as well as to raise the final accuracy of the photogrammetric product. Because of such a situation t he geometry of the resulting image blocks is far from the typical configuration of images . For fast images georeferencing automatic image matching algorithms are currently applied . They can create a model of a block in the local coordinate system or using initial exterior orientation and measured control points can provide image georeference in an external reference frame. In the case of non - metric image application, it is also possible to carry out self - calibration process at this stage . Image matching algorithm is also used in generation of dense point clouds reconstructing spatial shape of the object ( area. In subsequent processing steps it is possible to obtain typical photogrammetric products such as orthomosaic , DSM or DTM and a photorealistic solid model of an object . All aforementioned processing steps are implemented in a single program in contrary to standard commercial software dividing all steps into dedicated modules . I mage processing leading to final geo referenced products can be fully automated including sequential implementation of the processing steps at predetermined control parameters . The paper presents the practical results of the application fully automatic generation of othomosaic for both images obtained by a metric Vexell camera and a block of images acquired by a non - metric UAV system.

  5. Complex event processing for content-based text, image, and video retrieval

    NARCIS (Netherlands)

    Bowman, E.K.; Broome, B.D.; Holland, V.M.; Summers-Stay, D.; Rao, R.M.; Duselis, J.; Howe, J.; Madahar, B.K.; Boury-Brisset, A.C.; Forrester, B.; Kwantes, P.; Burghouts, G.; Huis, J. van; Mulayim, A.Y.

    2016-01-01

    This report summarizes the findings of an exploratory team of the North Atlantic Treaty Organization (NATO) Information Systems Technology panel into Content-Based Analytics (CBA). The team carried out a technical review into the current status of theoretical and practical developments of methods,

  6. Autoradiography and automated image analysis

    International Nuclear Information System (INIS)

    Vardy, P.H.; Willard, A.G.

    1982-01-01

    Limitations with automated image analysis and the solution of problems encountered are discussed. With transmitted light, unstained plastic sections with planar profiles should be used. Stains potentiate signal so that television registers grains as falsely larger areas of low light intensity. Unfocussed grains in paraffin sections will not be seen by image analysers due to change in darkness and size. With incident illumination, the use of crossed polars, oil objectives and an oil filled light trap continuous with the base of the slide will reduce glare. However this procedure so enormously attenuates the light reflected by silver grains, that detection may be impossible. Autoradiographs should then be photographed and the negative images of silver grains on film analysed automatically using transmitted light

  7. A review of content-based image retrieval systems in medical applications-clinical benefits and future directions.

    Science.gov (United States)

    Müller, Henning; Michoux, Nicolas; Bandon, David; Geissbuhler, Antoine

    2004-02-01

    Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of

  8. Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

    Directory of Open Access Journals (Sweden)

    Meiyan Huang

    Full Text Available This study aims to develop content-based image retrieval (CBIR system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor. Using the BoVW model with partition learning, the mean average precision (mAP of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

  9. Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

    Science.gov (United States)

    Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Gao, Yang; Chen, Yang; Feng, Qianjin; Chen, Wufan; Lu, Zhentai

    2014-01-01

    This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

  10. A Review of Content Based Image Classification using Machine Learning Approach

    OpenAIRE

    Sandeep Kumar; Zeeshan Khan; Anurag jain

    2012-01-01

    Image classification is vital field of research in computer vision. Increasing rate of multimedia data, remote sensing and web photo gallery need a category of different image for the proper retrieval of user. Various researchers apply different approach for image classification such as segmentation, clustering and some machine learning approach for the classification of image. Content of image such as color, texture and shape and size plays an important role in semantic image classification....

  11. Adaptive image content-based exposure control for scanning applications in radiography

    NARCIS (Netherlands)

    Schulerud, H.; Thielemann, J.; Kirkhus, T.; Kaspersen, K.; Østby, J.M.; Metaxas, M.G.; Royle, G.J.; Griffiths, J.; Cook, E.; Esbrand, C.; Pani, S.; Venanzi, C.; van der Stelt, P.F.; Li, G.; Turchetta, R.; Fant, A.; Theodoridis, S.; Georgiou, H.; Hall, G.; Noy, M.; Jones, J.; Leaver, J.; Triantis, F.; Asimidis, A.; Manthos, N.; Longo, R.; Bergamaschi, A.; Speller, R.D.

    2007-01-01

    I-ImaS (Intelligent Imaging Sensors) is a European project which has designed and developed a new adaptive X-ray imaging system using on-line exposure control, to create locally optimized images. The I-ImaS system allows for real-time image analysis during acquisition, thus enabling real-time

  12. Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis.

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Roberts M

    2009-06-01

    In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.

  13. Content-based Image Hiding Method for Secure Network Biometric Verification

    Directory of Open Access Journals (Sweden)

    Xiangjiu Che

    2011-08-01

    Full Text Available For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content correlation analysis to protect the network-based transmitted image. By using principal component analysis (PCA, the content correlation between the biometric image and the cover image is firstly analyzed. Then based on particle swarm optimization (PSO algorithm, some regions of the cover image are selected to represent the biometric image, in which the cover image can carry partial content of the biometric image. As a result of the correlation analysis, the unrepresented part of the biometric image is embedded into the cover image by using the discrete wavelet transform (DWT. Combined with human visual system (HVS model, this approach makes the hiding result perceptually invisible. The extensive experimental results demonstrate that the proposed hiding approach is robust against some common frequency and geometric attacks; it also provides an effective protection for the secure biometric verification.

  14. Content-based image retrieval using a signature graph and a self-organizing map

    Directory of Open Access Journals (Sweden)

    Van Thanh The

    2016-06-01

    Full Text Available In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

  15. A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

    Directory of Open Access Journals (Sweden)

    Sudeep Thepade

    2014-01-01

    Full Text Available A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

  16. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Directory of Open Access Journals (Sweden)

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  17. A rapid automatic analyzer and its methodology for effective bentonite content based on image recognition technology

    Directory of Open Access Journals (Sweden)

    Wei Long

    2016-09-01

    Full Text Available Fast and accurate determination of effective bentonite content in used clay bonded sand is very important for selecting the correct mixing ratio and mixing process to obtain high-performance molding sand. Currently, the effective bentonite content is determined by testing the ethylene blue absorbed in used clay bonded sand, which is usually a manual operation with some disadvantages including complicated process, long testing time and low accuracy. A rapid automatic analyzer of the effective bentonite content in used clay bonded sand was developed based on image recognition technology. The instrument consists of auto stirring, auto liquid removal, auto titration, step-rotation and image acquisition components, and processor. The principle of the image recognition method is first to decompose the color images into three-channel gray images based on the photosensitive degree difference of the light blue and dark blue in the three channels of red, green and blue, then to make the gray values subtraction calculation and gray level transformation of the gray images, and finally, to extract the outer circle light blue halo and the inner circle blue spot and calculate their area ratio. The titration process can be judged to reach the end-point while the area ratio is higher than the setting value.

  18. Developing a comprehensive system for content-based retrieval of image and text data from a national survey

    Science.gov (United States)

    Antani, Sameer K.; Natarajan, Mukil; Long, Jonathan L.; Long, L. Rodney; Thoma, George R.

    2005-04-01

    The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES II). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data; flexibility to incorporate new research work; provides a means to control access to system components under development; and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.

  19. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    We propose a scheme for automating power law transformations which are used for image enhancement. The scheme we propose does not require the user to choose the exponent in the power law transformation. This method works well for images having poor contrast, especially to those images in which the peaks ...

  20. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    Abstract. We propose a scheme for automating power law transformations which are used for image enhancement. The scheme we propose does not require the user to choose the exponent in the power law transformation. This method works well for images having poor contrast, especially to those images in which the ...

  1. Automated imaging system for single molecules

    Science.gov (United States)

    Schwartz, David Charles; Runnheim, Rodney; Forrest, Daniel

    2012-09-18

    There is provided a high throughput automated single molecule image collection and processing system that requires minimal initial user input. The unique features embodied in the present disclosure allow automated collection and initial processing of optical images of single molecules and their assemblies. Correct focus may be automatically maintained while images are collected. Uneven illumination in fluorescence microscopy is accounted for, and an overall robust imaging operation is provided yielding individual images prepared for further processing in external systems. Embodiments described herein are useful in studies of any macromolecules such as DNA, RNA, peptides and proteins. The automated image collection and processing system and method of same may be implemented and deployed over a computer network, and may be ergonomically optimized to facilitate user interaction.

  2. Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Muhammad Imran

    2014-01-01

    Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

  3. ARTIP: Automated Radio Telescope Image Processing Pipeline

    Science.gov (United States)

    Sharma, Ravi; Gyanchandani, Dolly; Kulkarni, Sarang; Gupta, Neeraj; Pathak, Vineet; Pande, Arti; Joshi, Unmesh

    2018-02-01

    The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data. ARTIP starts with raw data, i.e. a measurement set and goes through multiple stages, such as flux calibration, bandpass calibration, phase calibration, and imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs. It is written using standard python libraries and the CASA package. The pipeline can deal with datasets with multiple spectral windows and also multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators.

  4. A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images

    Science.gov (United States)

    Thies, Christian; Schmidt Borreda, Marcel; Seidl, Thomas; Lehmann, Thomas M.

    2006-03-01

    Multiscale analysis provides a complete hierarchical partitioning of images into visually plausible regions. Each of them is formally characterized by a feature vector describing shape, texture and scale properties. Consequently, object extraction becomes a classification of the feature vectors. Classifiers are trained by relevant and irrelevant regions labeled as object and remaining partitions, respectively. A trained classifier is applicable to yet uncategorized partitionings to identify the corresponding region's classes. Such an approach enables retrieval of a-priori unknown objects within a point-and-click interface. In this work, the classification pipeline consists of a framework for data selection, feature selection, classifier training, classification of testing data, and evaluation. According to the no-free-lunch-theorem of supervised learning, the appropriate classification pipeline is determined experimentally. Therefore, each of the steps is varied by state-of-the-art methods and the respective classification quality is measured. Selection of training data from the ground truth is supported by bootstrapping, variance pooling, virtual training data, and cross validation. Feature selection for dimension reduction is performed by linear discriminant analysis, principal component analysis, and greedy selection. Competing classifiers are k-nearest-neighbor, Bayesian classifier, and the support vector machine. Quality is measured by precision and recall to reflect the retrieval task. A set of 105 hand radiographs from clinical routine serves as ground truth, where the metacarpal bones have been labeled manually. In total, 368 out of 39.017 regions are identified as relevant. In initial experiments for feature selection with the support vector machine have been obtained recall, precision and F-measure of 0.58, 0.67, and 0,62, respectively.

  5. A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

    Science.gov (United States)

    Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

    2018-01-01

    The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

  6. İçerik Tabanlı Görüntü Erişimi / Content-Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    İrem Soydal

    2005-10-01

    Full Text Available Digital image collections are expanding day by day, and image retrieval becomes even harder. Both individuals and institutions encounter serious problems when building their image archives and later when retrieving the archived images. Visual information cannot be fully expressed in words and normally depends on intuitive human perception. Consequently, this causes us to find the plain text-based information inadequate, and as a result, increases the value of the visual content. However describing, storing and retrieving the visual content is not simple. The research activities in this area, which escalated in the 90’s, have brought several solutions to the understanding, design and development of the image retrieval systems. This article reviews the studies on image retrieval systems in general, and content-based image retrieval systems specifically. The article also examines the features of content-based image retrieval systems.

  7. Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.

    Science.gov (United States)

    Sparks, Rachel; Madabhushi, Anant

    2016-06-06

    Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01.

  8. Semi-automated Image Processing for Preclinical Bioluminescent Imaging.

    Science.gov (United States)

    Slavine, Nikolai V; McColl, Roderick W

    Bioluminescent imaging is a valuable noninvasive technique for investigating tumor dynamics and specific biological molecular events in living animals to better understand the effects of human disease in animal models. The purpose of this study was to develop and test a strategy behind automated methods for bioluminescence image processing from the data acquisition to obtaining 3D images. In order to optimize this procedure a semi-automated image processing approach with multi-modality image handling environment was developed. To identify a bioluminescent source location and strength we used the light flux detected on the surface of the imaged object by CCD cameras. For phantom calibration tests and object surface reconstruction we used MLEM algorithm. For internal bioluminescent sources we used the diffusion approximation with balancing the internal and external intensities on the boundary of the media and then determined an initial order approximation for the photon fluence we subsequently applied a novel iterative deconvolution method to obtain the final reconstruction result. We find that the reconstruction techniques successfully used the depth-dependent light transport approach and semi-automated image processing to provide a realistic 3D model of the lung tumor. Our image processing software can optimize and decrease the time of the volumetric imaging and quantitative assessment. The data obtained from light phantom and lung mouse tumor images demonstrate the utility of the image reconstruction algorithms and semi-automated approach for bioluminescent image processing procedure. We suggest that the developed image processing approach can be applied to preclinical imaging studies: characteristics of tumor growth, identify metastases, and potentially determine the effectiveness of cancer treatment.

  9. Automated Segmentation of Cardiac Magnetic Resonance Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Nilsson, Jens Chr.; Grønning, Bjørn A.

    2001-01-01

    Magnetic resonance imaging (MRI) has been shown to be an accurate and precise technique to assess cardiac volumes and function in a non-invasive manner and is generally considered to be the current gold-standard for cardiac imaging [1]. Measurement of ventricular volumes, muscle mass and function...... is based on determination of the left-ventricular endocardial and epicardial borders. Since manual border detection is laborious, automated segmentation is highly desirable as a fast, objective and reproducible alternative. Automated segmentation will thus enhance comparability between and within cardiac...... studies and increase accuracy by allowing acquisition of thinner MRI-slices. This abstract demonstrates that statistical models of shape and appearance, namely the deformable models: Active Appearance Models, can successfully segment cardiac MRIs....

  10. Automated image segmentation using information theory

    International Nuclear Information System (INIS)

    Hibbard, L.S.

    2001-01-01

    Full text: Our development of automated contouring of CT images for RT planning is based on maximum a posteriori (MAP) analyses of region textures, edges, and prior shapes, and assumes stationary Gaussian distributions for voxel textures and contour shapes. Since models may not accurately represent image data, it would be advantageous to compute inferences without relying on models. The relative entropy (RE) from information theory can generate inferences based solely on the similarity of probability distributions. The entropy of a distribution of a random variable X is defined as -Σ x p(x)log 2 p(x) for all the values x which X may assume. The RE (Kullback-Liebler divergence) of two distributions p(X), q(X), over X is Σ x p(x)log 2 {p(x)/q(x)}. The RE is a kind of 'distance' between p,q, equaling zero when p=q and increasing as p,q are more different. Minimum-error MAP and likelihood ratio decision rules have RE equivalents: minimum error decisions obtain with functions of the differences between REs of compared distributions. One applied result is the contour ideally separating two regions is that which maximizes the relative entropy of the two regions' intensities. A program was developed that automatically contours the outlines of patients in stereotactic headframes, a situation most often requiring manual drawing. The relative entropy of intensities inside the contour (patient) versus outside (background) was maximized by conjugate gradient descent over the space of parameters of a deformable contour. shows the computed segmentation of a patient from headframe backgrounds. This program is particularly useful for preparing images for multimodal image fusion. Relative entropy and allied measures of distribution similarity provide automated contouring criteria that do not depend on statistical models of image data. This approach should have wide utility in medical image segmentation applications. Copyright (2001) Australasian College of Physical Scientists and

  11. Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

    Directory of Open Access Journals (Sweden)

    Andreia V. Faria

    2015-01-01

    Full Text Available Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA. PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA and principal component analysis (PCA. The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8 was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.

  12. Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

    Science.gov (United States)

    Faria, Andreia V.; Oishi, Kenichi; Yoshida, Shoko; Hillis, Argye; Miller, Michael I.; Mori, Susumu

    2015-01-01

    Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support. PMID:25685706

  13. Automated landmark-guided deformable image registration

    International Nuclear Information System (INIS)

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-01

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency. (paper)

  14. Automated landmark-guided deformable image registration.

    Science.gov (United States)

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-07

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

  15. Automated Quality Assurance Applied to Mammographic Imaging

    Directory of Open Access Journals (Sweden)

    Anne Davis

    2002-07-01

    Full Text Available Quality control in mammography is based upon subjective interpretation of the image quality of a test phantom. In order to suppress subjectivity due to the human observer, automated computer analysis of the Leeds TOR(MAM test phantom is investigated. Texture analysis via grey-level co-occurrence matrices is used to detect structures in the test object. Scoring of the substructures in the phantom is based on grey-level differences between regions and information from grey-level co-occurrence matrices. The results from scoring groups of particles within the phantom are presented.

  16. Automated Aesthetic Analysis of Photographic Images.

    Science.gov (United States)

    Aydın, Tunç Ozan; Smolic, Aljoscha; Gross, Markus

    2015-01-01

    We present a perceptually calibrated system for automatic aesthetic evaluation of photographic images. Our work builds upon the concepts of no-reference image quality assessment, with the main difference being our focus on rating image aesthetic attributes rather than detecting image distortions. In contrast to the recent attempts on the highly subjective aesthetic judgment problems such as binary aesthetic classification and the prediction of an image's overall aesthetics rating, our method aims on providing a reliable objective basis of comparison between aesthetic properties of different photographs. To that end our system computes perceptually calibrated ratings for a set of fundamental and meaningful aesthetic attributes, that together form an "aesthetic signature" of an image. We show that aesthetic signatures can still be used to improve upon the current state-of-the-art in automatic aesthetic judgment, but also enable interesting new photo editing applications such as automated aesthetic analysis, HDR tone mapping evaluation, and providing aesthetic feedback during multi-scale contrast manipulation.

  17. Automated Image Analysis Corrosion Working Group Update: February 1, 2018

    Energy Technology Data Exchange (ETDEWEB)

    Wendelberger, James G. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-02-01

    These are slides for the automated image analysis corrosion working group update. The overall goals were: automate the detection and quantification of features in images (faster, more accurate), how to do this (obtain data, analyze data), focus on Laser Scanning Confocal Microscope (LCM) data (laser intensity, laser height/depth, optical RGB, optical plus laser RGB).

  18. Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix

    Science.gov (United States)

    Srivastava, Prashant; Khare, Manish; Khare, Ashish

    2017-06-01

    The rapid growth of different types of images has posed a great challenge to the scientific fraternity. As the images are increasing everyday, it is becoming a challenging task to organize the images for efficient and easy access. The field of image retrieval attempts to solve this problem through various techniques. This paper proposes a novel technique of image retrieval by combining Scale Invariant Feature Transform (SIFT) and Co-occurrence matrix. For construction of feature vector, SIFT descriptors of gray scale images are computed and normalized using z-score normalization followed by construction of Gray-Level Co-occurrence Matrix (GLCM) of normalized SIFT keypoints. The constructed feature vector is matched with those of images in database to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and the performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods.

  19. Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

    Science.gov (United States)

    Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

    2014-01-01

    Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

  20. Automatic content-based analysis of georeferenced image data: Detection of Beggiatoa mats in seafloor video mosaics from the HÅkon Mosby Mud Volcano

    Science.gov (United States)

    Jerosch, K.; Lüdtke, A.; Schlüter, M.; Ioannidis, G. T.

    2007-02-01

    The combination of new underwater technology as remotely operating vehicles (ROVs), high-resolution video imagery, and software to compute georeferenced mosaics of the seafloor provides new opportunities for marine geological or biological studies and applications in offshore industry. Even during single surveys by ROVs or towed systems large amounts of images are compiled. While these underwater techniques are now well-engineered, there is still a lack of methods for the automatic analysis of the acquired image data. During ROV dives more than 4200 georeferenced video mosaics were compiled for the HÅkon Mosby Mud Volcano (HMMV). Mud volcanoes as HMMV are considered as significant source locations for methane characterised by unique chemoautotrophic communities as Beggiatoa mats. For the detection and quantification of the spatial distribution of Beggiatoa mats an automated image analysis technique was developed, which applies watershed transformation and relaxation-based labelling of pre-segmented regions. Comparison of the data derived by visual inspection of 2840 video images with the automated image analysis revealed similarities with a precision better than 90%. We consider this as a step towards a time-efficient and accurate analysis of seafloor images for computation of geochemical budgets and identification of habitats at the seafloor.

  1. Automated mapping of the intertidal beach from video images

    NARCIS (Netherlands)

    Uunk, L.; Uunk, L.; Wijnberg, Kathelijne Mariken; Morelissen, R.; Morelissen, R.

    2010-01-01

    This paper presents a fully automated procedure to derive the intertidal beach bathymetry on a daily basis from video images of low-sloping beaches that are characterised by the intermittent emergence of intertidal bars. Bathymetry data are obtained by automated and repeated mapping of shorelines

  2. Automated image analysis of the pathological lung in CT

    NARCIS (Netherlands)

    Sluimer, Ingrid Christine

    2005-01-01

    The general objective of the thesis is automation of the analysis of the pathological lung from CT images. Specifically, we aim for automated detection and classification of abnormalities in the lung parenchyma. We first provide a review of computer analysis techniques applied to CT of the

  3. Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

    Science.gov (United States)

    Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

    2016-08-01

    Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

  4. Automated feature extraction and classification from image sources

    Science.gov (United States)

    ,

    1995-01-01

    The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

  5. Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric.

    Science.gov (United States)

    Yang, Wei; Feng, Qianjin; Yu, Mei; Lu, Zhentai; Gao, Yang; Xu, Yikai; Chen, Wufan

    2012-11-01

    A content-based image retrieval (CBIR) method for T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors is presented for diagnosis aid. The method is thoroughly evaluated on a large image dataset. Using the tumor region as a query, the authors' CBIR system attempts to retrieve tumors of the same pathological category. Aside from commonly used features such as intensity, texture, and shape features, the authors use a margin information descriptor (MID), which is capable of describing the characteristics of tissue surrounding a tumor, for representing image contents. In addition, the authors designed a distance metric learning algorithm called Maximum mean average Precision Projection (MPP) to maximize the smooth approximated mean average precision (mAP) to optimize retrieval performance. The effectiveness of MID and MPP algorithms was evaluated using a brain CE-MRI dataset consisting of 3108 2D scans acquired from 235 patients with three categories of brain tumors (meningioma, glioma, and pituitary tumor). By combining MID and other features, the mAP of retrieval increased by more than 6% with the learned distance metrics. The distance metric learned by MPP significantly outperformed the other two existing distance metric learning methods in terms of mAP. The CBIR system using the proposed strategies achieved a mAP of 87.3% and a precision of 89.3% when top 10 images were returned by the system. Compared with scale-invariant feature transform, the MID, which uses the intensity profile as descriptor, achieves better retrieval performance. Incorporating tumor margin information represented by MID with the distance metric learned by the MPP algorithm can substantially improve the retrieval performance for brain tumors in CE-MRI.

  6. Automated Detection of Optic Disc in Fundus Images

    Science.gov (United States)

    Burman, R.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.

    Optic disc (OD) localization is an important preprocessing step in the automated image detection of fundus image infected with glaucoma. An Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution (DE) is applied to determine the location of the OD in the right of left eye retinal fundus image. The algorithm, when applied to 460 fundus images from the MESSIDOR dataset, shows a success rate of 99.07 % for 217 normal images and 95.47 % for 243 pathological images. The mean computational time is 1.709 s for normal images and 1.753 s for pathological images. These results are important for automated detection of glaucoma and for telemedicine purposes.

  7. Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation.

    Directory of Open Access Journals (Sweden)

    Oscar Beijbom

    Full Text Available Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys.

  8. Automated diabetic retinopathy imaging in Indian eyes: A pilot study

    Directory of Open Access Journals (Sweden)

    Rupak Roy

    2014-01-01

    Full Text Available Aim: To evaluate the efficacy of an automated retinal image grading system in diabetic retinopathy (DR screening. Materials and Methods: Color fundus images of patients of a DR screening project were analyzed for the purpose of the study. For each eye two set of images were acquired, one centerd on the disk and the other centerd on the macula. All images were processed by automated DR screening software (Retmarker. The results were compared to ophthalmologist grading of the same set of photographs. Results: 5780 images of 1445 patients were analyzed. Patients were screened into two categories DR or no DR. Image quality was high, medium and low in 71 (4.91%, 1117 (77.30% and 257 (17.78% patients respectively. Specificity and sensitivity for detecting DR in the high, medium and low group were (0.59, 0.91; (0.11, 0.95 and (0.93, 0.14. Conclusion: Automated retinal image screening system for DR had a high sensitivity in high and medium quality images. Automated DR grading software′s hold promise in future screening programs.

  9. Automated identification of animal species in camera trap images

    NARCIS (Netherlands)

    Yu, X.; Wang, J.; Kays, R.; Jansen, P.A.; Wang, T.; Huang, T.

    2013-01-01

    Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species

  10. Automated Registration Of Images From Multiple Sensors

    Science.gov (United States)

    Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

    1994-01-01

    Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

  11. Automation of Cassini Support Imaging Uplink Command Development

    Science.gov (United States)

    Ly-Hollins, Lisa; Breneman, Herbert H.; Brooks, Robert

    2010-01-01

    "Support imaging" is imagery requested by other Cassini science teams to aid in the interpretation of their data. The generation of the spacecraft command sequences for these images is performed by the Cassini Instrument Operations Team. The process initially established for doing this was very labor-intensive, tedious and prone to human error. Team management recognized this process as one that could easily benefit from automation. Team members were tasked to document the existing manual process, develop a plan and strategy to automate the process, implement the plan and strategy, test and validate the new automated process, and deliver the new software tools and documentation to Flight Operations for use during the Cassini extended mission. In addition to the goals of higher efficiency and lower risk in the processing of support imaging requests, an effort was made to maximize adaptability of the process to accommodate uplink procedure changes and the potential addition of new capabilities outside the scope of the initial effort.

  12. Cardiac imaging: working towards fully-automated machine analysis & interpretation.

    Science.gov (United States)

    Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

    2017-03-01

    Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

  13. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    Automatically enhancing contrast of an image has been a challenging task since the digital image can represent variety of scene types. Trifonov et al (2001) performed automatic contrast enhancement by automatically determining the measure of central tendency of the brightness histogram of an image and shifting and ...

  14. Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

    Science.gov (United States)

    Cao, Jianfang; Chen, Lichao

    2015-01-01

    With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

  15. Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

    Directory of Open Access Journals (Sweden)

    Jianfang Cao

    2015-01-01

    Full Text Available With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance.

  16. Automated Acquisition and Analysis of Digital Radiographic Images

    International Nuclear Information System (INIS)

    Poland, R.

    1999-01-01

    Engineers at the Savannah River Technology Center have designed, built, and installed a fully automated small field-of-view, lens-coupled, digital radiography imaging system. The system is installed in one of the Savannah River Site''s production facilities to be used for the evaluation of production components. Custom software routines developed for the system automatically acquire, enhance, and diagnostically evaluate critical geometric features of various components that have been captured radiographically. Resolution of the digital radiograms and accuracy of the acquired measurements approaches 0.001 inches. To date, there has been zero deviation in measurement repeatability. The automated image acquisition methodology will be discussed, unique enhancement algorithms will be explained, and the automated routines for measuring the critical component features will be presented. An additional feature discussed is the independent nature of the modular software components, which allows images to be automatically acquired, processed, and evaluated by the computer in the background, while the operator reviews other images on the monitor. System components were also a key in gaining the required image resolution. System factors such as scintillator selection, x-ray source energy, optical components and layout, as well as geometric unsharpness issues are considered in the paper. Finally the paper examines the numerous quality improvement factors and cost saving advantages that will be realized at the Savannah River Site due to the implementation of the Automated Pinch Weld Analysis System (APWAS)

  17. Automated ion imaging with the NanoSIMS ion microprobe

    Science.gov (United States)

    Gröner, E.; Hoppe, P.

    2006-07-01

    Automated ion imaging systems developed for Cameca IMS3f and IMS6f ion microprobes are very useful for the analysis of large numbers of presolar dust grains, in particular with respect to the identification of rare types of presolar grains. The application of these systems is restricted to the study of micrometer-sized grains, thereby by-passing the major fraction of presolar grains which are sub-micrometer in size. The new generation Cameca NanoSIMS 50 ion microprobe combines high spatial resolution, high sensitivity, and simultaneous detection of up to 6 isotopes which makes the NanoSIMS an unprecedented tool for the analysis of presolar materials. Here, we report on the development of a fully automated ion imaging system for the NanoSIMS at MPI for Chemistry in order to extend its analytical capabilities further. The ion imaging consists of five steps: (i) Removal of surface contamination on the area of interest. (ii) Secondary ion image acquisition of up to 5 isotopes in multi-detection. (iii) Automated particle recognition in a pre-selected image. (iv) Automated measurement of all recognised particles with appropriate raster sizes and measurement times. (v) Stage movement to new area and repetition of steps (ii)-(iv).

  18. Automated ion imaging with the NanoSIMS ion microprobe

    International Nuclear Information System (INIS)

    Groener, E.; Hoppe, P.

    2006-01-01

    Automated ion imaging systems developed for Cameca IMS3f and IMS6f ion microprobes are very useful for the analysis of large numbers of presolar dust grains, in particular with respect to the identification of rare types of presolar grains. The application of these systems is restricted to the study of micrometer-sized grains, thereby by-passing the major fraction of presolar grains which are sub-micrometer in size. The new generation Cameca NanoSIMS 50 ion microprobe combines high spatial resolution, high sensitivity, and simultaneous detection of up to 6 isotopes which makes the NanoSIMS an unprecedented tool for the analysis of presolar materials. Here, we report on the development of a fully automated ion imaging system for the NanoSIMS at MPI for Chemistry in order to extend its analytical capabilities further. The ion imaging consists of five steps: (i) Removal of surface contamination on the area of interest. (ii) Secondary ion image acquisition of up to 5 isotopes in multi-detection. (iii) Automated particle recognition in a pre-selected image. (iv) Automated measurement of all recognised particles with appropriate raster sizes and measurement times. (v) Stage movement to new area and repetition of steps (ii)-(iv)

  19. Benchmarking, Research, Development, and Support for ORNL Automated Image and Signature Retrieval (AIR/ASR) Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Tobin, K.W.

    2004-06-01

    This report describes the results of a Cooperative Research and Development Agreement (CRADA) with Applied Materials, Inc. (AMAT) of Santa Clara, California. This project encompassed the continued development and integration of the ORNL Automated Image Retrieval (AIR) technology, and an extension of the technology denoted Automated Signature Retrieval (ASR), and other related technologies with the Defect Source Identification (DSI) software system that was under development by AMAT at the time this work was performed. In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train yield management engineers, and examine historical data for trends. Image management in semiconductor data systems is a growing cause of concern in the industry as fabricators are now collecting up to 20,000 images each week. In response to this concern, researchers at the Oak Ridge National Laboratory (ORNL) developed a semiconductor-specific content-based image retrieval method and system, also known as AIR. The system uses an image-based query-by-example method to locate and retrieve similar imagery from a database of digital imagery using visual image characteristics. The query method is based on a unique architecture that takes advantage of the statistical, morphological, and structural characteristics of image data, generated by inspection equipment in industrial applications. The system improves the manufacturing process by allowing rapid access to historical records of similar events so that errant process equipment can be isolated and corrective actions can be quickly taken to improve yield. The combined ORNL and AMAT technology is referred to hereafter as DSI-AIR and DSI-ASR.

  20. Automated image registration for FDOPA PET studies

    International Nuclear Information System (INIS)

    Kang-Ping Lin; Sung-Cheng Huang, Dan-Chu Yu; Melega, W.; Barrio, J.R.; Phelps, M.E.

    1996-01-01

    In this study, various image registration methods are investigated for their suitability for registration of L-6-[18F]-fluoro-DOPA (FDOPA) PET images. Five different optimization criteria including sum of absolute difference (SAD), mean square difference (MSD), cross-correlation coefficient (CC), standard deviation of pixel ratio (SDPR), and stochastic sign change (SSC) were implemented and Powell's algorithm was used to optimize the criteria. The optimization criteria were calculated either unidirectionally (i.e. only evaluating the criteria for comparing the resliced image 1 with the original image 2) or bidirectionally (i.e. averaging the criteria for comparing the resliced image 1 with the original image 2 and those for the sliced image 2 with the original image 1). Monkey FDOPA images taken at various known orientations were used to evaluate the accuracy of different methods. A set of human FDOPA dynamic images was used to investigate the ability of the methods for correcting subject movement. It was found that a large improvement in performance resulted when bidirectional rather than unidirectional criteria were used. Overall, the SAD, MSD and SDPR methods were found to be comparable in performance and were suitable for registering FDOPA images. The MSD method gave more adequate results for frame-to-frame image registration for correcting subject movement during a dynamic FDOPA study. The utility of the registration method is further demonstrated by registering FDOPA images in monkeys before and after amphetamine injection to reveal more clearly the changes in spatial distribution of FDOPA due to the drug intervention. (author)

  1. FULLY AUTOMATED IMAGE ORIENTATION IN THE ABSENCE OF TARGETS

    Directory of Open Access Journals (Sweden)

    C. Stamatopoulos

    2012-07-01

    Full Text Available Automated close-range photogrammetric network orientation has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO and subsequent spatial resection of the images. Over the past decade, automated orientation via feature-based matching (FBM techniques has attracted renewed research attention in both the photogrammetry and computer vision (CV communities. This is largely due to advances made towards the goal of automated relative orientation of multi-image networks covering untargetted (markerless objects. There are now a number of CV-based algorithms, with accompanying open-source software, that can achieve multi-image orientation within narrow-baseline networks. From a photogrammetric standpoint, the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown, while the number of outliers within the image matching and triangulation is large, and generally too large to allow relative orientation (RO via the commonly used coplanarity equations. On the other hand, there are few examples within the photogrammetric research field of automated markerless camera calibration to metric tolerances, and these too are restricted to narrow-baseline, low-convergence imaging geometry. The objective addressed in this paper is markerless automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. By wide-baseline we imply convergent multi-image configurations with convergence angles of up to around 90°. An associated aim is provision of a fast, fully automated process, which can be performed without user intervention. For this purpose, various algorithms require optimisation to allow parallel processing utilising multiple PC cores and graphics processing units (GPUs.

  2. Automated designation of tie-points for image-to-image coregistration.

    Science.gov (United States)

    R.E. Kennedy; W.B. Cohen

    2003-01-01

    Image-to-image registration requires identification of common points in both images (image tie-points: ITPs). Here we describe software implementing an automated, area-based technique for identifying ITPs. The ITP software was designed to follow two strategies: ( I ) capitalize on human knowledge and pattern recognition strengths, and (2) favour robustness in many...

  3. Automated image analysis in the study of collagenous colitis

    DEFF Research Database (Denmark)

    Fiehn, Anne-Marie Kanstrup; Kristensson, Martin; Engel, Ulla

    2016-01-01

    PURPOSE: The aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic...

  4. An automated vessel segmentation of retinal images using multiscale vesselness

    International Nuclear Information System (INIS)

    Ben Abdallah, M.; Malek, J.; Tourki, R.; Krissian, K.

    2011-01-01

    The ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases, such as diabetes and hypertension. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. In this paper, we introduce an implementation of the anisotropic diffusion which allows reducing the noise and better preserving small structures like vessels in 2D images. A vessel detection filter, based on a multi-scale vesselness function, is then applied to enhance vascular structures.

  5. Automated hybridization/imaging device for fluorescent multiplex DNA sequencing

    Science.gov (United States)

    Weiss, Robert B.; Kimball, Alvin W.; Gesteland, Raymond F.; Ferguson, F. Mark; Dunn, Diane M.; Di Sera, Leonard J.; Cherry, Joshua L.

    1995-01-01

    A method is disclosed for automated multiplex sequencing of DNA with an integrated automated imaging hybridization chamber system. This system comprises an hybridization chamber device for mounting a membrane containing size-fractionated multiplex sequencing reaction products, apparatus for fluid delivery to the chamber device, imaging apparatus for light delivery to the membrane and image recording of fluorescence emanating from the membrane while in the chamber device, and programmable controller apparatus for controlling operation of the system. The multiplex reaction products are hybridized with a probe, then an enzyme (such as alkaline phosphatase) is bound to a binding moiety on the probe, and a fluorogenic substrate (such as a benzothiazole derivative) is introduced into the chamber device by the fluid delivery apparatus. The enzyme converts the fluorogenic substrate into a fluorescent product which, when illuminated in the chamber device with a beam of light from the imaging apparatus, excites fluorescence of the fluorescent product to produce a pattern of hybridization. The pattern of hybridization is imaged by a CCD camera component of the imaging apparatus to obtain a series of digital signals. These signals are converted by the controller apparatus into a string of nucleotides corresponding to the nucleotide sequence an automated sequence reader. The method and apparatus are also applicable to other membrane-based applications such as colony and plaque hybridization and Southern, Northern, and Western blots.

  6. An Automated, Image Processing System for Concrete Evaluation

    International Nuclear Information System (INIS)

    Baumgart, C.W.; Cave, S.P.; Linder, K.E.

    1998-01-01

    Allied Signal Federal Manufacturing ampersand Technologies (FM ampersand T) was asked to perform a proof-of-concept study for the Missouri Highway and Transportation Department (MHTD), Research Division, in June 1997. The goal of this proof-of-concept study was to ascertain if automated scanning and imaging techniques might be applied effectively to the problem of concrete evaluation. In the current evaluation process, a concrete sample core is manually scanned under a microscope. Voids (or air spaces) within the concrete are then detected visually by a human operator by incrementing the sample under the cross-hairs of a microscope and by counting the number of ''pixels'' which fall within a void. Automation of the scanning and image analysis processes is desired to improve the speed of the scanning process, to improve evaluation consistency, and to reduce operator fatigue. An initial, proof-of-concept image analysis approach was successfully developed and demonstrated using acquired black and white imagery of concrete samples. In this paper, the automated scanning and image capture system currently under development will be described and the image processing approach developed for the proof-of-concept study will be demonstrated. A development update and plans for future enhancements are also presented

  7. Automated vasculature extraction from placenta images

    Science.gov (United States)

    Almoussa, Nizar; Dutra, Brittany; Lampe, Bryce; Getreuer, Pascal; Wittman, Todd; Salafia, Carolyn; Vese, Luminita

    2011-03-01

    Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important information on how certain diseases progress. One important property is the structure of the placental blood vessels, which supply a fetus with all of its oxygen and nutrition. An essential step in the analysis of the vascular network pattern is the extraction of the blood vessels, which has only been done manually through a costly and time-consuming process. There is no existing method to automatically detect placental blood vessels; in addition, the large variation in the shape, color, and texture of the placenta makes it difficult to apply standard edge-detection algorithms. We describe a method to automatically detect and extract blood vessels from a given image by using image processing techniques and neural networks. We evaluate several local features for every pixel, in addition to a novel modification to an existing road detector. Pixels belonging to blood vessel regions have recognizable responses; hence, we use an artificial neural network to identify the pattern of blood vessels. A set of images where blood vessels are manually highlighted is used to train the network. We then apply the neural network to recognize blood vessels in new images. The network is effective in capturing the most prominent vascular structures of the placenta.

  8. Automated Pointing of Cardiac Imaging Catheters.

    Science.gov (United States)

    Loschak, Paul M; Brattain, Laura J; Howe, Robert D

    2013-12-31

    Intracardiac echocardiography (ICE) catheters enable high-quality ultrasound imaging within the heart, but their use in guiding procedures is limited due to the difficulty of manually pointing them at structures of interest. This paper presents the design and testing of a catheter steering model for robotic control of commercial ICE catheters. The four actuated degrees of freedom (4-DOF) are two catheter handle knobs to produce bi-directional bending in combination with rotation and translation of the handle. An extra degree of freedom in the system allows the imaging plane (dependent on orientation) to be directed at an object of interest. A closed form solution for forward and inverse kinematics enables control of the catheter tip position and the imaging plane orientation. The proposed algorithms were validated with a robotic test bed using electromagnetic sensor tracking of the catheter tip. The ability to automatically acquire imaging targets in the heart may improve the efficiency and effectiveness of intracardiac catheter interventions by allowing visualization of soft tissue structures that are not visible using standard fluoroscopic guidance. Although the system has been developed and tested for manipulating ICE catheters, the methods described here are applicable to any long thin tendon-driven tool (with single or bi-directional bending) requiring accurate tip position and orientation control.

  9. Automated Image Registration Using Morphological Region of Interest Feature Extraction

    Science.gov (United States)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2005-01-01

    With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

  10. Automated delineation of stroke lesions using brain CT images

    Directory of Open Access Journals (Sweden)

    Céline R. Gillebert

    2014-01-01

    Full Text Available Computed tomographic (CT images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner.

  11. Quantifying biodiversity using digital cameras and automated image analysis.

    Science.gov (United States)

    Roadknight, C. M.; Rose, R. J.; Barber, M. L.; Price, M. C.; Marshall, I. W.

    2009-04-01

    Monitoring the effects on biodiversity of extensive grazing in complex semi-natural habitats is labour intensive. There are also concerns about the standardization of semi-quantitative data collection. We have chosen to focus initially on automating the most time consuming aspect - the image analysis. The advent of cheaper and more sophisticated digital camera technology has lead to a sudden increase in the number of habitat monitoring images and information that is being collected. We report on the use of automated trail cameras (designed for the game hunting market) to continuously capture images of grazer activity in a variety of habitats at Moor House National Nature Reserve, which is situated in the North of England at an average altitude of over 600m. Rainfall is high, and in most areas the soil consists of deep peat (1m to 3m), populated by a mix of heather, mosses and sedges. The cameras have been continuously in operation over a 6 month period, daylight images are in full colour and night images (IR flash) are black and white. We have developed artificial intelligence based methods to assist in the analysis of the large number of images collected, generating alert states for new or unusual image conditions. This paper describes the data collection techniques, outlines the quantitative and qualitative data collected and proposes online and offline systems that can reduce the manpower overheads and increase focus on important subsets in the collected data. By converting digital image data into statistical composite data it can be handled in a similar way to other biodiversity statistics thus improving the scalability of monitoring experiments. Unsupervised feature detection methods and supervised neural methods were tested and offered solutions to simplifying the process. Accurate (85 to 95%) categorization of faunal content can be obtained, requiring human intervention for only those images containing rare animals or unusual (undecidable) conditions, and

  12. Tools for automating the imaging of zebrafish larvae.

    Science.gov (United States)

    Pulak, Rock

    2016-03-01

    The VAST BioImager system is a set of tools developed for zebrafish researchers who require the collection of images from a large number of 2-7 dpf zebrafish larvae. The VAST BioImager automates larval handling, positioning and orientation tasks. Color images at about 10 μm resolution are collected from the on-board camera of the system. If images of greater resolution and detail are required, this system is mounted on an upright microscope, such as a confocal or fluorescence microscope, to utilize their capabilities. The system loads a larvae, positions it in view of the camera, determines orientation using pattern recognition analysis, and then more precisely positions to user-defined orientation for optimal imaging of any desired tissue or organ system. Multiple images of the same larva can be collected. The specific part of each larva and the desired orientation and position is identified by the researcher and an experiment defining the settings and a series of steps can be saved and repeated for imaging of subsequent larvae. The system captures images, then ejects and loads another larva from either a bulk reservoir, a well of a 96 well plate using the LP Sampler, or individually targeted larvae from a Petri dish or other container using the VAST Pipettor. Alternative manual protocols for handling larvae for image collection are tedious and time consuming. The VAST BioImager automates these steps to allow for greater throughput of assays and screens requiring high-content image collection of zebrafish larvae such as might be used in drug discovery and toxicology studies. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

  13. Automated Dermoscopy Image Analysis of Pigmented Skin Lesions

    Directory of Open Access Journals (Sweden)

    Alfonso Baldi

    2010-03-01

    Full Text Available Dermoscopy (dermatoscopy, epiluminescence microscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs, allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis. This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR.

  14. An automated and simple method for brain MR image extraction

    Directory of Open Access Journals (Sweden)

    Zhu Zixin

    2011-09-01

    Full Text Available Abstract Background The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model. Methods The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository. Results The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set. Conclusions The proposed method can provide automated and accurate brain extraction result with high efficiency.

  15. Image mosaicing for automated pipe scanning

    International Nuclear Information System (INIS)

    Summan, Rahul; Dobie, Gordon; Guarato, Francesco; MacLeod, Charles; Marshall, Stephen; Pierce, Gareth; Forrester, Cailean; Bolton, Gary

    2015-01-01

    Remote visual inspection (RVI) is critical for the inspection of the interior condition of pipelines particularly in the nuclear and oil and gas industries. Conventional RVI equipment produces a video which is analysed online by a trained inspector employing expert knowledge. Due to the potentially disorientating nature of the footage, this is a time intensive and difficult activity. In this paper a new probe for such visual inspections is presented. The device employs a catadioptric lens coupled with feature based structure from motion to create a 3D model of the interior surface of a pipeline. Reliance upon the availability of image features is mitigated through orientation and distance estimates from an inertial measurement unit and encoder respectively. Such a model affords a global view of the data thus permitting a greater appreciation of the nature and extent of defects. Furthermore, the technique estimates the 3D position and orientation of the probe thus providing information to direct remedial action. Results are presented for both synthetic and real pipe sections. The former enables the accuracy of the generated model to be assessed while the latter demonstrates the efficacy of the technique in a practice

  16. Image mosaicing for automated pipe scanning

    Energy Technology Data Exchange (ETDEWEB)

    Summan, Rahul, E-mail: rahul.summan@strath.ac.uk; Dobie, Gordon, E-mail: rahul.summan@strath.ac.uk; Guarato, Francesco, E-mail: rahul.summan@strath.ac.uk; MacLeod, Charles, E-mail: rahul.summan@strath.ac.uk; Marshall, Stephen, E-mail: rahul.summan@strath.ac.uk; Pierce, Gareth [Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW (United Kingdom); Forrester, Cailean [Inspectahire Instrument Company Ltd, Units 10 -12 Whitemyres Business Centre, Whitemyres Avenue, Aberdeen, AB16 6HQ (United Kingdom); Bolton, Gary [National Nuclear Laboratory, Chadwick House, Warrington Road, Birchwood Park, Warrington, WA3 6AE (United Kingdom)

    2015-03-31

    Remote visual inspection (RVI) is critical for the inspection of the interior condition of pipelines particularly in the nuclear and oil and gas industries. Conventional RVI equipment produces a video which is analysed online by a trained inspector employing expert knowledge. Due to the potentially disorientating nature of the footage, this is a time intensive and difficult activity. In this paper a new probe for such visual inspections is presented. The device employs a catadioptric lens coupled with feature based structure from motion to create a 3D model of the interior surface of a pipeline. Reliance upon the availability of image features is mitigated through orientation and distance estimates from an inertial measurement unit and encoder respectively. Such a model affords a global view of the data thus permitting a greater appreciation of the nature and extent of defects. Furthermore, the technique estimates the 3D position and orientation of the probe thus providing information to direct remedial action. Results are presented for both synthetic and real pipe sections. The former enables the accuracy of the generated model to be assessed while the latter demonstrates the efficacy of the technique in a practice.

  17. Automated imaging dark adaptometer for investigating hereditary retinal degenerations

    Science.gov (United States)

    Azevedo, Dario F. G.; Cideciyan, Artur V.; Regunath, Gopalakrishnan; Jacobson, Samuel G.

    1995-05-01

    We designed and built an automated imaging dark adaptometer (AIDA) to increase accuracy, reliability, versatility and speed of dark adaptation testing in patients with hereditary retinal degenerations. AIDA increases test accuracy by imaging the ocular fundus for precise positioning of bleaching and stimulus lights. It improves test reliability by permitting continuous monitoring of patient fixation. Software control of stimulus presentation provides broad testing versatility without sacrificing speed. AIDA promises to facilitate the measurement of dark adaptation in studies of the pathophysiology of retinal degenerations and in future treatment trials of these diseases.

  18. An automated system for whole microscopic image acquisition and analysis.

    Science.gov (United States)

    Bueno, Gloria; Déniz, Oscar; Fernández-Carrobles, María Del Milagro; Vállez, Noelia; Salido, Jesús

    2014-09-01

    The field of anatomic pathology has experienced major changes over the last decade. Virtual microscopy (VM) systems have allowed experts in pathology and other biomedical areas to work in a safer and more collaborative way. VMs are automated systems capable of digitizing microscopic samples that were traditionally examined one by one. The possibility of having digital copies reduces the risk of damaging original samples, and also makes it easier to distribute copies among other pathologists. This article describes the development of an automated high-resolution whole slide imaging (WSI) system tailored to the needs and problems encountered in digital imaging for pathology, from hardware control to the full digitization of samples. The system has been built with an additional digital monochromatic camera together with the color camera by default and LED transmitted illumination (RGB). Monochrome cameras are the preferred method of acquisition for fluorescence microscopy. The system is able to digitize correctly and form large high resolution microscope images for both brightfield and fluorescence. The quality of the digital images has been quantified using three metrics based on sharpness, contrast and focus. It has been proved on 150 tissue samples of brain autopsies, prostate biopsies and lung cytologies, at five magnifications: 2.5×, 10×, 20×, 40×, and 63×. The article is focused on the hardware set-up and the acquisition software, although results of the implemented image processing techniques included in the software and applied to the different tissue samples are also presented. © 2014 Wiley Periodicals, Inc.

  19. Usefulness of automated biopsy guns in image-guided biopsy

    International Nuclear Information System (INIS)

    Lee, Jung Hyung; Rhee, Chang Soo; Lee, Sung Moon; Kim, Hong; Woo, Sung Ku; Suh, Soo Jhi

    1994-01-01

    To evaluate the usefulness of automated biopsy guns in image-guided biopsy of lung, liver, pancreas and other organs. Using automated biopsy devices, 160 biopsies of variable anatomic sites were performed: Biopsies were performed under ultrasonographic(US) guidance in 95 and computed tomographic (CT) guidance in 65. We retrospectively analyzed histologic results and complications. Specimens were adequate for histopathologic diagnosis in 143 of the 160 patients(89.4%)-Diagnostic tissue was obtained in 130 (81.3%), suggestive tissue obtained in 13(8.1%), and non-diagnostic tissue was obtained in 14(8.7%). Inadequate tissue was obtained in only 3(1.9%). There was no statistically significant difference between US-guided and CT-guided percutaneous biopsy. There was no occurrence of significant complication. We have experienced mild complications in only 5 patients-2 hematuria and 2 hematochezia in transrectal prostatic biopsy, and 1 minimal pneumothorax in CT-guided percutaneous lung biopsy. All of them were resolved spontaneously. The image-guided biopsy using the automated biopsy gun was a simple, safe and accurate method of obtaining adequate specimen for the histopathologic diagnosis

  20. Automated curved planar reformation of 3D spine images

    International Nuclear Information System (INIS)

    Vrtovec, Tomaz; Likar, Bostjan; Pernus, Franjo

    2005-01-01

    Traditional techniques for visualizing anatomical structures are based on planar cross-sections from volume images, such as images obtained by computed tomography (CT) or magnetic resonance imaging (MRI). However, planar cross-sections taken in the coordinate system of the 3D image often do not provide sufficient or qualitative enough diagnostic information, because planar cross-sections cannot follow curved anatomical structures (e.g. arteries, colon, spine, etc). Therefore, not all of the important details can be shown simultaneously in any planar cross-section. To overcome this problem, reformatted images in the coordinate system of the inspected structure must be created. This operation is usually referred to as curved planar reformation (CPR). In this paper we propose an automated method for CPR of 3D spine images, which is based on the image transformation from the standard image-based to a novel spine-based coordinate system. The axes of the proposed spine-based coordinate system are determined on the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The proposed method was qualitatively and quantitatively evaluated on five CT spine images. The method performed well on both normal and pathological cases and was consistent with manually obtained ground truth data. The proposed spine-based CPR benefits from reduced structural complexity in favour of improved feature perception of the spine. The reformatted images are diagnostically valuable and enable easier navigation, manipulation and orientation in 3D space. Moreover, reformatted images may prove useful for segmentation and other image analysis tasks

  1. AUTOMATED DATA ANALYSIS FOR CONSECUTIVE IMAGES FROM DROPLET COMBUSTION EXPERIMENTS

    Directory of Open Access Journals (Sweden)

    Christopher Lee Dembia

    2012-09-01

    Full Text Available A simple automated image analysis algorithm has been developed that processes consecutive images from high speed, high resolution digital images of burning fuel droplets. The droplets burn under conditions that promote spherical symmetry. The algorithm performs the tasks of edge detection of the droplet’s boundary using a grayscale intensity threshold, and shape fitting either a circle or ellipse to the droplet’s boundary. The results are compared to manual measurements of droplet diameters done with commercial software. Results show that it is possible to automate data analysis for consecutive droplet burning images even in the presence of a significant amount of noise from soot formation. An adaptive grayscale intensity threshold provides the ability to extract droplet diameters for the wide range of noise encountered. In instances where soot blocks portions of the droplet, the algorithm manages to provide accurate measurements if a circle fit is used instead of an ellipse fit, as an ellipse can be too accommodating to the disturbance.

  2. Automated Processing of Zebrafish Imaging Data: A Survey

    Science.gov (United States)

    Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

    2013-01-01

    Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

  3. Automated image analysis for quantification of filamentous bacteria

    DEFF Research Database (Denmark)

    Fredborg, M.; Rosenvinge, F. S.; Spillum, E.

    2015-01-01

    Background: Antibiotics of the beta-lactam group are able to alter the shape of the bacterial cell wall, e.g. filamentation or a spheroplast formation. Early determination of antimicrobial susceptibility may be complicated by filamentation of bacteria as this can be falsely interpreted as growth...... displaying different resistant profiles and differences in filamentation kinetics were used to study a novel image analysis algorithm to quantify length of bacteria and bacterial filamentation. A total of 12 beta-lactam antibiotics or beta-lactam-beta-lactamase inhibitor combinations were analyzed...... in systems relying on colorimetry or turbidometry (such as Vitek-2, Phoenix, MicroScan WalkAway). The objective was to examine an automated image analysis algorithm for quantification of filamentous bacteria using the 3D digital microscopy imaging system, oCelloScope. Results: Three E. coli strains...

  4. Automated rice leaf disease detection using color image analysis

    Science.gov (United States)

    Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

    2011-06-01

    In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

  5. Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy.

    Science.gov (United States)

    Mudie, Lucy I; Wang, Xueyang; Friedman, David S; Brady, Christopher J

    2017-09-23

    As the number of people with diabetic retinopathy (DR) in the USA is expected to increase threefold by 2050, the need to reduce health care costs associated with screening for this treatable disease is ever present. Crowdsourcing and automated retinal image analysis (ARIA) are two areas where new technology has been applied to reduce costs in screening for DR. This paper reviews the current literature surrounding these new technologies. Crowdsourcing has high sensitivity for normal vs abnormal images; however, when multiple categories for severity of DR are added, specificity is reduced. ARIAs have higher sensitivity and specificity, and some commercial ARIA programs are already in use. Deep learning enhanced ARIAs appear to offer even more improvement in ARIA grading accuracy. The utilization of crowdsourcing and ARIAs may be a key to reducing the time and cost burden of processing images from DR screening.

  6. Automated tracking of the vascular tree on DSA images

    International Nuclear Information System (INIS)

    Alperin, N.; Hoffmann, K.R.; Doi, K.

    1990-01-01

    Determination of the vascular tree structure is important for reconstruction of three-dimensional vascular tree from biplane images, for assessment of the significance of a lesion, and for planning treatment for arteriovenous malformation. To automate these analyses, the authors of this paper are developing a method to determine the vascular tree structure from digital subtraction angiography (DSA) images. The authors have previously described a vessel tracking method, based on the double-square-box technique. To improve the tracking accuracy, they have developed and integrated with the previous method a connectivity test and guided-sector-search technique. The connectivity test, based on region growing techniques, eliminates tracking across nonvessel regions. The guided sector-search method incorporates information from a larger are of the image to guide the search for the next tracking point

  7. Automated Identification of Fiducial Points on 3D Torso Images

    Directory of Open Access Journals (Sweden)

    Manas M. Kawale

    2013-01-01

    Full Text Available Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D coordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship.

  8. Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

    Science.gov (United States)

    Wihardi, Y.; Setiawan, W.; Nugraha, E.

    2018-01-01

    On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

  9. Granulometric profiling of aeolian dust deposits by automated image analysis

    Science.gov (United States)

    Varga, György; Újvári, Gábor; Kovács, János; Jakab, Gergely; Kiss, Klaudia; Szalai, Zoltán

    2016-04-01

    Determination of granulometric parameters is of growing interest in the Earth sciences. Particle size data of sedimentary deposits provide insights into the physicochemical environment of transport, accumulation and post-depositional alterations of sedimentary particles, and are important proxies applied in paleoclimatic reconstructions. It is especially true for aeolian dust deposits with a fairly narrow grain size range as a consequence of the extremely selective nature of wind sediment transport. Therefore, various aspects of aeolian sedimentation (wind strength, distance to source(s), possible secondary source regions and modes of sedimentation and transport) can be reconstructed only from precise grain size data. As terrestrial wind-blown deposits are among the most important archives of past environmental changes, proper explanation of the proxy data is a mandatory issue. Automated imaging provides a unique technique to gather direct information on granulometric characteristics of sedimentary particles. Granulometric data obtained from automatic image analysis of Malvern Morphologi G3-ID is a rarely applied new technique for particle size and shape analyses in sedimentary geology. Size and shape data of several hundred thousand (or even million) individual particles were automatically recorded in this study from 15 loess and paleosoil samples from the captured high-resolution images. Several size (e.g. circle-equivalent diameter, major axis, length, width, area) and shape parameters (e.g. elongation, circularity, convexity) were calculated by the instrument software. At the same time, the mean light intensity after transmission through each particle is automatically collected by the system as a proxy of optical properties of the material. Intensity values are dependent on chemical composition and/or thickness of the particles. The results of the automated imaging were compared to particle size data determined by three different laser diffraction instruments

  10. Automated and unsupervised detection of malarial parasites in microscopic images

    Directory of Open Access Journals (Sweden)

    Purwar Yashasvi

    2011-12-01

    Full Text Available Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis and prone to human error (leading to erroneous diagnosis, even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method

  11. Automated extraction of chemical structure information from digital raster images

    Science.gov (United States)

    Park, Jungkap; Rosania, Gus R; Shedden, Kerby A; Nguyen, Mandee; Lyu, Naesung; Saitou, Kazuhiro

    2009-01-01

    Background To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research

  12. Automated extraction of chemical structure information from digital raster images

    Directory of Open Access Journals (Sweden)

    Shedden Kerby A

    2009-02-01

    Full Text Available Abstract Background To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links

  13. Automated exploration of the radio plasma imager data

    Science.gov (United States)

    Galkin, Ivan; Reinisch, Bodo; Grinstein, Georges; Khmyrov, Grigori; Kozlov, Alexander; Huang, Xueqin; Fung, Shing

    2004-12-01

    As research instruments with large information capacities become a reality, automated systems for intelligent data analysis become a necessity. Scientific archives containing huge volumes of data preclude manual manipulation or intervention and require automated exploration and mining that can at least preclassify information in categories. The large data set from the radio plasma imager (RPI) instrument on board the IMAGE satellite shows a critical need for such exploration in order to identify and archive features of interest in the volumes of visual information. In this research we have developed such a preclassifier through a model of preattentive vision capable of detecting and extracting traces of echoes from the RPI plasmagrams. The overall design of our model complies with Marr's paradigm of vision, where elements of increasing perceptual strength are built bottom up under the Gestalt constraints of good continuation and smoothness. The specifics of the RPI data, however, demanded extension of this paradigm to achieve greater robustness for signature analysis. Our preattentive model now employs a feedback neural network that refines alignment of the oriented edge elements (edgels) detected in the plasmagram image by subjecting them to collective global-scale optimization. The level of interaction between the oriented edgels is determined by their distance and mutual orientation in accordance with the Yen and Finkel model of the striate cortex that encompasses findings in psychophysical studies of human vision. The developed models have been implemented in an operational system "CORPRAL" (Cognitive Online RPI Plasmagram Ranking Algorithm) that currently scans daily submissions of the RPI plasmagrams for the presence of echo traces. Qualifying plasmagrams are tagged in the mission database, making them available for a variety of queries. We discuss CORPRAL performance and its impact on scientific analysis of RPI data.

  14. Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

    Science.gov (United States)

    Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

    2017-06-01

    This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

  15. Automated regional behavioral analysis for human brain images.

    Science.gov (United States)

    Lancaster, Jack L; Laird, Angela R; Eickhoff, Simon B; Martinez, Michael J; Fox, P Mickle; Fox, Peter T

    2012-01-01

    Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMap's 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score ≥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of ~100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains (~75%) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten "major representative" functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions.

  16. Automated Recognition of 3D Features in GPIR Images

    Science.gov (United States)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

    A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a

  17. Automated region definition for cardiac nitrogen-13-ammonia PET imaging.

    Science.gov (United States)

    Muzik, O; Beanlands, R; Wolfe, E; Hutchins, G D; Schwaiger, M

    1993-02-01

    In combination with PET, the tracer 13N-ammonia can be employed for the noninvasive quantification of myocardial perfusion at rest and after pharmacological stress. The purpose of this study was to develop an analysis method for the quantification of regional myocardial blood flow in the clinical setting. The algorithm includes correction for patient motion, an automated definition of multiple regions and display of absolute flows in polar map format. The effects of partial volume and blood to tissue cross-contamination were accounted for by optimizing the radial position of regions to meet fundamental assumptions of the kinetic model. In order to correct for motion artifacts, the myocardial displacement was manually determined based on edge-enhanced images. The obtained results exhibit the capability of the presented algorithm to noninvasively assess regional myocardial perfusion in the clinical environment.

  18. Automated 3D ultrasound image segmentation to aid breast cancer image interpretation.

    Science.gov (United States)

    Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A; Yuan, Jie; Wang, Xueding; Carson, Paul L

    2016-02-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Automated Image Analysis of Offshore Infrastructure Marine Biofouling

    Directory of Open Access Journals (Sweden)

    Kate Gormley

    2018-01-01

    Full Text Available In the UK, some of the oldest oil and gas installations have been in the water for over 40 years and have considerable colonisation by marine organisms, which may lead to both industry challenges and/or potential biodiversity benefits (e.g., artificial reefs. The project objective was to test the use of an automated image analysis software (CoralNet on images of marine biofouling from offshore platforms on the UK continental shelf, with the aim of (i training the software to identify the main marine biofouling organisms on UK platforms; (ii testing the software performance on 3 platforms under 3 different analysis criteria (methods A–C; (iii calculating the percentage cover of marine biofouling organisms and (iv providing recommendations to industry. Following software training with 857 images, and testing of three platforms, results showed that diversity of the three platforms ranged from low (in the central North Sea to moderate (in the northern North Sea. The two central North Sea platforms were dominated by the plumose anemone Metridium dianthus; and the northern North Sea platform showed less obvious species domination. Three different analysis criteria were created, where the method of selection of points, number of points assessed and confidence level thresholds (CT varied: (method A random selection of 20 points with CT 80%, (method B stratified random of 50 points with CT of 90% and (method C a grid approach of 100 points with CT of 90%. Performed across the three platforms, the results showed that there were no significant differences across the majority of species and comparison pairs. No significant difference (across all species was noted between confirmed annotations methods (A, B and C. It was considered that the software performed well for the classification of the main fouling species in the North Sea. Overall, the study showed that the use of automated image analysis software may enable a more efficient and consistent

  20. Automated image analysis of microstructure changes in metal alloys

    Science.gov (United States)

    Hoque, Mohammed E.; Ford, Ralph M.; Roth, John T.

    2005-02-01

    The ability to identify and quantify changes in the microstructure of metal alloys is valuable in metal cutting and shaping applications. For example, certain metals, after being cryogenically and electrically treated, have shown large increases in their tool life when used in manufacturing cutting and shaping processes. However, the mechanisms of microstructure changes in alloys under various treatments, which cause them to behave differently, are not yet fully understood. The changes are currently evaluated in a semi-quantitative manner by visual inspection of images of the microstructure. This research applies pattern recognition technology to quantitatively measure the changes in microstructure and to validate the initial assertion of increased tool life under certain treatments. Heterogeneous images of aluminum and tungsten carbide of various categories were analyzed using a process including background correction, adaptive thresholding, edge detection and other algorithms for automated analysis of microstructures. The algorithms are robust across a variety of operating conditions. This research not only facilitates better understanding of the effects of electric and cryogenic treatment of these materials, but also their impact on tooling and metal-cutting processes.

  1. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Baofeng Li

    2009-01-01

    Full Text Available Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

  2. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Li Baofeng

    2009-01-01

    Full Text Available Abstract Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

  3. Automated X-ray image analysis for cargo security: Critical review and future promise.

    Science.gov (United States)

    Rogers, Thomas W; Jaccard, Nicolas; Morton, Edward J; Griffin, Lewis D

    2017-01-01

    We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

  4. Automated force volume image processing for biological samples.

    Directory of Open Access Journals (Sweden)

    Pavel Polyakov

    2011-04-01

    Full Text Available Atomic force microscopy (AFM has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.

  5. Automated Nanofiber Diameter Measurement in SEM Images Using a Robust Image Analysis Method

    Directory of Open Access Journals (Sweden)

    Ertan Öznergiz

    2014-01-01

    Full Text Available Due to the high surface area, porosity, and rigidity, applications of nanofibers and nanosurfaces have developed in recent years. Nanofibers and nanosurfaces are typically produced by electrospinning method. In the production process, determination of average fiber diameter is crucial for quality assessment. Average fiber diameter is determined by manually measuring the diameters of randomly selected fibers on scanning electron microscopy (SEM images. However, as the number of the images increases, manual fiber diameter determination becomes a tedious and time consuming task as well as being sensitive to human errors. Therefore, an automated fiber diameter measurement system is desired. In the literature, this task is achieved by using image analysis algorithms. Typically, these methods first isolate each fiber in the image and measure the diameter of each isolated fiber. Fiber isolation is an error-prone process. In this study, automated calculation of nanofiber diameter is achieved without fiber isolation using image processing and analysis algorithms. Performance of the proposed method was tested on real data. The effectiveness of the proposed method is shown by comparing automatically and manually measured nanofiber diameter values.

  6. Automated processing of webcam images for phenological classification.

    Directory of Open Access Journals (Sweden)

    Ludwig Bothmann

    Full Text Available Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the

  7. Internet of things and automation of imaging: beyond representationalism

    Directory of Open Access Journals (Sweden)

    2016-09-01

    Full Text Available It is no doubt that the production of digital imagery invites for the major update of theoretical apparatus: what up until now was perceived solely or primarily as the stable representation of the world gives way to the image understood in terms of “the continuous actualization of networked data” or “networked terminal.” In my article I would like to argue that analysis of this new visual environment should not be limited to the procedures of data processing. What also invites serious investigation is acknowledging the reliance of contemporary media ecology on wireless communication which according to Adrian Mackenzie functions as “prepositions (‘at,’ ‘in,’ ‘with,’ by’, ‘between,’ ‘near,’ etc in the grammar of contemporary media” It seems especially important in the case of the imagery accompanying some instances of internet of things, where the considerable part of networked imagery is produced in a fully automated and machinic way. This crowdsourced air pollution monitoring platform consists of networked sensors transmitting signals and data which are then visualized as graphs and maps through the IoT service provider, Xively.

  8. Automated counting of bacterial colonies by image analysis.

    Science.gov (United States)

    Chiang, Pei-Ju; Tseng, Min-Jen; He, Zong-Sian; Li, Chia-Hsun

    2015-01-01

    Research on microorganisms often involves culturing as a means to determine the survival and proliferation of bacteria. The number of colonies in a culture is counted to calculate the concentration of bacteria in the original broth; however, manual counting can be time-consuming and imprecise. To save time and prevent inconsistencies, this study proposes a fully automated counting system using image processing methods. To accurately estimate the number of viable bacteria in a known volume of suspension, colonies distributing over the whole surface area of a plate, including the central and rim areas of a Petri dish are taken into account. The performance of the proposed system is compared with verified manual counts, as well as with two freely available counting software programs. Comparisons show that the proposed system is an effective method with excellent accuracy with mean value of absolute percentage error of 3.37%. A user-friendly graphical user interface is also developed and freely available for download, providing researchers in biomedicine with a more convenient instrument for the enumeration of bacterial colonies. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Automated Detection of Firearms and Knives in a CCTV Image

    Directory of Open Access Journals (Sweden)

    Michał Grega

    2016-01-01

    Full Text Available Closed circuit television systems (CCTV are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

  10. Automated Detection of Firearms and Knives in a CCTV Image.

    Science.gov (United States)

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

  11. Automated image analysis of atomic force microscopy images of rotavirus particles

    Energy Technology Data Exchange (ETDEWEB)

    Venkataraman, S. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Allison, D.P. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Biochemistry, Cellular, and Molecular Biology, University of Tennessee, Knoxville, TN 37996 (United States); Molecular Imaging Inc. Tempe, AZ, 85282 (United States); Qi, H. [Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Morrell-Falvey, J.L. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Kallewaard, N.L. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Crowe, J.E. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Doktycz, M.J. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States)]. E-mail: doktyczmj@ornl.gov

    2006-06-15

    A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.

  12. Automated endoscopic navigation and advisory system from medical image

    Science.gov (United States)

    Kwoh, Chee K.; Khan, Gul N.; Gillies, Duncan F.

    1999-05-01

    , which is developed to obtain the relative depth of the colon surface in the image by assuming a point light source very close to the camera. If we assume the colon has a shape similar to a tube, then a reasonable approximation of the position of the center of the colon (lumen) will be a function of the direction in which the majority of the normal vectors of shape are pointing. The second layer is the control layer and at this level, a decision model must be built for endoscope navigation and advisory system. The system that we built is the models of probabilistic networks that create a basic, artificial intelligence system for navigation in the colon. We have constructed the probabilistic networks from correlated objective data using the maximum weighted spanning tree algorithm. In the construction of a probabilistic network, it is always assumed that the variables starting from the same parent are conditionally independent. However, this may not hold and will give rise to incorrect inferences. In these cases, we proposed the creation of a hidden node to modify the network topology, which in effect models the dependency of correlated variables, to solve the problem. The conditional probability matrices linking the hidden node to its neighbors are determined using a gradient descent method which minimizing the objective cost function. The error gradients can be treated as updating messages and ca be propagated in any direction throughout any singly connected network to adjust the network parameters. With the above two- level approach, we have been able to build an automated endoscope navigation and advisory system successfully.

  13. Application of automated image analysis to coal petrography

    Science.gov (United States)

    Chao, E.C.T.; Minkin, J.A.; Thompson, C.L.

    1982-01-01

    The coal petrologist seeks to determine the petrographic characteristics of organic and inorganic coal constituents and their lateral and vertical variations within a single coal bed or different coal beds of a particular coal field. Definitive descriptions of coal characteristics and coal facies provide the basis for interpretation of depositional environments, diagenetic changes, and burial history and determination of the degree of coalification or metamorphism. Numerous coal core or columnar samples must be studied in detail in order to adequately describe and define coal microlithotypes, lithotypes, and lithologic facies and their variations. The large amount of petrographic information required can be obtained rapidly and quantitatively by use of an automated image-analysis system (AIAS). An AIAS can be used to generate quantitative megascopic and microscopic modal analyses for the lithologic units of an entire columnar section of a coal bed. In our scheme for megascopic analysis, distinctive bands 2 mm or more thick are first demarcated by visual inspection. These bands consist of either nearly pure microlithotypes or lithotypes such as vitrite/vitrain or fusite/fusain, or assemblages of microlithotypes. Megascopic analysis with the aid of the AIAS is next performed to determine volume percentages of vitrite, inertite, minerals, and microlithotype mixtures in bands 0.5 to 2 mm thick. The microlithotype mixtures are analyzed microscopically by use of the AIAS to determine their modal composition in terms of maceral and optically observable mineral components. Megascopic and microscopic data are combined to describe the coal unit quantitatively in terms of (V) for vitrite, (E) for liptite, (I) for inertite or fusite, (M) for mineral components other than iron sulfide, (S) for iron sulfide, and (VEIM) for the composition of the mixed phases (Xi) i = 1,2, etc. in terms of the maceral groups vitrinite V, exinite E, inertinite I, and optically observable mineral

  14. Microvascular glycocalyx dimension estimated by automated SDF imaging is not related to cardiovascular disease

    NARCIS (Netherlands)

    Amraoui, Fouad; Olde Engberink, Rik H. G.; van Gorp, Jacqueline; Ramdani, Amal; Vogt, Liffert; van den Born, Bert-Jan H.

    2014-01-01

    The EG regulates vascular homeostasis and has anti-atherogenic properties. SDF imaging allows for noninvasive visualization of microvessels and automated estimation of EG dimensions. We aimed to assess whether microcirculatory EG dimension is related to cardiovascular disease. Sublingual EG

  15. Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

    OpenAIRE

    Staley, Tim D.; Anderson, Gemma E.

    2015-01-01

    In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of severa...

  16. 3-D image pre-processing algorithms for improved automated tracing of neuronal arbors.

    Science.gov (United States)

    Narayanaswamy, Arunachalam; Wang, Yu; Roysam, Badrinath

    2011-09-01

    The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.

  17. The effect of JPEG compression on automated detection of microaneurysms in retinal images

    Science.gov (United States)

    Cree, M. J.; Jelinek, H. F.

    2008-02-01

    As JPEG compression at source is ubiquitous in retinal imaging, and the block artefacts introduced are known to be of similar size to microaneurysms (an important indicator of diabetic retinopathy) it is prudent to evaluate the effect of JPEG compression on automated detection of retinal pathology. Retinal images were acquired at high quality and then compressed to various lower qualities. An automated microaneurysm detector was run on the retinal images of various qualities of JPEG compression and the ability to predict the presence of diabetic retinopathy based on the detected presence of microaneurysms was evaluated with receiver operating characteristic (ROC) methodology. The negative effect of JPEG compression on automated detection was observed even at levels of compression sometimes used in retinal eye-screening programmes and these may have important clinical implications for deciding on acceptable levels of compression for a fully automated eye-screening programme.

  18. Twelve automated thresholding methods for segmentation of PET images: a phantom study

    Science.gov (United States)

    Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

    2012-06-01

    Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

  19. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

    Science.gov (United States)

    Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

    2017-08-01

    Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

  20. Automated interpretation of PET/CT images in patients with lung cancer

    DEFF Research Database (Denmark)

    Gutte, Henrik; Jakobsson, David; Olofsson, Fredrik

    2007-01-01

    standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used...... cancer. METHODS: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold......PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung...

  1. Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images.

    Science.gov (United States)

    Kim, Kwang-Min; Son, Kilho; Palmore, G Tayhas R

    2015-11-23

    Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation.

  2. Quantization of polyphenolic compounds in histological sections of grape berries by automated color image analysis

    Science.gov (United States)

    Clement, Alain; Vigouroux, Bertnand

    2003-04-01

    We present new results in applied color image analysis that put in evidence the significant influence of soil on localization and appearance of polyphenols in grapes. These results have been obtained with a new unsupervised classification algorithm founded on hierarchical analysis of color histograms. The process is automated thanks to a software platform we developed specifically for color image analysis and it's applications.

  3. Fluorescence In Situ Hybridization (FISH Signal Analysis Using Automated Generated Projection Images

    Directory of Open Access Journals (Sweden)

    Xingwei Wang

    2012-01-01

    Full Text Available Fluorescence in situ hybridization (FISH tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.

  4. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease.

    Science.gov (United States)

    Kim, Youngwoo; Ge, Yinghui; Tao, Cheng; Zhu, Jianbing; Chapman, Arlene B; Torres, Vicente E; Yu, Alan S L; Mrug, Michal; Bennett, William M; Flessner, Michael F; Landsittel, Doug P; Bae, Kyongtae T

    2016-04-07

    Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method. Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed. Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (Pkidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good

  5. Integrating two spectral imaging systems in an automated mineralogy application

    CSIR Research Space (South Africa)

    Harris, D

    2009-11-01

    Full Text Available A system for the automated analysis and sorting of mineral samples has been developed to assist in the concentration of heavy mineral samples in the diamond exploration process. These samples consist of irregularly shaped mineral grains ranging from...

  6. Automated morphometry of transgenic mouse brains in MR images

    NARCIS (Netherlands)

    Scheenstra, Alize Elske Hiltje

    2011-01-01

    Quantitative and local morphometry of mouse brain MRI is a relatively new field of research, where automated methods can be exploited to rapidly provide accurate and repeatable results. In this thesis we reviewed several existing methods and applications of quantitative morphometry to brain MR

  7. Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

    International Nuclear Information System (INIS)

    Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

    2007-01-01

    Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development

  8. How automated image analysis techniques help scientists in species identification and classification?

    Science.gov (United States)

    Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

    2017-09-04

    Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

  9. Automated striatal uptake analysis of 18F-FDOPA PET images applied to Parkinson's disease patients

    International Nuclear Information System (INIS)

    Chang Icheng; Lue Kunhan; Hsieh Hungjen; Liu Shuhsin; Kao, Chinhao K.

    2011-01-01

    6-[ 18 F]Fluoro-L-DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson's disease (PD) patients. Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist. The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were -0.023, -0.029, and -0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively. We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer. (author)

  10. A semi-automated image analysis procedure for in situ plankton imaging systems.

    Directory of Open Access Journals (Sweden)

    Hongsheng Bi

    Full Text Available Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects ( 95%. First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that

  11. Automated Photogrammetric Image Matching with Sift Algorithm and Delaunay Triangulation

    DEFF Research Database (Denmark)

    Karagiannis, Georgios; Antón Castro, Francesc/François; Mioc, Darka

    2016-01-01

    An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features...

  12. Application of Bayesian Classification to Content-Based Data Management

    Science.gov (United States)

    Lynnes, Christopher; Berrick, S.; Gopalan, A.; Hua, X.; Shen, S.; Smith, P.; Yang, K-Y.; Wheeler, K.; Curry, C.

    2004-01-01

    The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

  13. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

    Directory of Open Access Journals (Sweden)

    Mohendra Roy

    2016-05-01

    Full Text Available Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al., we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

  14. Comparison of the automated evaluation of phantom mama in digital and digitalized images

    International Nuclear Information System (INIS)

    Santana, Priscila do Carmo

    2011-01-01

    Mammography is an essential tool for diagnosis and early detection of breast cancer if it is provided as a very good quality service. The process of evaluating the quality of radiographic images in general, and mammography in particular, can be much more accurate, practical and fast with the help of computer analysis tools. This work compare the automated methodology for the evaluation of scanned digital images the phantom mama. By applied the DIP method techniques was possible determine geometrical and radiometric images evaluated. The evaluated parameters include circular details of low contrast, contrast ratio, spatial resolution, tumor masses, optical density and background in Phantom Mama scanned and digitized images. The both results of images were evaluated. Through this comparison was possible to demonstrate that this automated methodology is presented as a promising alternative for the reduction or elimination of subjectivity in both types of images, but the Phantom Mama present insufficient parameters for spatial resolution evaluation. (author)

  15. A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.

    Science.gov (United States)

    Banerjee, Pat; Hu, Mengqi; Kannan, Rahul; Krishnaswamy, Srinivasan

    2017-08-01

    The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.

  16. Content-based management service for medical videos.

    Science.gov (United States)

    Mendi, Engin; Bayrak, Coskun; Cecen, Songul; Ermisoglu, Emre

    2013-01-01

    Development of health information technology has had a dramatic impact to improve the efficiency and quality of medical care. Developing interoperable health information systems for healthcare providers has the potential to improve the quality and equitability of patient-centered healthcare. In this article, we describe an automated content-based medical video analysis and management service that provides convenience and ease in accessing the relevant medical video content without sequential scanning. The system facilitates effective temporal video segmentation and content-based visual information retrieval that enable a more reliable understanding of medical video content. The system is implemented as a Web- and mobile-based service and has the potential to offer a knowledge-sharing platform for the purpose of efficient medical video content access.

  17. A method for fast automated microscope image stitching.

    Science.gov (United States)

    Yang, Fan; Deng, Zhen-Sheng; Fan, Qiu-Hong

    2013-05-01

    Image stitching is an important technology to produce a panorama or larger image by combining several images with overlapped areas. In many biomedical researches, image stitching is highly desirable to acquire a panoramic image which represents large areas of certain structures or whole sections, while retaining microscopic resolution. In this study, we develop a fast normal light microscope image stitching algorithm based on feature extraction. At first, an algorithm of scale-space reconstruction of speeded-up robust features (SURF) was proposed to extract features from the images to be stitched with a short time and higher repeatability. Then, the histogram equalization (HE) method was employed to preprocess the images to enhance their contrast for extracting more features. Thirdly, the rough overlapping zones of the images preprocessed were calculated by phase correlation, and the improved SURF was used to extract the image features in the rough overlapping areas. Fourthly, the features were corresponded by matching algorithm and the transformation parameters were estimated, then the images were blended seamlessly. Finally, this procedure was applied to stitch normal light microscope images to verify its validity. Our experimental results demonstrate that the improved SURF algorithm is very robust to viewpoint, illumination, blur, rotation and zoom of the images and our method is able to stitch microscope images automatically with high precision and high speed. Also, the method proposed in this paper is applicable to registration and stitching of common images as well as stitching the microscope images in the field of virtual microscope for the purpose of observing, exchanging, saving, and establishing a database of microscope images. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

    Science.gov (United States)

    Staley, T. D.; Anderson, G. E.

    2015-11-01

    In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of several component libraries which have been packaged separately for open-source release. The most scientifically significant of these is chimenea, which implements a telescope-agnostic algorithm for automated imaging of pre-calibrated multi-epoch radio-synthesis data, of the sort typically acquired for transient surveys or follow-up. The algorithm aims to improve upon standard imaging pipelines by utilizing iterative RMS-estimation and automated source-detection to avoid so called 'Clean-bias', and makes use of CASA subroutines for the underlying image-synthesis operations. At a lower level, AMIsurvey relies upon two libraries, drive-ami and drive-casa, built to allow use of mature radio-astronomy software packages from within Python scripts. While targeted at automated imaging, the drive-casa interface can also be used to automate interaction with any of the CASA subroutines from a generic Python process. Additionally, these packages may be of wider technical interest beyond radio-astronomy, since they demonstrate use of the Python library pexpect to emulate terminal interaction with an external process. This approach allows for rapid development of a Python interface to any legacy or externally-maintained pipeline which accepts command-line input, without requiring alterations to the original code.

  19. Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

    Science.gov (United States)

    Welikala, R A; Fraz, M M; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

    2016-04-01

    Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Automated three-dimensional analysis of particle measurements using an optical profilometer and image analysis software.

    Science.gov (United States)

    Bullman, V

    2003-07-01

    The automated collection of topographic images from an optical profilometer coupled with existing image analysis software offers the unique ability to quantify three-dimensional particle morphology. Optional software available with most optical profilers permits automated collection of adjacent topographic images of particles dispersed onto a suitable substrate. Particles are recognized in the image as a set of continuous pixels with grey-level values above the grey level assigned to the substrate, whereas particle height or thickness is represented in the numerical differences between these grey levels. These images are loaded into remote image analysis software where macros automate image processing, and then distinguish particles for feature analysis, including standard two-dimensional measurements (e.g. projected area, length, width, aspect ratios) and third-dimensional measurements (e.g. maximum height, mean height). Feature measurements from each calibrated image are automatically added to cumulative databases and exported to a commercial spreadsheet or statistical program for further data processing and presentation. An example is given that demonstrates the superiority of quantitative three-dimensional measurements by optical profilometry and image analysis in comparison with conventional two-dimensional measurements for the characterization of pharmaceutical powders with plate-like particles.

  1. Microscopic images dataset for automation of RBCs counting

    Directory of Open Access Journals (Sweden)

    Sherif Abbas

    2015-12-01

    Full Text Available A method for Red Blood Corpuscles (RBCs counting has been developed using RBCs light microscopic images and Matlab algorithm. The Dataset consists of Red Blood Corpuscles (RBCs images and there RBCs segmented images. A detailed description using flow chart is given in order to show how to produce RBCs mask. The RBCs mask was used to count the number of RBCs in the blood smear image.

  2. [Clinical application of automated digital image analysis for morphology review of peripheral blood leukocyte].

    Science.gov (United States)

    Xing, Ying; Yan, Xiaohua; Pu, Chengwei; Shang, Ke; Dong, Ning; Wang, Run; Wang, Jianzhong

    2016-03-01

    To explore the clinical application of automated digital image analysis in leukocyte morphology examination when review criteria of hematology analyzer are triggered. The reference range of leukocyte differentiation by automated digital image analysis was established by analyzing 304 healthy blood samples from Peking University First Hospital. Six hundred and ninty-seven blood samples from Peking University First Hospital were randomly collected from November 2013 to April 2014, complete blood cells were counted on hematology analyzer, blood smears were made and stained at the same time. Blood smears were detected by automated digital image analyzer and the results were checked (reclassification) by a staff with abundant morphology experience. The same smear was examined manually by microscope. The results by manual microscopic differentiation were used as"golden standard", and diagnostic efficiency of abnormal specimens by automated digital image analysis was calculated, including sensitivity, specificity and accuracy. The difference of abnormal leukocytes detected by two different methods was analyzed in 30 samples of hematological and infectious diseases. Specificity of identifying abnormalities of white blood cells by automated digital image analysis was more than 90% except monocyte. Sensitivity of neutrophil toxic abnormities (including Döhle body, toxic granulate and vacuolization) was 100%; sensitivity of blast cells, immature granulates and atypical lymphocytes were 91.7%, 60% to 81.5% and 61.5%, respectively. Sensitivity of leukocyte differential count was 91.8% for neutrophils, 88.5% for lymphocytes, 69.1% for monocytes, 78.9% for eosinophils and 36.3 for basophils. The positive rate of recognizing abnormal cells (blast, immature granulocyte and atypical lymphocyte) by manual microscopic method was 46.7%, 53.3% and 10%, respectively. The positive rate of automated digital image analysis was 43.3%, 60% and 10%, respectively. There was no statistic

  3. Evaluation of an improved technique for automated center lumen line definition in cardiovascular image data

    International Nuclear Information System (INIS)

    Gratama van Andel, Hugo A.F.; Meijering, Erik; Vrooman, Henri A.; Stokking, Rik; Lugt, Aad van der; Monye, Cecile de

    2006-01-01

    The aim of the study was to evaluate a new method for automated definition of a center lumen line in vessels in cardiovascular image data. This method, called VAMPIRE, is based on improved detection of vessel-like structures. A multiobserver evaluation study was conducted involving 40 tracings in clinical CTA data of carotid arteries to compare VAMPIRE with an established technique. This comparison showed that VAMPIRE yields considerably more successful tracings and improved handling of stenosis, calcifications, multiple vessels, and nearby bone structures. We conclude that VAMPIRE is highly suitable for automated definition of center lumen lines in vessels in cardiovascular image data. (orig.)

  4. A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

    Science.gov (United States)

    Wang, Xingwei; Li, Yuhua; Liu, Hong; Li, Shibo; Zhang, Roy R.; Zheng, Bin

    2012-02-01

    Fluorescence in situ hybridization (FISH) technology provides a promising molecular imaging tool to detect cervical cancer. Since manual FISH analysis is difficult, time-consuming, and inconsistent, the automated FISH image scanning systems have been developed. Due to limited focal depth of scanned microscopic image, a FISH-probed specimen needs to be scanned in multiple layers that generate huge image data. To improve diagnostic efficiency of using automated FISH image analysis, we developed a computer-aided detection (CAD) scheme. In this experiment, four pap-smear specimen slides were scanned by a dual-detector fluorescence image scanning system that acquired two spectrum images simultaneously, which represent images of interphase cells and FISH-probed chromosome X. During image scanning, once detecting a cell signal, system captured nine image slides by automatically adjusting optical focus. Based on the sharpness index and maximum intensity measurement, cells and FISH signals distributed in 3-D space were projected into a 2-D con-focal image. CAD scheme was applied to each con-focal image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm and detect FISH-probed signals using a top-hat transform. The ratio of abnormal cells was calculated to detect positive cases. In four scanned specimen slides, CAD generated 1676 con-focal images that depicted analyzable cells. FISH-probed signals were independently detected by our CAD algorithm and an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots. The study demonstrated the feasibility of applying automated FISH image and signal analysis to assist cyto-geneticists in detecting cervical cancers.

  5. Automated quantification of budding Saccharomyces cerevisiae using a novel image cytometry method.

    Science.gov (United States)

    Laverty, Daniel J; Kury, Alexandria L; Kuksin, Dmitry; Pirani, Alnoor; Flanagan, Kevin; Chan, Leo Li-Ying

    2013-06-01

    The measurements of concentration, viability, and budding percentages of Saccharomyces cerevisiae are performed on a routine basis in the brewing and biofuel industries. Generation of these parameters is of great importance in a manufacturing setting, where they can aid in the estimation of product quality, quantity, and fermentation time of the manufacturing process. Specifically, budding percentages can be used to estimate the reproduction rate of yeast populations, which directly correlates with metabolism of polysaccharides and bioethanol production, and can be monitored to maximize production of bioethanol during fermentation. The traditional method involves manual counting using a hemacytometer, but this is time-consuming and prone to human error. In this study, we developed a novel automated method for the quantification of yeast budding percentages using Cellometer image cytometry. The automated method utilizes a dual-fluorescent nucleic acid dye to specifically stain live cells for imaging analysis of unique morphological characteristics of budding yeast. In addition, cell cycle analysis is performed as an alternative method for budding analysis. We were able to show comparable yeast budding percentages between manual and automated counting, as well as cell cycle analysis. The automated image cytometry method is used to analyze and characterize corn mash samples directly from fermenters during standard fermentation. Since concentration, viability, and budding percentages can be obtained simultaneously, the automated method can be integrated into the fermentation quality assurance protocol, which may improve the quality and efficiency of beer and bioethanol production processes.

  6. Automated, non-linear registration between 3-dimensional brain map and medical head image

    International Nuclear Information System (INIS)

    Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

    1998-01-01

    In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

  7. SU-E-I-94: Automated Image Quality Assessment of Radiographic Systems Using An Anthropomorphic Phantom

    International Nuclear Information System (INIS)

    Wells, J; Wilson, J; Zhang, Y; Samei, E; Ravin, Carl E.

    2014-01-01

    Purpose: In a large, academic medical center, consistent radiographic imaging performance is difficult to routinely monitor and maintain, especially for a fleet consisting of multiple vendors, models, software versions, and numerous imaging protocols. Thus, an automated image quality control methodology has been implemented using routine image quality assessment with a physical, stylized anthropomorphic chest phantom. Methods: The “Duke” Phantom (Digital Phantom 07-646, Supertech, Elkhart, IN) was imaged twice on each of 13 radiographic units from a variety of vendors at 13 primary care clinics. The first acquisition used the clinical PA chest protocol to acquire the post-processed “FOR PRESENTATION” image. The second image was acquired without an antiscatter grid followed by collection of the “FOR PROCESSING” image. Manual CNR measurements were made from the largest and thickest contrast-detail inserts in the lung, heart, and abdominal regions of the phantom in each image. An automated image registration algorithm was used to estimate the CNR of the same insert using similar ROIs. Automated measurements were then compared to the manual measurements. Results: Automatic and manual CNR measurements obtained from “FOR PRESENTATION” images had average percent differences of 0.42%±5.18%, −3.44%±4.85%, and 1.04%±3.15% in the lung, heart, and abdominal regions, respectively; measurements obtained from “FOR PROCESSING” images had average percent differences of -0.63%±6.66%, −0.97%±3.92%, and −0.53%±4.18%, respectively. The maximum absolute difference in CNR was 15.78%, 10.89%, and 8.73% in the respective regions. In addition to CNR assessment of the largest and thickest contrast-detail inserts, the automated method also provided CNR estimates for all 75 contrast-detail inserts in each phantom image. Conclusion: Automated analysis of a radiographic phantom has been shown to be a fast, robust, and objective means for assessing radiographic

  8. Infrared thermal imaging for automated detection of diabetic foot complications

    NARCIS (Netherlands)

    van Netten, Jaap J.; van Baal, Jeff G.; Liu, Chanjuan; van der Heijden, Ferdi; Bus, Sicco A.

    2013-01-01

    Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability

  9. Infrared thermal imaging for automated detection of diabetic foot complications

    NARCIS (Netherlands)

    van Netten, Jaap J.; van Baal, Jeff G.; Liu, C.; van der Heijden, Ferdinand; Bus, Sicco A.

    Background: Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the

  10. Automated measurement of pressure injury through image processing.

    Science.gov (United States)

    Li, Dan; Mathews, Carol

    2017-11-01

    To develop an image processing algorithm to automatically measure pressure injuries using electronic pressure injury images stored in nursing documentation. Photographing pressure injuries and storing the images in the electronic health record is standard practice in many hospitals. However, the manual measurement of pressure injury is time-consuming, challenging and subject to intra/inter-reader variability with complexities of the pressure injury and the clinical environment. A cross-sectional algorithm development study. A set of 32 pressure injury images were obtained from a western Pennsylvania hospital. First, we transformed the images from an RGB (i.e. red, green and blue) colour space to a YC b C r colour space to eliminate inferences from varying light conditions and skin colours. Second, a probability map, generated by a skin colour Gaussian model, guided the pressure injury segmentation process using the Support Vector Machine classifier. Third, after segmentation, the reference ruler - included in each of the images - enabled perspective transformation and determination of pressure injury size. Finally, two nurses independently measured those 32 pressure injury images, and intraclass correlation coefficient was calculated. An image processing algorithm was developed to automatically measure the size of pressure injuries. Both inter- and intra-rater analysis achieved good level reliability. Validation of the size measurement of the pressure injury (1) demonstrates that our image processing algorithm is a reliable approach to monitoring pressure injury progress through clinical pressure injury images and (2) offers new insight to pressure injury evaluation and documentation. Once our algorithm is further developed, clinicians can be provided with an objective, reliable and efficient computational tool for segmentation and measurement of pressure injuries. With this, clinicians will be able to more effectively monitor the healing process of pressure

  11. Automated whole animal bio-imaging assay for human cancer dissemination.

    Directory of Open Access Journals (Sweden)

    Veerander P S Ghotra

    Full Text Available A quantitative bio-imaging platform is developed for analysis of human cancer dissemination in a short-term vertebrate xenotransplantation assay. Six days after implantation of cancer cells in zebrafish embryos, automated imaging in 96 well plates coupled to image analysis algorithms quantifies spreading throughout the host. Findings in this model correlate with behavior in long-term rodent xenograft models for panels of poorly- versus highly malignant cell lines derived from breast, colorectal, and prostate cancer. In addition, cancer cells with scattered mesenchymal characteristics show higher dissemination capacity than cell types with epithelial appearance. Moreover, RNA interference establishes the metastasis-suppressor role for E-cadherin in this model. This automated quantitative whole animal bio-imaging assay can serve as a first-line in vivo screening step in the anti-cancer drug target discovery pipeline.

  12. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    NARCIS (Netherlands)

    Lee, K.; Buitendijk, G.H.; Bogunovic, H.; Springelkamp, H.; Hofman, A.; Wahle, A.; Sonka, M.; Vingerling, J.R.; Klaver, C.C.W.; Abramoff, M.D.

    2016-01-01

    PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm3)

  13. An Automated Method for Semantic Classification of Regions in Coastal Images

    NARCIS (Netherlands)

    Hoonhout, B.M.; Radermacher, M.; Baart, F.; Van der Maaten, L.J.P.

    2015-01-01

    Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness.

  14. Automated and unbiased image analyses as tools in phenotypic classification of small-spored Alternaria species

    DEFF Research Database (Denmark)

    Andersen, Birgitte; Hansen, Michael Edberg; Smedsgaard, Jørn

    2005-01-01

    often has been broadly applied to various morphologically and chemically distinct groups of isolates from different hosts. The purpose of this study was to develop and evaluate automated and unbiased image analysis systems that will analyze different phenotypic characters and facilitate testing...

  15. Automated image mosaics by non-automated light microscopes: the MicroMos software tool.

    Science.gov (United States)

    Piccinini, F; Bevilacqua, A; Lucarelli, E

    2013-12-01

    Light widefield microscopes and digital imaging are the basis for most of the analyses performed in every biological laboratory. In particular, the microscope's user is typically interested in acquiring high-detailed images for analysing observed cells and tissues, meanwhile being representative of a wide area to have reliable statistics. The microscopist has to choose between higher magnification factor and extension of the observed area, due to the finite size of the camera's field of view. To overcome the need of arrangement, mosaicing techniques have been developed in the past decades for increasing the camera's field of view by stitching together more images. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Or alternatively, the methods are conceived just to provide visually pleasant mosaics not suitable for quantitative analyses. This work presents a tool for building mosaics of images acquired with nonautomated light microscopes. The method proposed is based on visual information only and the mosaics are built by incrementally stitching couples of images, making the approach available also for online applications. Seams in the stitching regions as well as tonal inhomogeneities are corrected by compensating the vignetting effect. In the experiments performed, we tested different registration approaches, confirming that the translation model is not always the best, despite the fact that the motion of the sample holder of the microscope is apparently translational and typically considered as such. The method's implementation is freely distributed as an open source tool called MicroMos. Its usability makes building mosaics of microscope images at subpixel accuracy easier. Furthermore, optional parameters for building mosaics according to different strategies make MicroMos an easy and reliable tool to compare different registration approaches, warping models and tonal corrections. © 2013 The Authors Journal of

  16. Automated registration of multispectral MR vessel wall images of the carotid artery

    Energy Technology Data Exchange (ETDEWEB)

    Klooster, R. van ' t; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der, E-mail: rvdgeest@lumc.nl [Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden (Netherlands); Klein, S. [Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 GE (Netherlands); Kwee, R. M.; Kooi, M. E. [Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht 6202 AZ (Netherlands)

    2013-12-15

    Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

  17. An image-processing program for automated counting

    Science.gov (United States)

    Cunningham, D.J.; Anderson, W.H.; Anthony, R.M.

    1996-01-01

    An image-processing program developed by the National Institute of Health, IMAGE, was modified in a cooperative project between remote sensing specialists at the Ohio State University Center for Mapping and scientists at the Alaska Science Center to facilitate estimating numbers of black brant (Branta bernicla nigricans) in flocks at Izembek National Wildlife Refuge. The modified program, DUCK HUNT, runs on Apple computers. Modifications provide users with a pull down menu that optimizes image quality; identifies objects of interest (e.g., brant) by spectral, morphometric, and spatial parameters defined interactively by users; counts and labels objects of interest; and produces summary tables. Images from digitized photography, videography, and high- resolution digital photography have been used with this program to count various species of waterfowl.

  18. An automated image analysis system to measure and count organisms in laboratory microcosms.

    Directory of Open Access Journals (Sweden)

    François Mallard

    Full Text Available 1. Because of recent technological improvements in the way computer and digital camera perform, the potential use of imaging for contributing to the study of communities, populations or individuals in laboratory microcosms has risen enormously. However its limited use is due to difficulties in the automation of image analysis. 2. We present an accurate and flexible method of image analysis for detecting, counting and measuring moving particles on a fixed but heterogeneous substrate. This method has been specifically designed to follow individuals, or entire populations, in experimental laboratory microcosms. It can be used in other applications. 3. The method consists in comparing multiple pictures of the same experimental microcosm in order to generate an image of the fixed background. This background is then used to extract, measure and count the moving organisms, leaving out the fixed background and the motionless or dead individuals. 4. We provide different examples (springtails, ants, nematodes, daphnia to show that this non intrusive method is efficient at detecting organisms under a wide variety of conditions even on faintly contrasted and heterogeneous substrates. 5. The repeatability and reliability of this method has been assessed using experimental populations of the Collembola Folsomia candida. 6. We present an ImageJ plugin to automate the analysis of digital pictures of laboratory microcosms. The plugin automates the successive steps of the analysis and recursively analyses multiple sets of images, rapidly producing measurements from a large number of replicated microcosms.

  19. Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

    Czech Academy of Sciences Publication Activity Database

    Habart, D.; Švihlík, J.; Schier, Jan; Cahová, M.; Girman, P.; Zacharovová, K.; Berková, Z.; Kříž, J.; Fabryová, E.; Kosinová, L.; Papáčková, Z.; Kybic, J.; Saudek, F.

    2016-01-01

    Roč. 25, č. 12 (2016), s. 2145-2156 ISSN 0963-6897 Grant - others:GA ČR(CZ) GA14-10440S Institutional support: RVO:67985556 Keywords : enumeration of islets * image processing * image segmentation * islet transplantation * machine-learning * quality control Subject RIV: IN - Informatics, Computer Science Impact factor: 3.006, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/schier-0465945.pdf

  20. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images

    International Nuclear Information System (INIS)

    Shahidi, Shoaleh; Bahrampour, Ehsan; Soltanimehr, Elham; Zamani, Ali; Oshagh, Morteza; Moattari, Marzieh; Mehdizadeh, Alireza

    2014-01-01

    Two-dimensional projection radiographs have been traditionally considered the modality of choice for cephalometric analysis. To overcome the shortcomings of two-dimensional images, three-dimensional computed tomography (CT) has been used to evaluate craniofacial structures. However, manual landmark detection depends on medical expertise, and the process is time-consuming. The present study was designed to produce software capable of automated localization of craniofacial landmarks on cone beam (CB) CT images based on image registration and to evaluate its accuracy. The software was designed using MATLAB programming language. The technique was a combination of feature-based (principal axes registration) and voxel similarity-based methods for image registration. A total of 8 CBCT images were selected as our reference images for creating a head atlas. Then, 20 CBCT images were randomly selected as the test images for evaluating the method. Three experts twice located 14 landmarks in all 28 CBCT images during two examinations set 6 weeks apart. The differences in the distances of coordinates of each landmark on each image between manual and automated detection methods were calculated and reported as mean errors. The combined intraclass correlation coefficient for intraobserver reliability was 0.89 and for interobserver reliability 0.87 (95% confidence interval, 0.82 to 0.93). The mean errors of all 14 landmarks were <4 mm. Additionally, 63.57% of landmarks had a mean error of <3 mm compared with manual detection (gold standard method). The accuracy of our approach for automated localization of craniofacial landmarks, which was based on combining feature-based and voxel similarity-based methods for image registration, was acceptable. Nevertheless we recommend repetition of this study using other techniques, such as intensity-based methods

  1. Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

    Science.gov (United States)

    Fotin, Sergei V.; Yin, Yin; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter L.

    2012-02-01

    Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 +/- 0.33 mm and 3.10 +/- 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

  2. Automated tissue segmentation of MR brain images in the presence of white matter lesions.

    Science.gov (United States)

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

    2017-01-01

    Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections.

    Science.gov (United States)

    Yang, Chen; Niedieker, Daniel; Grosserüschkamp, Frederik; Horn, Melanie; Tannapfel, Andrea; Kallenbach-Thieltges, Angela; Gerwert, Klaus; Mosig, Axel

    2015-11-25

    In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

  4. An Automated Platform for High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Lanekoff, Ingela T.; Heath, Brandi S.; Liyu, Andrey V.; Thomas, Mathew; Carson, James P.; Laskin, Julia

    2012-10-02

    An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours. This is achieved by controlling the distance between the sample and the probe by mounting the sample holder onto an automated XYZ stage and defining the tilt of the sample plane. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSI QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. High resolution mass analysis combined with MS/MS experiments enabled identification of lipids and metabolites in the tissue section. In addition, high dynamic range and sensitivity of the technique allowed us to generate ion images of low-abundance isobaric lipids. High-spatial resolution image acquired over a small region of the tissue section revealed the spatial distribution of an abundant brain metabolite, creatine, in the white and gray matter that is consistent with the literature data obtained using magnetic resonance spectroscopy.

  5. Automated analysis of image mammogram for breast cancer diagnosis

    Science.gov (United States)

    Nurhasanah, Sampurno, Joko; Faryuni, Irfana Diah; Ivansyah, Okto

    2016-03-01

    Medical imaging help doctors in diagnosing and detecting diseases that attack the inside of the body without surgery. Mammogram image is a medical image of the inner breast imaging. Diagnosis of breast cancer needs to be done in detail and as soon as possible for determination of next medical treatment. The aim of this work is to increase the objectivity of clinical diagnostic by using fractal analysis. This study applies fractal method based on 2D Fourier analysis to determine the density of normal and abnormal and applying the segmentation technique based on K-Means clustering algorithm to image abnormal for determine the boundary of the organ and calculate the area of organ segmentation results. The results show fractal method based on 2D Fourier analysis can be used to distinguish between the normal and abnormal breast and segmentation techniques with K-Means Clustering algorithm is able to generate the boundaries of normal and abnormal tissue organs, so area of the abnormal tissue can be determined.

  6. Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing.

    Science.gov (United States)

    Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

    2018-02-15

    Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED light target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, direction location algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system.

  7. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  8. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

    Science.gov (United States)

    Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

    2016-01-01

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  9. Automation of chromosomes analysis. Automatic system for image processing

    International Nuclear Information System (INIS)

    Le Go, R.; Cosnac, B. de; Spiwack, A.

    1975-01-01

    The A.S.T.I. is an automatic system relating to the fast conversational processing of all kinds of images (cells, chromosomes) converted to a numerical data set (120000 points, 16 grey levels stored in a MOS memory) through a fast D.O. analyzer. The system performs automatically the isolation of any individual image, the area and weighted area of which are computed. These results are directly displayed on the command panel and can be transferred to a mini-computer for further computations. A bright spot allows parts of an image to be picked out and the results to be displayed. This study is particularly directed towards automatic karyo-typing [fr

  10. Automated Structure Detection in HRTEM Images: An Example with Graphene

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm

    Graphene, as the forefather of 2D-materials, attracts much attention due to its extraordinary properties like transparency, flexibility and outstanding high conductivity, together with a thickness of only one atom. The properties seem to be dependent on the atomic structure of graphene...... of time making it difficult to resolve dynamic processes or unstable structures. Tools that assist to get the maximum of information out of recorded images are therefore greatly appreciated. In order to get the most accurate results out of the structure detection, we have optimized the imaging conditions...

  11. System and method for automated object detection in an image

    Energy Technology Data Exchange (ETDEWEB)

    Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

    2015-10-06

    A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

  12. Automated interpretation of PET/CT images in patients with lung cancer

    DEFF Research Database (Denmark)

    Gutte, Henrik; Jakobsson, David; Olofsson, Fredrik

    2007-01-01

    cancer. METHODS: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold...... method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. CONCLUSIONS: A completely automated method using artificial neural networks can be used to detect lung cancer......PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung...

  13. Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Cheng; Chan, Qing N., E-mail: qing.chan@unsw.edu.au; Zhang, Renlin; Kook, Sanghoon; Hawkes, Evatt R.; Yeoh, Guan H. [UNSW, School of Mechanical and Manufacturing Engineering (Australia); Medwell, Paul R. [The University of Adelaide, Centre for Energy Technology (Australia)

    2016-05-15

    The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.

  14. Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.

    Science.gov (United States)

    Singh, P K; Hernandez-Herrera, P; Labate, D; Papadakis, M

    2017-10-01

    Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length, spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.

  15. Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

    International Nuclear Information System (INIS)

    Wang, Cheng; Chan, Qing N.; Zhang, Renlin; Kook, Sanghoon; Hawkes, Evatt R.; Yeoh, Guan H.; Medwell, Paul R.

    2016-01-01

    The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.

  16. Automated Coronal Loop Identification Using Digital Image Processing Techniques

    Science.gov (United States)

    Lee, Jong K.; Gary, G. Allen; Newman, Timothy S.

    2003-01-01

    The results of a master thesis project on a study of computer algorithms for automatic identification of optical-thin, 3-dimensional solar coronal loop centers from extreme ultraviolet and X-ray 2-dimensional images will be presented. These center splines are proxies of associated magnetic field lines. The project is pattern recognition problems in which there are no unique shapes or edges and in which photon and detector noise heavily influence the images. The study explores extraction techniques using: (1) linear feature recognition of local patterns (related to the inertia-tensor concept), (2) parametric space via the Hough transform, and (3) topological adaptive contours (snakes) that constrains curvature and continuity as possible candidates for digital loop detection schemes. We have developed synthesized images for the coronal loops to test the various loop identification algorithms. Since the topology of these solar features is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information in the identification process. Results from both synthesized and solar images will be presented.

  17. Automated identification of retained surgical items in radiological images

    Science.gov (United States)

    Agam, Gady; Gan, Lin; Moric, Mario; Gluncic, Vicko

    2015-03-01

    Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

  18. Transportation informatics : advanced image processing techniques automated pavement distress evaluation.

    Science.gov (United States)

    2010-01-01

    The current project, funded by MIOH-UTC for the period 1/1/2009- 4/30/2010, is concerned : with the development of the framework for a transportation facility inspection system using : advanced image processing techniques. The focus of this study is ...

  19. Automation of the method gamma of comparison dosimetry images

    International Nuclear Information System (INIS)

    Moreno Reyes, J. C.; Macias Jaen, J.; Arrans Lara, R.

    2013-01-01

    The objective of this work was the development of JJGAMMA application analysis software, which enables this task systematically, minimizing intervention specialist and therefore the variability due to the observer. Both benefits, allow comparison of images is done in practice with the required frequency and objectivity. (Author)

  20. Adaptive Algorithms for Automated Processing of Document Images

    Science.gov (United States)

    2011-01-01

    IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 21(8):761 –768, Aug. 1999. [84] N. Stamatopoulos, B. Gatos , and T. Georgiou. Automatic...2007. [85] N. Stamatopoulos, B. Gatos , and T. Georgiou. Page frame detection for double page document images. In Proc. of the 9th IAPR Int’l

  1. Unsupervised fully automated inline analysis of global left ventricular function in CINE MR imaging.

    Science.gov (United States)

    Theisen, Daniel; Sandner, Torleif A; Bauner, Kerstin; Hayes, Carmel; Rist, Carsten; Reiser, Maximilian F; Wintersperger, Bernd J

    2009-08-01

    To implement and evaluate the accuracy of unsupervised fully automated inline analysis of global ventricular function and myocardial mass (MM). To compare automated with manual segmentation in patients with cardiac disorders. In 50 patients, cine imaging of the left ventricle was performed with an accelerated retrogated steady state free precession sequence (GRAPPA; R = 2) on a 1.5 Tesla whole body scanner (MAGNETOM Avanto, Siemens Healthcare, Germany). A spatial resolution of 1.4 x 1.9 mm was achieved with a slice thickness of 8 mm and a temporal resolution of 42 milliseconds. Ventricular coverage was based on 9 to 12 short axis slices extending from the annulus of the mitral valve to the apex with 2 mm gaps. Fully automated segmentation and contouring was performed instantaneously after image acquisition. In addition to automated processing, cine data sets were also manually segmented using a semi-automated postprocessing software. Results of both methods were compared with regard to end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and MM. A subgroup analysis was performed in patients with normal (> or =55%) and reduced EF (<55%) based on the results of the manual analysis. Thirty-two percent of patients had a reduced left ventricular EF of <55%. Volumetric results of the automated inline analysis for EDV (r = 0.96), ESV (r = 0.95), EF (r = 0.89), and MM (r = 0.96) showed high correlation with the results of manual segmentation (all P < 0.001). Head-to-head comparison did not show significant differences between automated and manual evaluation for EDV (153.6 +/- 52.7 mL vs. 149.1 +/- 48.3 mL; P = 0.05), ESV (61.6 +/- 31.0 mL vs. 64.1 +/- 31.7 mL; P = 0.08), and EF (58.0 +/- 11.6% vs. 58.6 +/- 11.6%; P = 0.5). However, differences were significant for MM (150.0 +/- 61.3 g vs. 142.4 +/- 59.0 g; P < 0.01). The standard error was 15.6 (EDV), 9.7 (ESV), 5.0 (EF), and 17.1 (mass). The mean time for manual analysis was 15 minutes

  2. Extending and applying active appearance models for automated, high precision segmentation in different image modalities

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Fisker, Rune; Ersbøll, Bjarne Kjær

    2001-01-01

    , an initialization scheme is designed thus making the usage of AAMs fully automated. Using these extensions it is demonstrated that AAMs can segment bone structures in radiographs, pork chops in perspective images and the left ventricle in cardiovascular magnetic resonance images in a robust, fast and accurate...... object class description, which can be employed to rapidly search images for new object instances. The proposed extensions concern enhanced shape representation, handling of homogeneous and heterogeneous textures, refinement optimization using Simulated Annealing and robust statistics. Finally...

  3. Automated detection of new impact sites on Martian surface from HiRISE images

    Science.gov (United States)

    Xin, Xin; Di, Kaichang; Wang, Yexin; Wan, Wenhui; Yue, Zongyu

    2017-10-01

    In this study, an automated method for Martian new impact site detection from single images is presented. It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm. Experimental results using 100 HiRISE images show that the overall detection rate of proposed method is 84.5%, with a true positive rate of 86.9%. The detection rate and true positive rate in the flat regions are 93.0% and 91.5%, respectively.

  4. Automated segmentation of pigmented skin lesions in multispectral imaging

    International Nuclear Information System (INIS)

    Carrara, Mauro; Tomatis, Stefano; Bono, Aldo; Bartoli, Cesare; Moglia, Daniele; Lualdi, Manuela; Colombo, Ambrogio; Santinami, Mario; Marchesini, Renato

    2005-01-01

    The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions. (note)

  5. Scoring of radiation-induced micronuclei in cytokinesis-blocked human lymphocytes by automated image analysis

    International Nuclear Information System (INIS)

    Verhaegen, F.; Seuntjens, J.; Thierens, H.

    1994-01-01

    The micronucleus assay in human lymphocytes is, at present, frequently used to assess chromosomal damage caused by ionizing radiation or mutagens. Manual scoring of micronuclei (MN) by trained personnel is very time-consuming, tiring work, and the results depend on subjective interpretation of scoring criteria. More objective scoring can be accomplished only if the test can be automated. Furthermore, an automated system allows scoring of large numbers of cells, thereby increasing the statistical significance of the results. This is of special importance for screening programs for low doses of chromosome-damaging agents. In this paper, the first results of our effort to automate the micronucleus assay with an image-analysis system are represented. The method we used is described in detail, and the results are compared to those of other groups. Our system is able to detect 88% of the binucleated lymphocytes on the slides. The procedure consists of a fully automated localization of binucleated cells and counting of the MN within these cells, followed by a simple and fast manual operation in which the false positives are removed. Preliminary measurements for blood samples irradiated with a dose of 1 Gy X-rays indicate that the automated system can find 89% ± 12% of the micronuclei within the binucleated cells compared to a manual screening. 18 refs., 8 figs., 1 tab

  6. AUTOMATED INSPECTION OF POWER LINE CORRIDORS TO MEASURE VEGETATION UNDERCUT USING UAV-BASED IMAGES

    Directory of Open Access Journals (Sweden)

    M. Maurer

    2017-08-01

    Full Text Available Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line, and on the other hand solid objects (surrounding. The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

  7. Automation of disbond detection in aircraft fuselage through thermal image processing

    Science.gov (United States)

    Prabhu, D. R.; Winfree, W. P.

    1992-01-01

    A procedure for interpreting thermal images obtained during the nondestructive evaluation of aircraft bonded joints is presented. The procedure operates on time-derivative thermal images and resulted in a disbond image with disbonds highlighted. The size of the 'black clusters' in the output disbond image is a quantitative measure of disbond size. The procedure is illustrated using simulation data as well as data obtained through experimental testing of fabricated samples and aircraft panels. Good results are obtained, and, except in pathological cases, 'false calls' in the cases studied appeared only as noise in the output disbond image which was easily filtered out. The thermal detection technique coupled with an automated image interpretation capability will be a very fast and effective method for inspecting bonded joints in an aircraft structure.

  8. Automated gas bubble imaging at sea floor - a new method of in situ gas flux quantification

    Science.gov (United States)

    Thomanek, K.; Zielinski, O.; Sahling, H.; Bohrmann, G.

    2010-06-01

    Photo-optical systems are common in marine sciences and have been extensively used in coastal and deep-sea research. However, due to technical limitations in the past photo images had to be processed manually or semi-automatically. Recent advances in technology have rapidly improved image recording, storage and processing capabilities which are used in a new concept of automated in situ gas quantification by photo-optical detection. The design for an in situ high-speed image acquisition and automated data processing system is reported ("Bubblemeter"). New strategies have been followed with regards to back-light illumination, bubble extraction, automated image processing and data management. This paper presents the design of the novel method, its validation procedures and calibration experiments. The system will be positioned and recovered from the sea floor using a remotely operated vehicle (ROV). It is able to measure bubble flux rates up to 10 L/min with a maximum error of 33% for worst case conditions. The Bubblemeter has been successfully deployed at a water depth of 1023 m at the Makran accretionary prism offshore Pakistan during a research expedition with R/V Meteor in November 2007.

  9. An image processing framework for automated analysis of swimming behavior in tadpoles with vestibular alterations

    Science.gov (United States)

    Zarei, Kasra; Fritzsch, Bernd; Buchholz, James H. J.

    2017-03-01

    Micogravity, as experienced during prolonged space flight, presents a problem for space exploration. Animal models, specifically tadpoles, with altered connections of the vestibular ear allow the examination of the effects of microgravity and can be quantitatively monitored through tadpole swimming behavior. We describe an image analysis framework for performing automated quantification of tadpole swimming behavior. Speckle reducing anisotropic diffusion is used to smooth tadpole image signals by diffusing noise while retaining edges. A narrow band level set approach is used for sharp tracking of the tadpole body. The use of level set method for interface tracking provides an inherent advantage of using level set based image segmentation algorithm (active contouring). Active contour segmentation is followed by two-dimensional skeletonization, which allows the automated quantification of tadpole deflection angles, and subsequently tadpole escape (or C-start) response times. Evaluation of the image analysis methodology was performed by comparing the automated quantifications of deflection angles to manual assessments (obtained using a standard grading scheme), and produced a high correlation (r2 = 0.99) indicating high reliability and accuracy of the proposed method. The methods presented form an important element of objective quantification of the escape response of the tadpole vestibular system to mechanical and biochemical manipulations, and can ultimately contribute to a better understanding of the effects of altered gravity perception on humans.

  10. Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

    Science.gov (United States)

    Yousef Kalafi, Elham; Tan, Wooi Boon; Town, Christopher; Dhillon, Sarinder Kaur

    2016-12-22

    Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in

  11. Extended Field Laser Confocal Microscopy (EFLCM): Combining automated Gigapixel image capture with in silico virtual microscopy

    International Nuclear Information System (INIS)

    Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

    2008-01-01

    Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes

  12. Automated detection of diabetic retinopathy in retinal images

    Directory of Open Access Journals (Sweden)

    Carmen Valverde

    2016-01-01

    Full Text Available Diabetic retinopathy (DR is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis.

  13. Automated extraction of metastatic liver cancer regions from abdominal contrast CT images

    International Nuclear Information System (INIS)

    Yamakawa, Junki; Matsubara, Hiroaki; Kimura, Shouta; Hasegawa, Junichi; Shinozaki, Kenji; Nawano, Shigeru

    2010-01-01

    In this paper, automated extraction of metastatic liver cancer regions from abdominal contrast X-ray CT images is investigated. Because even in Japan, cases of metastatic liver cancers are increased due to recent Europeanization and/or Americanization of Japanese eating habits, development of a system for computer aided diagnosis of them is strongly expected. Our automated extraction procedure consists of following four steps; liver region extraction, density transformation for enhancement of cancer regions, segmentation for obtaining candidate cancer regions, and reduction of false positives by shape feature. Parameter values used in each step of the procedure are decided based on density and shape features of typical metastatic liver cancers. In experiments using practical 20 cases of metastatic liver tumors, it is shown that 56% of true cancers can be detected successfully from CT images by the proposed procedure. (author)

  14. A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

    Directory of Open Access Journals (Sweden)

    Phlypo Ronald

    2010-01-01

    Full Text Available We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

  15. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  16. Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

  17. Automated Image-Based Procedures for Adaptive Radiotherapy

    DEFF Research Database (Denmark)

    Bjerre, Troels

    Fractionated radiotherapy for cancer treatment is a field of constant innovation. Developments in dose delivery techniques have made it possible to precisely direct ionizing radiation at complicated targets. In order to further increase tumour control probability (TCP) and decrease normal...... to encourage bone rigidity and local tissue volume change only in the gross tumour volume and the lungs. This is highly relevant in adaptive radiotherapy when modelling significant tumour volume changes. - It is described how cone beam CT reconstruction can be modelled as a deformation of a planning CT scan...... be employed for contour propagation in adaptive radiotherapy. - MRI-radiotherapy devices have the potential to offer near real-time intrafraction imaging without any additional ionising radiation. It is detailed how the use of multiple, orthogonal slices can form the basis for reliable 3D soft tissue tracking....

  18. Feature representation and compression for content-based retrieval

    Science.gov (United States)

    Xie, Hua; Ortega, Antonio

    2000-12-01

    In semantic content-based image/video browsing and navigation systems, efficient mechanisms to represent and manage a large collection of digital images/videos are needed. Traditional keyword-based indexing describes the content of multimedia data through annotations such as text or keywords extracted manually by the user from a controlled vocabulary. This textual indexing technique lacks the flexibility of satisfying various kinds of queries requested by database users and also requires huge amount of work for updating the information. Current content-based retrieval systems often extract a set of features such as color, texture, shape motion, speed, and position from the raw multimedia data automatically and store them as content descriptors. This content-based metadata differs from text-based metadata in that it supports wider varieties of queries and can be extracted automatically, thus providing a promising approach for efficient database access and management. When the raw data volume grows very large, explicitly extracting the content-information and storing it as metadata along with the images will improve querying performance since metadata requires much less storage than the raw image data and thus will be easier to manipulate. In this paper we maintain that storing metadata together with images will enable effective information management and efficient remote query. We also show, using a texture classification example, that this side information can be compressed while guaranteeing that the desired query accuracy is satisfied. We argue that the compact representation of the image contents not only reduces significantly the storage and transmission rate requirement, but also facilitates certain types of queries. Algorithms are developed for optimized compression of this texture feature metadata given that the goal is to maximize the classification performance for a given rate budget.

  19. The impact of air pollution on the level of micronuclei measured by automated image analysis

    Czech Academy of Sciences Publication Activity Database

    Rössnerová, Andrea; Špátová, Milada; Rossner, P.; Solanský, I.; Šrám, Radim

    2009-01-01

    Roč. 669, 1-2 (2009), s. 42-47 ISSN 0027-5107 R&D Projects: GA AV ČR 1QS500390506; GA MŠk 2B06088; GA MŠk 2B08005 Institutional research plan: CEZ:AV0Z50390512 Keywords : micronuclei * binucleated cells * automated image analysis Subject RIV: DN - Health Impact of the Environment Quality Impact factor: 3.556, year: 2009

  20. OpenComet: An automated tool for comet assay image analysis

    Directory of Open Access Journals (Sweden)

    Benjamin M. Gyori

    2014-01-01

    Full Text Available Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.

  1. Development of a methodology for automated assessment of the quality of digitized images in mammography

    International Nuclear Information System (INIS)

    Santana, Priscila do Carmo

    2010-01-01

    The process of evaluating the quality of radiographic images in general, and mammography in particular, can be much more accurate, practical and fast with the help of computer analysis tools. The purpose of this study is to develop a computational methodology to automate the process of assessing the quality of mammography images through techniques of digital imaging processing (PDI), using an existing image processing environment (ImageJ). With the application of PDI techniques was possible to extract geometric and radiometric characteristics of the images evaluated. The evaluated parameters include spatial resolution, high-contrast detail, low contrast threshold, linear detail of low contrast, tumor masses, contrast ratio and background optical density. The results obtained by this method were compared with the results presented in the visual evaluations performed by the Health Surveillance of Minas Gerais. Through this comparison was possible to demonstrate that the automated methodology is presented as a promising alternative for the reduction or elimination of existing subjectivity in the visual assessment methodology currently in use. (author)

  2. Automated seed detection and three-dimensional reconstruction. I. Seed localization from fluoroscopic images or radiographs

    International Nuclear Information System (INIS)

    Tubic, Dragan; Zaccarin, Andre; Pouliot, Jean; Beaulieu, Luc

    2001-01-01

    An automated procedure for the detection of the position and the orientation of radioactive seeds on fluoroscopic images or scanned radiographs is presented. The extracted positions of seed centers and the orientations are used for three-dimensional reconstruction of permanent prostate implants. The extraction procedure requires several steps: correction of image intensifier distortions, normalization, background removal, automatic threshold selection, thresholding, and finally, moment analysis and classification of the connected components. The algorithm was tested on 75 fluoroscopic images. The results show that, on average, 92% of the seeds are detected automatically. The orientation is found with an error smaller than 5 deg. for 75% of the seeds. The orientation of overlapping seeds (10%) should be considered as an estimate at best. The image processing procedure can also be used for seed or catheter detection in CT images, with minor modifications

  3. Automated Region of Interest Retrieval of Metallographic Images for Quality Classification in Industry

    Directory of Open Access Journals (Sweden)

    Petr Kotas

    2012-01-01

    Full Text Available The aim of the research is development and testing of new methods to classify the quality of metallographic samples of steels with high added value (for example grades X70 according API. In this paper, we address the development of methods to classify the quality of slab samples images with the main emphasis on the quality of the image center called as segregation area. For this reason, we introduce an alternative method for automated retrieval of region of interest. In the first step, the metallographic image is segmented using both spectral method and thresholding. Then, the extracted macrostructure of the metallographic image is automatically analyzed by statistical methods. Finally, automatically extracted region of interests are compared with results of human experts.  Practical experience with retrieval of non-homogeneous noised digital images in industrial environment is discussed as well.

  4. English Institute Content-Based Program Manual.

    Science.gov (United States)

    Canada Coll., Redwood City, CA.

    Instructional materials designed for the content-based English as a Second Language program at Canada College's English Institute (EI) are presented in this manual. First, an introduction provides background information on the college, its student body, and the program. Drawing on relevant second language theory, this section offers a definition…

  5. Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

    Science.gov (United States)

    Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

    2009-09-01

    We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

  6. Automated 3D-Objectdocumentation on the Base of an Image Set

    Directory of Open Access Journals (Sweden)

    Sebastian Vetter

    2011-12-01

    Full Text Available Digital stereo-photogrammetry allows users an automatic evaluation of the spatial dimension and the surface texture of objects. The integration of image analysis techniques simplifies the automation of evaluation of large image sets and offers a high accuracy [1]. Due to the substantial similarities of stereoscopic image pairs, correlation techniques provide measurements of subpixel precision for corresponding image points. With the help of an automated point search algorithm in image sets identical points are used to associate pairs of images to stereo models and group them. The found identical points in all images are basis for calculation of the relative orientation of each stereo model as well as defining the relation of neighboured stereo models. By using proper filter strategies incorrect points are removed and the relative orientation of the stereo model can be made automatically. With the help of 3D-reference points or distances at the object or a defined distance of camera basis the stereo model is orientated absolute. An adapted expansion- and matching algorithm offers the possibility to scan the object surface automatically. The result is a three dimensional point cloud; the scan resolution depends on image quality. With the integration of the iterative closest point- algorithm (ICP these partial point clouds are fitted to a total point cloud. In this way, 3D-reference points are not necessary. With the help of the implemented triangulation algorithm a digital surface models (DSM can be created. The texturing can be made automatically by the usage of the images that were used for scanning the object surface. It is possible to texture the surface model directly or to generate orthophotos automatically. By using of calibrated digital SLR cameras with full frame sensor a high accuracy can be reached. A big advantage is the possibility to control the accuracy and quality of the 3d-objectdocumentation with the resolution of the images. The

  7. Automated construction of arterial and venous trees in retinal images.

    Science.gov (United States)

    Hu, Qiao; Abràmoff, Michael D; Garvin, Mona K

    2015-10-01

    While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.

  8. Automated color classification of urine dipstick image in urine examination

    Science.gov (United States)

    Rahmat, R. F.; Royananda; Muchtar, M. A.; Taqiuddin, R.; Adnan, S.; Anugrahwaty, R.; Budiarto, R.

    2018-03-01

    Urine examination using urine dipstick has long been used to determine the health status of a person. The economical and convenient use of urine dipstick is one of the reasons urine dipstick is still used to check people health status. The real-life implementation of urine dipstick is done manually, in general, that is by comparing it with the reference color visually. This resulted perception differences in the color reading of the examination results. In this research, authors used a scanner to obtain the urine dipstick color image. The use of scanner can be one of the solutions in reading the result of urine dipstick because the light produced is consistent. A method is required to overcome the problems of urine dipstick color matching and the test reference color that have been conducted manually. The method proposed by authors is Euclidean Distance, Otsu along with RGB color feature extraction method to match the colors on the urine dipstick with the standard reference color of urine examination. The result shows that the proposed approach was able to classify the colors on a urine dipstick with an accuracy of 95.45%. The accuracy of color classification on urine dipstick against the standard reference color is influenced by the level of scanner resolution used, the higher the scanner resolution level, the higher the accuracy.

  9. Automated construction of arterial and venous trees in retinal images

    Science.gov (United States)

    Hu, Qiao; Abràmoff, Michael D.; Garvin, Mona K.

    2015-01-01

    Abstract. While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input. PMID:26636114

  10. Automated vehicle counting using image processing and machine learning

    Science.gov (United States)

    Meany, Sean; Eskew, Edward; Martinez-Castro, Rosana; Jang, Shinae

    2017-04-01

    Vehicle counting is used by the government to improve roadways and the flow of traffic, and by private businesses for purposes such as determining the value of locating a new store in an area. A vehicle count can be performed manually or automatically. Manual counting requires an individual to be on-site and tally the traffic electronically or by hand. However, this can lead to miscounts due to factors such as human error A common form of automatic counting involves pneumatic tubes, but pneumatic tubes disrupt traffic during installation and removal, and can be damaged by passing vehicles. Vehicle counting can also be performed via the use of a camera at the count site recording video of the traffic, with counting being performed manually post-recording or using automatic algorithms. This paper presents a low-cost procedure to perform automatic vehicle counting using remote video cameras with an automatic counting algorithm. The procedure would utilize a Raspberry Pi micro-computer to detect when a car is in a lane, and generate an accurate count of vehicle movements. The method utilized in this paper would use background subtraction to process the images and a machine learning algorithm to provide the count. This method avoids fatigue issues that are encountered in manual video counting and prevents the disruption of roadways that occurs when installing pneumatic tubes

  11. Content-based analysis and indexing of sports video

    Science.gov (United States)

    Luo, Ming; Bai, Xuesheng; Xu, Guang-you

    2001-12-01

    An explosion of on-line image and video data in digital form is already well underway. With the exponential rise in interactive information exploration and dissemination through the World-Wide Web, the major inhibitors of rapid access to on-line video data are the management of capture and storage, and content-based intelligent search and indexing techniques. This paper proposes an approach for content-based analysis and event-based indexing of sports video. It includes a novel method to organize shots - classifying shots as close shots and far shots, an original idea of blur extent-based event detection, and an innovative local mutation-based algorithm for caption detection and retrieval. Results on extensive real TV programs demonstrate the applicability of our approach.

  12. Semi-automated Digital Imaging and Processing System for Measuring Lake Ice Thickness

    Science.gov (United States)

    Singh, Preetpal

    Canada is home to thousands of freshwater lakes and rivers. Apart from being sources of infinite natural beauty, rivers and lakes are an important source of water, food and transportation. The northern hemisphere of Canada experiences extreme cold temperatures in the winter resulting in a freeze up of regional lakes and rivers. Frozen lakes and rivers tend to offer unique opportunities in terms of wildlife harvesting and winter transportation. Ice roads built on frozen rivers and lakes are vital supply lines for industrial operations in the remote north. Monitoring the ice freeze-up and break-up dates annually can help predict regional climatic changes. Lake ice impacts a variety of physical, ecological and economic processes. The construction and maintenance of a winter road can cost millions of dollars annually. A good understanding of ice mechanics is required to build and deem an ice road safe. A crucial factor in calculating load bearing capacity of ice sheets is the thickness of ice. Construction costs are mainly attributed to producing and maintaining a specific thickness and density of ice that can support different loads. Climate change is leading to warmer temperatures causing the ice to thin faster. At a certain point, a winter road may not be thick enough to support travel and transportation. There is considerable interest in monitoring winter road conditions given the high construction and maintenance costs involved. Remote sensing technologies such as Synthetic Aperture Radar have been successfully utilized to study the extent of ice covers and record freeze-up and break-up dates of ice on lakes and rivers across the north. Ice road builders often used Ultrasound equipment to measure ice thickness. However, an automated monitoring system, based on machine vision and image processing technology, which can measure ice thickness on lakes has not been thought of. Machine vision and image processing techniques have successfully been used in manufacturing

  13. Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

    Directory of Open Access Journals (Sweden)

    B. Kuhlenkötter

    2006-01-01

    Full Text Available In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

  14. Automated recognition and characterization of solar active regions based on the SOHO/MDI images

    Science.gov (United States)

    Pap, J. M.; Turmon, M.; Mukhtar, S.; Bogart, R.; Ulrich, R.; Froehlich, C.; Wehrli, C.

    1997-01-01

    The first results of a new method to identify and characterize the various surface structures on the sun, which may contribute to the changes in solar total and spectral irradiance, are shown. The full disk magnetograms (1024 x 1024 pixels) of the Michelson Doppler Imager (MDI) experiment onboard SOHO are analyzed. Use of a Bayesian inference scheme allows objective, uniform, automated processing of a long sequence of images. The main goal is to identify the solar magnetic features causing irradiance changes. The results presented are based on a pilot time interval of August 1996.

  15. An Automated Reference Frame Selection (ARFS) Algorithm for Cone Imaging with Adaptive Optics Scanning Light Ophthalmoscopy.

    Science.gov (United States)

    Salmon, Alexander E; Cooper, Robert F; Langlo, Christopher S; Baghaie, Ahmadreza; Dubra, Alfredo; Carroll, Joseph

    2017-04-01

    To develop an automated reference frame selection (ARFS) algorithm to replace the subjective approach of manually selecting reference frames for processing adaptive optics scanning light ophthalmoscope (AOSLO) videos of cone photoreceptors. Relative distortion was measured within individual frames before conducting image-based motion tracking and sorting of frames into distinct spatial clusters. AOSLO images from nine healthy subjects were processed using ARFS and human-derived reference frames, then aligned to undistorted AO-flood images by nonlinear registration and the registration transformations were compared. The frequency at which humans selected reference frames that were rejected by ARFS was calculated in 35 datasets from healthy subjects, and subjects with achromatopsia, albinism, or retinitis pigmentosa. The level of distortion in this set of human-derived reference frames was assessed. The average transformation vector magnitude required for registration of AOSLO images to AO-flood images was significantly reduced from 3.33 ± 1.61 pixels when using manual reference frame selection to 2.75 ± 1.60 pixels (mean ± SD) when using ARFS ( P = 0.0016). Between 5.16% and 39.22% of human-derived frames were rejected by ARFS. Only 2.71% to 7.73% of human-derived frames were ranked in the top 5% of least distorted frames. ARFS outperforms expert observers in selecting minimally distorted reference frames in AOSLO image sequences. The low success rate in human frame choice illustrates the difficulty in subjectively assessing image distortion. Manual reference frame selection represented a significant barrier to a fully automated image-processing pipeline (including montaging, cone identification, and metric extraction). The approach presented here will aid in the clinical translation of AOSLO imaging.

  16. Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis.

    Directory of Open Access Journals (Sweden)

    Jean-Claude Gilhodes

    Full Text Available Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg. A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (p<0.0001 was found between automated analysis and the above standard evaluation methods. This correlation

  17. Automated low-contrast pattern recognition algorithm for magnetic resonance image quality assessment.

    Science.gov (United States)

    Ehman, Morgan O; Bao, Zhonghao; Stiving, Scott O; Kasam, Mallik; Lanners, Dianna; Peterson, Teresa; Jonsgaard, Renee; Carter, Rickey; McGee, Kiaran P

    2017-08-01

    Low contrast (LC) detectability is a common test criterion for diagnostic radiologic quality control (QC) programs. Automation of this test is desirable in order to reduce human variability and to speed up analysis. However, automation is challenging due to the complexity of the human visual perception system and the ability to create algorithms that mimic this response. This paper describes the development and testing of an automated LC detection algorithm for use in the analysis of magnetic resonance (MR) images of the American College of Radiology (ACR) QC phantom. The detection algorithm includes fuzzy logic decision processes and various edge detection methods to quantify LC detectability. Algorithm performance was first evaluated using a single LC phantom MR image with the addition of incremental zero mean Gaussian noise resulting in a total of 200 images. A c-statistic was calculated to determine the role of CNR to indicate when the algorithm would detect ten spokes. To evaluate inter-rater agreement between experienced observers and the algorithm, a blinded observer study was performed on 196 LC phantom images acquired from nine clinical MR scanners. The nine scanners included two MR manufacturers and two field strengths (1.5 T, 3.0 T). Inter-rater and algorithm-rater agreement was quantified using Krippendorff's alpha. For the Gaussian noise added data, CNR ranged from 0.519 to 11.7 with CNR being considered an excellent discriminator of algorithm performance (c-statistic = 0.9777). Reviewer scoring of the clinical phantom data resulted in an inter-rater agreement of 0.673 with the agreement between observers and algorithm equal to 0.652, both of which indicate significant agreement. This study demonstrates that the detection of LC test patterns for MR imaging QC programs can be successfully developed and that their response can model the human visual detection system of expert MR QC readers. © 2017 American Association of Physicists in Medicine.

  18. Quantification of Pulmonary Fibrosis in a Bleomycin Mouse Model Using Automated Histological Image Analysis.

    Science.gov (United States)

    Gilhodes, Jean-Claude; Julé, Yvon; Kreuz, Sebastian; Stierstorfer, Birgit; Stiller, Detlef; Wollin, Lutz

    2017-01-01

    Current literature on pulmonary fibrosis induced in animal models highlights the need of an accurate, reliable and reproducible histological quantitative analysis. One of the major limits of histological scoring concerns the fact that it is observer-dependent and consequently subject to variability, which may preclude comparative studies between different laboratories. To achieve a reliable and observer-independent quantification of lung fibrosis we developed an automated software histological image analysis performed from digital image of entire lung sections. This automated analysis was compared to standard evaluation methods with regard to its validation as an end-point measure of fibrosis. Lung fibrosis was induced in mice by intratracheal administration of bleomycin (BLM) at 0.25, 0.5, 0.75 and 1 mg/kg. A detailed characterization of BLM-induced fibrosis was performed 14 days after BLM administration using lung function testing, micro-computed tomography and Ashcroft scoring analysis. Quantification of fibrosis by automated analysis was assessed based on pulmonary tissue density measured from thousands of micro-tiles processed from digital images of entire lung sections. Prior to analysis, large bronchi and vessels were manually excluded from the original images. Measurement of fibrosis has been expressed by two indexes: the mean pulmonary tissue density and the high pulmonary tissue density frequency. We showed that tissue density indexes gave access to a very accurate and reliable quantification of morphological changes induced by BLM even for the lowest concentration used (0.25 mg/kg). A reconstructed 2D-image of the entire lung section at high resolution (3.6 μm/pixel) has been performed from tissue density values allowing the visualization of their distribution throughout fibrotic and non-fibrotic regions. A significant correlation (pfibrosis in mice, which will be very valuable for future preclinical drug explorations.

  19. An automated algorithm for photoreceptors counting in adaptive optics retinal images

    Science.gov (United States)

    Liu, Xu; Zhang, Yudong; Yun, Dai

    2012-10-01

    Eyes are important organs of humans that detect light and form spatial and color vision. Knowing the exact number of cones in retinal image has great importance in helping us understand the mechanism of eyes' function and the pathology of some eye disease. In order to analyze data in real time and process large-scale data, an automated algorithm is designed to label cone photoreceptors in adaptive optics (AO) retinal images. Images acquired by the flood-illuminated AO system are taken to test the efficiency of this algorithm. We labeled these images both automatically and manually, and compared the results of the two methods. A 94.1% to 96.5% agreement rate between the two methods is achieved in this experiment, which demonstrated the reliability and efficiency of the algorithm.

  20. SAND: an automated VLBI imaging and analysing pipeline - I. Stripping component trajectories

    Science.gov (United States)

    Zhang, M.; Collioud, A.; Charlot, P.

    2018-02-01

    We present our implementation of an automated very long baseline interferometry (VLBI) data-reduction pipeline that is dedicated to interferometric data imaging and analysis. The pipeline can handle massive VLBI data efficiently, which makes it an appropriate tool to investigate multi-epoch multiband VLBI data. Compared to traditional manual data reduction, our pipeline provides more objective results as less human interference is involved. The source extraction is carried out in the image plane, while deconvolution and model fitting are performed in both the image plane and the uv plane for parallel comparison. The output from the pipeline includes catalogues of CLEANed images and reconstructed models, polarization maps, proper motion estimates, core light curves and multiband spectra. We have developed a regression STRIP algorithm to automatically detect linear or non-linear patterns in the jet component trajectories. This algorithm offers an objective method to match jet components at different epochs and to determine their proper motions.

  1. Semi-automated camera trap image processing for the detection of ungulate fence crossing events.

    Science.gov (United States)

    Janzen, Michael; Visser, Kaitlyn; Visscher, Darcy; MacLeod, Ian; Vujnovic, Dragomir; Vujnovic, Ksenija

    2017-09-27

    Remote cameras are an increasingly important tool for ecological research. While remote camera traps collect field data with minimal human attention, the images they collect require post-processing and characterization before it can be ecologically and statistically analyzed, requiring the input of substantial time and money from researchers. The need for post-processing is due, in part, to a high incidence of non-target images. We developed a stand-alone semi-automated computer program to aid in image processing, categorization, and data reduction by employing background subtraction and histogram rules. Unlike previous work that uses video as input, our program uses still camera trap images. The program was developed for an ungulate fence crossing project and tested against an image dataset which had been previously processed by a human operator. Our program placed images into categories representing the confidence of a particular sequence of images containing a fence crossing event. This resulted in a reduction of 54.8% of images that required further human operator characterization while retaining 72.6% of the known fence crossing events. This program can provide researchers using remote camera data the ability to reduce the time and cost required for image post-processing and characterization. Further, we discuss how this procedure might be generalized to situations not specifically related to animal use of linear features.

  2. High-resolution imaging optomechatronics for precise liquid crystal display module bonding automated optical inspection

    Science.gov (United States)

    Ni, Guangming; Liu, Lin; Zhang, Jing; Liu, Juanxiu; Liu, Yong

    2018-01-01

    With the development of the liquid crystal display (LCD) module industry, LCD modules become more and more precise with larger sizes, which demands harsh imaging requirements for automated optical inspection (AOI). Here, we report a high-resolution and clearly focused imaging optomechatronics for precise LCD module bonding AOI inspection. It first presents and achieves high-resolution imaging for LCD module bonding AOI inspection using a line scan camera (LSC) triggered by a linear optical encoder, self-adaptive focusing for the whole large imaging region using LSC, and a laser displacement sensor, which reduces the requirements of machining, assembly, and motion control of AOI devices. Results show that this system can directly achieve clearly focused imaging for AOI inspection of large LCD module bonding with 0.8 μm image resolution, 2.65-mm scan imaging width, and no limited imaging width theoretically. All of these are significant for AOI inspection in the LCD module industry and other fields that require imaging large regions with high resolution.

  3. Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

    Science.gov (United States)

    Wang, Jason J.; Perrin, Marshall D.; Savransky, Dmitry; Arriaga, Pauline; Chilcote, Jeffrey K.; De Rosa, Robert J.; Millar-Blanchaer, Maxwell A.; Marois, Christian; Rameau, Julien; Wolff, Schuyler G.; Shapiro, Jacob; Ruffio, Jean-Baptiste; Maire, Jérôme; Marchis, Franck; Graham, James R.; Macintosh, Bruce; Ammons, S. Mark; Bailey, Vanessa P.; Barman, Travis S.; Bruzzone, Sebastian; Bulger, Joanna; Cotten, Tara; Doyon, René; Duchêne, Gaspard; Fitzgerald, Michael P.; Follette, Katherine B.; Goodsell, Stephen; Greenbaum, Alexandra Z.; Hibon, Pascale; Hung, Li-Wei; Ingraham, Patrick; Kalas, Paul; Konopacky, Quinn M.; Larkin, James E.; Marley, Mark S.; Metchev, Stanimir; Nielsen, Eric L.; Oppenheimer, Rebecca; Palmer, David W.; Patience, Jennifer; Poyneer, Lisa A.; Pueyo, Laurent; Rajan, Abhijith; Rantakyrö, Fredrik T.; Schneider, Adam C.; Sivaramakrishnan, Anand; Song, Inseok; Soummer, Remi; Thomas, Sandrine; Wallace, J. Kent; Ward-Duong, Kimberly; Wiktorowicz, Sloane J.

    2018-01-01

    The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

  4. An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network

    Directory of Open Access Journals (Sweden)

    Amiri S

    2013-12-01

    Full Text Available Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM, white matter (WM and cerebrospinal fluid (CSF, but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classifcation errors in the results of image segmentation. Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features and is classifed using a MLPNN into one of the three classes WM and GM or unknown. Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.

  5. Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function.

    Science.gov (United States)

    Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A

    2016-08-01

    Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.

  6. An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network.

    Science.gov (United States)

    Amiri, S; Movahedi, M M; Kazemi, K; Parsaei, H

    2013-12-01

    Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors in the results of image segmentation. An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classified using a MLPNN into one of the three classes WM and GM or unknown. The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.

  7. SlideJ: An ImageJ plugin for automated processing of whole slide images.

    Directory of Open Access Journals (Sweden)

    Vincenzo Della Mea

    Full Text Available The digital slide, or Whole Slide Image, is a digital image, acquired with specific scanners, that represents a complete tissue sample or cytological specimen at microscopic level. While Whole Slide image analysis is recognized among the most interesting opportunities, the typical size of such images-up to Gpixels- can be very demanding in terms of memory requirements. Thus, while algorithms and tools for processing and analysis of single microscopic field images are available, Whole Slide images size makes the direct use of such tools prohibitive or impossible. In this work a plugin for ImageJ, named SlideJ, is proposed with the objective to seamlessly extend the application of image analysis algorithms implemented in ImageJ for single microscopic field images to a whole digital slide analysis. The plugin has been complemented by examples of macro in the ImageJ scripting language to demonstrate its use in concrete situations.

  8. Microscope image based fully automated stomata detection and pore measurement method for grapevines

    Directory of Open Access Journals (Sweden)

    Hiranya Jayakody

    2017-11-01

    Full Text Available Abstract Background Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. Results First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area

  9. Microscope image based fully automated stomata detection and pore measurement method for grapevines.

    Science.gov (United States)

    Jayakody, Hiranya; Liu, Scarlett; Whitty, Mark; Petrie, Paul

    2017-01-01

    Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area, 94.06% for major axis length, 93.31% for minor

  10. An Automated Tracking Approach for Extraction of Retinal Vasculature in Fundus Images

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2010-01-01

    Full Text Available Purpose: To present a novel automated method for tracking and detection of retinal blood vessels in fundus images. Methods: For every pixel in retinal images, a feature vector was computed utilizing multiscale analysis based on Gabor filters. To classify the pixels based on their extracted features as vascular or non-vascular, various classifiers including Quadratic Gaussian (QG, K-Nearest Neighbors (KNN, and Neural Networks (NN were investigated. The accuracy of classifiers was evaluated using Receiver Operating Characteristic (ROC curve analysis in addition to sensitivity and specificity measurements. We opted for an NN model due to its superior performance in classification of retinal pixels as vascular and non-vascular. Results: The proposed method achieved an overall accuracy of 96.9%, sensitivity of 96.8%, and specificity of 97.3% for identification of retinal blood vessels using a dataset of 40 images. The area under the ROC curve reached a value of 0.967. Conclusion: Automated tracking and identification of retinal blood vessels based on Gabor filters and neural network classifiers seems highly successful. Through a comprehensive optimization process of operational parameters, our proposed scheme does not require any user intervention and has consistent performance for both normal and abnormal images.

  11. Fully automated segmentation of left ventricle using dual dynamic programming in cardiac cine MR images

    Science.gov (United States)

    Jiang, Luan; Ling, Shan; Li, Qiang

    2016-03-01

    Cardiovascular diseases are becoming a leading cause of death all over the world. The cardiac function could be evaluated by global and regional parameters of left ventricle (LV) of the heart. The purpose of this study is to develop and evaluate a fully automated scheme for segmentation of LV in short axis cardiac cine MR images. Our fully automated method consists of three major steps, i.e., LV localization, LV segmentation at end-diastolic phase, and LV segmentation propagation to the other phases. First, the maximum intensity projection image along the time phases of the midventricular slice, located at the center of the image, was calculated to locate the region of interest of LV. Based on the mean intensity of the roughly segmented blood pool in the midventricular slice at each phase, end-diastolic (ED) and end-systolic (ES) phases were determined. Second, the endocardial and epicardial boundaries of LV of each slice at ED phase were synchronously delineated by use of a dual dynamic programming technique. The external costs of the endocardial and epicardial boundaries were defined with the gradient values obtained from the original and enhanced images, respectively. Finally, with the advantages of the continuity of the boundaries of LV across adjacent phases, we propagated the LV segmentation from the ED phase to the other phases by use of dual dynamic programming technique. The preliminary results on 9 clinical cardiac cine MR cases show that the proposed method can obtain accurate segmentation of LV based on subjective evaluation.

  12. A method for the automated detection phishing websites through both site characteristics and image analysis

    Science.gov (United States)

    White, Joshua S.; Matthews, Jeanna N.; Stacy, John L.

    2012-06-01

    Phishing website analysis is largely still a time-consuming manual process of discovering potential phishing sites, verifying if suspicious sites truly are malicious spoofs and if so, distributing their URLs to the appropriate blacklisting services. Attackers increasingly use sophisticated systems for bringing phishing sites up and down rapidly at new locations, making automated response essential. In this paper, we present a method for rapid, automated detection and analysis of phishing websites. Our method relies on near real-time gathering and analysis of URLs posted on social media sites. We fetch the pages pointed to by each URL and characterize each page with a set of easily computed values such as number of images and links. We also capture a screen-shot of the rendered page image, compute a hash of the image and use the Hamming distance between these image hashes as a form of visual comparison. We provide initial results demonstrate the feasibility of our techniques by comparing legitimate sites to known fraudulent versions from Phishtank.com, by actively introducing a series of minor changes to a phishing toolkit captured in a local honeypot and by performing some initial analysis on a set of over 2.8 million URLs posted to Twitter over a 4 days in August 2011. We discuss the issues encountered during our testing such as resolvability and legitimacy of URL's posted on Twitter, the data sets used, the characteristics of the phishing sites we discovered, and our plans for future work.

  13. AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. O. Ok

    2015-03-01

    Full Text Available This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012. The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

  14. Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

    Science.gov (United States)

    Paesen, Rik; Smolders, Sophie; Vega, José Manolo de Hoyos; Eijnde, Bert O.; Hansen, Dominique; Ameloot, Marcel

    2016-02-01

    Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

  15. A Container Horizontal Positioning Method with Image Sensors for Cranes in Automated Container Terminals

    Directory of Open Access Journals (Sweden)

    FU Yonghua

    2014-03-01

    Full Text Available Automation is a trend for large container terminals nowadays, and container positioning techniques are key factor in the automating process. Vision based positioning techniques are inexpensive and rather accurate in nature, while the effect with insufficient illumination is left in question. This paper proposed a vision-based procedure with image sensors to determine the position of one container in the horizontal plane. The points found by the edge detection operator are clustered, and only the peak points in the parameter space of the Hough transformation is selected, in order that the effect of noises could be much decreased. The effectiveness of our procedure is verified in experiments, in which the efficiency of the procedure is also investigated.

  16. Automated identification of copepods using digital image processing and artificial neural network.

    Science.gov (United States)

    Leow, Lee Kien; Chew, Li-Lee; Chong, Ving Ching; Dhillon, Sarinder Kaur

    2015-01-01

    Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.

  17. ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples.

    Science.gov (United States)

    Kudella, Patrick Wolfgang; Moll, Kirsten; Wahlgren, Mats; Wixforth, Achim; Westerhausen, Christoph

    2016-04-18

    Rosetting is associated with severe malaria and a primary cause of death in Plasmodium falciparum infections. Detailed understanding of this adhesive phenomenon may enable the development of new therapies interfering with rosette formation. For this, it is crucial to determine parameters such as rosetting and parasitaemia of laboratory strains or patient isolates, a bottleneck in malaria research due to the time consuming and error prone manual analysis of specimens. Here, the automated, free, stand-alone analysis software automated rosetting analyzer for micrographs (ARAM) to determine rosetting rate, rosette size distribution as well as parasitaemia with a convenient graphical user interface is presented. Automated rosetting analyzer for micrographs is an executable with two operation modes for automated identification of objects on images. The default mode detects red blood cells and fluorescently labelled parasitized red blood cells by combining an intensity-gradient with a threshold filter. The second mode determines object location and size distribution from a single contrast method. The obtained results are compared with standardized manual analysis. Automated rosetting analyzer for micrographs calculates statistical confidence probabilities for rosetting rate and parasitaemia. Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis. For the first time rosette size distribution is determined in a precise and quantitative manner employing ARAM in combination with established inhibition tests. Additionally ARAM measures the essential observables parasitaemia, rosetting rate and size as well as location of all detected objects and provides confidence intervals for the determined observables. No other existing software solution offers this range of function. The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects

  18. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Ani eEloyan

    2012-08-01

    Full Text Available Successful automated diagnoses of attention deficit hyperactive disorder (ADHD using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions, CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

  19. SIFT optimization and automation for matching images from multiple temporal sources

    Science.gov (United States)

    Castillo-Carrión, Sebastián; Guerrero-Ginel, José-Emilio

    2017-05-01

    Scale Invariant Feature Transformation (SIFT) was applied to extract tie-points from multiple source images. Although SIFT is reported to perform reliably under widely different radiometric and geometric conditions, using the default input parameters resulted in too few points being found. We found that the best solution was to focus on large features as these are more robust and not prone to scene changes over time, which constitutes a first approach to the automation of processes using mapping applications such as geometric correction, creation of orthophotos and 3D models generation. The optimization of five key SIFT parameters is proposed as a way of increasing the number of correct matches; the performance of SIFT is explored in different images and parameter values, finding optimization values which are corroborated using different validation imagery. The results show that the optimization model improves the performance of SIFT in correlating multitemporal images captured from different sources.

  20. Technique for Automated Recognition of Sunspots on Full-Disk Solar Images

    Directory of Open Access Journals (Sweden)

    Zharkov S

    2005-01-01

    Full Text Available A new robust technique is presented for automated identification of sunspots on full-disk white-light (WL solar images obtained from SOHO/MDI instrument and Ca II K1 line images from the Meudon Observatory. Edge-detection methods are applied to find sunspot candidates followed by local thresholding using statistical properties of the region around sunspots. Possible initial oversegmentation of images is remedied with a median filter. The features are smoothed by using morphological closing operations and filled by applying watershed, followed by dilation operator to define regions of interest containing sunspots. A number of physical and geometrical parameters of detected sunspot features are extracted and stored in a relational database along with umbra-penumbra information in the form of pixel run-length data within a bounding rectangle. The detection results reveal very good agreement with the manual synoptic maps and a very high correlation with those produced manually by NOAA Observatory, USA.

  1. Automated detection of acute haemorrhagic stroke in non-contrasted CT images

    International Nuclear Information System (INIS)

    Meetz, K.; Buelow, T.

    2007-01-01

    An efficient treatment of stroke patients implies a profound differential diagnosis that includes the detection of acute haematoma. The proposed approach provides an automated detection of acute haematoma, assisting the non-stroke expert in interpreting non-contrasted CT images. It consists of two steps: First, haematoma candidates are detected applying multilevel region growing approach based on a typical grey value characteristic. Second, true haematomas are differentiated from partial volume artefacts, relying on spatial features derived from distance-based histograms. This approach achieves a specificity of 77% and a sensitivity of 89.7% in detecting acute haematoma in non-contrasted CT images when applied to a set of 25 non-contrasted CT images. (orig.)

  2. Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya

    DEFF Research Database (Denmark)

    Juul Bøgelund Hansen, Morten; Abramoff, M. D.; Folk, J. C.

    2015-01-01

    Objective Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased...... gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. Conclusions In this epidemiological sample, the IDP's grading was comparable...... workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields...

  3. Automated system for acquisition and image processing for the control and monitoring boned nopal

    Science.gov (United States)

    Luevano, E.; de Posada, E.; Arronte, M.; Ponce, L.; Flores, T.

    2013-11-01

    This paper describes the design and fabrication of a system for acquisition and image processing to control the removal of thorns nopal vegetable (Opuntia ficus indica) in an automated machine that uses pulses of a laser of Nd: YAG. The areolas, areas where thorns grow on the bark of the Nopal, are located applying segmentation algorithms to the images obtained by a CCD. Once the position of the areolas is known, coordinates are sent to a motors system that controls the laser to interact with all areolas and remove the thorns of the nopal. The electronic system comprises a video decoder, memory for image and software storage, and digital signal processor for system control. The firmware programmed tasks on acquisition, preprocessing, segmentation, recognition and interpretation of the areolas. This system achievement identifying areolas and generating table of coordinates of them, which will be send the motor galvo system that controls the laser for removal

  4. High-Throughput Light Sheet Microscopy for the Automated Live Imaging of Larval Zebrafish

    Science.gov (United States)

    Baker, Ryan; Logan, Savannah; Dudley, Christopher; Parthasarathy, Raghuveer

    The zebrafish is a model organism with a variety of useful properties; it is small and optically transparent, it reproduces quickly, it is a vertebrate, and there are a large variety of transgenic animals available. Because of these properties, the zebrafish is well suited to study using a variety of optical technologies including light sheet fluorescence microscopy (LSFM), which provides high-resolution three-dimensional imaging over large fields of view. Research progress, however, is often not limited by optical techniques but instead by the number of samples one can examine over the course of an experiment, which in the case of light sheet imaging has so far been severely limited. Here we present an integrated fluidic circuit and microscope which provides rapid, automated imaging of zebrafish using several imaging modes, including LSFM, Hyperspectral Imaging, and Differential Interference Contrast Microscopy. Using this system, we show that we can increase our imaging throughput by a factor of 10 compared to previous techniques. We also show preliminary results visualizing zebrafish immune response, which is sensitive to gut microbiota composition, and which shows a strong variability between individuals that highlights the utility of high throughput imaging. National Science Foundation, Award No. DBI-1427957.

  5. The use of the Kalman filter in the automated segmentation of EIT lung images.

    Science.gov (United States)

    Zifan, A; Liatsis, P; Chapman, B E

    2013-06-01

    In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

  6. Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features

    Science.gov (United States)

    Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yu, Lianbo; Gurcan, Metin

    2017-03-01

    In this study, we propose an automated otoscopy image analysis system called Autoscope. To the best of our knowledge, Autoscope is the first system designed to detect a wide range of eardrum abnormalities by using high-resolution otoscope images and report the condition of the eardrum as "normal" or "abnormal." In order to achieve this goal, first, we developed a preprocessing step to reduce camera-specific problems, detect the region of interest in the image, and prepare the image for further analysis. Subsequently, we designed a new set of clinically motivated eardrum features (CMEF). Furthermore, we evaluated the potential of the visual MPEG-7 descriptors for the task of tympanic membrane image classification. Then, we fused the information extracted from the CMEF and state-of-the-art computer vision features (CVF), which included MPEG-7 descriptors and two additional features together, using a state of the art classifier. In our experiments, 247 tympanic membrane images with 14 different types of abnormality were used, and Autoscope was able to classify the given tympanic membrane images as normal or abnormal with 84.6% accuracy.

  7. automated image analysis system for homogeneity evaluation of nuclear fuel plates

    International Nuclear Information System (INIS)

    Hassan, A.H.H.

    2005-01-01

    the main aim of this work is to design an automated image analysis system developed for inspection of fuel plates manufactured for the operation of ETRR-2 of egypt. the proposed system aims to evaluate homogeneity of the core of the fuel plate, and detecting white spot outside the fuel core. A vision system has been introduced to capture images for plates to be characterized and software has been developed to analyze the captured images based on the gray level co-occurrence matrix (GLCM). the images are digitized using digital camera. it is common practice to adopt a preprocessing step for the images with a special purpose of reduction/eliminating the noise. two preprocessing steps are carried out, application of a median type low pass filter and contrast improvement by extending the image's histogram. the analysis of texture features of co-occurrence matrix (COM) is a good tool to investigate the identification of fuel plates images based on different structures of COM considering neighbouring distance, direction

  8. Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

    Science.gov (United States)

    Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

    2018-01-10

    We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

  9. Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle †

    Science.gov (United States)

    Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

    2018-01-01

    We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy. PMID:29320434

  10. Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort.

    Science.gov (United States)

    Welikala, R A; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

    2017-11-01

    The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835,914 centreline pixels derived from 100 retinal images selected from the 135,867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Automated detection and tracking of many cells by using 4D live-cell imaging data.

    Science.gov (United States)

    Tokunaga, Terumasa; Hirose, Osamu; Kawaguchi, Shotaro; Toyoshima, Yu; Teramoto, Takayuki; Ikebata, Hisaki; Kuge, Sayuri; Ishihara, Takeshi; Iino, Yuichi; Yoshida, Ryo

    2014-06-15

    Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves. The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans. http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 SUPPLEMENTARY INFORMATION: Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 © The Author 2014. Published by Oxford University Press.

  12. Comparison of manually produced and automated cross country movement maps using digital image processing techniques

    Science.gov (United States)

    Wynn, L. K.

    1985-01-01

    The Image-Based Information System (IBIS) was used to automate the cross country movement (CCM) mapping model developed by the Defense Mapping Agency (DMA). Existing terrain factor overlays and a CCM map, produced by DMA for the Fort Lewis, Washington area, were digitized and reformatted into geometrically registered images. Terrain factor data from Slope, Soils, and Vegetation overlays were entered into IBIS, and were then combined utilizing IBIS-programmed equations to implement the DMA CCM model. The resulting IBIS-generated CCM map was then compared with the digitized manually produced map to test similarity. The numbers of pixels comprising each CCM region were compared between the two map images, and percent agreement between each two regional counts was computed. The mean percent agreement equalled 86.21%, with an areally weighted standard deviation of 11.11%. Calculation of Pearson's correlation coefficient yielded +9.997. In some cases, the IBIS-calculated map code differed from the DMA codes: analysis revealed that IBIS had calculated the codes correctly. These highly positive results demonstrate the power and accuracy of IBIS in automating models which synthesize a variety of thematic geographic data.

  13. Automated measurement of CT noise in patient images with a novel structure coherence feature

    International Nuclear Information System (INIS)

    Chun, Minsoo; Kim, Jong Hyo; Choi, Young Hun

    2015-01-01

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCC within   =  0.75, PCC between   =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine. (paper)

  14. Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imaging.

    Science.gov (United States)

    Jenkins, Cesare H; Naczynski, Dominik J; Yu, Shu-Jung S; Yang, Yong; Xing, Lei

    2016-09-07

    Performing mechanical and geometric quality assurance (QA) tests for medical linear accelerators (LINAC) is a predominantly manual process that consumes significant time and resources. In order to alleviate this burden this study proposes a novel strategy to automate the process of performing these tests. The autonomous QA system consists of three parts: (1) a customized phantom coated with radioluminescent material; (2) an optical imaging system capable of visualizing the incidence of the radiation beam, light field or lasers on the phantom; and (3) software to process the captured signals. The radioluminescent phantom, which enables visualization of the radiation beam on the same surface as the light field and lasers, is placed on the couch and imaged while a predefined treatment plan is delivered from the LINAC. The captured images are then processed to self-calibrate the system and perform measurements for evaluating light field/radiation coincidence, jaw position indicators, cross-hair centering, treatment couch position indicators and localizing laser alignment. System accuracy is probed by intentionally introducing errors and by comparing with current clinical methods. The accuracy of self-calibration is evaluated by examining measurement repeatability under fixed and variable phantom setups. The integrated system was able to automatically collect, analyze and report the results for the mechanical alignment tests specified by TG-142. The average difference between introduced and measured errors was 0.13 mm. The system was shown to be consistent with current techniques. Measurement variability increased slightly from 0.1 mm to 0.2 mm when the phantom setup was varied, but no significant difference in the mean measurement value was detected. Total measurement time was less than 10 minutes for all tests as a result of automation. The system's unique features of a phosphor-coated phantom and fully automated, operator independent self-calibration offer the

  15. Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year.

    Science.gov (United States)

    Majid, D S Adnan; Stoffers, Diederick; Sheldon, Sarah; Hamza, Samar; Thompson, Wesley K; Goldstein, Jody; Corey-Bloom, Jody; Aron, Adam R

    2011-07-01

    Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1-year longitudinal design, we analyzed T(1) -weighted structural scans in 35 preHD individuals and 22 age-matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel-level changes in atrophy. We calculated sample sizes for a hypothetical disease-modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, -0.149%; preHD, -0.388%; P = .031, Cohen's d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close-to-onset preHD, -0.510%; P = .019, Cohen's d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well-established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close-to-onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole-brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. Copyright © 2011 Movement Disorder Society.

  16. Fully automated quantitative analysis of breast cancer risk in DCE-MR images

    Science.gov (United States)

    Jiang, Luan; Hu, Xiaoxin; Gu, Yajia; Li, Qiang

    2015-03-01

    Amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance (DCE-MR) images are two important indices for breast cancer risk assessment in the clinical practice. The purpose of this study is to develop and evaluate a fully automated scheme for quantitative analysis of FGT and BPE in DCE-MR images. Our fully automated method consists of three steps, i.e., segmentation of whole breast, fibroglandular tissues, and enhanced fibroglandular tissues. Based on the volume of interest extracted automatically, dynamic programming method was applied in each 2-D slice of a 3-D MR scan to delineate the chest wall and breast skin line for segmenting the whole breast. This step took advantages of the continuity of chest wall and breast skin line across adjacent slices. We then further used fuzzy c-means clustering method with automatic selection of cluster number for segmenting the fibroglandular tissues within the segmented whole breast area. Finally, a statistical method was used to set a threshold based on the estimated noise level for segmenting the enhanced fibroglandular tissues in the subtraction images of pre- and post-contrast MR scans. Based on the segmented whole breast, fibroglandular tissues, and enhanced fibroglandular tissues, FGT and BPE were automatically computed. Preliminary results of technical evaluation and clinical validation showed that our fully automated scheme could obtain good segmentation of the whole breast, fibroglandular tissues, and enhanced fibroglandular tissues to achieve accurate assessment of FGT and BPE for quantitative analysis of breast cancer risk.

  17. Bacterial growth on surfaces: Automated image analysis for quantification of growth rate-related parameters

    DEFF Research Database (Denmark)

    Møller, S.; Sternberg, Claus; Poulsen, L. K.

    1995-01-01

    species-specific hybridizations with fluorescence-labelled ribosomal probes to estimate the single-cell concentration of RNA. By automated analysis of digitized images of stained cells, we determined four independent growth rate-related parameters: cellular RNA and DNA contents, cell volume......, and the frequency of dividing cells in a cell population. These parameters were used to compare physiological states of liquid-suspended and surfacegrowing Pseudomonas putida KT2442 in chemostat cultures. The major finding is that the correlation between substrate availability and cellular growth rate found...

  18. Automated registration of freehand B-mode ultrasound and magnetic resonance imaging of the carotid arteries based on geometric features

    DEFF Research Database (Denmark)

    Carvalho, Diego D. B.; Arias Lorza, Andres Mauricio; Niessen, Wiro J.

    2017-01-01

    An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual an...

  19. Empirical gradient threshold technique for automated segmentation across image modalities and cell lines.

    Science.gov (United States)

    Chalfoun, J; Majurski, M; Peskin, A; Breen, C; Bajcsy, P; Brady, M

    2015-10-01

    New microscopy technologies are enabling image acquisition of terabyte-sized data sets consisting of hundreds of thousands of images. In order to retrieve and analyze the biological information in these large data sets, segmentation is needed to detect the regions containing cells or cell colonies. Our work with hundreds of large images (each 21,000×21,000 pixels) requires a segmentation method that: (1) yields high segmentation accuracy, (2) is applicable to multiple cell lines with various densities of cells and cell colonies, and several imaging modalities, (3) can process large data sets in a timely manner, (4) has a low memory footprint and (5) has a small number of user-set parameters that do not require adjustment during the segmentation of large image sets. None of the currently available segmentation methods meet all these requirements. Segmentation based on image gradient thresholding is fast and has a low memory footprint. However, existing techniques that automate the selection of the gradient image threshold do not work across image modalities, multiple cell lines, and a wide range of foreground/background densities (requirement 2) and all failed the requirement for robust parameters that do not require re-adjustment with time (requirement 5). We present a novel and empirically derived image gradient threshold selection method for separating foreground and background pixels in an image that meets all the requirements listed above. We quantify the difference between our approach and existing ones in terms of accuracy, execution speed, memory usage and number of adjustable parameters on a reference data set. This reference data set consists of 501 validation images with manually determined segmentations and image sizes ranging from 0.36 Megapixels to 850 Megapixels. It includes four different cell lines and two image modalities: phase contrast and fluorescent. Our new technique, called Empirical Gradient Threshold (EGT), is derived from this reference

  20. CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    M. Ehlers

    2012-08-01

    Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

  1. AI (artificial intelligence in histopathology--from image analysis to automated diagnosis.

    Directory of Open Access Journals (Sweden)

    Aleksandar Bogovac

    2010-02-01

    Full Text Available The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures and pixel based (texture measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and

  2. Automation of PCXMC and ImPACT for NASA Astronaut Medical Imaging Dose and Risk Tracking

    Science.gov (United States)

    Bahadori, Amir; Picco, Charles; Flores-McLaughlin, John; Shavers, Mark; Semones, Edward

    2011-01-01

    To automate astronaut organ and effective dose calculations from occupational X-ray and computed tomography (CT) examinations incorporating PCXMC and ImPACT tools and to estimate the associated lifetime cancer risk per the National Council on Radiation Protection & Measurements (NCRP) using MATLAB(R). Methods: NASA follows guidance from the NCRP on its operational radiation safety program for astronauts. NCRP Report 142 recommends that astronauts be informed of the cancer risks from reported exposures to ionizing radiation from medical imaging. MATLAB(R) code was written to retrieve exam parameters for medical imaging procedures from a NASA database, calculate associated dose and risk, and return results to the database, using the Microsoft .NET Framework. This code interfaces with the PCXMC executable and emulates the ImPACT Excel spreadsheet to calculate organ doses from X-rays and CTs, respectively, eliminating the need to utilize the PCXMC graphical user interface (except for a few special cases) and the ImPACT spreadsheet. Results: Using MATLAB(R) code to interface with PCXMC and replicate ImPACT dose calculation allowed for rapid evaluation of multiple medical imaging exams. The user inputs the exam parameter data into the database and runs the code. Based on the imaging modality and input parameters, the organ doses are calculated. Output files are created for record, and organ doses, effective dose, and cancer risks associated with each exam are written to the database. Annual and post-flight exposure reports, which are used by the flight surgeon to brief the astronaut, are generated from the database. Conclusions: Automating PCXMC and ImPACT for evaluation of NASA astronaut medical imaging radiation procedures allowed for a traceable and rapid method for tracking projected cancer risks associated with over 12,000 exposures. This code will be used to evaluate future medical radiation exposures, and can easily be modified to accommodate changes to the risk

  3. Semi-automated International Cartilage Repair Society scoring of equine articular cartilage lesions in optical coherence tomography images.

    Science.gov (United States)

    Te Moller, N C R; Pitkänen, M; Sarin, J K; Väänänen, S; Liukkonen, J; Afara, I O; Puhakka, P H; Brommer, H; Niemelä, T; Tulamo, R-M; Argüelles Capilla, D; Töyräs, J

    2017-07-01

    Arthroscopic optical coherence tomography (OCT) is a promising tool for the detailed evaluation of articular cartilage injuries. However, OCT-based articular cartilage scoring still relies on the operator's visual estimation. To test the hypothesis that semi-automated International Cartilage Repair Society (ICRS) scoring of chondral lesions seen in OCT images could enhance intra- and interobserver agreement of scoring and its accuracy. Validation study using equine cadaver tissue. Osteochondral samples (n = 99) were prepared from 18 equine metacarpophalangeal joints and imaged using OCT. Custom-made software was developed for semi-automated ICRS scoring of cartilage lesions on OCT images. Scoring was performed visually and semi-automatically by five observers, and levels of inter- and intraobserver agreement were calculated. Subsequently, OCT-based scores were compared with ICRS scores based on light microscopy images of the histological sections of matching locations (n = 82). When semi-automated scoring of the OCT images was performed by multiple observers, mean levels of intraobserver and interobserver agreement were higher than those achieved with visual OCT scoring (83% vs. 77% and 74% vs. 33%, respectively). Histology-based scores from matching regions of interest agreed better with visual OCT-based scoring than with semi-automated OCT scoring; however, the accuracy of the software was improved by optimising the threshold combinations used to determine the ICRS score. Images were obtained from cadavers. Semi-automated scoring software improved the reproducibility of ICRS scoring of chondral lesions in OCT images and made scoring less observer-dependent. The image analysis and segmentation techniques adopted in this study warrant further optimisation to achieve better accuracy with semi-automated ICRS scoring. In addition, studies on in vivo applications are required. © 2016 EVJ Ltd.

  4. Automated segmentation and geometrical modeling of the tricuspid aortic valve in 3D echocardiographic images.

    Science.gov (United States)

    Pouch, Alison M; Wang, Hongzhi; Takabe, Manabu; Jackson, Benjamin M; Sehgal, Chandra M; Gorman, Joseph H; Gorman, Robert C; Yushkevich, Paul A

    2013-01-01

    The aortic valve has been described with variable anatomical definitions, and the consistency of 2D manual measurement of valve dimensions in medical image data has been questionable. Given the importance of image-based morphological assessment in the diagnosis and surgical treatment of aortic valve disease, there is considerable need to develop a standardized framework for 3D valve segmentation and shape representation. Towards this goal, this work integrates template-based medial modeling and multi-atlas label fusion techniques to automatically delineate and quantitatively describe aortic leaflet geometry in 3D echocardiographic (3DE) images, a challenging task that has been explored only to a limited extent. The method makes use of expert knowledge of aortic leaflet image appearance, generates segmentations with consistent topology, and establishes a shape-based coordinate system on the aortic leaflets that enables standardized automated measurements. In this study, the algorithm is evaluated on 11 3DE images of normal human aortic leaflets acquired at mid systole. The clinical relevance of the method is its ability to capture leaflet geometry in 3DE image data with minimal user interaction while producing consistent measurements of 3D aortic leaflet geometry.

  5. Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Teramoto, Atsushi; Tsukamoto, Tetsuya; Kiriyama, Yuka; Fujita, Hiroshi

    2017-01-01

    Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

  6. PyDBS: an automated image processing workflow for deep brain stimulation surgery.

    Science.gov (United States)

    D'Albis, Tiziano; Haegelen, Claire; Essert, Caroline; Fernández-Vidal, Sara; Lalys, Florent; Jannin, Pierre

    2015-02-01

    Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. The results obtained are compatible with the adoption of PyDBS in clinical practice.

  7. A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

    Science.gov (United States)

    Novas, Romulo Bourget; Fazan, Valeria Paula Sassoli; Felipe, Joaquim Cezar

    2016-02-01

    Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

  8. Automating the Analysis of Spatial Grids A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications

    CERN Document Server

    Lakshmanan, Valliappa

    2012-01-01

    The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.

  9. Quantitative Assessment of Mouse Mammary Gland Morphology Using Automated Digital Image Processing and TEB Detection.

    Science.gov (United States)

    Blacher, Silvia; Gérard, Céline; Gallez, Anne; Foidart, Jean-Michel; Noël, Agnès; Péqueux, Christel

    2016-04-01

    The assessment of rodent mammary gland morphology is largely used to study the molecular mechanisms driving breast development and to analyze the impact of various endocrine disruptors with putative pathological implications. In this work, we propose a methodology relying on fully automated digital image analysis methods including image processing and quantification of the whole ductal tree and of the terminal end buds as well. It allows to accurately and objectively measure both growth parameters and fine morphological glandular structures. Mammary gland elongation was characterized by 2 parameters: the length and the epithelial area of the ductal tree. Ductal tree fine structures were characterized by: 1) branch end-point density, 2) branching density, and 3) branch length distribution. The proposed methodology was compared with quantification methods classically used in the literature. This procedure can be transposed to several software and thus largely used by scientists studying rodent mammary gland morphology.

  10. The Automation and Exoplanet Orbital Characterization from the Gemini Planet Imager Exoplanet Survey

    Science.gov (United States)

    Jinfei Wang, Jason; Graham, James; Perrin, Marshall; Pueyo, Laurent; Savransky, Dmitry; Kalas, Paul; arriaga, Pauline; Chilcote, Jeffrey K.; De Rosa, Robert J.; Ruffio, Jean-Baptiste; Sivaramakrishnan, Anand; Gemini Planet Imager Exoplanet Survey Collaboration

    2018-01-01

    The Gemini Planet Imager (GPI) Exoplanet Survey (GPIES) is a multi-year 600-star survey to discover and characterize young Jovian exoplanets and their planet forming environments. For large surveys like GPIES, it is critical to have a uniform dataset processed with the latest techniques and calibrations. I will describe the GPI Data Cruncher, an automated data processing framework that is able to generate fully reduced data minutes after the data are taken and can also reprocess the entire campaign in a single day on a supercomputer. The Data Cruncher integrates into a larger automated data processing infrastructure which syncs, logs, and displays the data. I will discuss the benefits of the GPIES data infrastructure, including optimizing observing strategies, finding planets, characterizing instrument performance, and constraining giant planet occurrence. I will also discuss my work in characterizing the exoplanets we have imaged in GPIES through monitoring their orbits. Using advanced data processing algorithms and GPI's precise astrometric calibration, I will show that GPI can achieve one milliarcsecond astrometry on the extensively-studied planet Beta Pic b. With GPI, we can confidently rule out a possible transit of Beta Pic b, but have precise timings on a Hill sphere transit, and I will discuss efforts to search for transiting circumplanetary material this year. I will also discuss the orbital monitoring of other exoplanets as part of GPIES.

  11. Analysis of irradiated U-7wt%Mo dispersion fuel microstructures using automated image processing

    Energy Technology Data Exchange (ETDEWEB)

    Collette, R. [Colorado School of Mines, Nuclear Science and Engineering Program, 1500 Illinois St, Golden, CO 80401 (United States); King, J., E-mail: kingjc@mines.edu [Colorado School of Mines, Nuclear Science and Engineering Program, 1500 Illinois St, Golden, CO 80401 (United States); Buesch, C. [Oregon State University, 1500 SW Jefferson St., Corvallis, OR 97331 (United States); Keiser, D.D.; Williams, W.; Miller, B.D.; Schulthess, J. [Nuclear Fuels and Materials Division, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-6188 (United States)

    2016-07-15

    The High Performance Research Reactor Fuel Development (HPPRFD) program is responsible for developing low enriched uranium (LEU) fuel substitutes for high performance reactors fueled with highly enriched uranium (HEU) that have not yet been converted to LEU. The uranium-molybdenum (U-Mo) fuel system was selected for this effort. In this study, fission gas pore segmentation was performed on U-7wt%Mo dispersion fuel samples at three separate fission densities using an automated image processing interface developed in MATLAB. Pore size distributions were attained that showed both expected and unexpected fission gas behavior. In general, it proved challenging to identify any dominant trends when comparing fission bubble data across samples from different fuel plates due to varying compositions and fabrication techniques. The results exhibited fair agreement with the fission density vs. porosity correlation developed by the Russian reactor conversion program. - Highlights: • Automated image processing is used to extract fission gas bubble data from irradiated U−Mo fuel samples. • Verification and validation tests are performed to ensure the algorithm's accuracy. • Fission bubble parameters are predictably difficult to compare across samples of varying compositions. • The 2-D results suggest the need for more homogenized fuel sampling in future studies. • The results also demonstrate the value of 3-D reconstruction techniques.

  12. Global optimal hybrid geometric active contour for automated lung segmentation on CT images.

    Science.gov (United States)

    Zhang, Weihang; Wang, Xue; Zhang, Pengbo; Chen, Junfeng

    2017-12-01

    Lung segmentation on thoracic CT images plays an important role in early detection, diagnosis and 3D visualization of lung cancer. The segmentation accuracy, stability, and efficiency of serial CT scans have a significant impact on the performance of computer-aided detection. This paper proposes a global optimal hybrid geometric active contour model for automated lung segmentation on CT images. Firstly, the combination of global region and edge information leads to high segmentation accuracy in lung regions with weak boundaries or narrow bands. Secondly, due to the global optimality of energy functional, the proposed model is robust to the initial position of level set function and requires fewer iterations. Thus, the stability and efficiency of lung segmentation on serial CT slices can be greatly improved by taking advantage of the information between adjacent slices. In addition, to achieve the whole process of automated segmentation for lung cancer, two assistant algorithms based on prior shape and anatomical knowledge are proposed. The algorithms not only automatically separate the left and right lungs, but also include juxta-pleural tumors into the segmentation result. The proposed method was quantitatively validated on subjects from the publicly available LIDC-IDRI and our own data sets. Exhaustive experimental results demonstrate the superiority and competency of our method, especially compared with the typical edge-based geometric active contour model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Automated image classification applied to reconstituted human corneal epithelium for the early detection of toxic damage

    Science.gov (United States)

    Crosta, Giovanni Franco; Urani, Chiara; De Servi, Barbara; Meloni, Marisa

    2010-02-01

    For a long time acute eye irritation has been assessed by means of the DRAIZE rabbit test, the limitations of which are known. Alternative tests based on in vitro models have been proposed. This work focuses on the "reconstituted human corneal epithelium" (R-HCE), which resembles the corneal epithelium of the human eye by thickness, morphology and marker expression. Testing a substance on R-HCE involves a variety of methods. Herewith quantitative morphological analysis is applied to optical microscope images of R-HCE cross sections resulting from exposure to benzalkonium chloride (BAK). The short term objectives and the first results are the analysis and classification of said images. Automated analysis relies on feature extraction by the spectrum-enhancement algorithm, which is made sensitive to anisotropic morphology, and classification based on principal components analysis. The winning strategy has been the separate analysis of the apical and basal layers, which carry morphological information of different types. R-HCE specimens have been ranked by gross damage. The onset of early damage has been detected and an R-HCE specimen exposed to a low BAK dose has been singled out from the negative and positive control. These results provide a proof of principle for the automated classification of the specimens of interest on a purely morphological basis by means of the spectrum enhancement algorithm.

  14. Automated static image analysis as a novel tool in describing the physical properties of dietary fiber

    Directory of Open Access Journals (Sweden)

    Marcin Andrzej KUREK

    2015-01-01

    Full Text Available Abstract The growing interest in the usage of dietary fiber in food has caused the need to provide precise tools for describing its physical properties. This research examined two dietary fibers from oats and beets, respectively, in variable particle sizes. The application of automated static image analysis for describing the hydration properties and particle size distribution of dietary fiber was analyzed. Conventional tests for water holding capacity (WHC were conducted. The particles were measured at two points: dry and after water soaking. The most significant water holding capacity (7.00 g water/g solid was achieved by the smaller sized oat fiber. Conversely, the water holding capacity was highest (4.20 g water/g solid in larger sized beet fiber. There was evidence for water absorption increasing with a decrease in particle size in regards to the same fiber source. Very strong correlations were drawn between particle shape parameters, such as fiber length, straightness, width and hydration properties measured conventionally. The regression analysis provided the opportunity to estimate whether the automated static image analysis method could be an efficient tool in describing the hydration properties of dietary fiber. The application of the method was validated using mathematical model which was verified in comparison to conventional WHC measurement results.

  15. Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices.

    Science.gov (United States)

    Wu, Qiuyu; Chubykin, Alexander A

    2017-07-31

    Whole-cell patch clamp is the gold-standard method to measure the electrical properties of single cells. However, the in vitro patch clamp remains a challenging and low-throughput technique due to its complexity and high reliance on user operation and control. This manuscript demonstrates an image-guided automatic patch clamp system for in vitro whole-cell patch clamp experiments in acute brain slices. Our system implements a computer vision-based algorithm to detect fluorescently labeled cells and to target them for fully automatic patching using a micromanipulator and internal pipette pressure control. The entire process is highly automated, with minimal requirements for human intervention. Real-time experimental information, including electrical resistance and internal pipette pressure, are documented electronically for future analysis and for optimization to different cell types. Although our system is described in the context of acute brain slice recordings, it can also be applied to the automated image-guided patch clamp of dissociated neurons, organotypic slice cultures, and other non-neuronal cell types.

  16. An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

    Science.gov (United States)

    Weberg, Micah J.; Morton, Richard J.; McLaughlin, James A.

    2018-01-01

    Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%–2% for displacement amplitudes and 4%–10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%–41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.

  17. Automated analysis of heterogeneous carbon nanostructures by high-resolution electron microscopy and on-line image processing

    Energy Technology Data Exchange (ETDEWEB)

    Toth, P., E-mail: toth.pal@uni-miskolc.hu [Department of Chemical Engineering, University of Utah, 50 S. Central Campus Drive, Salt Lake City, UT 84112-9203 (United States); Farrer, J.K. [Department of Physics and Astronomy, Brigham Young University, N283 ESC, Provo, UT 84602 (United States); Palotas, A.B. [Department of Combustion Technology and Thermal Energy, University of Miskolc, H3515, Miskolc-Egyetemvaros (Hungary); Lighty, J.S.; Eddings, E.G. [Department of Chemical Engineering, University of Utah, 50 S. Central Campus Drive, Salt Lake City, UT 84112-9203 (United States)

    2013-06-15

    High-resolution electron microscopy is an efficient tool for characterizing heterogeneous nanostructures; however, currently the analysis is a laborious and time-consuming manual process. In order to be able to accurately and robustly quantify heterostructures, one must obtain a statistically high number of micrographs showing images of the appropriate sub-structures. The second step of analysis is usually the application of digital image processing techniques in order to extract meaningful structural descriptors from the acquired images. In this paper it will be shown that by applying on-line image processing and basic machine vision algorithms, it is possible to fully automate the image acquisition step; therefore, the number of acquired images in a given time can be increased drastically without the need for additional human labor. The proposed automation technique works by computing fields of structural descriptors in situ and thus outputs sets of the desired structural descriptors in real-time. The merits of the method are demonstrated by using combustion-generated black carbon samples. - Highlights: ► The HRTEM analysis of heterogeneous nanostructures is a tedious manual process. ► Automatic HRTEM image acquisition and analysis can improve data quantity and quality. ► We propose a method based on on-line image analysis for the automation of HRTEM image acquisition. ► The proposed method is demonstrated using HRTEM images of soot particles.

  18. Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya.

    Science.gov (United States)

    Hansen, Morten B; Abràmoff, Michael D; Folk, James C; Mathenge, Wanjiku; Bastawrous, Andrew; Peto, Tunde

    2015-01-01

    Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya. Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]). First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR. The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard. Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.

  19. Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture.

    Science.gov (United States)

    Esses, Steven J; Lu, Xiaoguang; Zhao, Tiejun; Shanbhogue, Krishna; Dane, Bari; Bruno, Mary; Chandarana, Hersh

    2018-03-01

    To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 -weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728. © 2017 International Society for Magnetic Resonance in Medicine.

  20. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

    Directory of Open Access Journals (Sweden)

    Hojjat Seyed Mousavi

    2015-01-01

    Full Text Available Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images. To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG and high-grade glioma (HGG which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1 A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2 a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP. In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1 Successful detection rates of pseudopalisading necrosis

  1. Long-term live cell imaging and automated 4D analysis of drosophila neuroblast lineages.

    Directory of Open Access Journals (Sweden)

    Catarina C F Homem

    Full Text Available The developing Drosophila brain is a well-studied model system for neurogenesis and stem cell biology. In the Drosophila central brain, around 200 neural stem cells called neuroblasts undergo repeated rounds of asymmetric cell division. These divisions typically generate a larger self-renewing neuroblast and a smaller ganglion mother cell that undergoes one terminal division to create two differentiating neurons. Although single mitotic divisions of neuroblasts can easily be imaged in real time, the lack of long term imaging procedures has limited the use of neuroblast live imaging for lineage analysis. Here we describe a method that allows live imaging of cultured Drosophila neuroblasts over multiple cell cycles for up to 24 hours. We describe a 4D image analysis protocol that can be used to extract cell cycle times and growth rates from the resulting movies in an automated manner. We use it to perform lineage analysis in type II neuroblasts where clonal analysis has indicated the presence of a transit-amplifying population that potentiates the number of neurons. Indeed, our experiments verify type II lineages and provide quantitative parameters for all cell types in those lineages. As defects in type II neuroblast lineages can result in brain tumor formation, our lineage analysis method will allow more detailed and quantitative analysis of tumorigenesis and asymmetric cell division in the Drosophila brain.

  2. Automated detection of synapses in serial section transmission electron microscopy image stacks.

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

    Full Text Available We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem.

  3. Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

    Science.gov (United States)

    Hu, Zhihong; Medioni, Gerard G; Hernandez, Matthias; Sadda, Srinivas R

    2015-01-01

    Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A [Formula: see text]-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson's [Formula: see text]), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

  4. Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images

    Directory of Open Access Journals (Sweden)

    Pachiyappan Arulmozhivarman

    2012-06-01

    Full Text Available Abstract We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

  5. An automated retinal imaging method for the early diagnosis of diabetic retinopathy.

    Science.gov (United States)

    Franklin, S Wilfred; Rajan, S Edward

    2013-01-01

    Diabetic retinopathy is a microvascular complication of long-term diabetes and is the major cause for eyesight loss due to changes in blood vessels of the retina. Major vision loss due to diabetic retinopathy is highly preventable with regular screening and timely intervention at the earlier stages. Retinal blood vessel segmentation methods help to identify the successive stages of such sight threatening diseases like diabetes. To develop and test a novel retinal imaging method which segments the blood vessels automatically from retinal images, which helps the ophthalmologists in the diagnosis and follow-up of diabetic retinopathy. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels were identified by means of a multilayer perceptron neural network, for which the inputs were derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network, is utilized in our method. Quantitative results of sensitivity, specificity and predictive values were obtained in our method and the measured accuracy of our segmentation algorithm was 95.3%, which is better than that presented by state-of-the-art approaches. The evaluation procedure used and the demonstrated effectiveness of our automated retinal imaging method proves itself as the most powerful tool to diagnose diabetic retinopathy in the earlier stages.

  6. Integrating image processing and classification technology into automated polarizing film defect inspection

    Science.gov (United States)

    Kuo, Chung-Feng Jeffrey; Lai, Chun-Yu; Kao, Chih-Hsiang; Chiu, Chin-Hsun

    2018-05-01

    In order to improve the current manual inspection and classification process for polarizing film on production lines, this study proposes a high precision automated inspection and classification system for polarizing film, which is used for recognition and classification of four common defects: dent, foreign material, bright spot, and scratch. First, the median filter is used to remove the impulse noise in the defect image of polarizing film. The random noise in the background is smoothed by the improved anisotropic diffusion, while the edge detail of the defect region is sharpened. Next, the defect image is transformed by Fourier transform to the frequency domain, combined with a Butterworth high pass filter to sharpen the edge detail of the defect region, and brought back by inverse Fourier transform to the spatial domain to complete the image enhancement process. For image segmentation, the edge of the defect region is found by Canny edge detector, and then the complete defect region is obtained by two-stage morphology processing. For defect classification, the feature values, including maximum gray level, eccentricity, the contrast, and homogeneity of gray level co-occurrence matrix (GLCM) extracted from the images, are used as the input of the radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) classifier, 96 defect images are then used as training samples, and 84 defect images are used as testing samples to validate the classification effect. The result shows that the classification accuracy by using RBFNN is 98.9%. Thus, our proposed system can be used by manufacturing companies for a higher yield rate and lower cost. The processing time of one single image is 2.57 seconds, thus meeting the practical application requirement of an industrial production line.

  7. Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

    Science.gov (United States)

    Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

    2016-02-01

    The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7

  8. Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging

    Science.gov (United States)

    Orologas, F.; Saitis, P.; Kallergi, M.

    2017-11-01

    Patients with lung tumors or inflammatory lung disease could greatly benefit in terms of treatment and follow-up by PET/CT quantitative imaging, namely measurements of metabolic tumor volume (MTV), standardized uptake values (SUVs) and total lesion glycolysis (TLG). The purpose of this study was the development of an unsupervised or partially supervised algorithm using standard image processing tools for measuring MTV, SUV, and TLG from lung PET/CT scans. Automated metabolic lesion volume and metabolic parameter measurements were achieved through a 5 step algorithm: (i) The segmentation of the lung areas on the CT slices, (ii) the registration of the CT segmented lung regions on the PET images to define the anatomical boundaries of the lungs on the functional data, (iii) the segmentation of the regions of interest (ROIs) on the PET images based on adaptive thresholding and clinical criteria, (iv) the estimation of the number of pixels and pixel intensities in the PET slices of the segmented ROIs, (v) the estimation of MTV, SUVs, and TLG from the previous step and DICOM header data. Whole body PET/CT scans of patients with sarcoidosis were used for training and testing the algorithm. Lung area segmentation on the CT slices was better achieved with semi-supervised techniques that reduced false positive detections significantly. Lung segmentation results agreed with the lung volumes published in the literature while the agreement between experts and algorithm in the segmentation of the lesions was around 88%. Segmentation results depended on the image resolution selected for processing. The clinical parameters, SUV (either mean or max or peak) and TLG estimated by the segmented ROIs and DICOM header data provided a way to correlate imaging data to clinical and demographic data. In conclusion, automated MTV, SUV, and TLG measurements offer powerful analysis tools in PET/CT imaging of the lungs. Custom-made algorithms are often a better approach than the manufacturer

  9. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  10. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  11. Automated Analysis of 123I-beta-CIT SPECT Images with Statistical Probabilistic Anatomical Mapping

    International Nuclear Information System (INIS)

    Eo, Jae Seon; Lee, Hoyoung; Lee, Jae Sung; Kim, Yu Kyung; Jeon, Bumseok; Lee, Dong Soo

    2014-01-01

    Population-based statistical probabilistic anatomical maps have been used to generate probabilistic volumes of interest for analyzing perfusion and metabolic brain imaging. We investigated the feasibility of automated analysis for dopamine transporter images using this technique and evaluated striatal binding potentials in Parkinson's disease and Wilson's disease. We analyzed 2β-Carbomethoxy-3β-(4- 123 I-iodophenyl)tropane ( 123 I-beta-CIT) SPECT images acquired from 26 people with Parkinson's disease (M:F=11:15,mean age=49±12 years), 9 people with Wilson's disease (M: F=6:3, mean age=26±11 years) and 17 normal controls (M:F=5:12, mean age=39±16 years). A SPECT template was created using striatal statistical probabilistic map images. All images were spatially normalized onto the template, and probability-weighted regional counts in striatal structures were estimated. The binding potential was calculated using the ratio of specific and nonspecific binding activities at equilibrium. Voxel-based comparisons between groups were also performed using statistical parametric mapping. Qualitative assessment showed that spatial normalizations of the SPECT images were successful for all images. The striatal binding potentials of participants with Parkinson's disease and Wilson's disease were significantly lower than those of normal controls. Statistical parametric mapping analysis found statistically significant differences only in striatal regions in both disease groups compared to controls. We successfully evaluated the regional 123 I-beta-CIT distribution using the SPECT template and probabilistic map data automatically. This procedure allows an objective and quantitative comparison of the binding potential, which in this case showed a significantly decreased binding potential in the striata of patients with Parkinson's disease or Wilson's disease

  12. Development of an automated imaging pipeline for the analysis of the zebrafish larval kidney.

    Directory of Open Access Journals (Sweden)

    Jens H Westhoff

    Full Text Available The analysis of kidney malformation caused by environmental influences during nephrogenesis or by hereditary nephropathies requires animal models allowing the in vivo observation of developmental processes. The zebrafish has emerged as a useful model system for the analysis of vertebrate organ development and function, and it is suitable for the identification of organotoxic or disease-modulating compounds on a larger scale. However, to fully exploit its potential in high content screening applications, dedicated protocols are required allowing the consistent visualization of inner organs such as the embryonic kidney. To this end, we developed a high content screening compatible pipeline for the automated imaging of standardized views of the developing pronephros in zebrafish larvae. Using a custom designed tool, cavities were generated in agarose coated microtiter plates allowing for accurate positioning and orientation of zebrafish larvae. This enabled the subsequent automated acquisition of stable and consistent dorsal views of pronephric kidneys. The established pipeline was applied in a pilot screen for the analysis of the impact of potentially nephrotoxic drugs on zebrafish pronephros development in the Tg(wt1b:EGFP transgenic line in which the developing pronephros is highlighted by GFP expression. The consistent image data that was acquired allowed for quantification of gross morphological pronephric phenotypes, revealing concentration dependent effects of several compounds on nephrogenesis. In addition, applicability of the imaging pipeline was further confirmed in a morpholino based model for cilia-associated human genetic disorders associated with different intraflagellar transport genes. The developed tools and pipeline can be used to study various aspects in zebrafish kidney research, and can be readily adapted for the analysis of other organ systems.

  13. Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis.

    Science.gov (United States)

    Karisto, Petteri; Hund, Andreas; Yu, Kang; Anderegg, Jonas; Walter, Achim; Mascher, Fabio; McDonald, Bruce A; Mikaberidze, Alexey

    2017-12-06

    Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis (AIA) of 21420 scanned wheat leaves to obtain quantitative measures of conditional STB intesity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize

  14. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

    Energy Technology Data Exchange (ETDEWEB)

    Drukker, Karen, E-mail: kdrukker@uchicago.edu; Sennett, Charlene A.; Giger, Maryellen L. [Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

    2014-01-15

    Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

  15. AUTOMATED DETECTION OF GALAXY-SCALE GRAVITATIONAL LENSES IN HIGH-RESOLUTION IMAGING DATA

    International Nuclear Information System (INIS)

    Marshall, Philip J.; Bradac, Marusa; Hogg, David W.; Moustakas, Leonidas A.; Fassnacht, Christopher D.; Schrabback, Tim; Blandford, Roger D.

    2009-01-01

    We expect direct lens modeling to be the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling 'robot' that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Having optimized a simple model for 'typical' galaxy-scale gravitational lenses, we generate four assessments of model quality that are then used in an automated classification. The robot infers from these four data the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set of candidates, including realistic simulated lenses and known false positives drawn from the Hubble Space Telescope (HST) Extended Groth Strip (EGS) survey. We compute the expected purity, completeness, and rejection rate, and find that these statistics can be optimized for a particular application by changing the prior probability distribution for H; this is equivalent to defining the robot's 'character'. Adopting a realistic prior based on expectations for the abundance of lenses, we find that a lens sample may be generated that is ∼100% pure, but only ∼20% complete. This shortfall is due primarily to the oversimplicity of the model of both the lens light and mass. With a more optimistic robot, ∼90% completeness can be achieved while rejecting ∼90% of the candidate objects. The remaining candidates must be classified by human inspectors. Displaying the images used and produced by the robot on a custom 'one-click' web interface, we are able to inspect and classify lens candidates at a rate of a few seconds per system, suggesting that a future 1000 deg. 2 imaging survey containing 10 7 BRGs, and some 10 4 lenses, could be successfully, and reproducibly, searched in a modest amount of time. We have verified our projected survey statistics, albeit at low significance, using the HST EGS data

  16. Automated localization and segmentation techniques for B-mode ultrasound images: A review.

    Science.gov (United States)

    Meiburger, Kristen M; Acharya, U Rajendra; Molinari, Filippo

    2018-01-01

    B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Vision 20/20: Perspectives on automated image segmentation for radiotherapy

    Science.gov (United States)

    Sharp, Gregory; Fritscher, Karl D.; Pekar, Vladimir; Peroni, Marta; Shusharina, Nadya; Veeraraghavan, Harini; Yang, Jinzhong

    2014-01-01

    Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods’ strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology. PMID:24784366

  18. Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin

    Science.gov (United States)

    Kłeczek, Paweł; Dyduch, Grzegorz; Jaworek-Korjakowska, Joanna; Tadeusiewicz, Ryszard

    2017-03-01

    Background: Epidermis area is an important observation area for the diagnosis of inflammatory skin diseases and skin cancers. Therefore, in order to develop a computer-aided diagnosis system, segmentation of the epidermis area is usually an essential, initial step. This study presents an automated and robust method for epidermis segmentation in whole slide histopathological images of human skin, stained with hematoxylin and eosin. Methods: The proposed method performs epidermis segmentation based on the information about shape and distribution of transparent regions in a slide image and information about distribution and concentration of hematoxylin and eosin stains. It utilizes domain-specific knowledge of morphometric and biochemical properties of skin tissue elements to segment the relevant histopathological structures in human skin. Results: Experimental results on 88 skin histopathological images from three different sources show that the proposed method segments the epidermis with a mean sensitivity of 87 %, a mean specificity of 95% and a mean precision of 57%. It is robust to inter- and intra-image variations in both staining and illumination, and makes no assumptions about the type of skin disorder. The proposed method provides a superior performance compared to the existing techniques.

  19. Vision 20/20: Perspectives on automated image segmentation for radiotherapy

    International Nuclear Information System (INIS)

    Sharp, Gregory; Fritscher, Karl D.; Shusharina, Nadya; Pekar, Vladimir; Peroni, Marta; Veeraraghavan, Harini; Yang, Jinzhong

    2014-01-01

    Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods’ strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology

  20. Automated Segmentation of High-Resolution Photospheric Images of Active Regions

    Science.gov (United States)

    Yang, Meng; Tian, Yu; Rao, Changhui

    2018-02-01

    Due to the development of ground-based, large-aperture solar telescopes with adaptive optics (AO) resulting in increasing resolving ability, more accurate sunspot identifications and characterizations are required. In this article, we have developed a set of automated segmentation methods for high-resolution solar photospheric images. Firstly, a local-intensity-clustering level-set method is applied to roughly separate solar granulation and sunspots. Then reinitialization-free level-set evolution is adopted to adjust the boundaries of the photospheric patch; an adaptive intensity threshold is used to discriminate between umbra and penumbra; light bridges are selected according to their regional properties from candidates produced by morphological operations. The proposed method is applied to the solar high-resolution TiO 705.7-nm images taken by the 151-element AO system and Ground-Layer Adaptive Optics prototype system at the 1-m New Vacuum Solar Telescope of the Yunnan Observatory. Experimental results show that the method achieves satisfactory robustness and efficiency with low computational cost on high-resolution images. The method could also be applied to full-disk images, and the calculated sunspot areas correlate well with the data given by the National Oceanic and Atmospheric Administration (NOAA).

  1. Automated tracking of lava lake level using thermal images at Kīlauea Volcano, Hawai’i

    Science.gov (United States)

    Patrick, Matthew R.; Swanson, Don; Orr, Tim R.

    2016-01-01

    Tracking the level of the lava lake in Halema‘uma‘u Crater, at the summit of Kīlauea Volcano, Hawai’i, is an essential part of monitoring the ongoing eruption and forecasting potentially hazardous changes in activity. We describe a simple automated image processing routine that analyzes continuously-acquired thermal images of the lava lake and measures lava level. The method uses three image segmentation approaches, based on edge detection, short-term change analysis, and composite temperature thresholding, to identify and track the lake margin in the images. These relative measurements from the images are periodically calibrated with laser rangefinder measurements to produce real-time estimates of lake elevation. Continuous, automated tracking of the lava level has been an important tool used by the U.S. Geological Survey’s Hawaiian Volcano Observatory since 2012 in real-time operational monitoring of the volcano and its hazard potential.

  2. Automated spine and vertebrae detection in CT images using object-based image analysis.

    Science.gov (United States)

    Schwier, M; Chitiboi, T; Hülnhagen, T; Hahn, H K

    2013-09-01

    Although computer assistance has become common in medical practice, some of the most challenging tasks that remain unsolved are in the area of automatic detection and recognition. The human visual perception is in general far superior to computer vision algorithms. Object-based image analysis is a relatively new approach that aims to lift image analysis from a pixel-based processing to a semantic region-based processing of images. It allows effective integration of reasoning processes and contextual concepts into the recognition method. In this paper, we present an approach that applies object-based image analysis to the task of detecting the spine in computed tomography images. A spine detection would be of great benefit in several contexts, from the automatic labeling of vertebrae to the assessment of spinal pathologies. We show with our approach how region-based features, contextual information and domain knowledge, especially concerning the typical shape and structure of the spine and its components, can be used effectively in the analysis process. The results of our approach are promising with a detection rate for vertebral bodies of 96% and a precision of 99%. We also gain a good two-dimensional segmentation of the spine along the more central slices and a coarse three-dimensional segmentation. Copyright © 2013 John Wiley & Sons, Ltd.

  3. Content-based retrieval in videos from laparoscopic surgery

    Science.gov (United States)

    Schoeffmann, Klaus; Beecks, Christian; Lux, Mathias; Uysal, Merih Seran; Seidl, Thomas

    2016-03-01

    In the field of medical endoscopy more and more surgeons are changing over to record and store videos of their endoscopic procedures for long-term archival. These endoscopic videos are a good source of information for explanations to patients and follow-up operations. As the endoscope is the "eye of the surgeon", the video shows the same information the surgeon has seen during the operation, and can describe the situation inside the patient much more precisely than an operation report would do. Recorded endoscopic videos can also be used for training young surgeons and in some countries the long-term archival of video recordings from endoscopic procedures is even enforced by law. A major challenge, however, is to efficiently access these very large video archives for later purposes. One problem, for example, is to locate specific images in the videos that show important situations, which are additionally captured as static images during the procedure. This work addresses this problem and focuses on contentbased video retrieval in data from laparoscopic surgery. We propose to use feature signatures, which can appropriately and concisely describe the content of laparoscopic images, and show that by using this content descriptor with an appropriate metric, we are able to efficiently perform content-based retrieval in laparoscopic videos. In a dataset with 600 captured static images from 33 hours recordings, we are able to find the correct video segment for more than 88% of these images.

  4. Automated segmentation of dental CBCT image with prior-guided sequential random forests

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Chen, Ken-Chung; Tang, Zhen; Xia, James J.; Shen, Dinggang

    2016-01-01

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

  5. Automated segmentation of dental CBCT image with prior-guided sequential random forests

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 (United States); Chen, Ken-Chung; Tang, Zhen [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030 (United States); Xia, James J., E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery, Shanghai Jiao Tong University School of Medicine, Shanghai Ninth People’s Hospital, Shanghai 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841 (Korea, Republic of)

    2016-01-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

  6. Small sample sorting of primary adherent cells by automated micropallet imaging and release.

    Science.gov (United States)

    Shah, Pavak K; Herrera-Loeza, Silvia Gabriela; Sims, Christopher E; Yeh, Jen Jen; Allbritton, Nancy L

    2014-07-01

    Primary patient samples are the gold standard for molecular investigations of tumor biology yet are difficult to acquire, heterogeneous in nature and variable in size. Patient-derived xenografts (PDXs) comprised of primary tumor tissue cultured in host organisms such as nude mice permit the propagation of human tumor samples in an in vivo environment and closely mimic the phenotype and gene expression profile of the primary tumor. Although PDX models reduce the cost and complexity of acquiring sample tissue and permit repeated sampling of the primary tumor, these samples are typically contaminated by immune, blood, and vascular tissues from the host organism while also being limited in size. For very small tissue samples (on the order of 10(3) cells) purification by fluorescence-activated cell sorting (FACS) is not feasible while magnetic activated cell sorting (MACS) of small samples results in very low purity, low yield, and poor viability. We developed a platform for imaging cytometry integrated with micropallet array technology to perform automated cell sorting on very small samples obtained from PDX models of pancreatic and colorectal cancer using antibody staining of EpCAM (CD326) as a selection criteria. These data demonstrate the ability to automate and efficiently separate samples with very low number of cells. © 2014 International Society for Advancement of Cytometry.

  7. MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images.

    Science.gov (United States)

    Mungle, T; Tewary, S; DAS, D K; Arun, I; Basak, B; Agarwal, S; Ahmed, R; Chatterjee, S; Chakraborty, C

    2017-08-01

    Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells. The proposed segmentation methodology is found to have F-measure 0.95. Artificial neural network is subsequently used to obtain intensity-based score for ER cells, from pixel colour intensity features. Simultaneously, proportion score - percentage of ER positive cells is computed via cell counting. The final ER score is computed by adding intensity and proportion scores - a standard Allred scoring system followed by pathologists. The classification accuracy for classification of cells by classifier in terms of F-measure is 0.9626. The problem of subjective interobserver ability is addressed by quantifying ER score from two expert pathologist and proposed methodology. The intraclass correlation achieved is greater than 0.90. The study has potential advantage of assisting pathologist in decision making over manual procedure and could evolve as a part of automated decision support system with other receptor scoring/analysis procedure. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

  8. Sci-Thur AM: YIS – 08: Automated Imaging Quality Assurance for Image-Guided Small Animal Irradiators

    International Nuclear Information System (INIS)

    Johnstone, Chris; Bazalova-Carter, Magdalena

    2016-01-01

    Purpose: To develop quality assurance (QA) standards and tolerance levels for image quality of small animal irradiators. Methods: A fully automated in-house QA software for image analysis of a commercial microCT phantom was created. Quantitative analyses of CT linearity, signal-to-noise ratio (SNR), uniformity and noise, geometric accuracy, modulation transfer function (MTF), and CT number evaluation was performed. Phantom microCT scans from seven institutions acquired with varying parameters (kVp, mA, time, voxel size, and frame rate) and five irradiator units (Xstrahl SARRP, PXI X-RAD 225Cx, PXI X-RAD SmART, GE explore CT/RT 140, and GE Explore CT 120) were analyzed. Multi-institutional data sets were compared using our in-house software to establish pass/fail criteria for each QA test. Results: CT linearity (R2>0.996) was excellent at all but Institution 2. Acceptable SNR (>35) and noise levels (<55HU) were obtained at four of the seven institutions, where failing scans were acquired with less than 120mAs. Acceptable MTF (>1.5 lp/mm for MTF=0.2) was obtained at all but Institution 6 due to the largest scan voxel size (0.35mm). The geometric accuracy passed (<1.5%) at five of the seven institutions. Conclusion: Our QA software can be used to rapidly perform quantitative imaging QA for small animal irradiators, accumulate results over time, and display possible changes in imaging functionality from its original performance and/or from the recommended tolerance levels. This tool will aid researchers in maintaining high image quality, enabling precise conformal dose delivery to small animals.

  9. Automated Image Analysis of Lung Branching Morphogenesis from Microscopic Images of Fetal Rat Explants

    Directory of Open Access Journals (Sweden)

    Pedro L. Rodrigues

    2014-01-01

    Full Text Available Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers.

  10. Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Alize E.H. Scheenstra

    2009-01-01

    Full Text Available Segmentation of magnetic resonance imaging (MRI data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.

  11. Automated detection of midsagittal plane in MR images of the head

    Science.gov (United States)

    Wang, Deming; Chalk, Jonathan B.; Doddrell, David M.; Semple, James

    2001-07-01

    A fully automated and robust method is presented for dividing MR 3D images of the human brain into two hemispheres. The method is developed specifically to deal with pathologically affected brains or brains in which the longitudinal fissure (LF) is significantly widened due to ageing or atrophy associated with neuro-degenerative processes. To provide a definitive estimate of the mid- sagittal plane, the method combines longitudinal fissure lines detected in both axial and corona slices of T1- weighted MR images and then fit these lines to a 3D plane. The method was applied to 36 brain MR image data sets (15 of them arising from subjects with probable Alzheimer's disease) all exhibiting some degrees of widened fissures and/or significant asymmetry due to pathology. Visual inspection of the results revealed that the separation was highly accurate and satisfactory. In some cases (5 in total), there were minor degrees of asymmetry in the posterior fossa structures despite successful splitting of cerebral cortex.

  12. Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images.

    Science.gov (United States)

    Maheshwari, Shishir; Pachori, Ram Bilas; Acharya, U Rajendra

    2017-05-01

    Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.

  13. Automated analysis of retinal imaging using machine learning techniques for computer vision.

    Science.gov (United States)

    De Fauw, Jeffrey; Keane, Pearse; Tomasev, Nenad; Visentin, Daniel; van den Driessche, George; Johnson, Mike; Hughes, Cian O; Chu, Carlton; Ledsam, Joseph; Back, Trevor; Peto, Tunde; Rees, Geraint; Montgomery, Hugh; Raine, Rosalind; Ronneberger, Olaf; Cornebise, Julien

    2016-01-01

    There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular ("wet") age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the 'back' of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  14. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  15. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

    Energy Technology Data Exchange (ETDEWEB)

    Loh, K.B.; Ramli, N.; Tan, L.K.; Roziah, M. [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Kuala Lumpur (Malaysia); Rahmat, K. [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Kuala Lumpur (Malaysia); University Malaya, Biomedical Imaging Department, Kuala Lumpur (Malaysia); Ariffin, H. [University of Malaya, Department of Paediatrics, Faculty of Medicine, Kuala Lumpur (Malaysia)

    2012-07-15

    The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas. Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction. DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued. DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data. (orig.)

  16. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

    International Nuclear Information System (INIS)

    Loh, K.B.; Ramli, N.; Tan, L.K.; Roziah, M.; Rahmat, K.; Ariffin, H.

    2012-01-01

    The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas. Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction. DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued. DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data. (orig.)

  17. Hyper-Cam automated calibration method for continuous hyperspectral imaging measurements

    Science.gov (United States)

    Gagnon, Jean-Philippe; Habte, Zewdu; George, Jacks; Farley, Vincent; Tremblay, Pierre; Chamberland, Martin; Romano, Joao; Rosario, Dalton

    2010-04-01

    The midwave and longwave infrared regions of the electromagnetic spectrum contain rich information which can be captured by hyperspectral sensors thus enabling enhanced detection of targets of interest. A continuous hyperspectral imaging measurement capability operated 24/7 over varying seasons and weather conditions permits the evaluation of hyperspectral imaging for detection of different types of targets in real world environments. Such a measurement site was built at Picatinny Arsenal under the Spectral and Polarimetric Imagery Collection Experiment (SPICE), where two Hyper-Cam hyperspectral imagers are installed at the Precision Armament Laboratory (PAL) and are operated autonomously since Fall of 2009. The Hyper-Cam are currently collecting a complete hyperspectral database that contains the MWIR and LWIR hyperspectral measurements of several targets under day, night, sunny, cloudy, foggy, rainy and snowy conditions. The Telops Hyper-Cam sensor is an imaging spectrometer that enables the spatial and spectral analysis capabilities using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The MWIR version covers the 3 to 5 μm spectral range and the LWIR version covers the 8 to 12 μm spectral range. This paper describes the automated operation of the two Hyper-Cam sensors being used in the SPICE data collection. The Reveal Automation Control Software (RACS) developed collaboratively between Telops, ARDEC, and ARL enables flexible operating parameters and autonomous calibration. Under the RACS software, the Hyper-Cam sensors can autonomously calibrate itself using their internal blackbody targets, and the calibration events are initiated by user defined time intervals and on internal beamsplitter temperature monitoring. The RACS software is the first software developed for

  18. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study

    International Nuclear Information System (INIS)

    Tomatis, Stefano; Carrara, Mauro; Bono, Aldo; Bartoli, Cesare; Lualdi, Manuela; Tragni, Gabrina; Colombo, Ambrogio; Marchesini, Renato

    2005-01-01

    The aim of this research was to evaluate the performance of a new spectroscopic system in the diagnosis of melanoma. This study involves a consecutive series of 1278 patients with 1391 cutaneous pigmented lesions including 184 melanomas. In an attempt to approach the 'real world' of lesion population, a further set of 1022 not excised clinically reassuring lesions was also considered for analysis. Each lesion was imaged in vivo by a multispectral imaging system. The system operates at wavelengths between 483 and 950 nm by acquiring 15 images at equally spaced wavelength intervals. From the images, different lesion descriptors were extracted related to the colour distribution and morphology of the lesions. Data reduction techniques were applied before setting up a neural network classifier designed to perform automated diagnosis. The data set was randomly divided into three sets: train (696 lesions, including 90 melanomas) and verify (348 lesions, including 53 melanomas) for the instruction of a proper neural network, and an independent test set (347 lesions, including 41 melanomas). The neural network was able to discriminate between melanomas and non-melanoma lesions with a sensitivity of 80.4% and a specificity of 75.6% in the 1391 histologized cases data set. No major variations were found in classification scores when train, verify and test subsets were separately evaluated. Following receiver operating characteristic (ROC) analysis, the resulting area under the curve was 0.85. No significant differences were found among areas under train, verify and test set curves, supporting the good network ability to generalize for new cases. In addition, specificity and area under ROC curve increased up to 90% and 0.90, respectively, when the additional set of 1022 lesions without histology was added to the test set. Our data show that performance of an automated system is greatly population dependent, suggesting caution in the comparison with results reported in the

  19. A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data.

    Science.gov (United States)

    Kerz, Maximilian; Folarin, Amos; Meleckyte, Ruta; Watt, Fiona M; Dobson, Richard J; Danovi, Davide

    2016-10-01

    Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmentation of single-channel images with a robust R-based software solution to reduce the dimension of time to a single data point. These two packages combined allow robust segmentation of iPSCs solely on phase-contrast single-channel images and enable live imaging data to be easily integrated to endpoint data sets while retaining the dynamics of cellular responses. The described workflow facilitates characterization of the response of live-imaged iPSCs to external stimuli and definition of cell line-specific, phenotypic signatures. We present an efficient tool set for automated high-content analysis suitable for cells with challenging morphology. This approach has potentially widespread applications for human pluripotent stem cells and other cell types. © 2016 Society for Laboratory Automation and Screening.

  20. Development of Automated Image Analysis Tools for Verification of Radiotherapy Field Accuracy with AN Electronic Portal Imaging Device.

    Science.gov (United States)

    Dong, Lei

    1995-01-01

    The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5^ circ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1^ circ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross -correlation technique were

  1. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks.

    Science.gov (United States)

    Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

    2017-04-01

    Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams

  2. Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images.

    Science.gov (United States)

    Vermeer, K A; van der Schoot, J; Lemij, H G; de Boer, J F

    2011-06-01

    Current OCT devices provide three-dimensional (3D) in-vivo images of the human retina. The resulting very large data sets are difficult to manually assess. Automated segmentation is required to automatically process the data and produce images that are clinically useful and easy to interpret. In this paper, we present a method to segment the retinal layers in these images. Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples. When applied to new data, these classifiers produce labels for every pixel. After regularization of the 3D labeled volume to produce a surface, this results in consistent, three-dimensionally segmented layers that match known retinal morphology. Six labels were defined, corresponding to the following layers: Vitreous, retinal nerve fiber layer (RNFL), ganglion cell layer & inner plexiform layer, inner nuclear layer & outer plexiform layer, photoreceptors & retinal pigment epithelium and choroid. For both normal and glaucomatous eyes that were imaged with a Spectralis (Heidelberg Engineering) OCT system, the five resulting interfaces were compared between automatic and manual segmentation. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. The resulting total retinal thickness maps corresponded with known retinal morphology. RNFL thickness maps were compared to GDx (Carl Zeiss Meditec) thickness maps. Both maps were mostly consistent but local defects were better visualized in OCT-derived thickness maps.

  3. Identification and red blood cell automated counting from blood smear images using computer-aided system.

    Science.gov (United States)

    Acharya, Vasundhara; Kumar, Preetham

    2018-03-01

    Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the hemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually lead to erroneous results, and medical laboratory technicians are put under stress. A computer-aided system will help to attain precise results in less amount of time. This research work proposes an image-processing technique for counting the number of red blood cells. It aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. K-medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the red blood cells from the white blood cells. The red blood cells obtained are counted using the labeling algorithm and circular Hough transform. The radius range for the circle-drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. A comparison is done between the counts obtained using the labeling algorithm and circular Hough transform. Results of the work showed that circular Hough transform was more accurate in counting the red blood cells than the labeling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The work is designed to address all the drawbacks of the previous research work. The research work can be extended to extract various texture and shape features of abnormal cells identified so that diseases like anemia of inflammation and chronic disease can be detected at the earliest.

  4. Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

    Energy Technology Data Exchange (ETDEWEB)

    Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

    2006-02-01

    The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

  5. Primary histologic diagnosis using automated whole slide imaging: a validation study

    Directory of Open Access Journals (Sweden)

    Jukic Drazen M

    2006-04-01

    Full Text Available Abstract Background Only prototypes 5 years ago, high-speed, automated whole slide imaging (WSI systems (also called digital slide systems, virtual microscopes or wide field imagers are becoming increasingly capable and robust. Modern devices can capture a slide in 5 minutes at spatial sampling periods of less than 0.5 micron/pixel. The capacity to rapidly digitize large numbers of slides should eventually have a profound, positive impact on pathology. It is important, however, that pathologists validate these systems during development, not only to identify their limitations but to guide their evolution. Methods Three pathologists fully signed out 25 cases representing 31 parts. The laboratory information system was used to simulate real-world sign-out conditions including entering a full diagnostic field and comment (when appropriate and ordering special stains and recuts. For each case, discrepancies between diagnoses were documented by committee and a "consensus" report was formed and then compared with the microscope-based, sign-out report from the clinical archive. Results In 17 of 25 cases there were no discrepancies between the individual study pathologist reports. In 8 of the remaining cases, there were 12 discrepancies, including 3 in which image quality could be at least partially implicated. When the WSI consensus diagnoses were compared with the original sign-out diagnoses, no significant discrepancies were found. Full text of the pathologist reports, the WSI consensus diagnoses, and the original sign-out diagnoses are available as an attachment to this publication. Conclusion The results indicated that the image information contained in current whole slide images is sufficient for pathologists to make reliable diagnostic decisions and compose complex diagnostic reports. This is a very positive result; however, this does not mean that WSI is as good as a microscope. Virtually every slide had focal areas in which image quality (focus

  6. Automated vessel shadow segmentation of fovea-centered spectral-domain images from multiple OCT devices

    Science.gov (United States)

    Wu, Jing; Gerendas, Bianca S.; Waldstein, Sebastian M.; Simader, Christian; Schmidt-Erfurth, Ursula

    2014-03-01

    Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.1 Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses

  7. Automated analysis of images acquired with electronic portal imaging device during delivery of quality assurance plans for inversely optimized arc therapy

    DEFF Research Database (Denmark)

    Fredh, Anna; Korreman, Stine; Rosenschöld, Per Munck af

    2010-01-01

    This work presents an automated method for comprehensively analyzing EPID images acquired for quality assurance of RapidArc treatment delivery. In-house-developed software has been used for the analysis and long-term results from measurements on three linacs are presented....

  8. Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Mei Zhan

    2015-04-01

    Full Text Available Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM. These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a

  9. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

    Directory of Open Access Journals (Sweden)

    Ramakrishnan Mukundan

    2018-02-01

    Full Text Available This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis.

  10. Quantitative measurements of human sperm nuclei using automated microscopy and image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Wyrobek, A.J.; Firpo, M. (Lawrence Livermore National Lab., CA (United States)); Sudar, D. (Univ. of California, San Francisco (United States))

    1993-01-01

    A package of computer codes, called Morphometry Automation Program (MAP), was developed to (a) detect human sperm smeared onto glass slides, (b) measure more than 30 aspects of the size, shape, texture, and staining of their nuclei, and (c) retain operator evaluation of the process. MAP performs the locating and measurement functions automatically, without operator assistance. In addition to standard measurements, MAP utilizes axial projections of nuclear area and stain intensity to detect asymmetries. MAP also stores for each cell the gray-scale images for later display and evaluation, and it retains coordinates for optional relocation and inspection under the microscope. MAP operates on the Quantitative Image Processing System (QUIPS) at LLNL. MAP has potential applications in the evaluation of infertility and in reproductive toxicology, such as (a) classifying sperm into clinical shape categories for assessing fertility status, (b) identifying subtle effects of host factors (diet, stress, etc.), (c) assessing the risk of potential spermatogenic toxicants (tobacco, drugs, etc.), and (d) investigating associations with abnormal pregnancy outcomes (time to pregnancy, early fetal loss, etc.).

  11. Automated imaging of cellular spheroids with selective plane illumination microscopy on a chip (Conference Presentation)

    Science.gov (United States)

    Paiè, Petra; Bassi, Andrea; Bragheri, Francesca; Osellame, Roberto

    2017-02-01

    Selective plane illumination microscopy (SPIM) is an optical sectioning technique that allows imaging of biological samples at high spatio-temporal resolution. Standard SPIM devices require dedicated set-ups, complex sample preparation and accurate system alignment, thus limiting the automation of the technique, its accessibility and throughput. We present a millimeter-scaled optofluidic device that incorporates selective plane illumination and fully automatic sample delivery and scanning. To this end an integrated cylindrical lens and a three-dimensional fluidic network were fabricated by femtosecond laser micromachining into a single glass chip. This device can upgrade any standard fluorescence microscope to a SPIM system. We used SPIM on a CHIP to automatically scan biological samples under a conventional microscope, without the need of any motorized stage: tissue spheroids expressing fluorescent proteins were flowed in the microchannel at constant speed and their sections were acquired while passing through the light sheet. We demonstrate high-throughput imaging of the entire sample volume (with a rate of 30 samples/min), segmentation and quantification in thick (100-300 μm diameter) cellular spheroids. This optofluidic device gives access to SPIM analyses to non-expert end-users, opening the way to automatic and fast screening of a high number of samples at subcellular resolution.

  12. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis

    Science.gov (United States)

    Chung, Howard; Cobzas, Dana; Birdsell, Laura; Lieffers, Jessica; Baracos, Vickie

    2009-02-01

    The ability to compute body composition in cancer patients lends itself to determining the specific clinical outcomes associated with fat and lean tissue stores. For example, a wasting syndrome of advanced disease associates with shortened survival. Moreover, certain tissue compartments represent sites for drug distribution and are likely determinants of chemotherapy efficacy and toxicity. CT images are abundant, but these cannot be fully exploited unless there exist practical and fast approaches for tissue quantification. Here we propose a fully automated method for segmenting muscle, visceral and subcutaneous adipose tissues, taking the approach of shape modeling for the analysis of skeletal muscle. Muscle shape is represented using PCA encoded Free Form Deformations with respect to a mean shape. The shape model is learned from manually segmented images and used in conjunction with a tissue appearance prior. VAT and SAT are segmented based on the final deformed muscle shape. In comparing the automatic and manual methods, coefficients of variation (COV) (1 - 2%), were similar to or smaller than inter- and intra-observer COVs reported for manual segmentation.

  13. Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images.

    Science.gov (United States)

    Frasconi, Paolo; Silvestri, Ludovico; Soda, Paolo; Cortini, Roberto; Pavone, Francesco S; Iannello, Giulio

    2014-09-01

    Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain. We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers. Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  14. AUTOMATED DATA PRODUCTION FOR A NOVEL AIRBORNE MULTIANGLE SPECTROPOLARIMETRIC IMAGER (AIRMSPI

    Directory of Open Access Journals (Sweden)

    V. M. Jovanovic

    2012-07-01

    Full Text Available A novel polarimetric imaging technique making use of rapid retardance modulation has been developed by JPL as a part of NASA's Instrument Incubator Program. It has been built into the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI under NASA's Airborne Instrument Technology Transition Program, and is aimed primarily at remote sensing of the amounts and microphysical properties of aerosols and clouds. AirMSPI includes an 8-band (355, 380, 445, 470, 555, 660, 865, 935 nm pushbroom camera that measures polarization in a subset of the bands (470, 660, and 865 nm. The camera is mounted on a gimbal and acquires imagery in a configurable set of along-track viewing angles ranging between +67°and –67° relative to nadir. As a result, near simultaneous multi-angle, multi-spectral, and polarimetric measurements of the targeted areas at a spatial resolution ranging from 7 m to 20 m (depending on the viewing angle can be derived. An automated data production system is being built to support high data acquisition rate in concert with co-registration and orthorectified mapping requirements. To date, a number of successful engineering checkout flights were conducted in October 2010, August-September 2011, and January 2012. Data products resulting from these flights will be presented.

  15. Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

    Directory of Open Access Journals (Sweden)

    Stefan Wiehle

    2015-01-01

    Full Text Available We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

  16. Automated analysis for early signs of cerebral infarctions on brain X-ray CT images

    International Nuclear Information System (INIS)

    Oshima, Kazuki; Hara, Takeshi; Zhou, X.; Muramatsu, Chisako; Fujita, Hiroshi; Sakashita, Keiji

    2010-01-01

    t-PA (tissue plasminogen activator) thrombolysis is an effective clinical treatment for the acute cerebral infarction by breakdown to blood clots. However there is a risk of hemorrhage with its use. The guideline of the treatment is denying cerebral hemorrhage and widespread Early CT sign (ECS) on CT images. In this study, we analyzed the CT value of normal brain and ECS with normal brain model by comparing patient brain CT scan with a statistical normal model. Our method has constructed normal brain models consisted of 60 normal brain X-ray CT images. We calculated Z-score based on statistical model for 16 cases of cerebral infarction with ECS, 3 cases of cerebral infarction without ECS, and 25 cases of normal brain. The results of statistical analysis showed that there was a statistically significant difference between control and abnormal groups. This result implied that the automated detection scheme for ECS by using Z-score would be a possible application for brain computer-aided diagnosis (CAD). (author)

  17. Chaotic secure content-based hidden transmission of biometric templates

    International Nuclear Information System (INIS)

    Khan, Muhammad Khurram; Zhang Jiashu; Tian Lei

    2007-01-01

    The large-scale proliferation of biometric verification systems creates a demand for effective and reliable security and privacy of its data. Like passwords and PIN codes, biometric data is also not secret and if it is compromised, the integrity of the whole verification system could be at high risk. To address these issues, this paper presents a novel chaotic secure content-based hidden transmission scheme of biometric data. Encryption and data hiding techniques are used to improve the security and secrecy of the transmitted templates. Secret keys are generated by the biometric image and used as the parameter value and initial condition of the chaotic map, and each transaction session has different secret keys to protect from the attacks. Two chaotic maps are incorporated for the encryption to resolve the finite word length effect and to improve the system's resistance against attacks. Encryption is applied on the biometric templates before hiding into the cover/host images to make them secure, and then templates are hidden into the cover image. Experimental results show that the security, performance, and accuracy of the presented scheme are encouraging comparable with other methods found in the current literature

  18. Automated processing of thermal infrared images of Osservatorio Vesuviano permanent surveillance network by using Matlab code

    Science.gov (United States)

    Sansivero, Fabio; Vilardo, Giuseppe; Caputo, Teresa

    2017-04-01

    The permanent thermal infrared surveillance network of Osservatorio Vesuviano (INGV) is composed of 6 stations which acquire IR frames of fumarole fields in the Campi Flegrei caldera and inside the Vesuvius crater (Italy). The IR frames are uploaded to a dedicated server in the Surveillance Center of Osservatorio Vesuviano in order to process the infrared data and to excerpt all the information contained. In a first phase the infrared data are processed by an automated system (A.S.I.R.A. Acq- Automated System of IR Analysis and Acquisition) developed in Matlab environment and with a user-friendly graphic user interface (GUI). ASIRA daily generates time-series of residual temperature values of the maximum temperatures observed in the IR scenes after the removal of seasonal effects. These time-series are displayed in the Surveillance Room of Osservatorio Vesuviano and provide information about the evolution of shallow temperatures field of the observed areas. In particular the features of ASIRA Acq include: a) efficient quality selection of IR scenes, b) IR images co-registration in respect of a reference frame, c) seasonal correction by using a background-removal methodology, a) filing of IR matrices and of the processed data in shared archives accessible to interrogation. The daily archived records can be also processed by ASIRA Plot (Matlab code with GUI) to visualize IR data time-series and to help in evaluating inputs parameters for further data processing and analysis. Additional processing features are accomplished in a second phase by ASIRA Tools which is Matlab code with GUI developed to extract further information from the dataset in automated way. The main functions of ASIRA Tools are: a) the analysis of temperature variations of each pixel of the IR frame in a given time interval, b) the removal of seasonal effects from temperature of every pixel in the IR frames by using an analytic approach (removal of sinusoidal long term seasonal component by using a

  19. Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

    Science.gov (United States)

    Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

    2016-03-01

    Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 +/- 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

  20. Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and IR Bands

    Science.gov (United States)

    Mozgovoy, Dmitry k.; Hnatushenko, Volodymyr V.; Vasyliev, Volodymyr V.

    2018-04-01

    Vegetation and water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. A methodology of automated recognition of vegetation and water bodies on the territory of megacities in satellite images of sub-meter spatial resolution of the visible and IR bands is proposed. By processing multispectral images from the satellite SuperView-1A, vector layers of recognized plant and water objects were obtained. Analysis of the results of image processing showed a sufficiently high accuracy of the delineation of the boundaries of recognized objects and a good separation of classes. The developed methodology provides a significant increase of the efficiency and reliability of updating maps of large cities while reducing financial costs. Due to the high degree of automation, the proposed methodology can be implemented in the form of a geo-information web service functioning in the interests of a wide range of public services and commercial institutions.

  1. Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

    Directory of Open Access Journals (Sweden)

    Michael Rzanny

    2017-11-01

    Full Text Available Abstract Background Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images. Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this

  2. Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain.

    Science.gov (United States)

    Rzanny, Michael; Seeland, Marco; Wäldchen, Jana; Mäder, Patrick

    2017-01-01

    Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf's top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf's boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend

  3. Optic disc boundary segmentation from diffeomorphic demons registration of monocular fundus image sequences versus 3D visualization of stereo fundus image pairs for automated early stage glaucoma assessment

    Science.gov (United States)

    Gatti, Vijay; Hill, Jason; Mitra, Sunanda; Nutter, Brian

    2014-03-01

    Despite the current availability in resource-rich regions of advanced technologies in scanning and 3-D imaging in current ophthalmology practice, world-wide screening tests for early detection and progression of glaucoma still consist of a variety of simple tools, including fundus image-based parameters such as CDR (cup to disc diameter ratio) and CAR (cup to disc area ratio), especially in resource -poor regions. Reliable automated computation of the relevant parameters from fundus image sequences requires robust non-rigid registration and segmentation techniques. Recent research work demonstrated that proper non-rigid registration of multi-view monocular fundus image sequences could result in acceptable segmentation of cup boundaries for automated computation of CAR and CDR. This research work introduces a composite diffeomorphic demons registration algorithm for segmentation of cup boundaries from a sequence of monocular images and compares the resulting CAR and CDR values with those computed manually by experts and from 3-D visualization of stereo pairs. Our preliminary results show that the automated computation of CDR and CAR from composite diffeomorphic segmentation of monocular image sequences yield values comparable with those from the other two techniques and thus may provide global healthcare with a cost-effective yet accurate tool for management of glaucoma in its early stage.

  4. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

    Science.gov (United States)

    Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

    2017-11-13

    Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

  5. Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

    Science.gov (United States)

    Brown, Richard B.; Smoot, James C.; Underwood, Lauren; Armstrong, C. Duane

    2012-01-01

    Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then

  6. Application of an Automated Discharge Imaging System and LSPIV during Typhoon Events in Taiwan

    Directory of Open Access Journals (Sweden)

    Wei-Che Huang

    2018-03-01

    Full Text Available An automated discharge imaging system (ADIS, which is a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all of the measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP and surface velocity radar (SVR, respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that the components till (τ and roll (θ of the camera are most sensitive parameters to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events.

  7. Content-Based tile Retrieval System

    Czech Academy of Sciences Publication Activity Database

    Vácha, Pavel; Haindl, Michal

    -, č. 85 (2011), s. 45-45 ISSN 0926-4981 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593; GA MŠk(CZ) LG11009 Institutional research plan: CEZ:AV0Z10750506 Keywords : CBIR * Markov random fields Subject RIV: BD - Theory of Information http://ercim-news.ercim.eu/images/stories/EN85/EN85-web.pdf

  8. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders.

    Science.gov (United States)

    Tufail, Adnan; Rudisill, Caroline; Egan, Catherine; Kapetanakis, Venediktos V; Salas-Vega, Sebastian; Owen, Christopher G; Lee, Aaron; Louw, Vern; Anderson, John; Liew, Gerald; Bolter, Louis; Srinivas, Sowmya; Nittala, Muneeswar; Sadda, SriniVas; Taylor, Paul; Rudnicka, Alicja R

    2017-03-01

    With the increasing prevalence of diabetes, annual screening for diabetic retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aimed to determine whether ARIAS can be safely introduced into DR screening pathways to replace human graders. Observational measurement comparison study of human graders following a national screening program for DR versus ARIAS. Retinal images from 20 258 consecutive patients attending routine annual diabetic eye screening between June 1, 2012, and November 4, 2013. Retinal images were manually graded following a standard national protocol for DR screening and were processed by 3 ARIAS: iGradingM, Retmarker, and EyeArt. Discrepancies between manual grades and ARIAS results were sent to a reading center for arbitration. Screening performance (sensitivity, false-positive rate) and diagnostic accuracy (95% confidence intervals of screening-performance measures) were determined. Economic analysis estimated the cost per appropriate screening outcome. Sensitivity point estimates (95% confidence intervals) of the ARIAS were as follows: EyeArt 94.7% (94.2%-95.2%) for any retinopathy, 93.8% (92.9%-94.6%) for referable retinopathy (human graded as either ungradable, maculopathy, preproliferative, or proliferative), 99.6% (97.0%-99.9%) for proliferative retinopathy; Retmarker 73.0% (72.0 %-74.0%) for any retinopathy, 85.0% (83.6%-86.2%) for referable retinopathy, 97.9% (94.9%-99.1%) for proliferative retinopathy. iGradingM classified all images as either having disease or being ungradable. EyeArt and Retmarker saved costs compared with manual grading both as a replacement for initial human grading and as a filter prior to primary human grading, although the latter approach was less cost-effective. Retmarker and EyeArt systems achieved acceptable sensitivity for referable retinopathy when compared

  9. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images

    Directory of Open Access Journals (Sweden)

    Hardy Craig Hall

    2016-02-01

    Full Text Available While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1 segment radial plant organs into individual cells, 2 classify cells into cell type categories based upon random forest classification, 3 divide each cell into sub-regions, and 4 quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

  10. Automated gas bubble imaging at sea floor – a new method of in situ gas flux quantification

    Directory of Open Access Journals (Sweden)

    G. Bohrmann

    2010-06-01

    Full Text Available Photo-optical systems are common in marine sciences and have been extensively used in coastal and deep-sea research. However, due to technical limitations in the past photo images had to be processed manually or semi-automatically. Recent advances in technology have rapidly improved image recording, storage and processing capabilities which are used in a new concept of automated in situ gas quantification by photo-optical detection. The design for an in situ high-speed image acquisition and automated data processing system is reported ("Bubblemeter". New strategies have been followed with regards to back-light illumination, bubble extraction, automated image processing and data management. This paper presents the design of the novel method, its validation procedures and calibration experiments. The system will be positioned and recovered from the sea floor using a remotely operated vehicle (ROV. It is able to measure bubble flux rates up to 10 L/min with a maximum error of 33% for worst case conditions. The Bubblemeter has been successfully deployed at a water depth of 1023 m at the Makran accretionary prism offshore Pakistan during a research expedition with R/V Meteor in November 2007.

  11. An Automated Segmentation of R2* Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme.

    Science.gov (United States)

    Saiviroonporn, Pairash; Korpraphong, Pornpim; Viprakasit, Vip; Krittayaphong, Rungroj

    2018-02-13

    The objectives of this study were to develop and test an automated segmentation of R2* iron-overloaded liver images using fuzzy c-mean (FCM) clustering and to evaluate the observer variations. Liver R2* images and liver iron concentration (LIC) maps of 660 thalassemia examinations were randomly separated into training (70%) and testing (30%) cohorts for development and evaluation purposes, respectively. Two-dimensional FCM used R2* images, and the LIC map was implemented to segment vessels from the parenchyma. Two automated FCM variables were investigated using new echo time and membership threshold selection criteria based on the FCM centroid distance and LIC levels, respectively. The new method was developed on a training cohort and compared with manual segmentation for segmentation accuracy and to a previous semiautomated method, and a semiautomated scheme was suggested to improve unsuccessful results. The automated variables found from the training cohort were assessed for their effectiveness in the testing cohort, both quantitatively and qualitatively (the latter by 2 abdominal radiologists using a grading method, with evaluations of observer variations). A segmentation error of less than 30% was considered to be a successful result in both cohorts, whereas, in the testing cohort, a good grade obtained from satisfactory automated results was considered a success. The centroid distance method has a segmentation accuracy comparable with the previous-best, semiautomated method. About 94% and 90% of the examinations in the training and testing cohorts were automatically segmented out successfully, respectively. The failed examinations were successfully segmented out with thresholding adjustment (3% and 8%) or by using alternative results from the previous 1-dimensional FCM method (3% and 2%) in the training and testing cohorts, respectively. There were no failed segmentation examinations in either cohort. The intraobserver and interobserver variabilities were

  12. Calibration of a semi-automated segmenting method for quantification of adipose tissue compartments from magnetic resonance images of mice.

    Science.gov (United States)

    Garteiser, Philippe; Doblas, Sabrina; Towner, Rheal A; Griffin, Timothy M

    2013-11-01

    To use an automated water-suppressed magnetic resonance imaging (MRI) method to objectively assess adipose tissue (AT) volumes in whole body and specific regional body components (subcutaneous, thoracic and peritoneal) of obese and lean mice. Water-suppressed MR images were obtained on a 7T, horizontal-bore MRI system in whole bodies (excluding head) of 26 week old male C57BL6J mice fed a control (10% kcal fat) or high-fat diet (60% kcal fat) for 20 weeks. Manual (outlined regions) versus automated (Gaussian fitting applied to threshold-weighted images) segmentation procedures were compared for whole body AT and regional AT volumes (i.e., subcutaneous, thoracic, and peritoneal). The AT automated segmentation method was compared to dual-energy X-ray (DXA) analysis. The average AT volumes for whole body and individual compartments correlated well between the manual outlining and the automated methods (R2>0.77, p<0.05). Subcutaneous, peritoneal, and total body AT volumes were increased 2-3 fold and thoracic AT volume increased more than 5-fold in diet-induced obese mice versus controls (p<0.05). MRI and DXA-based method comparisons were highly correlative (R2=0.94, p<0.0001). Automated AT segmentation of water-suppressed MRI data using a global Gaussian filtering algorithm resulted in a fairly accurate assessment of total and regional AT volumes in a pre-clinical mouse model of obesity. © 2013 Elsevier Inc. All rights reserved.

  13. Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images

    International Nuclear Information System (INIS)

    Pevsner, A.; Davis, B.; Joshi, S.; Hertanto, A.; Mechalakos, J.; Yorke, E.; Rosenzweig, K.; Nehmeh, S.; Erdi, Y.E.; Humm, J.L.; Larson, S.; Ling, C.C.; Mageras, G.S.

    2006-01-01

    We have evaluated an automated registration procedure for predicting tumor and lung deformation based on CT images of the thorax obtained at different respiration phases. The method uses a viscous fluid model of tissue deformation to map voxels from one CT dataset to another. To validate the deformable matching algorithm we used a respiration-correlated CT protocol to acquire images at different phases of the respiratory cycle for six patients with nonsmall cell lung carcinoma. The position and shape of the deformable gross tumor volumes (GTV) at the end-inhale (EI) phase predicted by the algorithm was compared to those drawn by four observers. To minimize interobserver differences, all observers used the contours drawn by a single observer at end-exhale (EE) phase as a guideline to outline GTV contours at EI. The differences between model-predicted and observer-drawn GTV surfaces at EI, as well as differences between structures delineated by observers at EI (interobserver variations) were evaluated using a contour comparison algorithm written for this purpose, which determined the distance between the two surfaces along different directions. The mean and 90% confidence interval for model-predicted versus observer-drawn GTV surface differences over all patients and all directions were 2.6 and 5.1 mm, respectively, whereas the mean and 90% confidence interval for interobserver differences were 2.1 and 3.7 mm. We have also evaluated the algorithm's ability to predict normal tissue deformations by examining the three-dimensional (3-D) vector displacement of 41 landmarks placed by each observer at bronchial and vascular branch points in the lung between the EE and EI image sets (mean and 90% confidence interval displacements of 11.7 and 25.1 mm, respectively). The mean and 90% confidence interval discrepancy between model-predicted and observer-determined landmark displacements over all patients were 2.9 and 7.3 mm, whereas interobserver discrepancies were 2.8 and 6

  14. Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.

    Science.gov (United States)

    Bolaños, Federico; LeDue, Jeff M; Murphy, Timothy H

    2017-01-30

    Automation of animal experimentation improves consistency, reduces potential for error while decreasing animal stress and increasing well-being. Radio frequency identification (RFID) tagging can identify individual mice in group housing environments enabling animal-specific tracking of physiological parameters. We describe a simple protocol to radio frequency identification (RFID) tag and detect mice. RFID tags were injected sub-cutaneously after brief isoflurane anesthesia and do not require surgical steps such as suturing or incisions. We employ glass-encapsulated 125kHz tags that can be read within 30.2±2.4mm of the antenna. A raspberry pi single board computer and tag reader enable automated logging and cross platform support is possible through Python. We provide sample software written in Python to provide a flexible and cost effective system for logging the weights of multiple mice in relation to pre-defined targets. The sample software can serve as the basis of any behavioral or physiological task where users will need to identify and track specific animals. Recently, we have applied this system of tagging to automated mouse brain imaging within home-cages. We provide a cost effective solution employing open source software to facilitate adoption in applications such as automated imaging or tracking individual animal weights during tasks where food or water restriction is employed as motivation for a specific behavior. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

    Directory of Open Access Journals (Sweden)

    Eva Husson

    2016-09-01

    Full Text Available Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated the desired level of

  16. Automated MALDI Matrix Coating System for Multiple Tissue Samples for Imaging Mass Spectrometry

    Science.gov (United States)

    Mounfield, William P.; Garrett, Timothy J.

    2012-03-01

    Uniform matrix deposition on tissue samples for matrix-assisted laser desorption/ionization (MALDI) is key for reproducible analyte ion signals. Current methods often result in nonhomogenous matrix deposition, and take time and effort to produce acceptable ion signals. Here we describe a fully-automated method for matrix deposition using an enclosed spray chamber and spray nozzle for matrix solution delivery. A commercial air-atomizing spray nozzle was modified and combined with solenoid controlled valves and a Programmable Logic Controller (PLC) to control and deliver the matrix solution. A spray chamber was employed to contain the nozzle, sample, and atomized matrix solution stream, and to prevent any interference from outside conditions as well as allow complete control of the sample environment. A gravity cup was filled with MALDI matrix solutions, including DHB in chloroform/methanol (50:50) at concentrations up to 60 mg/mL. Various samples (including rat brain tissue sections) were prepared using two deposition methods (spray chamber, inkjet). A linear ion trap equipped with an intermediate-pressure MALDI source was used for analyses. Optical microscopic examination showed a uniform coating of matrix crystals across the sample. Overall, the mass spectral images gathered from tissues coated using the spray chamber system were of better quality and more reproducible than from tissue specimens prepared by the inkjet deposition method.

  17. Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

    Science.gov (United States)

    Phipps, Jennifer E.; Gorpas, Dimitris; Unger, Jakob; Darrow, Morgan; Bold, Richard J.; Marcu, Laura

    2018-01-01

    Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N  =  20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

  18. UAS imaging for automated crop lodging detection: a case study over an experimental maize field

    Science.gov (United States)

    Chu, Tianxing; Starek, Michael J.; Brewer, Michael J.; Masiane, Tiisetso; Murray, Seth C.

    2017-05-01

    Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in corn. A variety of contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may lead to lodging before harvesting season. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address this problem, this research focuses on the use of unmanned aircraft systems (UAS) for automated detection of crop lodging. The study was conducted over an experimental corn field at the Texas A and M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the corn field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height, and rates of growth. A lodging index for detecting corn lodging was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates well with the ground-measured height. More importantly, the lodging index can effectively reflect severity of corn lodging and yield after harvesting.

  19. Automated recognition of the pericardium contour on processed CT images using genetic algorithms.

    Science.gov (United States)

    Rodrigues, É O; Rodrigues, L O; Oliveira, L S N; Conci, A; Liatsis, P

    2017-08-01

    This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Experimental saltwater intrusion in coastal aquifers using automated image analysis: Applications to homogeneous aquifers

    Science.gov (United States)

    Robinson, G.; Ahmed, Ashraf A.; Hamill, G. A.

    2016-07-01

    This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimising manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

  1. A multiresolution approach to automated classification of protein subcellular location images

    Directory of Open Access Journals (Sweden)

    Srinivasa Gowri

    2007-06-01

    Full Text Available Abstract Background Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem. Results We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%. Conclusion We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.

  2. Automated analysis of retinal images for detection of referable diabetic retinopathy.

    Science.gov (United States)

    Abràmoff, Michael D; Folk, James C; Han, Dennis P; Walker, Jonathan D; Williams, David F; Russell, Stephen R; Massin, Pascale; Cochener, Beatrice; Gain, Philippe; Tang, Li; Lamard, Mathieu; Moga, Daniela C; Quellec, Gwénolé; Niemeijer, Meindert

    2013-03-01

    The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard. To determine the sensitivity and specificity of the Iowa Detection Program (IDP) to detect referable diabetic retinopathy (RDR). In primary care DR clinics in France, from January 1, 2005, through December 31, 2010, patients were photographed consecutively, and retinal color images were graded for retinopathy severity according to the International Clinical Diabetic Retinopathy scale and macular edema by 3 masked independent retinal specialists and regraded with adjudication until consensus. The IDP analyzed the same images at a predetermined and fixed set point. We defined RDR as more than mild nonproliferative retinopathy and/or macular edema. A total of 874 people with diabetes at risk for DR. Sensitivity and specificity of the IDP to detect RDR, area under the receiver operating characteristic curve, sensitivity and specificity of the retinal specialists' readings, and mean interobserver difference (κ). The RDR prevalence was 21.7% (95% CI, 19.0%-24.5%). The IDP sensitivity was 96.8% (95% CI, 94.4%-99.3%) and specificity was 59.4% (95% CI, 55.7%-63.0%), corresponding to 6 of 874 false-negative results (none met treatment criteria). The area under the receiver operating characteristic curve was 0.937 (95% CI, 0.916-0.959). Before adjudication and consensus, the sensitivity/specificity of the retinal specialists were 0.80/0.98, 0.71/1.00, and 0.91/0.95, and the mean intergrader κ was 0.822. The IDP has high sensitivity and specificity to detect RDR. Computer analysis of retinal photographs for DR and automated detection of RDR can be implemented safely into the DR screening pipeline, potentially improving access to screening and health care productivity and reducing visual loss

  3. Automated Synthesis of 18F-Fluoropropoxytryptophan for Amino Acid Transporter System Imaging

    Directory of Open Access Journals (Sweden)

    I-Hong Shih

    2014-01-01

    Full Text Available Objective. This study was to develop a cGMP grade of [18F]fluoropropoxytryptophan (18F-FTP to assess tryptophan transporters using an automated synthesizer. Methods. Tosylpropoxytryptophan (Ts-TP was reacted with K18F/kryptofix complex. After column purification, solvent evaporation, and hydrolysis, the identity and purity of the product were validated by radio-TLC (1M-ammonium acetate : methanol = 4 : 1 and HPLC (C-18 column, methanol : water = 7 : 3 analyses. In vitro cellular uptake of 18F-FTP and 18F-FDG was performed in human prostate cancer cells. PET imaging studies were performed with 18F-FTP and 18F-FDG in prostate and small cell lung tumor-bearing mice (3.7 MBq/mouse, iv. Results. Radio-TLC and HPLC analyses of 18F-FTP showed that the Rf and Rt values were 0.9 and 9 min, respectively. Radiochemical purity was >99%. The radiochemical yield was 37.7% (EOS 90 min, decay corrected. Cellular uptake of 18F-FTP and 18F-FDG showed enhanced uptake as a function of incubation time. PET imaging studies showed that 18F-FTP had less tumor uptake than 18F-FDG in prostate cancer model. However, 18F-FTP had more uptake than 18F-FDG in small cell lung cancer model. Conclusion. 18F-FTP could be synthesized with high radiochemical yield. Assessment of upregulated transporters activity by 18F-FTP may provide potential applications in differential diagnosis and prediction of early treatment response.

  4. A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

    Science.gov (United States)

    Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

    2016-01-15

    Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Quantification of Eosinophilic Granule Protein Deposition in Biopsies of Inflammatory Skin Diseases by Automated Image Analysis of Highly Sensitive Immunostaining

    Directory of Open Access Journals (Sweden)

    Peter Kiehl

    1999-01-01

    Full Text Available Eosinophilic granulocytes are major effector cells in inflammation. Extracellular deposition of toxic eosinophilic granule proteins (EGPs, but not the presence of intact eosinophils, is crucial for their functional effect in situ. As even recent morphometric approaches to quantify the involvement of eosinophils in inflammation have been only based on cell counting, we developed a new method for the cell‐independent quantification of EGPs by image analysis of immunostaining. Highly sensitive, automated immunohistochemistry was done on paraffin sections of inflammatory skin diseases with 4 different primary antibodies against EGPs. Image analysis of immunostaining was performed by colour translation, linear combination and automated thresholding. Using strictly standardized protocols, the assay was proven to be specific and accurate concerning segmentation in 8916 fields of 520 sections, well reproducible in repeated measurements and reliable over 16 weeks observation time. The method may be valuable for the cell‐independent segmentation of immunostaining in other applications as well.

  6. Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction.

    Science.gov (United States)

    White, Tonya; Jansen, Philip R; Muetzel, Ryan L; Sudre, Gustavo; El Marroun, Hanan; Tiemeier, Henning; Qiu, Anqi; Shaw, Philip; Michael, Andrew M; Verhulst, Frank C

    2018-03-01

    Motion-related artifacts are one of the major challenges associated with pediatric neuroimaging. Recent studies have shown a relationship between visual quality ratings of T 1 images and cortical reconstruction measures. Automated algorithms offer more precision in quantifying movement-related artifacts compared to visual inspection. Thus, the goal of this study was to test three different automated quality assessment algorithms for structural MRI scans. The three algorithms included a Fourier-, integral-, and a gradient-based approach which were run on raw T 1 -weighted imaging data collected from four different scanners. The four cohorts included a total of 6,662 MRI scans from two waves of the Generation R Study, the NIH NHGRI Study, and the GUSTO Study. Using receiver operating characteristics with visually inspected quality ratings of the T 1 images, the area under the curve (AUC) for the gradient algorithm, which performed better than either the integral or Fourier approaches, was 0.95, 0.88, and 0.82 for the Generation R, NHGRI, and GUSTO studies, respectively. For scans of poor initial quality, repeating the scan often resulted in a better quality second image. Finally, we found that even minor differences in automated quality measurements were associated with FreeSurfer derived measures of cortical thickness and surface area, even in scans that were rated as good quality. Our findings suggest that the inclusion of automated quality assessment measures can augment visual inspection and may find use as a covariate in analyses or to identify thresholds to exclude poor quality data. © 2017 Wiley Periodicals, Inc.

  7. Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

    Science.gov (United States)

    Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

    2014-07-01

    To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

  8. Automated quantification and sizing of unbranched filamentous cyanobacteria by model-based object-oriented image analysis.

    Science.gov (United States)

    Zeder, Michael; Van den Wyngaert, Silke; Köster, Oliver; Felder, Kathrin M; Pernthaler, Jakob

    2010-03-01

    Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-oriented image analysis to simultaneously determine (i) filament number, (ii) individual filament lengths, and (iii) the cumulative filament length of unbranched cyanobacterial morphotypes in fluorescent microscope images in a fully automated high-throughput manner. Special emphasis was placed on correct detection of overlapping objects by image analysis and on appropriate coverage of filament length distribution by using large composite images. The method was validated with a data set for Planktothrix rubescens from field samples and was compared with manual filament tracing, the line intercept method, and the Utermöhl counting approach. The computer program described allows batch processing of large images from any appropriate source and annotation of detected filaments. It requires no user interaction, is available free, and thus might be a useful tool for basic research and drinking water quality control.

  9. Development and application of an automated analysis method for individual cerebral perfusion single photon emission tomography images

    CERN Document Server

    Cluckie, A J

    2001-01-01

    Neurological images may be analysed by performing voxel by voxel comparisons with a group of control subject images. An automated, 3D, voxel-based method has been developed for the analysis of individual single photon emission tomography (SPET) scans. Clusters of voxels are identified that represent regions of abnormal radiopharmaceutical uptake. Morphological operators are applied to reduce noise in the clusters, then quantitative estimates of the size and degree of the radiopharmaceutical uptake abnormalities are derived. Statistical inference has been performed using a Monte Carlo method that has not previously been applied to SPET scans, or for the analysis of individual images. This has been validated for group comparisons of SPET scans and for the analysis of an individual image using comparison with a group. Accurate statistical inference was obtained independent of experimental factors such as degrees of freedom, image smoothing and voxel significance level threshold. The analysis method has been eval...

  10. Prenatal Care: A Content-Based ESL Curriculum.

    Science.gov (United States)

    Hassel, Elissa Anne

    A content-based curriculum in English as a Second Language (ESL) focusing on prenatal self-care is presented. The course was designed as a solution to the problem of inadequate prenatal care for limited-English-proficient Mexican immigrant women. The first three sections offer background information on and discussion of (1) content-based ESL…

  11. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.

    Science.gov (United States)

    Jiang, Luan; Hu, Xiaoxin; Xiao, Qin; Gu, Yajia; Li, Qiang

    2017-06-01

    Amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) in breast dynamic contrast enhanced magnetic resonance images (DCE-MRI) are suggested as strong indices for assessing breast cancer risk. Whole breast segmentation is the first important task for quantitative analysis of FGT and BPE in three-dimensional (3-D) DCE-MRI. The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of the whole breast in 3-D fat-suppressed DCE-MRI. The whole breast segmentation consisted of two major steps, i.e., the delineation of chest wall line and breast skin line. First, a sectional dynamic programming method was employed to trace the upper and/or lower boundaries of the chest wall by use of the positive and/or negative gradient within a band along the chest wall in each 2-D slice. Second, another dynamic programming was applied to delineate the skin-air boundary slice-by-slice based on the saturated gradient of the enhanced image obtained with the prior statistical distribution of gray levels of the breast skin line. Starting from the central slice, these two steps employed a Gaussian function to limit the search range of boundaries in adjacent slices based on the continuity of chest wall line and breast skin line. Finally, local breast skin line detection was applied around armpit to complete the whole breast segmentation. The method was validated with a representative dataset of 100 3-D breast DCE-MRI scans through objective quantification and subjective evaluation. The MR scans in the dataset were acquired with four MR scanners in five spatial resolutions. The cases were assessed with four breast density ratings by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. Our segmentation algorithm achieved a Dice volume overlap measure of 95.8 ± 1.2% and volume difference measure of 8.4 ± 2.4% between the automatically and manually

  12. A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme.

    Science.gov (United States)

    Kapetanakis, Venediktos V; Rudnicka, Alicja R; Liew, Gerald; Owen, Christopher G; Lee, Aaron; Louw, Vern; Bolter, Louis; Anderson, John; Egan, Catherine; Salas-Vega, Sebastian; Rudisill, Caroline; Taylor, Paul; Tufail, Adnan

    2015-09-01

    Diabetic retinopathy screening in England involves labour intensive manual grading of digital retinal images. We present the plan for an observational retrospective study of whether automated systems could replace one or more steps of human grading. Patients aged 12 or older who attended the Diabetes Eye Screening programme, Homerton University Hospital (London) between 1 June 2012 and 4 November 2013 had macular and disc-centred retinal images taken. All screening episodes were manually graded and will additionally be graded by three automated systems. Each system will process all screening episodes, and screening performance (sensitivity, false positive rate, likelihood ratios) and diagnostic accuracy (95% confidence intervals of screening performance measures) will be quantified. A sub-set of gradings will be validated by an approved Reading Centre. Additional analyses will explore the effect of altering thresholds for disease detection within each automated system on screening performance. 2,782/20,258 diabetes patients were referred to ophthalmologists for further examination. Prevalence of maculopathy (M1), pre-proliferative retinopathy (R2), and proliferative retinopathy (R3) were 7.9%, 3.1% and 1.2%, respectively; 4749 (23%) patients were diagnosed with background retinopathy (R1); 1.5% were considered ungradable by human graders. Retinopathy prevalence was similar to other English diabetic screening programmes, so findings should be generalizable. The study population size will allow the detection of differences in screening performance between the human and automated grading systems as small as 2%. The project will compare performance and economic costs of manual versus automated systems. © The Author(s) 2015.

  13. CT angiography for planning transcatheter aortic valve replacement using automated tube voltage selection: Image quality and radiation exposure

    Energy Technology Data Exchange (ETDEWEB)

    Mangold, Stefanie [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Department of Diagnostic and Interventional Radiology, Eberhard-Karls University Tuebingen, Tuebingen (Germany); De Cecco, Carlo N. [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Schoepf, U. Joseph, E-mail: schoepf@musc.edu [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (United States); Kuhlman, Taylor S.; Varga-Szemes, Akos [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Caruso, Damiano [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza”, Rome (Italy); Duguay, Taylor M. [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Tesche, Christian [Department of Cardiology, Heart Centre Munich-Bogenhausen, Munich (Germany); Vogl, Thomas J. [Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC (United States); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt (Germany); Nikolaou, Konstantin [Department of Diagnostic and Interventional Radiology, Eberhard-Karls University Tuebingen, Tuebingen (Germany); and others

    2017-01-15

    Highlights: • TAVR-planning CT was performed with automated tube voltage selection. • Automated tube voltage selection enables individual tube voltage adaptation. • Image quality was diagnostic while radiation exposure was significantly decreased. - Abstract: Purpose: To assess image quality and accuracy of CT angiography (CTA) for transcatheter aortic valve replacement (TAVR) planning performed with 3rd generation dual-source CT (DSCT). Material and methods: We evaluated 125 patients who underwent TAVR-planning CTA on 3rd generation DSCT. A two-part protocol was performed including retrospectively ECG-gated coronary CTA (CCTA) and prospectively ECG-triggered aortoiliac CTA using 60 mL of contrast medium. Automated tube voltage selection and advanced iterative reconstruction were applied. Effective dose (ED), signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were calculated. Five-point scales were used for subjective image quality analysis. In patients who underwent TAVR, sizing parameters were obtained. Results: Image quality was rated good to excellent in 97.6% of CCTA and 100% of aortoiliac CTAs. CTA studies at >100 kV showed decreased objective image quality compared to 70–100 kV (SNR, all p ≤ 0.0459; CNR, all p ≤ 0.0462). Mean ED increased continuously from 70 to >100 kV (CCTA: 4.5 ± 1.7 mSv–13.6 ± 2.9 mSv, all p ≤ 0.0233; aortoiliac CTA: 2.4 ± 0.9 mSv–6.8 ± 2.7 mSv, all p ≤ 0.0414). In 39 patients TAVR was performed and annulus diameter was within the recommended range in all patients. No severe cardiac or vascular complications were noted. Conclusion: 3rd generation DSCT provides diagnostic image quality in TAVR-planning CTA and facilitates reliable assessment of TAVR device and delivery option while reducing radiation dose.

  14. Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

    International Nuclear Information System (INIS)

    Menten, Martin J.; Fast, Martin F.; Nill, Simeon; Oelfke, Uwe

    2015-01-01

    Purpose: Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. Methods: kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Results: Regular dual-energy imaging was able to increase tracking accuracy in left–right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. Conclusions: This study has highlighted the influence of

  15. Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy.

    Science.gov (United States)

    Menten, Martin J; Fast, Martin F; Nill, Simeon; Oelfke, Uwe

    2015-12-01

    Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Regular dual-energy imaging was able to increase tracking accuracy in left-right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. This study has highlighted the influence of patient anatomy on the success rate of real

  16. Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Menten, Martin J., E-mail: martin.menten@icr.ac.uk; Fast, Martin F.; Nill, Simeon; Oelfke, Uwe, E-mail: uwe.oelfke@icr.ac.uk [Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG (United Kingdom)

    2015-12-15

    Purpose: Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. Methods: kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Results: Regular dual-energy imaging was able to increase tracking accuracy in left–right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. Conclusions: This study has highlighted the influence of

  17. CUDA-based acceleration and BPN-assisted automation of bilateral filtering for brain MR image restoration.

    Science.gov (United States)

    Chang, Herng-Hua; Chang, Yu-Ning

    2017-04-01

    Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal. This article is in an attempt to investigate the optimal setting of the bilateral filter, from which an accelerated and automated restoration framework is developed. To reduce the computational burden of the bilateral filter, parallel computing with the graphics processing unit (GPU) architecture is first introduced. The NVIDIA Tesla K40c GPU with the compute unified device architecture (CUDA) functionality is specifically utilized to emphasize thread usages and memory resources. To correlate the filter parameters with image characteristics for automation, optimal image texture features are subsequently acquired based on the sequential forward floating selection (SFFS) scheme. Subsequently, the selected features are introduced into the back propagation network (BPN) model for filter parameter estimation. Finally, the k-fold cross validation method is adopted to evaluate the accuracy of the proposed filter parameter prediction framework. A wide variety of T1-weighted brain MR images with various scenarios of noise levels and anatomic structures were utilized to train and validate this new parameter decision system with CUDA-based bilateral filtering. For a common brain MR image volume of 256 × 256 × 256 pixels, the speed-up gain reached 284. Six optimal texture features were acquired and associated with the BPN to establish a "high accuracy" parameter prediction system, which achieved a mean absolute percentage error (MAPE) of 5.6%. Automatic restoration results on 2460 brain MR images received an average

  18. An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping.

    Science.gov (United States)

    Yu, Kang; Kirchgessner, Norbert; Grieder, Christoph; Walter, Achim; Hund, Andreas

    2017-01-01

    Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.

  19. Automated Segmentation of Light-Sheet Fluorescent Imaging to Characterize Experimental Doxorubicin-Induced Cardiac Injury and Repair.

    Science.gov (United States)

    Packard, René R Sevag; Baek, Kyung In; Beebe, Tyler; Jen, Nelson; Ding, Yichen; Shi, Feng; Fei, Peng; Kang, Bong Jin; Chen, Po-Heng; Gau, Jonathan; Chen, Michael; Tang, Jonathan Y; Shih, Yu-Huan; Ding, Yonghe; Li, Debiao; Xu, Xiaolei; Hsiai, Tzung K

    2017-08-17

    This study sought to develop an automated segmentation approach based on histogram analysis of raw axial images acquired by light-sheet fluorescent imaging (LSFI) to establish rapid reconstruction of the 3-D zebrafish cardiac architecture in response to doxorubicin-induced injury and repair. Input images underwent a 4-step automated image segmentation process consisting of stationary noise removal, histogram equalization, adaptive thresholding, and image fusion followed by 3-D reconstruction. We applied this method to 3-month old zebrafish injected intraperitoneally with doxorubicin followed by LSFI at 3, 30, and 60 days post-injection. We observed an initial decrease in myocardial and endocardial cavity volumes at day 3, followed by ventricular remodeling at day 30, and recovery at day 60 (P < 0.05, n = 7-19). Doxorubicin-injected fish developed ventricular diastolic dysfunction and worsening global cardiac function evidenced by elevated E/A ratios and myocardial performance indexes quantified by pulsed-wave Doppler ultrasound at day 30, followed by normalization at day 60 (P < 0.05, n = 9-20). Treatment with the γ-secretase inhibitor, DAPT, to inhibit cleavage and release of Notch Intracellular Domain (NICD) blocked cardiac architectural regeneration and restoration of ventricular function at day 60 (P < 0.05, n = 6-14). Our approach provides a high-throughput model with translational implications for drug discovery and genetic modifiers of chemotherapy-induced cardiomyopathy.

  20. Content-Based Object Movie Retrieval and Relevance Feedbacks

    Directory of Open Access Journals (Sweden)

    Lee Greg C

    2007-01-01

    Full Text Available Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie provides a good representation of a physical object because it can provide 3D interactive viewing effect, but does not require 3D model reconstruction. In this paper, we propose an efficient approach for content-based object movie retrieval. In order to retrieve the desired object movie from the database, we first map an object movie into the sampling of a manifold in the feature space. Two different layers of feature descriptors, dense and condensed, are designed to sample the manifold for representing object movies. Based on these descriptors, we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either an entire object movie or simply a subset of views. We further design a relevance feedback approach to improving retrieved results. Finally, some experimental results are presented to show the efficacy of our approach.

  1. Content-based video indexing and searching with wavelet transformation

    Science.gov (United States)

    Stumpf, Florian; Al-Jawad, Naseer; Du, Hongbo; Jassim, Sabah

    2006-05-01

    Biometric databases form an essential tool in the fight against international terrorism, organised crime and fraud. Various government and law enforcement agencies have their own biometric databases consisting of combination of fingerprints, Iris codes, face images/videos and speech records for an increasing number of persons. In many cases personal data linked to biometric records are incomplete and/or inaccurate. Besides, biometric data in different databases for the same individual may be recorded with different personal details. Following the recent terrorist atrocities, law enforcing agencies collaborate more than before and have greater reliance on database sharing. In such an environment, reliable biometric-based identification must not only determine who you are but also who else you are. In this paper we propose a compact content-based video signature and indexing scheme that can facilitate retrieval of multiple records in face biometric databases that belong to the same person even if their associated personal data are inconsistent. We shall assess the performance of our system using a benchmark audio visual face biometric database that has multiple videos for each subject but with different identity claims. We shall demonstrate that retrieval of relatively small number of videos that are nearest, in terms of the proposed index, to any video in the database results in significant proportion of that individual biometric data.

  2. Application of Reflectance Transformation Imaging Technique to Improve Automated Edge Detection in a Fossilized Oyster Reef

    Science.gov (United States)

    Djuricic, Ana; Puttonen, Eetu; Harzhauser, Mathias; Dorninger, Peter; Székely, Balázs; Mandic, Oleg; Nothegger, Clemens; Molnár, Gábor; Pfeifer, Norbert

    2016-04-01

    The world's largest fossilized oyster reef is located in Stetten, Lower Austria excavated during field campaigns of the Natural History Museum Vienna between 2005 and 2008. It is studied in paleontology to learn about change in climate from past events. In order to support this study, a laser scanning and photogrammetric campaign was organized in 2014 for 3D documentation of the large and complex site. The 3D point clouds and high resolution images from this field campaign are visualized by photogrammetric methods in form of digital surface models (DSM, 1 mm resolution) and orthophoto (0.5 mm resolution) to help paleontological interpretation of data. Due to size of the reef, automated analysis techniques are needed to interpret all digital data obtained from the field. One of the key components in successful automation is detection of oyster shell edges. We have tested Reflectance Transformation Imaging (RTI) to visualize the reef data sets for end-users through a cultural heritage viewing interface (RTIViewer). The implementation includes a Lambert shading method to visualize DSMs derived from terrestrial laser scanning using scientific software OPALS. In contrast to shaded RTI no devices consisting of a hardware system with LED lights, or a body to rotate the light source around the object are needed. The gray value for a given shaded pixel is related to the angle between light source and the normal at that position. Brighter values correspond to the slope surfaces facing the light source. Increasing of zenith angle results in internal shading all over the reef surface. In total, oyster reef surface contains 81 DSMs with 3 m x 2 m each. Their surface was illuminated by moving the virtual sun every 30 degrees (12 azimuth angles from 20-350) and every 20 degrees (4 zenith angles from 20-80). This technique provides paleontologists an interactive approach to virtually inspect the oyster reef, and to interpret the shell surface by changing the light source direction

  3. Screening of subfertile men for testicular carcinoma in situ by an automated image analysis-based cytological test of the ejaculate

    DEFF Research Database (Denmark)

    Almstrup, K; Lippert, Marianne; Mogensen, Hanne O

    2011-01-01

    and detected in ejaculates with specific CIS markers. We have built a high throughput framework involving automated immunocytochemical staining, scanning microscopy and in silico image analysis allowing automated detection and grading of CIS-like stained objects in semen samples. In this study, 1175 ejaculates...... a slightly lower sensitivity (0.51), possibly because of obstruction. We conclude that this novel non-invasive test combining automated immunocytochemistry and advanced image analysis allows identification of TC at the CIS stage with a high specificity, but a negative test does not completely exclude CIS...

  4. Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

    Science.gov (United States)

    Lian, Yanyun; Song, Zhijian

    2014-01-01

    Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

  5. Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.

    Directory of Open Access Journals (Sweden)

    Lucia Jañez-Garcia

    Full Text Available To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC from CT scans whereby the size and main morphometric characteristics of the canal can be determined.Cross-sectional study.36 eyes of 18 healthy individuals.Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and normality tests (Kolmogorov-Smirnov and Shapiro-Wilk were established.Time for segmentation, NLC volume, axis, sectional areas and length.Mean processing time was around 30 seconds for segmenting each canal. All the variables generated were normally distributed. Measurements obtained using the four models polygonal, 2nd, 3rd and 4th degree polynomial, respectively, were: mean canal length 14.74, 14.3, 14.80, and 15.03 mm; mean sectional area 15.15, 11.77, 11.43, and 11.56 mm2; minimum sectional area 8.69, 7.62, 7.40, and 7.19 mm2; and mean depth of minimum sectional area (craniocaudal 7.85, 7.71, 8.19, and 8.08 mm.The method proposed automatically reconstructs the NLC on CT scans. Using these reconstructions, morphometric measurements can be calculated from NLC axis estimates based on polygonal and 2nd, 3rd and 4th polynomial models.

  6. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report

    Energy Technology Data Exchange (ETDEWEB)

    Duberstein, Corey A.; Matzner, Shari; Cullinan, Valerie I.; Virden, Daniel J.; Myers, Joshua R.; Maxwell, Adam R.

    2012-09-01

    Surveying wildlife at risk from offshore wind energy development is difficult and expensive. Infrared video can be used to record birds and bats that pass through the camera view, but it is also time consuming and expensive to review video and determine what was recorded. We proposed to conduct algorithm and software development to identify and to differentiate thermally detected targets of interest that would allow automated processing of thermal image data to enumerate birds, bats, and insects. During FY2012 we developed computer code within MATLAB to identify objects recorded in video and extract attribute information that describes the objects recorded. We tested the efficiency of track identification using observer-based counts of tracks within segments of sample video. We examined object attributes, modeled the effects of random variability on attributes, and produced data smoothing techniques to limit random variation within attribute data. We also began drafting and testing methodology to identify objects recorded on video. We also recorded approximately 10 hours of infrared video of various marine birds, passerine birds, and bats near the Pacific Northwest National Laboratory (PNNL) Marine Sciences Laboratory (MSL) at Sequim, Washington. A total of 6 hours of bird video was captured overlooking Sequim Bay over a series of weeks. An additional 2 hours of video of birds was also captured during two weeks overlooking Dungeness Bay within the Strait of Juan de Fuca. Bats and passerine birds (swallows) were also recorded at dusk on the MSL campus during nine evenings. An observer noted the identity of objects viewed through the camera concurrently with recording. These video files will provide the information necessary to produce and test software developed during FY2013. The annotation will also form the basis for creation of a method to reliably identify recorded objects.

  7. Automated integer programming based separation of arteries and veins from thoracic CT images.

    Science.gov (United States)

    Payer, Christian; Pienn, Michael; Bálint, Zoltán; Shekhovtsov, Alexander; Talakic, Emina; Nagy, Eszter; Olschewski, Andrea; Olschewski, Horst; Urschler, Martin

    2016-12-01

    Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. To detect vascular changes which affect pulmonary arteries and veins differently, both compartments need to be identified. We present a novel, fully automatic method that separates arteries and veins in thoracic computed tomography images, by combining local as well as global properties of pulmonary vessels. We split the problem into two parts: the extraction of multiple distinct vessel subtrees, and their subsequent labeling into arteries and veins. Subtree extraction is performed with an integer program (IP), based on local vessel geometry. As naively solving this IP is time-consuming, we show how to drastically reduce computational effort by reformulating it as a Markov Random Field. Afterwards, each subtree is labeled as either arterial or venous by a second IP, using two anatomical properties of pulmonary vessels: the uniform distribution of arteries and veins, and the parallel configuration and close proximity of arteries and bronchi. We evaluate algorithm performance by comparing the results with 25 voxel-based manual reference segmentations. On this dataset, we show good performance of the subtree extraction, consisting of very few non-vascular structures (median value: 0.9%) and merged subtrees (median value: 0.6%). The resulting separation of arteries and veins achieves a median voxel-based overlap of 96.3% with the manual reference segmentations, outperforming a state-of-the-art interactive method. In conclusion, our novel approach provides an opportunity to become an integral part of computer aided pulmonary diagnosis, where artery/vein separation is important. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

    Science.gov (United States)

    Burns, Joseph E; Yao, Jianhua; Summers, Ronald M

    2017-09-01

    Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted κ of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.

  9. Automation of a high-speed imaging setup for differential viscosity measurements

    Science.gov (United States)

    Hurth, C.; Duane, B.; Whitfield, D.; Smith, S.; Nordquist, A.; Zenhausern, F.

    2013-12-01

    We present the automation of a setup previously used to assess the viscosity of pleural effusion samples and discriminate between transudates and exudates, an important first step in clinical diagnostics. The presented automation includes the design, testing, and characterization of a vacuum-actuated loading station that handles the 2 mm glass spheres used as sensors, as well as the engineering of electronic Printed Circuit Board (PCB) incorporating a microcontroller and their synchronization with a commercial high-speed camera operating at 10 000 fps. The hereby work therefore focuses on the instrumentation-related automation efforts