WorldWideScience

Sample records for biomedical image retrieval

  1. Biomedical image retrieval using microscopic configuration with ...

    Indian Academy of Sciences (India)

    G DEEP

    2018-03-10

    Mar 10, 2018 ... feature database. The selection of feature descriptors affects the image retrieval performance. In early years, Manjunath et al [7] used features based on intensity histogram for biomedical image retrieval. However, their retrieval performance is usually limited especially on large databases due to lack of ...

  2. Biomedical image retrieval using microscopic configuration with ...

    Indian Academy of Sciences (India)

    G DEEP

    2018-03-10

    Mar 10, 2018 ... uniform if they contain the main part of the noise of the images. A region with no transitions is considered as a background or a flat region of the image. The LBP feature vector is extracted from each cell ..... on a core2 Quad computer with 2.66 GHz, 4 GB of memory and all methods are implemented on the ...

  3. A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

    Science.gov (United States)

    You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

    2013-01-01

    Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

  4. A novel biomedical image indexing and retrieval system via deep preference learning.

    Science.gov (United States)

    Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

    2018-05-01

    The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

  5. Prototype client/server application for biomedical text/image retrieval on the Internet

    Science.gov (United States)

    Long, L. Rodney; Berman, Lewis E.; Thoma, George R.

    1996-03-01

    At the Lister Hill National Center for Biomedical Communications, a research and development division of the National Library of Medicine (NLM), a prototype image database retrieval system has been built. This medical information retrieval system (MIRS) is a client/server application which provides Internet access to biomedical databases, including both text search/retrieval and retrieval/display of medical images associated with the text records. The MIRS graphical user interface (GUI) allows a user to formulate queries by simple, intuitive interactions with screen buttons, list boxes, and edit boxes; these interactions create structured query language (SQL) queries, which are submitted to a database manager running at NLM. The result of a MIRS query is a display showing both scrollable text records and scrollable images returned for all of the 'hits' of the query. MIRS is designed as an information-delivery vehicle intended to provide access to multiple collections of medical text and image data. The database used for initial MIRS evaluation consists of national survey data collected by the National Center for Health Statistics, including 17,000 spinal x-ray images. This survey, conducted on a sample of 27,801 persons, collected demographic, socioeconomic, and medical information, including both interview results and results acquired by direct examination by physician.

  6. Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

    Science.gov (United States)

    You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

    2013-01-01

    Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

  7. A framework for biomedical figure segmentation towards image-based document retrieval.

    Science.gov (United States)

    Lopez, Luis D; Yu, Jingyi; Arighi, Cecilia; Tudor, Catalina O; Torii, Manabu; Huang, Hongzhan; Vijay-Shanker, K; Wu, Cathy

    2013-01-01

    The figures included in many of the biomedical publications play an important role in understanding the biological experiments and facts described within. Recent studies have shown that it is possible to integrate the information that is extracted from figures in classical document classification and retrieval tasks in order to improve their accuracy. One important observation about the figures included in biomedical publications is that they are often composed of multiple subfigures or panels, each describing different methodologies or results. The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures. Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels. This is a challenging task, however, as different panels can contain similar objects (i.e., barcharts and linecharts) with multiple layouts. Also, certain types of biomedical figures are text-heavy (e.g., DNA sequences and protein sequences images) and they differ from traditional images. As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels. In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure. Our framework starts by robustly harvesting figure-caption pairs from biomedical articles. We base our approach on the observation that the document layout can be used to identify encoded figures and figure boundaries within PDF files. Taking into consideration the document layout allows us to correctly extract figures from the PDF document and associate their corresponding caption. We combine pixel

  8. Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval.

    Science.gov (United States)

    Rahman, Md Mahmudur; Antani, Sameer K; Demner-Fushman, Dina; Thoma, George R

    2015-10-01

    This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.

  9. Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval

    Science.gov (United States)

    Cheng, Beibei; Antani, Sameer; Stanley, R. Joe; Thoma, George R.

    2011-01-01

    Biomedical images are often referenced for clinical decision support (CDS), educational purposes, and research. The task of automatically finding the images in a scientific article that are most useful for the purpose of determining relevance to a clinical situation is traditionally done using text and is quite challenging. We propose to improve this by associating image features from the entire image and from relevant regions of interest with biomedical concepts described in the figure caption or discussion in the article. However, images used in scientific article figures are often composed of multiple panels where each sub-figure (panel) is referenced in the caption using alphanumeric labels, e.g. Figure 1(a), 2(c), etc. It is necessary to separate individual panels from a multi-panel figure as a first step toward automatic annotation of images. In this work we present methods that add make robust our previous efforts reported here. Specifically, we address the limitation in segmenting figures that do not exhibit explicit inter-panel boundaries, e.g. illustrations, graphs, and charts. We present a novel hybrid clustering algorithm based on particle swarm optimization (PSO) with fuzzy logic controller (FLC) to locate related figure components in such images. Results from our evaluation are very promising with 93.64% panel detection accuracy for regular (non-illustration) figure images and 92.1% accuracy for illustration images. A computational complexity analysis also shows that PSO is an optimal approach with relatively low computation time. The accuracy of separating these two type images is 98.11% and is achieved using decision tree.

  10. A novel method for efficient archiving and retrieval of biomedical images using MPEG-7

    Science.gov (United States)

    Meyer, Joerg; Pahwa, Ash

    2004-10-01

    Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.

  11. Image retrieval

    DEFF Research Database (Denmark)

    Ørnager, Susanne

    1997-01-01

    The paper touches upon indexing and retrieval for effective searches of digitized images. Different conceptions of what subject indexing means are described as a basis for defining an operational subject indexing strategy for images. The methodology is based on the art historian Erwin Panofsky......), special knowledge about image codes, and special knowledge about history of ideas. The semiologist Roland Barthes has established a semiology for pictorial expressions based on advertising photos. Barthes uses the concepts denotation/connotation where denotations can be explained as the sober expression...

  12. Genre-based Search through Biomedical Images

    NARCIS (Netherlands)

    Geusebroek, J.M.; Hoang, M.A.; van Gemert, J.; Worring, M.

    2002-01-01

    We exploit the retrieval of visual information from biomedical scientific publication databses. Therefore, we consider the use of domain specific genres to automatically subdivide large image databases into smaller, consistent parts. Combination with Latent Semantic Indexing on the picture captions

  13. Information Retrieval in Biomedical Research: From Articles to Datasets

    Science.gov (United States)

    Wei, Wei

    2017-01-01

    Information retrieval techniques have been applied to biomedical research for a variety of purposes, such as textual document retrieval and molecular data retrieval. As biomedical research evolves over time, information retrieval is also constantly facing new challenges, including the growing number of available data, the emerging new data types,…

  14. Retrieve An Image

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Retrieve An Image. “A building”. “Box-shaped”. “Brown Color”. “Foreshortened view”. OR. Why not specify a similar looking picture? -- Main Motivation!

  15. Biomedical signals, imaging, and informatics

    CERN Document Server

    Bronzino, Joseph D

    2014-01-01

    Known as the bible of biomedical engineering, The Biomedical Engineering Handbook, Fourth Edition, sets the standard against which all other references of this nature are measured. As such, it has served as a major resource for both skilled professionals and novices to biomedical engineering.Biomedical Signals, Imaging, and Informatics, the third volume of the handbook, presents material from respected scientists with diverse backgrounds in biosignal processing, medical imaging, infrared imaging, and medical informatics.More than three dozen specific topics are examined, including biomedical s

  16. Molecular Biomedical Imaging Laboratory (MBIL)

    Data.gov (United States)

    Federal Laboratory Consortium — The Molecular Biomedical Imaging Laboratory (MBIL) is adjacent-a nd has access-to the Department of Radiology and Imaging Sciences clinical imaging facilities. MBIL...

  17. Computational intelligence in biomedical imaging

    CERN Document Server

    2014-01-01

    This book provides a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients’ medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational inte...

  18. Mobile medical image retrieval

    Science.gov (United States)

    Duc, Samuel; Depeursinge, Adrien; Eggel, Ivan; Müller, Henning

    2011-03-01

    Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in

  19. Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

    Science.gov (United States)

    Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

    2016-06-14

    In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

  20. Advanced biomedical image analysis

    CERN Document Server

    Haidekker, Mark A

    2010-01-01

    "This book covers the four major areas of image processing: Image enhancement and restoration, image segmentation, image quantification and classification, and image visualization. Image registration, storage, and compression are also covered. The text focuses on recently developed image processing and analysis operators and covers topical research"--Provided by publisher.

  1. Biomedical Imaging Principles and Applications

    CERN Document Server

    Salzer, Reiner

    2012-01-01

    This book presents and describes imaging technologies that can be used to study chemical processes and structural interactions in dynamic systems, principally in biomedical systems. The imaging technologies, largely biomedical imaging technologies such as MRT, Fluorescence mapping, raman mapping, nanoESCA, and CARS microscopy, have been selected according to their application range and to the chemical information content of their data. These technologies allow for the analysis and evaluation of delicate biological samples, which must not be disturbed during the profess. Ultimately, this may me

  2. Advancing biomedical imaging.

    Science.gov (United States)

    Weissleder, Ralph; Nahrendorf, Matthias

    2015-11-24

    Imaging reveals complex structures and dynamic interactive processes, located deep inside the body, that are otherwise difficult to decipher. Numerous imaging modalities harness every last inch of the energy spectrum. Clinical modalities include magnetic resonance imaging (MRI), X-ray computed tomography (CT), ultrasound, and light-based methods [endoscopy and optical coherence tomography (OCT)]. Research modalities include various light microscopy techniques (confocal, multiphoton, total internal reflection, superresolution fluorescence microscopy), electron microscopy, mass spectrometry imaging, fluorescence tomography, bioluminescence, variations of OCT, and optoacoustic imaging, among a few others. Although clinical imaging and research microscopy are often isolated from one another, we argue that their combination and integration is not only informative but also essential to discovering new biology and interpreting clinical datasets in which signals invariably originate from hundreds to thousands of cells per voxel.

  3. Mathematical modeling in biomedical imaging

    CERN Document Server

    2009-01-01

    This volume gives an introduction to a fascinating research area to applied mathematicians. It is devoted to providing the exposition of promising analytical and numerical techniques for solving challenging biomedical imaging problems, which trigger the investigation of interesting issues in various branches of mathematics.

  4. Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

    Science.gov (United States)

    Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

    2018-01-01

    Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

  5. Biomedical Image Processing

    CERN Document Server

    Deserno, Thomas Martin

    2011-01-01

    In modern medicine, imaging is the most effective tool for diagnostics, treatment planning and therapy. Almost all modalities have went to directly digital acquisition techniques and processing of this image data have become an important option for health care in future. This book is written by a team of internationally recognized experts from all over the world. It provides a brief but complete overview on medical image processing and analysis highlighting recent advances that have been made in academics. Color figures are used extensively to illustrate the methods and help the reader to understand the complex topics.

  6. 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.

  7. Biomedical signal and image processing

    CERN Document Server

    Najarian, Kayvan

    2012-01-01

    INTRODUCTION TO DIGITAL SIGNAL AND IMAGE PROCESSINGSignals and Biomedical Signal ProcessingIntroduction and OverviewWhat is a ""Signal""?Analog, Discrete, and Digital SignalsProcessing and Transformation of SignalsSignal Processing for Feature ExtractionSome Characteristics of Digital ImagesSummaryProblemsFourier TransformIntroduction and OverviewOne-Dimensional Continuous Fourier TransformSampling and NYQUIST RateOne-Dimensional Discrete Fourier TransformTwo-Dimensional Discrete Fourier TransformFilter DesignSummaryProblemsImage Filtering, Enhancement, and RestorationIntroduction and Overview

  8. Web Mining for Web Image Retrieval.

    Science.gov (United States)

    Chen, Zheng; Wenyin, Liu; Zhang, Feng; Li, Mingjing; Zhang, Hongjiang

    2001-01-01

    Presents a prototype system for image retrieval from the Internet using Web mining. Discusses the architecture of the Web image retrieval prototype; document space modeling; user log mining; and image retrieval experiments to evaluate the proposed system. (AEF)

  9. Interactive Exploration for Image Retrieval

    Directory of Open Access Journals (Sweden)

    Jérôme Fournier

    2005-08-01

    Full Text Available We present a new version of our content-based image retrieval system RETIN. It is based on adaptive quantization of the color space, together with new features aiming at representing the spatial relationship between colors. Color analysis is also extended to texture. Using these powerful indexes, an original interactive retrieval strategy is introduced. The process is based on two steps for handling the retrieval of very large image categories. First, a controlled exploration method of the database is presented. Second, a relevance feedback method based on statistical learning is proposed. All the steps are evaluated by experiments on a generalist database.

  10. Biomedical Optical Imaging Technologies Design and Applications

    CERN Document Server

    2013-01-01

    This book provides an introduction to design of biomedical optical imaging technologies and their applications. The main topics include: fluorescence imaging, confocal imaging, micro-endoscope, polarization imaging, hyperspectral imaging, OCT imaging, multimodal imaging and spectroscopic systems. Each chapter is written by the world leaders of the respective fields, and will cover: principles and limitations of optical imaging technology, system design and practical implementation for one or two specific applications, including design guidelines, system configuration, optical design, component requirements and selection, system optimization and design examples, recent advances and applications in biomedical researches and clinical imaging. This book serves as a reference for students and researchers in optics and biomedical engineering.

  11. Biomedical signal and image processing.

    Science.gov (United States)

    Cerutti, Sergio; Baselli, Giuseppe; Bianchi, Anna; Caiani, Enrico; Contini, Davide; Cubeddu, Rinaldo; Dercole, Fabio; Rienzo, Luca; Liberati, Diego; Mainardi, Luca; Ravazzani, Paolo; Rinaldi, Sergio; Signorini, Maria; Torricelli, Alessandro

    2011-01-01

    Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

  12. Review of Biomedical Image Processing

    Directory of Open Access Journals (Sweden)

    Ciaccio Edward J

    2011-11-01

    Full Text Available Abstract This article is a review of the book: 'Biomedical Image Processing', by Thomas M. Deserno, which is published by Springer-Verlag. Salient information that will be useful to decide whether the book is relevant to topics of interest to the reader, and whether it might be suitable as a course textbook, are presented in the review. This includes information about the book details, a summary, the suitability of the text in course and research work, the framework of the book, its specific content, and conclusions.

  13. Development of an information retrieval tool for biomedical patents.

    Science.gov (United States)

    Alves, Tiago; Rodrigues, Rúben; Costa, Hugo; Rocha, Miguel

    2018-06-01

    The volume of biomedical literature has been increasing in the last years. Patent documents have also followed this trend, being important sources of biomedical knowledge, technical details and curated data, which are put together along the granting process. The field of Biomedical text mining (BioTM) has been creating solutions for the problems posed by the unstructured nature of natural language, which makes the search of information a challenging task. Several BioTM techniques can be applied to patents. From those, Information Retrieval (IR) includes processes where relevant data are obtained from collections of documents. In this work, the main goal was to build a patent pipeline addressing IR tasks over patent repositories to make these documents amenable to BioTM tasks. The pipeline was developed within @Note2, an open-source computational framework for BioTM, adding a number of modules to the core libraries, including patent metadata and full text retrieval, PDF to text conversion and optical character recognition. Also, user interfaces were developed for the main operations materialized in a new @Note2 plug-in. The integration of these tools in @Note2 opens opportunities to run BioTM tools over patent texts, including tasks from Information Extraction, such as Named Entity Recognition or Relation Extraction. We demonstrated the pipeline's main functions with a case study, using an available benchmark dataset from BioCreative challenges. Also, we show the use of the plug-in with a user query related to the production of vanillin. This work makes available all the relevant content from patents to the scientific community, decreasing drastically the time required for this task, and provides graphical interfaces to ease the use of these tools. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. Mathematics and physics of emerging biomedical imaging

    National Research Council Canada - National Science Library

    National Research Council Staff; Commission on Physical Sciences, Mathematics, and Applications; Division on Engineering and Physical Sciences; National Research Council; National Academy of Sciences

    .... Incorporating input from dozens of biomedical researchers who described what they perceived as key open problems of imaging that are amenable to attack by mathematical scientists and physicists...

  15. Biomedical image understanding methods and applications

    CERN Document Server

    Lim, Joo-Hwee; Xiong, Wei

    2015-01-01

    A comprehensive guide to understanding and interpreting digital images in medical and functional applications Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. It covers image processing, image filtering, enhancement, de-noising, restoration, and reconstruction; image segmentation and feature extraction; registration; clustering, pattern classification, and data fusion. With contributions from ex

  16. Interactive radiographic image retrieval system.

    Science.gov (United States)

    Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

    2017-02-01

    Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance

  17. A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.

    Science.gov (United States)

    Sarrouti, Mourad; Ouatik El Alaoui, Said

    2017-04-01

    Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Double-compression method for biomedical images

    Science.gov (United States)

    Antonenko, Yevhenii A.; Mustetsov, Timofey N.; Hamdi, Rami R.; Małecka-Massalska, Teresa; Orshubekov, Nurbek; DzierŻak, RóŻa; Uvaysova, Svetlana

    2017-08-01

    This paper describes a double compression method (DCM) of biomedical images. A comparison of image compression factors in size JPEG, PNG and developed DCM was carried out. The main purpose of the DCM - compression of medical images while maintaining the key points that carry diagnostic information. To estimate the minimum compression factor an analysis of the coding of random noise image is presented.

  19. Optomechatronics for Biomedical Optical Imaging: An Overview

    Directory of Open Access Journals (Sweden)

    Cho Hyungsuck

    2015-01-01

    Full Text Available The use of optomechatronic technology, particularly in biomedical optical imaging, is becoming pronounced and ever increasing due to its synergistic effect of the integration of optics and mechatronics. The background of this trend is that the biomedical optical imaging for example in-vivo imaging related to retraction of tissues, diagnosis, and surgical operations have a variety of challenges due to complexity in internal structure and properties of biological body and the resulting optical phenomena. This paper addresses the technical issues related to tissue imaging, visualization of interior surfaces of organs, laparoscopic and endoscopic imaging and imaging of neuronal activities and structures. Within such problem domains the paper overviews the states of the art technology focused on how optical components are fused together with those of mechatronics to create the functionalities required for the imaging systems. Future perspective of the optical imaging in biomedical field is presented in short.

  20. Document image retrieval through word shape coding.

    Science.gov (United States)

    Lu, Shijian; Li, Linlin; Tan, Chew Lim

    2008-11-01

    This paper presents a document retrieval technique that is capable of searching document images without OCR (optical character recognition). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features including character ascenders/descenders, character holes, and character water reservoirs. With the annotated word shape codes, document images can be retrieved by either query keywords or a query document image. Experimental results show that the proposed document image retrieval technique is fast, efficient, and tolerant to various types of document degradation.

  1. Image Retrieval Based on Fractal Dictionary Parameters

    Directory of Open Access Journals (Sweden)

    Yuanyuan Sun

    2013-01-01

    Full Text Available Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

  2. Minimize the Percentage of Noise in Biomedical Images Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Abdul Khader Jilani Saudagar

    2014-01-01

    Full Text Available The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time. The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to accomplish the above objectives. This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia. We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR, and computation time. To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images. This work explores and tests biomedical images such as X-rays, computed tomography (CT, magnetic resonance imaging (MRI, and positron emission tomography (PET.

  3. University of Vermont Center for Biomedical Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Bernstein, Dr. Ira [University of Vermont and State Agricultural College

    2013-08-02

    This grant was awarded in support of Phase 2 of the University of Vermont Center for Biomedical Imaging. Phase 2 outlined several specific aims including: The development of expertise in MRI and fMRI imaging and their applications The acquisition of peer reviewed extramural funding in support of the Center The development of a Core Imaging Advisory Board, fee structure and protocol review and approval process.

  4. IEEE International Symposium on Biomedical Imaging.

    Science.gov (United States)

    2017-01-01

    The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2018 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers. High-quality papers are requested containing original contributions to the topics of interest including image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological, and statistical modeling. Accepted 4-page regular papers will be published in the symposium proceedings published by IEEE and included in IEEE Xplore. To encourage attendance by a broader audience of imaging scientists and offer additional presentation opportunities, ISBI 2018 will continue to have a second track featuring posters selected from 1-page abstract submissions without subsequent archival publication.

  5. Mathematics and physics of emerging biomedical imaging

    National Research Council Canada - National Science Library

    National Research Council Staff; Commission on Physical Sciences, Mathematics, and Applications; Division on Engineering and Physical Sciences; National Research Council; National Academy of Sciences

    ... of Emerging Dynamic Biomedical Imaging Board on Mathematical Sciences Board on Physics and Astronomy Commission on Physical Sciences, Mathematics, and Applications National Research Council and Board on Biobehavioral Sciences and Mental Disorders Institute of Medicine National Academy Press Washington, D.C. 1996 i Copyrightthe true use are Please ...

  6. A Semantics-Based Approach to Retrieving Biomedical Information

    DEFF Research Database (Denmark)

    Andreasen, Troels; Bulskov, Henrik; Zambach, Sine

    2011-01-01

    This paper describes an approach to representing, organising, and accessing conceptual content of biomedical texts using a formal ontology. The ontology is based on UMLS resources supplemented with domain ontologies developed in the project. The approach introduces the notion of ‘generative ontol...

  7. BIG: a Grid Portal for Biomedical Data and Images

    Directory of Open Access Journals (Sweden)

    Giovanni Aloisio

    2004-06-01

    Full Text Available Modern management of biomedical systems involves the use of many distributed resources, such as high performance computational resources to analyze biomedical data, mass storage systems to store them, medical instruments (microscopes, tomographs, etc., advanced visualization and rendering tools. Grids offer the computational power, security and availability needed by such novel applications. This paper presents BIG (Biomedical Imaging Grid, a Web-based Grid portal for management of biomedical information (data and images in a distributed environment. BIG is an interactive environment that deals with complex user's requests, regarding the acquisition of biomedical data, the "processing" and "delivering" of biomedical images, using the power and security of Computational Grids.

  8. Multi region based image retrieval system

    Indian Academy of Sciences (India)

    ... an image based on feature-based attention model which mimic viewer's attention. The Curvelet Transform in combination with colour descriptors are used to represent each significant region in an image. Experimental results are analysed and compared with the state-of-the-art Region Based Image Retrieval Technique.

  9. Toward privacy-preserving JPEG image retrieval

    Science.gov (United States)

    Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

    2017-07-01

    This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

  10. Multi region based image retrieval system

    Indian Academy of Sciences (India)

    Abstract. Multimedia information retrieval systems continue to be an active research area in the world of huge and voluminous data. The paramount challenge is to translate or convert a visual query from a human and find similar images or videos in large digital collection. In this paper, a technique of region based image.

  11. 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

  12. Adaptive wavelet lifting for image retrieval

    NARCIS (Netherlands)

    P.J. Oonincx; P.M. de Zeeuw (Paul); A.F. Laine; M.A. Unser; A. Aldroubi

    2001-01-01

    htmlabstractWe build a feature vector that can be used for content-based image retrieval of grayscale images of objects against a background of texture. The feature vector is based on moment invariants of detail coefficients produced by the lifting scheme. The prediction filters in this scheme are

  13. VICAR - VIDEO IMAGE COMMUNICATION AND RETRIEVAL

    Science.gov (United States)

    Wall, R. J.

    1994-01-01

    VICAR (Video Image Communication and Retrieval) is a general purpose image processing software system that has been under continuous development since the late 1960's. Originally intended for data from the NASA Jet Propulsion Laboratory's unmanned planetary spacecraft, VICAR is now used for a variety of other applications including biomedical image processing, cartography, earth resources, and geological exploration. The development of this newest version of VICAR emphasized a standardized, easily-understood user interface, a shield between the user and the host operating system, and a comprehensive array of image processing capabilities. Structurally, VICAR can be divided into roughly two parts; a suite of applications programs and an executive which serves as the interfaces between the applications, the operating system, and the user. There are several hundred applications programs ranging in function from interactive image editing, data compression/decompression, and map projection, to blemish, noise, and artifact removal, mosaic generation, and pattern recognition and location. An information management system designed specifically for handling image related data can merge image data with other types of data files. The user accesses these programs through the VICAR executive, which consists of a supervisor and a run-time library. From the viewpoint of the user and the applications programs, the executive is an environment that is independent of the operating system. VICAR does not replace the host computer's operating system; instead, it overlays the host resources. The core of the executive is the VICAR Supervisor, which is based on NASA Goddard Space Flight Center's Transportable Applications Executive (TAE). Various modifications and extensions have been made to optimize TAE for image processing applications, resulting in a user friendly environment. The rest of the executive consists of the VICAR Run-Time Library, which provides a set of subroutines (image

  14. Generic information can retrieve known biological associations: implications for biomedical knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Herman H H B M van Haagen

    Full Text Available MOTIVATION: Weighted semantic networks built from text-mined literature can be used to retrieve known protein-protein or gene-disease associations, and have been shown to anticipate associations years before they are explicitly stated in the literature. Our text-mining system recognizes over 640,000 biomedical concepts: some are specific (i.e., names of genes or proteins others generic (e.g., 'Homo sapiens'. Generic concepts may play important roles in automated information retrieval, extraction, and inference but may also result in concept overload and confound retrieval and reasoning with low-relevance or even spurious links. Here, we attempted to optimize the retrieval performance for protein-protein interactions (PPI by filtering generic concepts (node filtering or links to generic concepts (edge filtering from a weighted semantic network. First, we defined metrics based on network properties that quantify the specificity of concepts. Then using these metrics, we systematically filtered generic information from the network while monitoring retrieval performance of known protein-protein interactions. We also systematically filtered specific information from the network (inverse filtering, and assessed the retrieval performance of networks composed of generic information alone. RESULTS: Filtering generic or specific information induced a two-phase response in retrieval performance: initially the effects of filtering were minimal but beyond a critical threshold network performance suddenly drops. Contrary to expectations, networks composed exclusively of generic information demonstrated retrieval performance comparable to unfiltered networks that also contain specific concepts. Furthermore, an analysis using individual generic concepts demonstrated that they can effectively support the retrieval of known protein-protein interactions. For instance the concept "binding" is indicative for PPI retrieval and the concept "mutation abnormality" is

  15. Development of image mappers for hyperspectral biomedical imaging applications.

    Science.gov (United States)

    Kester, Robert T; Gao, Liang; Tkaczyk, Tomasz S

    2010-04-01

    A new design and fabrication method is presented for creating large-format (>100 mirror facets) image mappers for a snapshot hyperspectral biomedical imaging system called an image mapping spectrometer (IMS). To verify this approach a 250 facet image mapper with 25 multiple-tilt angles is designed for a compact IMS that groups the 25 subpupils in a 5 x 5 matrix residing within a single collecting objective's pupil. The image mapper is fabricated by precision diamond raster fly cutting using surface-shaped tools. The individual mirror facets have minimal edge eating, tilt errors of <1 mrad, and an average roughness of 5.4 nm.

  16. 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.

  17. Robust histogram-based image retrieval

    Czech Academy of Sciences Publication Activity Database

    Höschl, Cyril; Flusser, Jan

    2016-01-01

    Roč. 69, č. 1 (2016), s. 72-81 ISSN 0167-8655 R&D Projects: GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Image retrieval * Noisy image * Histogram * Convolution * Moments * Invariants Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.995, year: 2016 http://library.utia.cas.cz/separaty/2015/ZOI/hoschl-0452147.pdf

  18. Contextual Distance Refining for Image Retrieval

    KAUST Repository

    Islam, Almasri

    2014-09-16

    Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.

  19. Modeling and mining term association for improving biomedical information retrieval performance.

    Science.gov (United States)

    Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

    2012-06-11

    The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent

  20. Improving biomedical information retrieval by linear combinations of different query expansion techniques.

    Science.gov (United States)

    Abdulla, Ahmed AbdoAziz Ahmed; Lin, Hongfei; Xu, Bo; Banbhrani, Santosh Kumar

    2016-07-25

    Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user's needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user's information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries. The retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %. We propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher-quality search results.

  1. Ultrawideband radar imaging system for biomedical applications

    International Nuclear Information System (INIS)

    Jafari, H.M.; Liu, W.; Hranilovic, S.; Deen, M.J.

    2006-01-01

    Ultrawideband (UWB) (3-10 GHz) radar imaging systems offer much promise for biomedical applications such as cancer detection because of their good penetration and resolution characteristics. The underlying principle of UWB cancer detection is a significant contrast in dielectric properties, which is estimated to be greater than 2:1 between normal and cancerous tissue, compared to a few-percent contrast in radiographic density exploited by x rays. This article presents a feasibility study of the UWB imaging of liver cancer tumors, based on the frequency-dependent finite difference time domain method. The reflection, radiation, and scattering properties of UWB pulses as they propagate through the human body are studied. The reflected and back-scattered electromagnetic energies from cancer tumors inside the liver are also investigated. An optimized, ultrawideband antenna was designed for near field operation, allowing for the reduction of the air-skin interface. It will be placed on the fat-liver tissue phantom with a malignant tumor stimulant. By performing an incremental scan over the phantom and removing early time artifacts, including reflection from the antenna ends, images based on the back-scattered signal from the tumor can be constructed. This research is part of our effort to develop a UWB cancer detection system with good detection and localization properties

  2. Intelligent image retrieval based on radiology reports.

    Science.gov (United States)

    Gerstmair, Axel; Daumke, Philipp; Simon, Kai; Langer, Mathias; Kotter, Elmar

    2012-12-01

    To create an advanced image retrieval and data-mining system based on in-house radiology reports. Radiology reports are semantically analysed using natural language processing (NLP) techniques and stored in a state-of-the-art search engine. Images referenced by sequence and image number in the reports are retrieved from the picture archiving and communication system (PACS) and stored for later viewing. A web-based front end is used as an interface to query for images and show the results with the retrieved images and report text. Using a comprehensive radiological lexicon for the underlying terminology, the search algorithm also finds results for synonyms, abbreviations and related topics. The test set was 108 manually annotated reports analysed by different system configurations. Best results were achieved using full syntactic and semantic analysis with a precision of 0.929 and recall of 0.952. Operating successfully since October 2010, 258,824 reports have been indexed and a total of 405,146 preview images are stored in the database. Data-mining and NLP techniques provide quick access to a vast repository of images and radiology reports with both high precision and recall values. Consequently, the system has become a valuable tool in daily clinical routine, education and research. Radiology reports can now be analysed using sophisticated natural language-processing techniques. Semantic text analysis is backed by terminology of a radiological lexicon. The search engine includes results for synonyms, abbreviations and compositions. Key images are automatically extracted from radiology reports and fetched from PACS. Such systems help to find diagnoses, improve report quality and save time.

  3. CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

    Directory of Open Access Journals (Sweden)

    Perez-Rey David

    2012-04-01

    Full Text Available Abstract Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs. In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA, (ii identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH, automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard

  4. CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

    Science.gov (United States)

    Perez-Rey, David; Jimenez-Castellanos, Ana; Garcia-Remesal, Miguel; Crespo, Jose; Maojo, Victor

    2012-04-05

    Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset

  5. Proceedings of the international society for optical engineering biomedical image processing 2

    International Nuclear Information System (INIS)

    Bovik, A.G.; Howard, V.

    1991-01-01

    This book contains the proceedings of biomedical image processing. Topics covered include: Filtering and reconstruction of biomedical images; analysis, classification and recognition of biomedical images; and 3-D microscopy

  6. 78 FR 3009 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2013-01-15

    ... HUMAN SERVICES National Institutes of Health National Institute of Biomedical Imaging and Bioengineering... personal privacy. Name of Committee: National Institute of Biomedical Imaging and Bioengineering Special... Biomedical Imaging and Bioengineering, National Institutes of Health, 6707 Democracy Boulevard, Suite 959...

  7. 78 FR 66373 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2013-11-05

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  8. 75 FR 74068 - National Institute Of Biomedical Imaging And Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2010-11-30

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    Science.gov (United States)

    2013-02-07

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    2013-08-27

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    2013-11-12

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    2012-08-21

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    2012-01-20

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    2013-12-18

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    2013-04-26

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  16. 78 FR 25752 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meetings

    Science.gov (United States)

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  1. 75 FR 61769 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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  14. Terahertz Imaging for Biomedical Applications Pattern Recognition and Tomographic Reconstruction

    CERN Document Server

    Yin, Xiaoxia; Abbott, Derek

    2012-01-01

    Terahertz Imaging for Biomedical Applications: Pattern Recognition and Tomographic Reconstruction presents the necessary algorithms needed to assist screening, diagnosis, and treatment, and these algorithms will play a critical role in the accurate detection of abnormalities present in biomedical imaging. Terahertz biomedical imaging has become an area of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized with an increasing number of trials performed in a biomedical setting. Terahertz tomographic imaging and detection technology contributes to the ability to identify opaque objects with clear boundaries,and would be useful to both in vivo and ex vivo environments. This book also: Introduces terahertz radiation techniques and provides a number of topical examples of signal and image processing, as well as machine learning Presents the most recent developments in an emerging field, terahertz radiation Utilizes new methods...

  15. Semantics-driven modelling of user preferences for information retrieval in the biomedical domain.

    Science.gov (United States)

    Gladun, Anatoly; Rogushina, Julia; Valencia-García, Rafael; Béjar, Rodrigo Martínez

    2013-03-01

    A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

  16. Ontology-based retrieval of bio-medical information based on microarray text corpora

    DEFF Research Database (Denmark)

    Hansen, Kim Allan; Zambach, Sine; Have, Christian Theil

    Microarray technology is often used in gene expression exper- iments. Information retrieval in the context of microarrays has mainly been concerned with the analysis of the numeric data produced; how- ever, the experiments are often annotated with textual metadata. Al- though biomedical resources...... are exponentially growing, the text corpora are sparse and inconsistent in spite of attempts to standardize the format. Ordinary keyword search may in some cases be insucient to nd rele- vant information and the potential benet of using a semantic approach in this context has only been investigated to a limited...

  17. IMAGE RETRIEVAL: A STATE OF THE ART APPROACH FOR CBIR

    OpenAIRE

    AMANDEEP KHOKHER; DR. RAJNEESH TALWAR

    2011-01-01

    The emergence of multimedia, the availability of large image archives, and the rapid growth of the World Wide Web Web (WWW) have attracted significant research efforts in providing tools for effective retrieval and management of visual data. A major approach directed towards achieving this goal is to use visual contents ofthe image data to segment, index and retrieve relevant images from the image database. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) system...

  18. Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

    Science.gov (United States)

    Chen, Wei-Bang

    2012-01-01

    The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

  19. 76 FR 69748 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-11-09

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  20. Microlenses and microcameras for biomedical imaging

    Science.gov (United States)

    Kanhere, Aditi

    Liquid lens technology is a rapidly progressing field driven by the promise of low cost fabrication, faster response, fewer mechanical elements, versatility and ease of customization for different applications. Here we present the use of liquid lenses for biomedical optics and medical imaging. I will specifically focus on our approaches towards the development of two liquid-lens optical systems -- laparoscopic cameras and 3D microscopy. The first part of this work is based on the development of a multi-camera laparoscopic imaging system with tunable focusing capability. The work attempts to find a solution to overcome many of the fundamental challenges faced by current laparoscopic imaging systems. The system is developed upon the key idea that widely spread multiple, tunable microcameras can cover a large range of vantage points and field of view (FoV) for intra-abdominal visualization. Our design features multiple tunable-focus microcameras integrated with a surgical port to provide panoramic intra-abdominal visualization with enhanced depth perception. Our system can be optically tuned to focus in on objects within a range of 5 mm to infinity, with a FoV adjustable between 36 degrees and 130 degrees. This unique approach also eliminates the requirement of an exclusive imaging port and need for navigation of cameras between ports during surgery. The second part of this report focuses on the application of tunable lenses in microscopy. Conventional wide-field microscopy is one of the most widely used optical microscopy technique. This technique typically captures a two dimensional image of a specimen. For a volumetric visualization of the sample or to enable depth scanning along the axial direction, it is necessary to move the sample relative to the fixed focal plane of the microscope objective. For this purpose, a mechanical z-scanning stage is typically employed. The stage enables the focal plane to move through the sample. Typical approaches used to achieve

  1. Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

    Directory of Open Access Journals (Sweden)

    Rita Layona

    2014-12-01

    Full Text Available Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR, but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR. In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved.

  2. Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

    Directory of Open Access Journals (Sweden)

    Hirokazu Nosato

    2017-01-01

    Full Text Available Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.

  3. Advances in biomedical signal and image processing – A systematic review

    Directory of Open Access Journals (Sweden)

    J. Rajeswari

    Full Text Available Biomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical procedures for sophisticated medical practices and applications. In this paper, the relationship between electrophysiological signals, i.e., electrocardiogram (ECG, electromyogram (EMG, electroencephalogram (EEG and functional image processing and their derived interactions have been discussed. Examples have been investigated in various case studies such as neurosciences, functional imaging, and cardiovascular system, by using different algorithms and methods. The interaction between the extracted information obtained from multiple signals and modalities seems to be very promising. The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated. Finally, some examples of algorithms have been discussed in which the electrophysiological signals and functional images have been properly extracted and have a significant impact on various biomedical applications. Keywords: Biomedical signals and images, Processing, Analysis

  4. CLUE: cluster-based retrieval of images by unsupervised learning.

    Science.gov (United States)

    Chen, Yixin; Wang, James Z; Krovetz, Robert

    2005-08-01

    In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.

  5. Research on image retrieval algorithm based on LBP and LSH

    Science.gov (United States)

    Wu, Hongliang; Wu, Weimin; Zhang, Junyuan; Peng, Jiajin

    2017-08-01

    Using LBP (local binary pattern) to extract texture feature in the area of image recognition and retrieval has achieved good results. LSH (locality sensitive hashing) in the information retrieval, especially to solve the ANN (approximate nearest neighbor) problem has a more important Status. LSH has a solid theoretical basis and excellent performance in high-dimensional data space. Under the trend of cloud computing and Big Data, this paper proposes an image retrieval algorithm based on LBP and LSH. Firstly, LBP is used to extract the texture feature vector of the image. Then, the LBP texture feature is reduced dimensionally and indexed into different buckets using LSH. Finally, the image corresponding to the index value in the bucket is extracted for second retrieval by using LBP. This algorithm can adapt to the massive image retrieval and ensures the high accuracy of the image retrieval and reduces the time complexity. This algorithm is of great significance.

  6. Using cited references to improve the retrieval of related biomedical documents.

    Science.gov (United States)

    Ortuño, Francisco M; Rojas, Ignacio; Andrade-Navarro, Miguel A; Fontaine, Jean-Fred

    2013-03-27

    A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references. Data on cited references and text sections in 249,108 full-text biomedical articles was extracted from the Open Access subset of the PubMed Central® database (PMC-OA). Of the five standard sections of a scientific article, the Introduction and Discussion sections contained most of the citations (mean = 10.2 and 9.9 citations, respectively). A large proportion of articles (98.4%) and their cited references (79.5%) were indexed in the PubMed® database. Using the MedlineRanker abstract classification tool, cited references allowed accurate retrieval of the citing document in a test set of 10,000 documents and also of documents related to six biomedical topics defined by particular MeSH® terms from the entire PMC-OA (p-valuereferences were selected. Classifiers trained on the baseline (i.e., only text from the query document and not from the references) were outperformed in almost all the cases. Best performance was often obtained when using all cited references, though using the references from Introduction and Discussion sections led to similarly good results. This query expansion method performed significantly better than pseudo relevance feedback in 4 out of 6 topics. The retrieval of documents related to a single document can be significantly improved by using the references cited by this document (p-valuereferences from Introduction and Discussion performs almost as well as using all references, which might be useful for methods that require reduced datasets due to

  7. A Part-Of-Speech term weighting scheme for biomedical information retrieval.

    Science.gov (United States)

    Wang, Yanshan; Wu, Stephen; Li, Dingcheng; Mehrabi, Saeed; Liu, Hongfang

    2016-10-01

    In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature. We hypothesize that NLP can be incorporated into IR to strengthen the conventional IR models. In this study, we propose two NLP-empowered IR models, POS-BoW and POS-MRF, which incorporate automatic POS-based term weighting schemes into bag-of-word (BoW) and Markov Random Field (MRF) IR models, respectively. In the proposed models, the POS-based term weights are iteratively calculated by utilizing a cyclic coordinate method where golden section line search algorithm is applied along each coordinate to optimize the objective function defined by mean average precision (MAP). In the empirical experiments, we used the data sets from the Medical Records track in Text REtrieval Conference (TREC) 2011 and 2012 and the Genomics track in TREC 2004. The evaluation on TREC 2011 and 2012 Medical Records tracks shows that, for the POS-BoW models, the mean improvement rates for IR evaluation metrics, MAP, bpref, and P@10, are 10.88%, 4.54%, and 3.82%, compared to the BoW models; and for the POS-MRF models, these rates are 13.59%, 8.20%, and 8.78%, compared to the MRF models. Additionally, we experimentally verify that the proposed weighting approach is superior to the simple heuristic and frequency based weighting approaches, and validate our POS category selection. Using the optimal weights calculated in this experiment, we tested the proposed models on the TREC 2004 Genomics track and obtained average of 8.63% and 10.04% improvement rates for POS-BoW and POS

  8. G-Bean: an ontology-graph based web tool for biomedical literature retrieval.

    Science.gov (United States)

    Wang, James Z; Zhang, Yuanyuan; Dong, Liang; Li, Lin; Srimani, Pradip K; Yu, Philip S

    2014-01-01

    Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean

  9. 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.

  10. Boundary value problem for phase retrieval from unidirectional X-ray differential phase images.

    Science.gov (United States)

    Gasilov, Sergei; Mittone, Alberto; Horng, Annie; Bravin, Alberto; Baumbach, Tilo; Geith, Tobias; Reiser, Maximilian; Coan, Paola

    2015-05-18

    The phase retrieval problem can be reduced to the second order partial differential equation. In order to retrieve the absolute values of the X-ray phase and to minimize the reconstruction artifacts we defined the mixed inhomogeneous boundary condition using available a priori information about the sample. Finite element technique was used to solve the boundary value problem. The approach is validated on numerical and experimental phantoms. In order to demonstrate a possible application of the method, we have processed an entire tomographic set of differential phase images and estimated the magnitude of the refractive index decrement for some tissues inside complex biomedical samples.

  11. A Statistical Approach to Retrieving Historical Manuscript Images without Recognition

    National Research Council Canada - National Science Library

    Rath, Toni M; Lavrenko, Victor; Manmatha, R

    2003-01-01

    ...), and word spotting -- an image matching approach (computationally expensive). In this work, the authors present a novel retrieval approach for historical document collections that does not require recognition...

  12. 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

  13. Human factors in automatic image retrieval system design and evaluation

    Science.gov (United States)

    Jaimes, Alejandro

    2006-01-01

    Image retrieval is a human-centered task: images are created by people and are ultimately accessed and used by people for human-related activities. In designing image retrieval systems and algorithms, or measuring their performance, it is therefore imperative to consider the conditions that surround both the indexing of image content and the retrieval. This includes examining the different levels of interpretation for retrieval, possible search strategies, and image uses. Furthermore, we must consider different levels of similarity and the role of human factors such as culture, memory, and personal context. This paper takes a human-centered perspective in outlining levels of description, types of users, search strategies, image uses, and human factors that affect the construction and evaluation of automatic content-based retrieval systems, such as human memory, context, and subjectivity.

  14. 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

  15. Laser speckle contrast imaging in biomedical optics.

    Science.gov (United States)

    Boas, David A; Dunn, Andrew K

    2010-01-01

    First introduced in the 1980s, laser speckle contrast imaging is a powerful tool for full-field imaging of blood flow. Recently laser speckle contrast imaging has gained increased attention, in part due to its rapid adoption for blood flow studies in the brain. We review the underlying physics of speckle contrast imaging and discuss recent developments to improve the quantitative accuracy of blood flow measures. We also review applications of laser speckle contrast imaging in neuroscience, dermatology and ophthalmology.

  16. 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 ...

  17. PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

    Directory of Open Access Journals (Sweden)

    Lin Jimmy

    2008-06-01

    Full Text Available Abstract Background Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed® search interface, a MEDLINE® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. Results We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. Conclusion The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain.

  18. PageRank without hyperlinks: reranking with PubMed related article networks for biomedical text retrieval.

    Science.gov (United States)

    Lin, Jimmy

    2008-06-06

    Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed(R) search interface, a MEDLINE(R) citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain.

  19. Biomedical Imaging and Computational Modeling in Biomechanics

    CERN Document Server

    Iacoviello, Daniela

    2013-01-01

    This book collects the state-of-art and new trends in image analysis and biomechanics. It covers a wide field of scientific and cultural topics, ranging from remodeling of bone tissue under the mechanical stimulus up to optimizing the performance of sports equipment, through the patient-specific modeling in orthopedics, microtomography and its application in oral and implant research, computational modeling in the field of hip prostheses, image based model development and analysis of the human knee joint, kinematics of the hip joint, micro-scale analysis of compositional and mechanical properties of dentin, automated techniques for cervical cell image analysis, and iomedical imaging and computational modeling in cardiovascular disease.   The book will be of interest to researchers, Ph.D students, and graduate students with multidisciplinary interests related to image analysis and understanding, medical imaging, biomechanics, simulation and modeling, experimental analysis.

  20. Ultrasmall lanthanide oxide nanoparticles for biomedical imaging and therapy

    CERN Document Server

    Lee, Gang Ho

    2014-01-01

    Most books discuss general and broad topics regarding molecular imagings. However, Ultrasmall Lanthanide Oxide Nanoparticles for Biomedical Imaging and Therapy, will mainly focus on lanthanide oxide nanoparticles for molecular imaging and therapeutics. Multi-modal imaging capabilities will discussed, along with up-converting FI by using lanthanide oxide nanoparticles. The synthesis will cover polyol synthesis of lanthanide oxide nanoparticles, Surface coatings with biocompatible and hydrophilic ligands will be discussed and TEM images and dynamic light scattering (DLS) patterns will be

  1. Applications of Micro-Raman Imaging in Biomedical Research

    NARCIS (Netherlands)

    Otto, Cornelis; de Grauw, C.J.; de Grauw, C.J.; Duindam, J.J.; Duindam, J.J.; Sijtsema, N.M.; Greve, Jan

    1997-01-01

    Recent results are presented of the application of imaging micro-Raman spectrometers in cellular biophysics and biomedical research. Various micro-Raman spectrometers have been developed that are now routinely applied in these fields. Results are presented that were obtained with a linescan Raman

  2. An Effective Combined Feature For Web Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    H.M.R.B Herath

    2015-08-01

    Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

  3. Sparse color interest points for image retrieval and object categorization

    NARCIS (Netherlands)

    Stöttinger, J.; Hanbury, A.; Sebe, N.; Gevers, T.

    2012-01-01

    Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest

  4. Inscription Image Retrieval Using Bag of Visual Words

    Science.gov (United States)

    Jayanthi, N.; Indu, S.

    2017-08-01

    This paper presents a technique for efficient and veracious retrieval of ancient inscriptions and manuscripts from a large database of images by using the Bag of Visual Words (BoVW) technique. The proposed method can be used to recognize inscription images across the world. SURF (speeded up robust features) is used as an image feature extractor. A visual vocabulary is created by representing the image as a histogram of visual words which helps in the retrieval process. Usage of SURF ensures scalability, faster processing better results with darkened and blurred images. We demonstrate the method on a combination of 300 inscriptions images comprising of several inscription around the world.

  5. 76 FR 40922 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-07-12

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  6. 75 FR 57969 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2010-09-23

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    2010-02-05

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    2013-04-24

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    2010-04-09

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    2013-10-25

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    2013-11-29

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    2012-06-22

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    2010-05-07

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  18. 76 FR 28795 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-05-18

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  19. 76 FR 28055 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-05-13

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  20. 76 FR 62814 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-10-11

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  1. 77 FR 38845 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2012-06-29

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    2010-06-23

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  3. 76 FR 10910 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2011-02-28

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    2011-02-01

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    2013-09-10

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    2011-12-13

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  7. 78 FR 6126 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting.

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    2013-01-29

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  8. 77 FR 3480 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2012-01-24

    ... Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting Pursuant to section 10(d) of... Institute of Biomedical Imaging and Bioengineering Special Emphasis Panel, R13 (2012/05). Date: February 15...

  9. 77 FR 2737 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2012-01-19

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  10. 77 FR 49821 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2012-08-17

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  11. 78 FR 58547 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2013-09-24

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  12. Pareto-depth for multiple-query image retrieval.

    Science.gov (United States)

    Hsiao, Ko-Jen; Calder, Jeff; Hero, Alfred O

    2015-02-01

    Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper, we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method with efficient manifold ranking. We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.

  13. Density-based retrieval from high-similarity image databases

    DEFF Research Database (Denmark)

    Hansen, Michael Edberg; Carstensen, Jens Michael

    2004-01-01

    Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce...... a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita distances between distributions of local (pixelwise) features estimated from a set of automatically and consistently defined image regions. The weight coefficients are estimated based on optimal...... retrieval performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition....

  14. Design and implementation of Metta, a metasearch engine for biomedical literature retrieval intended for systematic reviewers.

    Science.gov (United States)

    Smalheiser, Neil R; Lin, Can; Jia, Lifeng; Jiang, Yu; Cohen, Aaron M; Yu, Clement; Davis, John M; Adams, Clive E; McDonagh, Marian S; Meng, Weiyi

    2014-01-01

    systematic reviewers in retrieving, filtering and assessing publications. As such, Metta may find wide utility for anyone who is carrying out a comprehensive search of the biomedical literature.

  15. Fluorescent quantum dots: synthesis, biomedical optical imaging, and biosafety assessment.

    Science.gov (United States)

    Ji, Xiaoyuan; Peng, Fei; Zhong, Yiling; Su, Yuanyuan; He, Yao

    2014-12-01

    The marriage of nanomaterials with biology has significantly promoted advancement of biological techniques, profoundly facilitating basic research and practical applications in biological and biomedical fields. Taking advantages of unique optical properties (e.g., strong fluorescence, robust photostability, size-tunable emission wavelengths, etc.), fluorescent quantum dots (QDs), appearing as high-performance biological fluorescent nanoprobes, have been extensively explored for a variety of biomedical optical imaging applications. In this review, we present representative synthetic strategies for preparation of QDs and their applications in biomedical optical imaging, as well as risk assessments in vitro and in vivo. Briefly, we first summarize recent progress in fabrication of QDs via two rudimentary approaches, i.e., organometallic route and aqueous synthesis. Next we present representative achievement in QDs-based in vitro and in vivo biomedical optical imaging applications. We further discuss the toxicity assessment of QDs, ranging from cell studies to animal models. In the final section, we discuss challenges and perspectives for the QDs-relative bioapplications in the future. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. 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)

  17. 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.

  18. Computational optical biomedical spectroscopy and imaging

    CERN Document Server

    Musa, Sarhan M

    2015-01-01

    Applications of Vibrational Spectroscopic Imaging in Personal Care Studies; Guojin Zhang, Roger L. McMullen, Richard Mendelsohn, and Osama M. MusaFluorescence Bioimaging with Applications to Chemistry; Ufana Riaz and S.M. AshrafNew Trends in Immunohistochemical, Genome, and Metabolomics Imaging; G. Livanos, Aditi Deshpande, C. Narayan, Ying Na, T. Quang, T. Farrahi, R. Koglin, Suman Shrestha, M. Zervakis, and George C. GiakosDeveloping a Comprehensive Taxonomy for Human Cell Types; Richard Conroy and Vinay PaiFunctional Near-Infrared S

  19. Coherent fiber supercontinuum laser for nonlinear biomedical imaging

    DEFF Research Database (Denmark)

    Tu, Haohua; Liu, Yuan; Liu, Xiaomin

    2012-01-01

    Nonlinear biomedical imaging has not benefited from the well-known techniques of fiber supercontinuum generation for reasons such as poor coherence (or high noise), insufficient controllability, low spectral power intensity, and inadequate portability. Fortunately, a few techniques involving...... nonlinear fiber optics and femtosecond fiber laser development have emerged to overcome these critical limitations. These techniques pave the way for conducting point-of-care nonlinear biomedical imaging by a low-maintenance cost-effective coherent fiber supercontinuum laser, which covers a broad emission...... wavelength of 350-1700 nm. A prototype of this laser has been demonstrated in label-free multimodal nonlinear imaging of cell and tissue samples.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only....

  20. 77 FR 13347 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meetings

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    2012-03-06

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  1. 76 FR 76744 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2011-12-08

    ... Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting Pursuant to section 10(d) of... Institute of Biomedical Imaging and Bioengineering Special Emphasis Panel. Date: January 30-31, 2012. Time... Sukhareva, PhD, Scientific Review Officer, National Institute of Biomedical Imaging and Bioengineering...

  2. 77 FR 27784 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2012-05-11

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    2013-08-02

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    2010-01-26

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    2012-09-05

    ... National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting Pursuant to section... Institute of Biomedical Imaging and Bioengineering Special Emphasis Panel, BTRC P41 Review. Date: October 10... Sukhareva, Ph.D., Scientific Review Officer, National Institute of Biomedical Imaging and Bioengineering...

  6. A Robust Color Object Analysis Approach to Efficient Image Retrieval

    Directory of Open Access Journals (Sweden)

    Zhang Ruofei

    2004-01-01

    Full Text Available We describe a novel indexing and retrieval methodology integrating color, texture, and shape information for content-based image retrieval in image databases. This methodology, we call CLEAR, applies unsupervised image segmentation to partition an image into a set of objects. Fuzzy color histogram, fuzzy texture, and fuzzy shape properties of each object are then calculated to be its signature. The fuzzification procedures effectively resolve the recognition uncertainty stemming from color quantization and human perception of colors. At the same time, the fuzzy scheme incorporates segmentation-related uncertainties into the retrieval algorithm. An adaptive and effective measure for the overall similarity between images is developed by integrating properties of all the objects in every image. In an effort to further improve the retrieval efficiency, a secondary clustering technique is developed and employed, which significantly saves query processing time without compromising retrieval precision. A prototypical system of CLEAR, we developed, demonstrated the promising retrieval performance and robustness in color variations and segmentation-related uncertainties for a test database containing general-purpose color images, as compared with its peer systems in the literature.

  7. A Learning State-Space Model for Image Retrieval

    Directory of Open Access Journals (Sweden)

    Lee Greg C

    2007-01-01

    Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

  8. 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.

  9. Visualization and classification in biomedical terahertz pulsed imaging

    International Nuclear Information System (INIS)

    Loeffler, Torsten; Siebert, Karsten; Czasch, Stephanie; Bauer, Tobias; Roskos, Hartmut G

    2002-01-01

    'Visualization' in imaging is the process of extracting useful information from raw data in such a way that meaningful physical contrasts are developed. 'Classification' is the subsequent process of defining parameter ranges which allow us to identify elements of images such as different tissues or different objects. In this paper, we explore techniques for visualization and classification in terahertz pulsed imaging (TPI) for biomedical applications. For archived (formalin-fixed, alcohol-dehydrated and paraffin-mounted) test samples, we investigate both time- and frequency-domain methods based on bright- and dark-field TPI. Successful tissue classification is demonstrated

  10. Nonlinear Polarimetric Microscopy for Biomedical Imaging

    Science.gov (United States)

    Samim, Masood

    A framework for the nonlinear optical polarimetry and polarimetric microscopy is developed. Mathematical equations are derived in terms of linear and nonlinear Stokes Mueller formalism, which comprehensively characterize the polarization properties of the incoming and outgoing radiations, and provide structural information about the organization of the investigated materials. The algebraic formalism developed in this thesis simplifies many predictions for a nonlinear polarimetry study and provides an intuitive understanding of various polarization properties for radiations and the intervening medium. For polarimetric microscopy experiments, a custom fast-scanning differential polarization microscope is developed, which is also capable of real-time three-dimensional imaging. The setup is equipped with a pair of high-speed resonant and galvanometric scanning mirrors, and supplemented by advanced adaptive optics and data acquisition modules. The scanning mirrors when combined with the adaptive optics deformable mirror enable fast 3D imaging. Deformable membrane mirrors and genetic algorithm optimization routines are employed to improve the imaging conditions including correcting the optical aberrations, maximizing signal intensities, and minimizing point-spread-functions of the focal volume. A field-programmable-gate array (FPGA) chip is exploited to rapidly acquire and process the multidimensional data. Using the nonlinear optical polarimetry framework and the home-built polarization microscope, a few biologically important tissues are measured and analyzed to gain insight as to their structure and dynamics. The structure and distribution of muscle sarcomere myosins, connective tissue collagen, carbohydrate-rich starch, and fruit fly eye retinal molecules are characterized with revealing polarization studies. In each case, using the theoretical framework, polarization sensitive data are analyzed to decipher the molecular orientations and nonlinear optical

  11. PAMS photo image retrieval prototype system requirements specification

    Energy Technology Data Exchange (ETDEWEB)

    Conner, M.L.

    1996-04-30

    This project is part of the Photo Audiovisual Management System (PAMS). The project was initially identified in 1989 and has since been has been worked on under various names such as Image Retrieval and Viewing System, Photo Image Retrieval Subsystem and Image Processing and Compression System. This document builds upon the information collected and the analysis performed in the earlier phases of this project. The PAMS Photo Imaging subsystem will provide the means of capturing low resolution digital images from Photography`s negative files and associating the digital images with a record in the PAMS photo database. The digital images and key photo identification information will be accessible to HAN users to assist in locating and identifying specific photographs. After identifying desired photographs, users may request photo prints or high resolution digital images directly from Photography. The digital images captured by this project are for identification purposes only and are not intended to be of sufficient quality for subsequent use.

  12. Biomedical imaging ontologies: A survey and proposal for future work

    Directory of Open Access Journals (Sweden)

    Barry Smith

    2015-01-01

    Full Text Available Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as "cell" or "image" or "tissue" or "microscope" that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.

  13. Review of spectral imaging technology in biomedical engineering: achievements and challenges.

    Science.gov (United States)

    Li, Qingli; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Dongrong; Guo, Fangmin

    2013-10-01

    Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

  14. Multi region based image retrieval system

    Indian Academy of Sciences (India)

    Wang et al (2002) generated a code book from training images to segment images ... code book from different categories of training images. The code book then is used to segment ..... Frintrop S, Rome E and Christensen H I 2010 Computational visual attention systems and their cognitive foundations: A Survey. ACM Trans.

  15. Photoacoustics, thermoacoustics, and acousto-optics for biomedical imaging.

    Science.gov (United States)

    Tang, M-X; Elson, D S; Li, R; Dunsby, C; Eckersley, R J

    2010-01-01

    Recently there have been significant advances in developing hybrid techniques combining electromagnetic waves with ultrasound for biomedical imaging, namely photoacoustic, thermoacoustic, and acousto-optic (or ultrasound modulated optical) tomography. All three techniques take advantage of tissue contrast offered by electromagnetic (EM) waves, while achieving good spatial resolution in deeper tissue facilitated by ultrasound. In this review the principles of the three techniques are introduced. A description of existing experimental and image reconstruction techniques is provided. Some recent key developments are highlighted and current issues in each of the areas are discussed.

  16. All-optoelectronic continuous wave THz imaging for biomedical applications

    International Nuclear Information System (INIS)

    Siebert, Karsten J; Loeffler, Torsten; Quast, Holger; Thomson, Mark; Bauer, Tobias; Leonhardt, Rainer; Czasch, Stephanie; Roskos, Hartmut G

    2002-01-01

    We present an all-optoelectronic THz imaging system for ex vivo biomedical applications based on photomixing of two continuous-wave laser beams using photoconductive antennas. The application of hyperboloidal lenses is discussed. They allow for f-numbers less than 1/2 permitting better focusing and higher spatial resolution compared to off-axis paraboloidal mirrors whose f-numbers for practical reasons must be larger than 1/2. For a specific histological sample, an analysis of image noise is discussed

  17. Image Retrieval by Color Semantics with Incomplete Knowledge.

    Science.gov (United States)

    Corridoni, Jacopo M.; Del Bimbo, Alberto; Vicario, Enrico

    1998-01-01

    Presents a system which supports image retrieval by high-level chromatic contents, the sensations that color accordances generate on the observer. Surveys Itten's theory of color semantics and discusses image description and query specification. Presents examples of visual querying. (AEF)

  18. Learning tag relevance by neighbor voting for social image retrieval

    NARCIS (Netherlands)

    Li, X.; Snoek, C.G.M.; Worring, M.

    2008-01-01

    Social image retrieval is important for exploiting the increasing amounts of amateur-tagged multimedia such as Flickr images. Since amateur tagging is known to be uncontrolled, ambiguous, and personalized, a fundamental problem is how to reliably interpret the relevance of a tag with respect to the

  19. Learning Short Binary Codes for Large-scale Image Retrieval.

    Science.gov (United States)

    Liu, Li; Yu, Mengyang; Shao, Ling

    2017-03-01

    Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications, such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices. In this paper, we propose a novel unsupervised hashing approach called min-cost ranking (MCR) specifically for learning powerful short binary codes (i.e., usually the code length shorter than 100 b) for scalable image retrieval tasks. By exploring the discriminative ability of each dimension of data, MCR can generate one bit binary code for each dimension and simultaneously rank the discriminative separability of each bit according to the proposed cost function. Only top-ranked bits with minimum cost-values are then selected and grouped together to compose the final salient binary codes. Extensive experimental results on large-scale retrieval demonstrate that MCR can achieve comparative performance as the state-of-the-art hashing algorithms but with significantly shorter codes, leading to much faster large-scale retrieval.

  20. Towards Adaptive High-Resolution Images Retrieval Schemes

    Science.gov (United States)

    Kourgli, A.; Sebai, H.; Bouteldja, S.; Oukil, Y.

    2016-10-01

    Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. High spatial resolution images are complex and differ widely in their content, even in the same category. All images are more or less textured and structured. During the last decade, different approaches for the retrieval of this type of images have been proposed. They differ mainly in the type of features extracted. As these features are supposed to efficiently represent the query image, they should be adapted to all kind of images contained in the database. However, if the image to recognize is somewhat or very structured, a shape feature will be somewhat or very effective. While if the image is composed of a single texture, a parameter reflecting the texture of the image will reveal more efficient. This yields to use adaptive schemes. For this purpose, we propose to investigate this idea to adapt the retrieval scheme to image nature. This is achieved by making some preliminary analysis so that indexing stage becomes supervised. First results obtained show that by this way, simple methods can give equal performances to those obtained using complex methods such as the ones based on the creation of bag of visual word using SIFT (Scale Invariant Feature Transform) descriptors and those based on multi scale features extraction using wavelets and steerable pyramids.

  1. Simultenious binary hash and features learning for image retrieval

    Science.gov (United States)

    Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

    2016-05-01

    Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

  2. Multi-Atlas Segmentation of Biomedical Images: A Survey

    Science.gov (United States)

    Iglesias, Juan Eugenio; Sabuncu, Mert R.

    2015-01-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation. PMID:26201875

  3. Semantic-based high resolution remote sensing image retrieval

    Science.gov (United States)

    Guo, Dihua

    High Resolution Remote Sensing (HRRS) imagery has been experiencing extraordinary development in the past decade. Technology development means increased resolution imagery is available at lower cost, making it a precious resource for planners, environmental scientists, as well as others who can learn from the ground truth. Image retrieval plays an important role in managing and accessing huge image database. Current image retrieval techniques, cannot satisfy users' requests on retrieving remote sensing images based on semantics. In this dissertation, we make two fundamental contributions to the area of content based image retrieval. First, we propose a novel unsupervised texture-based segmentation approach suitable for accurately segmenting HRRS images. The results of existing segmentation algorithms dramatically deteriorate if simply adopted to HRRS images. This is primarily clue to the multi-texture scales and the high level noise present in these images. Therefore, we propose an effective and efficient segmentation model, which is a two-step process. At high-level, we improved the unsupervised segmentation algorithm by coping with two special features possessed by HRRS images. By preprocessing images with wavelet transform, we not only obtain multi-resolution images but also denoise the original images. By optimizing the splitting results, we solve the problem of textons in HRRS images existing in different scales. At fine level, we employ fuzzy classification segmentation techniques with adjusted parameters for different land cover. We implement our algorithm using real world 1-foot resolution aerial images. Second, we devise methodologies to automatically annotate HRRS images based on semantics. In this, we address the issue of semantic feature selection, the major challenge faced by semantic-based image retrieval. To discover and make use of hidden semantics of images is application dependent. One type of the semantics in HRRS image is conveyed by composite

  4. 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.

  5. 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

  6. Accelerating of Image Retrieval in CBIR System with Relevance Feedback

    Directory of Open Access Journals (Sweden)

    Branimir Reljin

    2007-01-01

    Full Text Available Content-based image retrieval (CBIR system with relevance feedback, which uses the algorithm for feature-vector (FV dimension reduction, is described. Feature-vector reduction (FVR exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the “curse of dimensionality” is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90% is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets.

  7. A UNIX-based prototype biomedical virtual image processor

    International Nuclear Information System (INIS)

    Fahy, J.B.; Kim, Y.

    1987-01-01

    The authors have developed a multiprocess virtual image processor for the IBM PC/AT, in order to maximize image processing software portability for biomedical applications. An interprocess communication scheme, based on two-way metacode exchange, has been developed and verified for this purpose. Application programs call a device-independent image processing library, which transfers commands over a shared data bridge to one or more Autonomous Virtual Image Processors (AVIP). Each AVIP runs as a separate process in the UNIX operating system, and implements the device-independent functions on the image processor to which it corresponds. Application programs can control multiple image processors at a time, change the image processor configuration used at any time, and are completely portable among image processors for which an AVIP has been implemented. Run-time speeds have been found to be acceptable for higher level functions, although rather slow for lower level functions, owing to the overhead associated with sending commands and data over the shared data bridge

  8. Uyghur Text Matching in Graphic Images for Biomedical Semantic Analysis.

    Science.gov (United States)

    Fang, Shancheng; Xie, Hongtao; Chen, Zhineng; Liu, Yizhi; Li, Yan

    2018-01-19

    How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network. Second, to train the matching network effectively an online sampling method is applied, which generates synthetic data continually. Finally, we propose a GPU acceleration scheme for matching network to match a complete Uyghur text line directly rather than a single window. Experimental results on benchmark dataset show our method achieves a good performance of F-measure 74.5%. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme.

  9. Selection of Quantum Dot Wavelengths for Biomedical Assays and Imaging

    Directory of Open Access Journals (Sweden)

    Yong Taik Lim

    2003-01-01

    Full Text Available Fluorescent semiconductor nanocrystals (quantum dots [QDs] are hypothesized to be excellent contrast agents for biomedical assays and imaging. A unique property of QDs is that their absorbance increases with increasing separation between excitation and emission wavelengths. Much of the enthusiasm for using QDs in vivo stems from this property, since photon yield should be proportional to the integral of the broadband absorption. In this study, we demonstrate that tissue scatter and absorbance can sometimes offset increasing QD absorption at bluer wavelengths, and counteract this potential advantage. By using a previously validated mathematical model, we explored the effects of tissue absorbance, tissue scatter, wavelength dependence of the scatter, water-to- hemoglobin ratio, and tissue thickness on QD performance. We conclude that when embedded in biological fluids and tissues, QD excitation wavelengths will often be quite constrained, and that excitation and emission wavelengths should be selected carefully based on the particular application. Based on our results, we produced near-infrared QDs optimized for imaging surface vasculature with white light excitation and a silicon CCD camera, and used them to image the coronary vasculature in vivo. Taken together, our data should prove useful in designing fluorescent QD contrast agents optimized for specific biomedical applications.

  10. Multi-clues image retrieval based on improved color invariants

    Science.gov (United States)

    Liu, Liu; Li, Jian-Xun

    2012-05-01

    At present, image retrieval has a great progress in indexing efficiency and memory usage, which mainly benefits from the utilization of the text retrieval technology, such as the bag-of-features (BOF) model and the inverted-file structure. Meanwhile, because the robust local feature invariants are selected to establish BOF, the retrieval precision of BOF is enhanced, especially when it is applied to a large-scale database. However, these local feature invariants mainly consider the geometric variance of the objects in the images, and thus the color information of the objects fails to be made use of. Because of the development of the information technology and Internet, the majority of our retrieval objects is color images. Therefore, retrieval performance can be further improved through proper utilization of the color information. We propose an improved method through analyzing the flaw of shadow-shading quasi-invariant. The response and performance of shadow-shading quasi-invariant for the object edge with the variance of lighting are enhanced. The color descriptors of the invariant regions are extracted and integrated into BOF based on the local feature. The robustness of the algorithm and the improvement of the performance are verified in the final experiments.

  11. Contributions on biomedical imaging, with a side-look at molecular imaging

    International Nuclear Information System (INIS)

    Winkler, G.

    2004-05-01

    This report is intended as a brief introduction to the emerging scientific field of biomedical imaging. The breadth of the subject is shown and future fields of research are indicated, which hopefully will serve as a guide to the identification of starting points for the research in 'Biomedical and/or Molecular Imaging' at the GSF-National Research Center for Environment and Health. The report starts with a brief sketch of the history. Then a - necessarily incomplete - list of research topics is presented. It is organized in two parts: the first one addresses medical imaging, and the second one is concerned with biological point aspects of the matter. (orig.) [de

  12. Recent advances in intelligent image search and video retrieval

    CERN Document Server

    2017-01-01

    This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant conte...

  13. Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval.

    Science.gov (United States)

    Zhu, Lei; Shen, Jialie; Xie, Liang; Cheng, Zhiyong

    2016-10-21

    Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by exploiting auxiliary texts around images. In our method, relations between images and semantic topics are first discovered via robust collective non-negative matrix factorization. Afterwards, a unified topic hypergraph, where images and topics are represented with independent vertices and hyperedges, respectively, is constructed to model inherent high-order semantic correlations of images. Finally, hashing codes and functions are learned by simultaneously enforcing semantic consistence and preserving the discovered semantic relations. Experiments on publicly available datasets demonstrate that THH can achieve superior performance compared with several state-of-the-art methods, and it is more suitable for mobile image retrieval.

  14. Acquisition and manipulation of computed tomography images of the maxillofacial region for biomedical prototyping

    International Nuclear Information System (INIS)

    Meurer, Maria Ines; Silva, Jorge Vicente Lopes da; Santa Barbara, Ailton; Nobre, Luiz Felipe; Oliveira, Marilia Gerhardt de; Silva, Daniela Nascimento

    2008-01-01

    Biomedical prototyping has resulted from a merger of rapid prototyping and imaging diagnosis technologies. However, this process is complex, considering the necessity of interaction between biomedical sciences and engineering. Good results are highly dependent on the acquisition of computed tomography images and their subsequent manipulation by means of specific software. The present study describes the experience of a multidisciplinary group of researchers in the acquisition and manipulation of computed tomography images of the maxillofacial region aiming at biomedical prototyping for surgical purposes. (author)

  15. Optical projection tomography via phase retrieval algorithms for hidden three dimensional imaging

    Science.gov (United States)

    Ancora, Daniele; Di Battista, Diego; Giasafaki, Georgia; Psycharakis, Stylianos; Liapis, Evangelos; Zacharopoulos, Athanasios; Zacharakis, Giannis

    2017-02-01

    Optical tomography in biomedical imaging is a highly dynamic field in which non-invasive optical and computational techniques are combined to obtain a three dimensional representation of the specimen we are interested to image. Although at optical wavelengths scattering is the main obstacle to reach diffraction limited resolution, recently several studies have shown the possibility to image even objects fully hidden behind a turbid layer exploiting the information contained in the speckle autocorrelation via an iterative phase retrieval algorithm. In this work we explore the possibility of blind three dimensional reconstruction approach based on the Optical Projection Tomography principles, a widely used tool to image almost transparent model organism such as C. Elegans and D. Rerio. By using autocorrelation information rather than projections at each angle we prove, both numerically and experimentally, the possibility to perform exact three dimensional reconstructions via a specifically designed phase retrieval algorithm, extending the capability of the projection-based tomographic methods to image behind scattering curtains. The reconstruction scheme we propose is simple to implement, does not require post-processing data alignment and moreover can be trivially implemented in parallel to fully exploit the computing power offered by modern GPUs, further reducing the need for costly computational resources.

  16. AMARSI: Aerosol modeling and retrieval from multi-spectral imagers

    NARCIS (Netherlands)

    Leeuw, G. de; Curier, R.L.; Staroverova, A.; Kokhanovsky, A.; Hoyningen-Huene, W. van; Rozanov, V.V.; Burrows, J.P.; Hesselmans, G.; Gale, L.; Bouvet, M.

    2008-01-01

    The AMARSI project aims at the development and validation of aerosol retrieval algorithms over ocean. One algorithm will be developed for application with data from the Multi Spectral Imager (MSI) on EarthCARE. A second algorithm will be developed using the combined information from AATSR and MERIS,

  17. Density-based retrieval from high-similarity image databases

    DEFF Research Database (Denmark)

    Hansen, Michael Edberg; Carstensen, Jens Michael

    2004-01-01

    a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita distances between distributions of local (pixelwise) features estimated from a set of automatically and consistently defined image regions. The weight coefficients are estimated based on optimal...

  18. Grid-Independent Compressive Imaging and Fourier Phase Retrieval

    Science.gov (United States)

    Liao, Wenjing

    2013-01-01

    This dissertation is composed of two parts. In the first part techniques of band exclusion(BE) and local optimization(LO) are proposed to solve linear continuum inverse problems independently of the grid spacing. The second part is devoted to the Fourier phase retrieval problem. Many situations in optics, medical imaging and signal processing call…

  19. Query Interpretation – an Application of Semiotics in Image Retrieval

    NARCIS (Netherlands)

    Boer, M.H.T. de; Brandt, P.; Sappelli, M.; Daniele, L.M.; Schutte, K.; Kraaij, W.

    2015-01-01

    One of the challenges in the field of content-based image retrieval is to bridge the semantic gap that exists between the information extracted from visual data using classifiers, and the interpretation of this data made by the end users. The semantic gap is a cascade of 1) the transformation of

  20. Image Retrieval Algorithm Based on Discrete Fractional Transforms

    Science.gov (United States)

    Jindal, Neeru; Singh, Kulbir

    2013-06-01

    The discrete fractional transforms is a signal processing tool which suggests computational algorithms and solutions to various sophisticated applications. In this paper, a new technique to retrieve the encrypted and scrambled image based on discrete fractional transforms has been proposed. Two-dimensional image was encrypted using discrete fractional transforms with three fractional orders and two random phase masks placed in the two intermediate planes. The significant feature of discrete fractional transforms benefits from its extra degree of freedom that is provided by its fractional orders. Security strength was enhanced (1024!)4 times by scrambling the encrypted image. In decryption process, image retrieval is sensitive for both correct fractional order keys and scrambling algorithm. The proposed approach make the brute force attack infeasible. Mean square error and relative error are the recital parameters to verify validity of proposed method.

  1. 77 FR 54584 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

    Science.gov (United States)

    2012-09-05

    ... Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting Pursuant to section 10(d) of... Institute of Biomedical Imaging and Bioengineering Special Emphasis Panel, ZEB1 OSR-D(J2) P Tissue... Imaging and Bioengineering, National Institutes of Health, 6707 Democracy Boulevard, Room 959, Bethesda...

  2. Applying GA for Optimizing the User Query in Image and Video Retrieval

    OpenAIRE

    Ehsan Lotfi

    2014-01-01

    In an information retrieval system, the query can be made by user sketch. The new method presented here, optimizes the user sketch and applies the optimized query to retrieval the information. This optimization may be used in Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR) which is based on trajectory extraction. To optimize the retrieval process, one stage of retrieval is performed by the user sketch. The retrieval criterion is based on the proposed distance met...

  3. Supervised learning of semantic classes for image annotation and retrieval.

    Science.gov (United States)

    Carneiro, Gustavo; Chan, Antoni B; Moreno, Pedro J; Vasconcelos, Nuno

    2007-03-01

    A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning.

  4. Image based book cover recognition and retrieval

    Science.gov (United States)

    Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

    2017-11-01

    In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

  5. A Retrieval Architecture for JWST Observations of Directly Imaged Exoplanets

    Science.gov (United States)

    Howe, Alex

    2017-06-01

    I present a new modeling and retrieval code for atmospheres of directly imaged exoplanets designed for use on JWST observations, extending my previous work on transiting planets. I perform example retrievals of temperature-pressure profiles, common molecular abundances, and basic cloud properties on existing lower-resolution spectra and on simulated JWST data using forward model emission spectra for planned NIRISS and NIRCam targets. From these results, I estimate the expected return on prospective JWST observations in information-theoretic terms using the mutual information metric.

  6. 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.

  7. Nanoparticles for biomedical imaging, therapy, and quantitative diagnostics

    Science.gov (United States)

    Yust, Brian G.

    Nanoparticles and nanomaterials are known to exhibit extraordinary characteristics and have a wide range of application which utilizes their unique properties. In particular, nanoparticles have shown great promise towards advancing the state of biological and biomedical techniques such as in vivo and in vitro imaging modalities, biosensing, and disease detection and therapy. Nanocrystalline hosts: NaYF4, KYF4, KGdF4, NaMF3, and KMF3 (M=Mg, Ba, Mn, Fe, Co, Ni, Cr) doped with rare earth ions have been synthesized by thermolysis, solvothermal, and hydrothermal methods. The morphology and spectroscopic properties have been thoroughly characterized. These nanoparticles (NP) are particularly useful for biomedical purposes since both the exciting and emitting wavelengths are in the near-infrared, where most tissues do not strongly absorb or scatter light. In vivo and in vitro imaging was performed with a 980 nm excitation source. Finally, NPs were conjugated with zinc phthalocyanine, a photosensitizer with a large absorption coefficient in the red and NIR regions, to illustrate the efficacy of these NPs as a platform for dual-mode infrared-activated imaging and photodynamic platforms. In addition, nonlinear optical nanomaterials, such as BaTiO3 and Ag@BaTiO3, were also synthesized and characterized. The nonlinear optical properties were investigated, and it is demonstrated that these nanoparticles can produce phase conjugate waves when used in a counterpropagating four wave mixing setup. The third order susceptibility is quantified using the z-scan technique, and the toxicity of these nanoparticles is also explored.

  8. Feasibility of telemammography as biomedical application for breast imaging

    Science.gov (United States)

    Beckerman, Barbara G.; Batsell, Stephen G.; MacIntyre, Lawrence P.; Sarraf, Hamed S.; Gleason, Shaun S.; Schnall, Mitchell D.

    1999-07-01

    Mammographic screening is an important tool in the early detection of breast cancer. The migration of mammography from the current mode of x-ray mammography using a film screen image detector and display to a digital technology provides an opportunity to improve access and performance of breast cancer screening. The sheer size and volume of the typical screening exam, the need to have previous screening data readily available, and the need to view other breast imaging data together to provide a common consensus and to plan treatment, make telemammography an ideal application for breast imaging. For telemammography to be a viable option, it must overcome the technical challenges related to transmission, archiving, management, processing and retrieval of large data sets. Researchers from the University of Pennsylvania, the University of Chicago and Lockheed Martin Energy Systems/Oak Ridge National Laboratory have developed a framework for transmission of large-scale medical images over high-speed networks, leveraged existing high-speed networks between research and medical facilities; tested the feasibility of point-to-point transmission of mammographic images in a near-real time environment; evaluated network performance and transmission scenarios; and investigated the impact of image preprocessing on an experimental computer-aided diagnosis system. Results of the initial study are reported here.

  9. Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance

    Directory of Open Access Journals (Sweden)

    Park BoGun

    2008-01-01

    Full Text Available In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the most commonly used color descriptor. However, a cluster-based representation, or a signature, has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages, only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper, we extract the perceptual representation of an original color image, a statistical signature by modifying general color signature, which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance (PMHD that is based on the Hausdorff distance. In the result, the proposed retrieval system views an image as a statistical signature, and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.

  10. Biomedical nanotechnology for molecular imaging, diagnostics, and targeted therapy.

    Science.gov (United States)

    Nie, Shuming

    2009-01-01

    Biomedical nanotechnology is a cross-disciplinary area of research in science, engineering and medicine with broad applications for molecular imaging, molecular diagnosis, and targeted therapy. The basic rationale is that nanometer-sized particles such as semiconductor quantum dots and iron oxide nanocrystals have optical, magnetic or structural properties that are not available from either molecules or bulk solids. When linked with biotargeting ligands such as monoclonal antibodies, peptides or small molecules, these nanoparticles can be used to target diseased cells and organs (such as malignant tumors and cardiovascular plaques) with high affinity and specificity. In the "mesoscopic" size range of 5-100 nm diameter, nanoparticles also have large surface areas and functional groups for conjugating to multiple diagnostic (e.g., optical, radioisotopic, or magnetic) and therapeutic (e.g., anticancer) agents.

  11. 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.

  12. 75 FR 39547 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2010-07-09

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    2012-04-30

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    2013-12-27

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    2013-12-18

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  1. 78 FR 77474 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2013-12-23

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    2013-01-02

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  3. 77 FR 19675 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

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  5. 75 FR 81630 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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    2010-12-28

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  6. 76 FR 75888 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

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    2011-12-05

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  8. 78 FR 64506 - National Institute of Biomedical Imaging and Bioengineering; Amended Notice of Meeting

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    2013-10-29

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  9. 75 FR 14175 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

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    2010-03-24

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    2013-11-06

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  14. 77 FR 58146 - National Institute of Biomedical Imaging and Bioengineering; Notice of Closed Meeting

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  15. 78 FR 64966 - National Institute of Biomedical Imaging and Bioengineering; Amended Notice of Meeting

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    2013-10-30

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  16. 78 FR 55268 - National Institute of Biomedical Imaging and Bioengineering Amended; Notice of Meeting

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    2013-09-10

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  17. 76 FR 40923 - National Institute of Biomedical Imaging and Bioengineering; Notice of Meeting

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    2011-07-12

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  18. Research of image retrieval technology based on color feature

    Science.gov (United States)

    Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

    2009-10-01

    Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

  19. 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.

  20. Color-Based Image Retrieval from High-Similarity Image Databases

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg; Carstensen, Jens Michael

    2003-01-01

    Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce...... a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval...... performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition....

  1. Fast wavelet-based image characterization for highly adaptive image retrieval.

    Science.gov (United States)

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

    2012-04-01

    Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.

  2. Modality prediction of biomedical literature images using multimodal feature representation

    Directory of Open Access Journals (Sweden)

    Pelka, Obioma

    2016-08-01

    Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

  3. 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.

  4. 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

  5. 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

  6. Rational design of nanoparticles for biomedical imaging and photovoltaic applications

    Energy Technology Data Exchange (ETDEWEB)

    Qin, Haiyan

    2011-07-01

    This thesis aims to rationally design nanoparticles and promote their applications in biomedical imaging and photovoltaic cells. Quantum dots (QDs) are promising fluorescent probes for biomedical imaging. We have fabricated two types of MSA capped QDs: CdTe/ZnSe core/shell QDs synthesized via an aqueous method and CdTe QDs via a hydrothermal method. They present high quantum yields (QYs), narrow emission band widths, high photo- and pH-stability, and low cytotoxicity. QD-IgG probes were produced and applied for labeling breast cancer marker HER2 proteins on MCF-7 cells. For the purpose of single molecule tracking using QDs as fluorescent probes, we use small affibodies instead of antibodies to produce QD-affibody probes. Smaller QD-target protein complexes are obtained using a direct immunofluorescence approach. These QD-affibody probes are developed to study the dynamic motion of single HER2 proteins on A431 cell membranes. Fluorescence blinking in single QDs is harmful for dynamic tracking due to information loss. We have experimentally studied the blinking phenomenon and the mechanism behind. We have discovered an emission bunching effect that two nearby QDs tend to emit light synchronously. The long-range Coulomb potential induced by the negative charge on the QD surface is found to be the major cause for the single QD blinking and the emission bunching in QD pairs. We have studied the in vitro cytotoxicity of CdTe QDs to human umbilical vein endothelial cells (HUVECs). The QDs treatment increases the intracellular reactive oxygen species (ROS) level and disrupts the mitochondrial membrane potential. The protein expression levels indicate that the mitochondria apoptosis is the main cause of HUVCEs apoptosis induced by CdTe QDs. Gold nanorods (GNRs) are scattering probes due to their tunable surface plasmon resonance (SPR) enhanced scattering spectrum. In order to control the yield and morphology of GNRs, we have systematically studied the effects of composition

  7. Imaging Chronic Traumatic Encephalopathy: A Biomedical Engineering Perspective.

    Science.gov (United States)

    Polak, Paul; Tuyl, John Van; Engel, Robin

    2016-01-01

    A disease initially associated with boxers ninety years ago, chronic traumatic encephalopathy (CTE) is now recognized as a significant risk to boxers, American football players, ice hockey players, military personnel or anyone to whom recurrent head injuries are a distinct possibility. Diagnosis is currently confirmed at autopsy, although CTE's presumed sufferers have symptoms of depression, suicidal thoughts, mood and personality changes, and loss of memory. CTE sufferers also complain of losing cognitive ability, dysfunction in everyday activities, inability to keep regular employment, violent tendencies and marital strife. Dementia may develop over the long term. Unfortunately, there is no clear consensus in regards to pathology, with both number and severity of head injuries being linked to disease progression. Despite the slow advancement of this disease, there are no clinical methods to diagnose or monitor prognosis in presumed patients, limiting clinicians' efforts to symptom management. The lack of diagnostic tools fuels the need for biomedical engineers to develop techniques for in vivo detection of CTE. This review examines efforts made with various magnetic resonance and nuclear imaging techniques, with a view towards improving the sensitivity and specificity of diagnostic imaging for CTE.

  8. The Application of Similar Image Retrieval in Electronic Commerce

    Directory of Open Access Journals (Sweden)

    YuPing Hu

    2014-01-01

    Full Text Available Traditional online shopping platform (OSP, which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers’ experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

  9. The application of similar image retrieval in electronic commerce.

    Science.gov (United States)

    Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

    2014-01-01

    Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

  10. The Application of Similar Image Retrieval in Electronic Commerce

    Science.gov (United States)

    Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

    2014-01-01

    Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system. PMID:24883411

  11. 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.

  12. WFIRST: Retrieval Studies of Directly Imaged Extrasolar Giant Planets

    Science.gov (United States)

    Marley, Mark; Lupu, Roxana; Lewis, Nikole K.; WFIRST Coronagraph SITs

    2018-01-01

    The typical direct imaging and spectroscopy target for the WFIRST Coronagraph will be a mature Jupiter-mass giant planet at a few AU from an FGK star. The spectra of such planets is expected to be shaped primarily by scattering from H2O clouds and absorption by gaseous NH3 and CH4. We have computed forward model spectra of such typical planets and applied noise models to understand the quality of photometry and spectra we can expect. Using such simulated datasets we have conducted Markov Chain Monte Carlo and MultiNest retrievals to derive atmospheric abundance of CH4, cloud scattering properties, gravity, and other parameters for various planets and observing modes. Our focus has primarily been to understand which combinations of photometry and spectroscopy at what SNR allow retrievals of atmospheric methane mixing ratios to within a factor of ten of the true value. This is a challenging task for directly imaged planets as the planet mass and radius--and thus surface gravity--are not as well constrained as in the case of transiting planets. We find that for plausible planets and datasets of the quality expected to be obtained by WFIRST it should be possible to place such constraints, at least for some planets. We present some examples of our retrieval results and explain how they have been utilized to help set design requirements on the coronagraph camera and integrated field spectrometer.

  13. Model control of image processing for telerobotics and biomedical instrumentation

    Science.gov (United States)

    Nguyen, An Huu

    1993-06-01

    This thesis has model control of image processing (MCIP) as its major theme. By this it is meant that there is a top-down model approach which already knows the structure of the image to be processed. This top-down image processing under model control is used further as visual feedback to control robots and as feedforward information for biomedical instrumentation. The software engineering of the bioengineering instrumentation image processing is defined in terms of the task and the tools available. Early bottom-up image processing such as thresholding occurs only within the top-down control regions of interest (ROI's) or operating windows. Moment computation is an important bottom-up procedure as well as pyramiding to attain rapid computation, among other considerations in attaining programming efficiencies. A distinction is made between initialization procedures and stripped down run time operations. Even more detailed engineering design considerations are considered with respect to the ellipsoidal modeling of objects. Here the major axis orientation is an important additional piece of information, beyond the centroid moments. Careful analysis of various sources of errors and considerable benchmarking characterized the detailed considerations of the software engineering of the image processing procedures. Image processing for robotic control involves a great deal of 3D calibration of the robot working environment (RWE). Of special interest is the idea of adapting the machine scanpath to the current task. It was important to pay careful attention to the hardware aspects of the control of the toy robots that were used to demonstrate the general methodology. It was necessary to precalibrate the open loop gains for all motors so that after initialization the visual feedback, which depends on MCIP, would be able to supply enough information quickly enough to the control algorithms to govern the robots under a variety of control configurations and task operations

  14. Signal and image analysis for biomedical and life sciences

    CERN Document Server

    Sun, Changming; Pham, Tuan D; Vallotton, Pascal; Wang, Dadong

    2014-01-01

    With an emphasis on applications of computational models for solving modern challenging problems in biomedical and life sciences, this book aims to bring collections of articles from biologists, medical/biomedical and health science researchers together with computational scientists to focus on problems at the frontier of biomedical and life sciences. The goals of this book are to build interactions of scientists across several disciplines and to help industrial users apply advanced computational techniques for solving practical biomedical and life science problems. This book is for users in t

  15. 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

  16. Video retrieval by still-image analysis with ImageMiner

    Science.gov (United States)

    Kreyss, Jutta; Roeper, M.; Alshuth, Peter; Hermes, Thorsten; Herzog, Otthein

    1997-01-01

    The large amount of available multimedia information (e.g. videos, audio, images) requires efficient and effective annotation and retrieval methods. As videos start playing a more important role in the frame of multimedia, we want to make these available for content-based retrieval. The ImageMiner-System, which was developed at the University of Bremen in the AI group, is designed for content-based retrieval of single images by a new combination of techniques and methods from computer vision and artificial intelligence. In our approach to make videos available for retrieval in a large database of videos and images there are two necessary steps: First, the detection and extraction of shots from a video, which is done by a histogram based method and second, the construction of the separate frames in a shot to one still single images. This is performed by a mosaicing-technique. The resulting mosaiced image gives a one image visualization of the shot and can be analyzed by the ImageMiner-System. ImageMiner has been tested on several domains, (e.g. landscape images, technical drawings), which cover a wide range of applications.

  17. Phase retrieval for interferometry imaging from microlens array

    Science.gov (United States)

    Zhu, Zhihao; Qiu, Minpu

    2018-03-01

    It was considered to get interferometry data from microlens array and reconstruct initial image through it directly, while which used to be taken to calculate the phase difference to get the structure of objects in measurement technology. It broke through the depend of resolution improvement on the size of apertures, reducing the volume of image system vastly. Nevertheless, on account of the phase deficiency, this method could not show the details well enough to be generally used in measurement and control systems. Through support estimation of the target, with the feature extraction technology, the deconvolution function could be got, by which the sidelobe and pinniform structure in the "ditry" image caused by the lack of frequency could be eliminated, and phase retrieval was done. Simulation did the reconstruction experiment, yet had got relatively good detail presentations.

  18. Rotation invariant deep binary hashing for fast image retrieval

    Science.gov (United States)

    Dai, Lai; Liu, Jianming; Jiang, Aiwen

    2017-07-01

    In this paper, we study how to compactly represent image's characteristics for fast image retrieval. We propose supervised rotation invariant compact discriminative binary descriptors through combining convolutional neural network with hashing. In the proposed network, binary codes are learned by employing a hidden layer for representing latent concepts that dominate on class labels. A loss function is proposed to minimize the difference between binary descriptors that describe reference image and the rotated one. Compared with some other supervised methods, the proposed network doesn't have to require pair-wised inputs for binary code learning. Experimental results show that our method is effective and achieves state-of-the-art results on the CIFAR-10 and MNIST datasets.

  19. Region-Based Color Image Indexing and Retrieval

    DEFF Research Database (Denmark)

    Kompatsiaris, Ioannis; Triantafyllou, Evangelia; Strintzis, Michael G.

    2001-01-01

    In this paper a region-based color image indexing and retrieval algorithm is presented. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of the regions. A new color distance is also defined for this algorithm. Based...... on the extracted regions, characteristic features are estimated using color, texture and shape information. An important and unique aspect of the algorithm is that, in the context of similarity-based querying, the user is allowed to view the internal representation of the submitted image and the query results....... Experimental results demonstrate the performance of the algorithm. The development of an intelligent image content-based search engine for the World Wide Web is also presented, as a direct application of the presented algorithm....

  20. Intelligent retrieval of chest X-ray image database using sketches

    International Nuclear Information System (INIS)

    Hasegawa, Jun-ichi; Okada, Noritake; Toriwaki, Jun-ichiro

    1988-01-01

    This paper presents further experiments on intelligent retrieval in our chest X-ray image database system using 'sketches'. First, in the previous sketch extraction procedure, vertical-location-invariant thresholding and shape-oriented smoothing are newly developed to improve the precision of lung borders and rib images in each sketch, respectively. Then, two new ways for image retrieval using sketches; (1) image-description retrieval and (2) pattern-matching retrieval, are proposed. In each retrieval way, a procedure for understanding picture queries input through a sketch is described in detail. (author)

  1. Enhancing imaging contrast via weighted feedback for iterative multi-image phase retrieval

    Science.gov (United States)

    Guo, Cheng; Li, Qiang; Zhang, Xiaoqing; Tan, Jiubin; Liu, Shutian; Liu, Zhengjun

    2018-01-01

    Iterative phase retrieval (IPR) has developed into a feasible and simple computational method to retrieve a complex-valued sample. Due to coherent illumination, the reconstructed image quality is degraded by speckle noise arising from a laser. Accordingly, partially coherent illumination has been introduced to alleviate this restriction. We apply weighted feedback modality into multidistance and multiwavelength phase retrieval to realize high-contrast and fast imaging. In simulation, it is proved that IPR based on weighted feedback accelerates the convergence in partially coherent illumination and speckle illumination. In experiment, the resolution chart and biological specimen are reconstructed in lensless and lens-based systems, which also demonstrate the performance of weighted feedback. This work provides a simple and high-contrast imaging modality for IPR. Also, it facilitates compact and flexible experimental implementation for label-free imaging.

  2. Model-based magnetization retrieval from holographic phase images

    Energy Technology Data Exchange (ETDEWEB)

    Röder, Falk, E-mail: f.roeder@hzdr.de [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Vogel, Karin [Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Wolf, Daniel [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); Triebenberg Labor, Institut für Strukturphysik, Technische Universität Dresden, D-01062 Dresden (Germany); Hellwig, Olav [Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Materialforschung, Bautzner Landstr. 400, D-01328 Dresden (Germany); AG Magnetische Funktionsmaterialien, Institut für Physik, Technische Universität Chemnitz, D-09126 Chemnitz (Germany); HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wee, Sung Hun [HGST, A Western Digital Company, 3403 Yerba Buena Rd., San Jose, CA 95135 (United States); Wicht, Sebastian; Rellinghaus, Bernd [IFW Dresden, Institute for Metallic Materials, P.O. Box 270116, D-01171 Dresden (Germany)

    2017-05-15

    The phase shift of the electron wave is a useful measure for the projected magnetic flux density of magnetic objects at the nanometer scale. More important for materials science, however, is the knowledge about the magnetization in a magnetic nano-structure. As demonstrated here, a dominating presence of stray fields prohibits a direct interpretation of the phase in terms of magnetization modulus and direction. We therefore present a model-based approach for retrieving the magnetization by considering the projected shape of the nano-structure and assuming a homogeneous magnetization therein. We apply this method to FePt nano-islands epitaxially grown on a SrTiO{sub 3} substrate, which indicates an inclination of their magnetization direction relative to the structural easy magnetic [001] axis. By means of this real-world example, we discuss prospects and limits of this approach. - Highlights: • Retrieval of the magnetization from holographic phase images. • Magnetostatic model constructed for a magnetic nano-structure. • Decomposition into homogeneously magnetized components. • Discretization of a each component by elementary cuboids. • Analytic solution for the phase of a magnetized cuboid considered. • Fitting a set of magnetization vectors to experimental phase images.

  3. Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

    Directory of Open Access Journals (Sweden)

    Petra Bosilj

    2016-12-01

    Full Text Available The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.

  4. Medical Image Retrieval Using Multi-Texton Assignment.

    Science.gov (United States)

    Tang, Qiling; Yang, Jirong; Xia, Xianfu

    2018-02-01

    In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

  5. 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.

  6. Coupled binary embedding for large-scale image retrieval.

    Science.gov (United States)

    Zheng, Liang; Wang, Shengjin; Tian, Qi

    2014-08-01

    Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

  7. 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.

  8. 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

  9. Fashion-related Image Gathering and Retrieval Using Meta Data generated at Image Sharing Site

    Science.gov (United States)

    Konishi, Katsumi; Toyama, Toshiaki; Watanabe, Asuka

    This paper proposes a fashion-related image gathering algorithm and a retrieval system. Since it is difficult to define the fashion-related image exactly in mathematical sense, computers can not recognize whether given images are fashion-related even if they use computer vision techniques. It is also difficult to gather and search only fashion-related images on the Internet automatically for the same reason. In order to overcome these difficulties, we focus on human computing power, which helps computers to find fashion-related images from tons of images on the Internet. This paper provides an algorithm to gather high quality fashion-related images and propses a fashion-related image retrieval system, both of which utilize the information and meta data obtained in a fashion-related image sharing site. Evaluation experiments show that the proposed algorithm can gather fashion-related images efficiently and that the proposed retrival system can find desired images more effectively than Google Image Search.

  10. 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.

  11. Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.

    Science.gov (United States)

    Abd El Aziz, Mohamed; Selim, I M; Xiong, Shengwu

    2017-06-30

    This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

  12. Extracting bimodal representations for language-based image text retrieval

    NARCIS (Netherlands)

    Westerveld, T.H.W.; Hiemstra, Djoerd; de Jong, Franciska M.G.; Correia, N.; Chambel, T.; Davenport, G.

    2000-01-01

    This paper explores two approaches to multimedia indexing that might contribute to the advancement of text-based conceptual search for pictorial information. Insights from relatively mature retrieval areas (spoken document retrieval and cross-language retrieval) are taken as a starting point for an

  13. Interactive Processing and Visualization of Image Data forBiomedical and Life Science Applications

    Energy Technology Data Exchange (ETDEWEB)

    Staadt, Oliver G.; Natarjan, Vijay; Weber, Gunther H.; Wiley,David F.; Hamann, Bernd

    2007-02-01

    Background: Applications in biomedical science and life science produce large data sets using increasingly powerful imaging devices and computer simulations. It is becoming increasingly difficult for scientists to explore and analyze these data using traditional tools. Interactive data processing and visualization tools can support scientists to overcome these limitations. Results: We show that new data processing tools and visualization systems can be used successfully in biomedical and life science applications. We present an adaptive high-resolution display system suitable for biomedical image data, algorithms for analyzing and visualization protein surfaces and retinal optical coherence tomography data, and visualization tools for 3D gene expression data. Conclusion: We demonstrated that interactive processing and visualization methods and systems can support scientists in a variety of biomedical and life science application areas concerned with massive data analysis.

  14. Collaborative Initiative in Biomedical Imaging to Study Complex Diseases

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Weili [The University of North Carolina at Chapel Hill; Fiddy, Michael A. [The University of North Carolina at Charlotte

    2012-03-31

    The work reported addressed these topics: Fluorescence imaging; Optical coherence tomography; X-ray interferometer/phase imaging system; Quantitative imaging from scattered fields, Terahertz imaging and spectroscopy; and Multiphoton and Raman microscopy.

  15. Combining textual and visual information for image retrieval in the medical domain.

    Science.gov (United States)

    Gkoufas, Yiannis; Morou, Anna; Kalamboukis, Theodore

    2011-01-01

    In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP).

  16. A review of microwave-induced thermoacoustic imaging: Excitation source, data acquisition system and biomedical applications

    Directory of Open Access Journals (Sweden)

    Yongsheng Cui

    2017-07-01

    Full Text Available Microwave-induced thermoacoustic imaging (TAI is a noninvasive modality based on the differences in microwave absorption of various biological tissues. TAI has been extensively researched in recent years, and several studies have revealed that TAI possesses advantages such as high resolution, high contrast, high imaging depth and fast imaging speed. In this paper, we reviewed the development of the TAI technique, its excitation source, data acquisition system and biomedical applications. It is believed that TAI has great potential applications in biomedical research and clinical study.

  17. Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image

    Directory of Open Access Journals (Sweden)

    Suchitra S

    2017-08-01

    Full Text Available ABSTRACT In photography, face recognition and face retrieval play an important role in many applications such as security, criminology and image forensics. Advancements in face recognition make easier for identity matching of an individual with attributes. Latest development in computer vision technologies enables us to extract facial attributes from the input image and provide similar image results. In this paper, we propose a novel LOP and sparse codewords method to provide similar matching results with respect to input query image. To improve accuracy in image results with input image and dynamic facial attributes, Local octal pattern algorithm [LOP] and Sparse codeword applied in offline and online. The offline and online procedures in face image binning techniques apply with sparse code. Experimental results with Pubfig dataset shows that the proposed LOP along with sparse codewords able to provide matching results with increased accuracy of 90%.

  18. Biomedical informatics and translational medicine

    Directory of Open Access Journals (Sweden)

    Sarkar Indra

    2010-02-01

    Full Text Available Abstract Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians" can be essential members of translational medicine teams.

  19. Biomedical informatics and translational medicine.

    Science.gov (United States)

    Sarkar, Indra Neil

    2010-02-26

    Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

  20. New software developments for quality mesh generation and optimization from biomedical imaging data.

    Science.gov (United States)

    Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

    2014-01-01

    In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  1. Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

    Science.gov (United States)

    Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

    2017-12-01

    In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

  2. Bio-medical X-ray imaging with spectroscopic pixel detectors

    CERN Document Server

    Butler, A P H; Tipples, R; Cook, N; Watts, R; Meyer, J; Bell, A J; Melzer, T R; Butler, P H

    2008-01-01

    The aim of this study is to review the clinical potential of spectroscopic X-ray detectors and to undertake a feasibility study using a novel detector in a clinical hospital setting. Detectors currently in development, such as Medipix-3, will have multiple energy thresholds allowing for routine use of spectroscopic bio-medical imaging. We have coined the term MARS (Medipix All Resolution System) for bio-medical images that provide spatial, temporal, and energy information. The full clinical significance of spectroscopic X-ray imaging is difficult to predict but insights can be gained by examining both image reconstruction artifacts and the current uses of dual-energy techniques. This paper reviews the known uses of energy information in vascular imaging and mammography, clinically important fields. It then presents initial results from using Medipix-2, to image human tissues within a clinical radiology department. Detectors currently in development, such as Medipix-3, will have multiple energy thresholds allo...

  3. Biomedical image analysis recipes in Matlab for life scientists and engineers

    CERN Document Server

    Reyes-Aldasoro, Constantino Carlos

    2015-01-01

    As its title suggests, this innovative book has been written for life scientists needing to analyse their data sets, and programmers, wanting a better understanding of the types of experimental images life scientists investigate on a regular basis. Each chapter presents one self-contained biomedical experiment to be analysed. Part I of the book presents its two basic ingredients: essential concepts of image analysis and Matlab. In Part II, algorithms and techniques are shown as series of 'recipes' or solved examples that show how specific techniques are applied to a biomedical experiments like

  4. 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.

  5. Filled carbon nanotubes in biomedical imaging and drug delivery.

    Science.gov (United States)

    Martincic, Markus; Tobias, Gerard

    2015-04-01

    Carbon nanotubes have been advocated as promising candidates in the biomedical field in the areas of diagnosis and therapy. In terms of drug delivery, the use of carbon nanotubes can overcome some limitations of 'free' drugs by improving the formulation of poorly water-soluble drugs, allowing targeted delivery and even enabling the co-delivery of two or more drugs for combination therapy. Two different approaches are currently being explored for the delivery of diagnostic and therapeutic agents by carbon nanotubes, namely attachment of the payload to the external sidewalls or encapsulation into the inner cavities. Although less explored, the latter confers additional stability to the chosen diagnostic or therapeutic agents, and leaves the backbone structure of the nanotubes available for its functionalization with dispersing and targeting moieties. Several drug delivery systems and diagnostic agents have been developed in the last years employing the inner tubular cavities of carbon nanotubes. The research discussed in this review focuses on the use of carbon nanotubes that contain in their interior drug molecules and diagnosis-related compounds. The approaches employed for the development of such nanoscale vehicles along with targeting and releasing strategies are discussed. The encapsulation of both biomedical contrast agents and drugs inside carbon nanotubes is further expanding the possibilities to allow an early diagnosis and treatment of diseases.

  6. W-transform method for feature-oriented multiresolution image retrieval

    Energy Technology Data Exchange (ETDEWEB)

    Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

    1995-07-01

    Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

  7. Optical image transformation and encryption by phase-retrieval-based double random-phase encoding and compressive ghost imaging

    Science.gov (United States)

    Yuan, Sheng; Yang, Yangrui; Liu, Xuemei; Zhou, Xin; Wei, Zhenzhuo

    2018-01-01

    An optical image transformation and encryption scheme is proposed based on double random-phase encoding (DRPE) and compressive ghost imaging (CGI) techniques. In this scheme, a secret image is first transformed into a binary image with the phase-retrieval-based DRPE technique, and then encoded by a series of random amplitude patterns according to the ghost imaging (GI) principle. Compressive sensing, corrosion and expansion operations are implemented to retrieve the secret image in the decryption process. This encryption scheme takes the advantage of complementary capabilities offered by the phase-retrieval-based DRPE and GI-based encryption techniques. That is the phase-retrieval-based DRPE is used to overcome the blurring defect of the decrypted image in the GI-based encryption, and the CGI not only reduces the data amount of the ciphertext, but also enhances the security of DRPE. Computer simulation results are presented to verify the performance of the proposed encryption scheme.

  8. Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

    Science.gov (United States)

    Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

    2013-02-01

    Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

  9. 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)

  10. Research of image retrieval system framework based on ontology and content

    Science.gov (United States)

    Liu, Hong

    2012-01-01

    The current most desirable image retrieval feature is retrieving images based on their semantic content. In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human semantics. In this paper, we put forward a system framework of image retrieval based on content and ontology, which has the potential to fully describe the semantic content of an image, allowing the similarity between images and retrieval query to be computed accurately. In the system, we identify third major categories of techniques in narrowing down the "semantic gap": (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using ontology reasoning to extend image retrieval. Finally, the paper does some testing experiment, whose result shows the feasibility of the system framework.

  11. Upconversion nanoparticles and their composite nanostructures for biomedical imaging and cancer therapy

    Science.gov (United States)

    Cheng, Liang; Wang, Chao; Liu, Zhuang

    2012-12-01

    Upconversion nanoparticles (UCNPs), particularly lanthanide-doped nanocrystals, which emit high energy photons under excitation by the near-infrared (NIR) light, have found potential applications in many different fields, including biomedicine. Compared with traditional down-conversion fluorescence imaging, the NIR light excited upconversion luminescence (UCL) imaging relying on UCNPs exhibits improved tissue penetration depth, higher photochemical stability, and is free of auto-fluorescence background, which promises biomedical imaging with high sensitivity. On the other hand, the unique upconversion process of UCNPs may be utilized to activate photosensitive therapeutic agents for applications in cancer treatment. Moreover, the integration of UCNPs with other functional nanostructures could result in the obtained nanocomposites having highly enriched functionalities, useful in imaging-guided cancer therapies. This review article will focus on the biomedical imaging and cancer therapy applications of UCNPs and their nanocomposites, and discuss recent advances and future prospects in this emerging field.

  12. Biomedical applications of nanodiamonds in imaging and therapy.

    Science.gov (United States)

    Perevedentseva, Elena; Lin, Yu-Chung; Jani, Mona; Cheng, Chia-Liang

    2013-12-01

    Nanodiamonds have attracted remarkable scientific attention for bioimaging and therapeutic applications owing to their low toxicity with many cell lines, convenient surface properties and stable fluorescence without photobleaching. Newer techniques are being applied to enhance fluorescence. Interest is also growing in exploring the possibilities for modifying the nanodiamond surface and functionalities by attaching various biomolecules of interest for interaction with the targets. The potential of Raman spectroscopy and fluorescence properties of nanodiamonds has been explored for bioimaging and drug delivery tracing. The interest in nanodiamonds' biological/medical application appears to be continuing with enhanced focus. In this review an attempt is made to capture the scope, spirit and recent developments in the field of nanodiamonds for biomedical applications.

  13. 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

  14. Biomedical imaging modality classification using combined visual features and textual terms.

    Science.gov (United States)

    Han, Xian-Hua; Chen, Yen-Wei

    2011-01-01

    We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

  15. Image Retrieval based on Integration between Color and Geometric Moment Features

    International Nuclear Information System (INIS)

    Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

    2012-01-01

    Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

  16. Performance analysis of algorithms for retrieval of magnetic resonance images for interactive teleradiology

    Science.gov (United States)

    Atkins, M. Stella; Hwang, Robert; Tang, Simon

    2001-05-01

    We have implemented a prototype system consisting of a Java- based image viewer and a web server extension component for transmitting Magnetic Resonance Images (MRI) to an image viewer, to test the performance of different image retrieval techniques. We used full-resolution images, and images compressed/decompressed using the Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm. We examined the SPIHT decompression algorithm using both non- progressive and progressive transmission, focusing on the running times of the algorithm, client memory usage and garbage collection. We also compared the Java implementation with a native C++ implementation of the non- progressive SPIHT decompression variant. Our performance measurements showed that for uncompressed image retrieval using a 10Mbps Ethernet, a film of 16 MR images can be retrieved and displayed almost within interactive times. The native C++ code implementation of the client-side decoder is twice as fast as the Java decoder. If the network bandwidth is low, the high communication time for retrieving uncompressed images may be reduced by use of SPIHT-compressed images, although the image quality is then degraded. To provide diagnostic quality images, we also investigated the retrieval of up to 3 images on a MR film at full-resolution, using progressive SPIHT decompression. The Java-based implementation of progressive decompression performed badly, mainly due to the memory requirements for maintaining the image states, and the high cost of execution of the Java garbage collector. Hence, in systems where the bandwidth is high, such as found in a hospital intranet, SPIHT image compression does not provide advantages for image retrieval performance.

  17. 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.

  18. Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging

    International Nuclear Information System (INIS)

    Wang Zhentian; Huang Zhifeng; Chen Zhiqiang; Zhang Li; Jiang Xiaolei; Kang Kejun; Yin Hongxia; Wang Zhenchang; Stampanoni, Marco

    2011-01-01

    The present work proposes a low dose information retrieval algorithm for X-ray grating-based multiple-information imaging (GB-MII) method, which can retrieve the attenuation, refraction and scattering information of samples by only three images. This algorithm aims at reducing the exposure time and the doses delivered to the sample. The multiple-information retrieval problem in GB-MII is solved by transforming a nonlinear equations set to a linear equations and adopting the nature of the trigonometric functions. The proposed algorithm is validated by experiments both on conventional X-ray source and synchrotron X-ray source, and compared with the traditional multiple-image-based retrieval algorithm. The experimental results show that our algorithm is comparable with the traditional retrieval algorithm and especially suitable for high Signal-to-Noise system.

  19. Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging

    Energy Technology Data Exchange (ETDEWEB)

    Wang Zhentian, E-mail: wang.zhentian@gmail.co [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084 (China); Huang Zhifeng, E-mail: huangzhifeng@mail.tsinghua.edu.c [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084 (China); Chen Zhiqiang; Zhang Li; Jiang Xiaolei; Kang Kejun [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084 (China); Yin Hongxia; Wang Zhenchang [Medical Imaging Center, Beijing TongRen Hospital, Beijing 100084 (China); Stampanoni, Marco [Swiss Light Source, Paul Scherrer Institute, 5232 Villigen PSI (Switzerland); Institute for Biomedical Engineering, University and ETH Zurich, 8092 Zurich (Switzerland)

    2011-04-11

    The present work proposes a low dose information retrieval algorithm for X-ray grating-based multiple-information imaging (GB-MII) method, which can retrieve the attenuation, refraction and scattering information of samples by only three images. This algorithm aims at reducing the exposure time and the doses delivered to the sample. The multiple-information retrieval problem in GB-MII is solved by transforming a nonlinear equations set to a linear equations and adopting the nature of the trigonometric functions. The proposed algorithm is validated by experiments both on conventional X-ray source and synchrotron X-ray source, and compared with the traditional multiple-image-based retrieval algorithm. The experimental results show that our algorithm is comparable with the traditional retrieval algorithm and especially suitable for high Signal-to-Noise system.

  20. 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.

  1. Second order Statistical Texture Features from a New CSLBPGLCM for Ultrasound Kidney Images Retrieval

    Directory of Open Access Journals (Sweden)

    Chelladurai CALLINS CHRISTIYANA

    2013-12-01

    Full Text Available This work proposes a new method called Center Symmetric Local Binary Pattern Grey Level Co-occurrence Matrix (CSLBPGLCM for the purpose of extracting second order statistical texture features in ultrasound kidney images. These features are then feed into ultrasound kidney images retrieval system for the point of medical applications. This new GLCM matrix combines the benefit of CSLBP and conventional GLCM. The main intention of this CSLBPGLCM is to reduce the number of grey levels in an image by not simply accumulating the grey levels but incorporating another statistical texture feature in it. The proposed approach is cautiously evaluated in ultrasound kidney images retrieval system and has been compared with conventional GLCM. It is experimentally proved that the proposed method increases the retrieval efficiency, accuracy and reduces the time complexity of ultrasound kidney images retrieval system by means of second order statistical texture features.

  2. [Nondestructive imaging of elements distribution in biomedical samples by X-ray fluorescence computed tomography].

    Science.gov (United States)

    Yang, Qun; Deng, Biao; Lü, Wei-Wei; Du, Guo-Hao; Yan, Fu-Hua; Xiao, Ti-Qiao; Xu, Hong-Jie

    2011-10-01

    X-ray fluorescence computed tomography is a stimulated emission tomography that allows nondestructive reconstruction of the elements distribution in the sample, which is important for biomedical investigations. Owing to the high flux density and easy energy tunability of highly collimated synchrotron X-rays, it is possible to apply X-ray fluorescence CT to biomedical samples. Reported in the present paper, an X-ray fluorescence CT system was established at Shanghai Synchrotron Radiation Facility for the investigations of trace elements distribution inside biomedical samples. By optimizing the experiment setup, the spatial resolution was improved and the data acquisition process was obviously speeded up. The maximum-likelihood expectation-maximization algorithm was introduced for the image reconstruction, which remarkably improved the imaging accuracy of element distributions. The developed system was verified by the test sample and medical sample respectively. The results showed that the distribution of interested elements could be imaged correctly, and the spatial resolution of 150 m was achieved. In conclusion, the developed system could be applied to the research on large-size biomedical samples, concerning imaging accuracy, spatial resolution and data collection time.

  3. Endovascular Device Testing with Particle Image Velocimetry Enhances Undergraduate Biomedical Engineering Education

    Science.gov (United States)

    Nair, Priya; Ankeny, Casey J.; Ryan, Justin; Okcay, Murat; Frakes, David H.

    2016-01-01

    We investigated the use of a new system, HemoFlow™, which utilizes state of the art technologies such as particle image velocimetry to test endovascular devices as part of an undergraduate biomedical engineering curriculum. Students deployed an endovascular stent into an anatomical model of a cerebral aneurysm and measured intra-aneurysmal flow…

  4. 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

  5. Molecular mass spectrometry imaging in biomedical and life science research

    Czech Academy of Sciences Publication Activity Database

    Pól, Jaroslav; Strohalm, Martin; Havlíček, Vladimír; Volný, Michael

    2010-01-01

    Roč. 134, č. 5 (2010), s. 423-443 ISSN 0948-6143 R&D Projects: GA MŠk LC545; GA ČR GPP206/10/P018 Institutional research plan: CEZ:AV0Z50200510 Keywords : Mass spectrometry * Chemical imaging * Molecular imaging Subject RIV: EE - Microbiology, Virology Impact factor: 4.727, year: 2010

  6. Advanced Contrast Agents for Multimodal Biomedical Imaging Based on Nanotechnology.

    Science.gov (United States)

    Calle, Daniel; Ballesteros, Paloma; Cerdán, Sebastián

    2018-01-01

    Clinical imaging modalities have reached a prominent role in medical diagnosis and patient management in the last decades. Different image methodologies as Positron Emission Tomography, Single Photon Emission Tomography, X-Rays, or Magnetic Resonance Imaging are in continuous evolution to satisfy the increasing demands of current medical diagnosis. Progress in these methodologies has been favored by the parallel development of increasingly more powerful contrast agents. These are molecules that enhance the intrinsic contrast of the images in the tissues where they accumulate, revealing noninvasively the presence of characteristic molecular targets or differential physiopathological microenvironments. The contrast agent field is currently moving to improve the performance of these molecules by incorporating the advantages that modern nanotechnology offers. These include, mainly, the possibilities to combine imaging and therapeutic capabilities over the same theranostic platform or improve the targeting efficiency in vivo by molecular engineering of the nanostructures. In this review, we provide an introduction to multimodal imaging methods in biomedicine, the sub-nanometric imaging agents previously used and the development of advanced multimodal and theranostic imaging agents based in nanotechnology. We conclude providing some illustrative examples from our own laboratories, including recent progress in theranostic formulations of magnetoliposomes containing ω-3 poly-unsaturated fatty acids to treat inflammatory diseases, or the use of stealth liposomes engineered with a pH-sensitive nanovalve to release their cargo specifically in the acidic extracellular pH microenvironment of tumors.

  7. 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

  8. A software package for biomedical image processing and analysis

    International Nuclear Information System (INIS)

    Goncalves, J.G.M.; Mealha, O.

    1988-01-01

    The decreasing cost of computing power and the introduction of low cost imaging boards justifies the increasing number of applications of digital image processing techniques in the area of biomedicine. There is however a large software gap to be fulfilled, between the application and the equipment. The requirements to bridge this gap are twofold: good knowledge of the hardware provided and its interface to the host computer, and expertise in digital image processing and analysis techniques. A software package incorporating these two requirements was developed using the C programming language, in order to create a user friendly image processing programming environment. The software package can be considered in two different ways: as a data structure adapted to image processing and analysis, which acts as the backbone and the standard of communication for all the software; and as a set of routines implementing the basic algorithms used in image processing and analysis. Hardware dependency is restricted to a single module upon which all hardware calls are based. The data structure that was built has four main features: hierchical, open, object oriented, and object dependent dimensions. Considering the vast amount of memory needed by imaging applications and the memory available in small imaging systems, an effective image memory management scheme was implemented. This software package is being used for more than one and a half years by users with different applications. It proved to be an excellent tool for helping people to get adapted into the system, and for standardizing and exchanging software, yet preserving flexibility allowing for users' specific implementations. The philosophy of the software package is discussed and the data structure that was built is described in detail

  9. Using Fuzzy SOM Strategy for Satellite Image Retrieval and Information Mining

    Directory of Open Access Journals (Sweden)

    Yo-Ping Huang

    2008-02-01

    Full Text Available This paper proposes an efficient satellite image retrieval and knowledge discovery model. The strategy comprises two major parts. First, a computational algorithm is used for off-line satellite image feature extraction, image data representation and image retrieval. Low level features are automatically extracted from the segmented regions of satellite images. A self-organization feature map is used to construct a two-layer satellite image concept hierarchy. The events are stored in one layer and the corresponding feature vectors are categorized in the other layer. Second, a user friendly interface is provided that retrieves images of interest and mines useful information based on the events in the concept hierarchy. The proposed system is evaluated with prominent features such as typhoons or high-pressure masses.

  10. Melanin-Based Contrast Agents for Biomedical Optoacoustic Imaging and Theranostic Applications.

    Science.gov (United States)

    Longo, Dario Livio; Stefania, Rachele; Aime, Silvio; Oraevsky, Alexander

    2017-08-07

    Optoacoustic imaging emerged in early 1990s as a new biomedical imaging technology that generates images by illuminating tissues with short laser pulses and detecting resulting ultrasound waves. This technique takes advantage of the spectroscopic approach to molecular imaging, and delivers high-resolution images in the depth of tissue. Resolution of the optoacoustic imaging is scalable, so that biomedical systems from cellular organelles to large organs can be visualized and, more importantly, characterized based on their optical absorption coefficient, which is proportional to the concentration of absorbing chromophores. Optoacoustic imaging was shown to be useful in both preclinical research using small animal models and in clinical applications. Applications in the field of molecular imaging offer abundant opportunities for the development of highly specific and effective contrast agents for quantitative optoacoustic imaging. Recent efforts are being made in the direction of nontoxic biodegradable contrast agents (such as nanoparticles made of melanin) that are potentially applicable in clinical optoacoustic imaging. In order to increase the efficiency and specificity of contrast agents and probes, they need to be made smart and capable of controlled accumulation in the target cells. This review was written in recognition of the potential breakthroughs in medical optoacoustic imaging that can be enabled by efficient and nontoxic melanin-based optoacoustic contrast agents.

  11. Retrieval of bilingual autobiographical memories: effects of cue language and cue imageability.

    Science.gov (United States)

    Mortensen, Linda; Berntsen, Dorthe; Bohn, Ocke-Schwen

    2015-01-01

    An important issue in theories of bilingual autobiographical memory is whether linguistically encoded memories are represented in language-specific stores or in a common language-independent store. Previous research has found that autobiographical memory retrieval is facilitated when the language of the cue is the same as the language of encoding, consistent with language-specific memory stores. The present study examined whether this language congruency effect is influenced by cue imageability. Danish-English bilinguals retrieved autobiographical memories in response to Danish and English high- or low-imageability cues. Retrieval latencies were shorter to Danish than English cues and shorter to high- than low-imageability cues. Importantly, the cue language effect was stronger for low-than high-imageability cues. To examine the relationship between cue language and the language of internal retrieval, participants identified the language in which the memories were internally retrieved. More memories were retrieved when the cue language was the same as the internal language than when the cue was in the other language, and more memories were identified as being internally retrieved in Danish than English, regardless of the cue language. These results provide further evidence for language congruency effects in bilingual memory and suggest that this effect is influenced by cue imageability.

  12. High resolution satellite image indexing and retrieval using SURF features and bag of visual words

    Science.gov (United States)

    Bouteldja, Samia; Kourgli, Assia

    2017-03-01

    In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

  13. Medical Image Retrieval Based On the Parallelization of the Cluster Sampling Algorithm

    OpenAIRE

    Ali, Hesham Arafat; Attiya, Salah; El-henawy, Ibrahim

    2017-01-01

    In this paper we develop parallel cluster sampling algorithms and show that a multi-chain version is embarrassingly parallel and can be used efficiently for medical image retrieval among other applications.

  14. An improved ptychographical phase retrieval algorithm for diffractive imaging

    International Nuclear Information System (INIS)

    Maiden, Andrew M.; Rodenburg, John M.

    2009-01-01

    The ptychographical iterative engine (or PIE) is a recently developed phase retrieval algorithm that employs a series of diffraction patterns recorded as a known illumination function is translated to a set of overlapping positions relative to a target sample. The technique has been demonstrated successfully at optical and X-ray wavelengths and has been shown to be robust to detector noise and to converge considerably faster than support-based phase retrieval methods. In this paper, the PIE is extended so that the requirement for an accurate model of the illumination function is removed.

  15. Unsupervised Multi-Feature Tag Relevance Learning for Social Image Retrieval

    NARCIS (Netherlands)

    Li, X.; Snoek, C.G.M.; Worring, M.

    2010-01-01

    Interpreting the relevance of a user-contributed tag with respect to the visual content of an image is an emerging problem in social image retrieval. In the literature this problem is tackled by analyzing the correlation between tags and images represented by specific visual features. Unfortunately,

  16. 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.

  17. A Beowulf Class parallel remote-sensed image database retrieval system developed in ASSIST environment

    Science.gov (United States)

    Di Lecce, Vincenzo; Guerriero, Andrea; Guarino, I.

    2005-01-01

    Image databases are now currently utilized in a wide range of different areas, in particular, the development and application of remote sensing platforms result in the production of huge amounts of image data. One of the major problem in the practical implementation of a Content-based Image Retrieval (CBIR) for remotely sensed images is that the content-based indexing and searching process always requires extremely high computational power. On the other hand, the content-based image retrieval algorithms are very suitable for parallel computation as the algorithms can be broken into several data independent processes for running on a parallel computer. In this paper, we discuss the porting problem of a sequential application of remote sensed image retrieval in a parallel environment using the new paradigm of programming introduced by the birth of a new structured program languages (Assist 1.2) and compared performances to sequential and to commercial multiprocessors solutions.

  18. A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

    Science.gov (United States)

    Ali, Nouman; Bajwa, Khalid Bashir; Sablatnig, Robert; Chatzichristofis, Savvas A; Iqbal, Zeshan; Rashid, Muhammad; Habib, Hafiz Adnan

    2016-01-01

    With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

  19. A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

    Directory of Open Access Journals (Sweden)

    Nouman Ali

    Full Text Available With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR, high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT and Speeded-Up Robust Features (SURF. The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

  20. Diversification in an image retrieval system based on text and image processing

    Directory of Open Access Journals (Sweden)

    Adrian Iftene

    2014-11-01

    Full Text Available In this paper we present an image retrieval system created within the research project MUCKE (Multimedia and User Credibility Knowledge Extraction, a CHIST-ERA research project where UAIC{\\footnote{"Alexandru Ioan Cuza" University of Iasi}} is one of the partners{\\footnote{Together with Technical University from Wienna, Austria, CEA-LIST Institute from Paris, France and BILKENT University from Ankara, Turkey}}. Our discussion in this work will focus mainly on components that are part of our image retrieval system proposed in MUCKE, and we present the work done by the UAIC group. MUCKE incorporates modules for processing multimedia content in different modes and languages (like English, French, German and Romanian and UAIC is responsible with text processing tasks (for Romanian and English. One of the problems addressed by our work is related to search results diversification. In order to solve this problem, we first process the user queries in both languages and secondly, we create clusters of similar images.

  1. 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.

  2. Biocompatible astaxanthin as novel contrast agent for biomedical imaging.

    Science.gov (United States)

    Nguyen, Van Phuc; Park, Suhyun; Oh, Junghwan; Wook Kang, Hyun

    2017-08-01

    Photoacoustic imaging (PAI) is a hybrid imaging modality with high resolution and sensitivity that can be beneficial for cancer staging. Due to insufficient endogenous photoacoustic (PA) contrast, the development of exogenous agents is critical in targeting cancerous tumors. The current study demonstrates the feasibility of marine-oriented material, astaxanthin, as a biocompatible PA contrast agent. Both silicon tubing phantoms and ex vivo bladder tissues are tested at various concentrations (up to 5 mg/ml) of astaxanthin to quantitatively explore variations in PA responses. A Q-switched Nd : YAG laser (λ = 532 nm) in conjunction with a 5 MHz ultrasound transducer is employed to generate and acquire PA signals from the samples. The phantom results presented that the PA signal amplitudes increase linearly with the astaxanthin concentrations (threshold detection = 0.31 mg/ml). The tissue injected with astaxanthin yields up to 16-fold higher PA signals, compared with that with saline. Due to distribution of the injected astaxanthin, PAI can image the margin of astaxanthin boles as well as quantify their volume in 3D reconstruction. Further investigations on selective tumor targeting are required to validate astaxanthin as a potential biocompatible contrast agent for PAI-assisted bladder cancer detection. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. 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.

  4. Image Retrieval Based on Multiview Constrained Nonnegative Matrix Factorization and Gaussian Mixture Model Spectral Clustering Method

    Directory of Open Access Journals (Sweden)

    Qunyi Xie

    2016-01-01

    Full Text Available Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF and Gaussian mixture model- (GMM- based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme.

  5. 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.

  6. Document image recognition and retrieval: where are we?

    Science.gov (United States)

    Garris, Michael D.

    1999-01-01

    This paper discusses survey data collected as a result of planning a project to evaluate document recognition and information retrieval technologies. In the process of establishing the project, a Request for Comment (RFC) was widely distributed throughout the document recognition and information retrieval research and development (R&D) communities, and based on the responses, the project was discontinued. The purpose of this paper is to present `real' data collected from the R&D communities in regards to a `real' project, so that we may all form our own conclusions about where we are, where we are heading, and how we are going to get there. Background on the project is provided and responses to the RFC are summarized.

  7. 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.

  8. An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

    Science.gov (United States)

    Liu, Fei; Zhang, Xi; Jia, Yan

    2015-01-01

    In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

  9. Euler vector for search and retrieval of gray-tone images.

    Science.gov (United States)

    Bishnu, Arijit; Bhattacharya, Bhargab B; Kundu, Malay K; Murthy, C A; Acharya, Tinku

    2005-08-01

    A new combinatorial characterization of a gray-tone image called Euler Vector is proposed. The Euler number of a binary image is a well-known topological feature, which remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. The Euler vector comprises a 4-tuple, where each element is an integer representing the Euler number of the partial binary image formed by the gray-code representation of the four most significant bit planes of the gray-tone image. Computation of Euler vector requires only integer and Boolean operations. The Euler vector is experimentally observed to be robust against noise and compression. For efficient image indexing, storage and retrieval from an image database using this vector, a bucket searching technique based on a simple modification of Kd-tree, is employed successfully. The Euler vector can also be used to perform an efficient four-dimensional range query. The set of retrieved images are finally ranked on the basis of Mahalanobis distance measure. Experiments are performed on the COIL database and results are reported. The retrieval success can be improved significantly by augmentiong the Euler vector by a few additional simple shape features. Since Euler vector can be computed very fast, the proposed technique is likely to find many applications to content-based image retrieval.

  10. Large-scale retrieval for medical image analytics: A comprehensive review.

    Science.gov (United States)

    Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

    2018-01-01

    Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Retrieve polarization aberration from image degradation: a new measurement method in DUV lithography

    Science.gov (United States)

    Xiang, Zhongbo; Li, Yanqiu

    2017-10-01

    Detailed knowledge of polarization aberration (PA) of projection lens in higher-NA DUV lithographic imaging is necessary due to its impact to imaging degradations, and precise measurement of PA is conductive to computational lithography techniques such as RET and OPC. Current in situ measurement method of PA thorough the detection of degradations of aerial images need to do linear approximation and apply the assumption of 3-beam/2-beam interference condition. The former approximation neglects the coupling effect of the PA coefficients, which would significantly influence the accuracy of PA retrieving. The latter assumption restricts the feasible pitch of test masks in higher-NA system, conflicts with the Kirhhoff diffraction model of test mask used in retrieving model, and introduces 3D mask effect as a source of retrieving error. In this paper, a new in situ measurement method of PA is proposed. It establishes the analytical quadratic relation between the PA coefficients and the degradations of aerial images of one-dimensional dense lines in coherent illumination through vector aerial imaging, which does not rely on the assumption of 3-beam/2- beam interference and linear approximation. In this case, the retrieval of PA from image degradation can be convert from the nonlinear system of m-quadratic equations to a multi-objective quadratic optimization problem, and finally be solved by nonlinear least square method. Some preliminary simulation results are given to demonstrate the correctness and accuracy of the new PA retrieving model.

  12. A novel 3D shape descriptor for automatic retrieval of anatomical structures from medical images

    Science.gov (United States)

    Nunes, Fátima L. S.; Bergamasco, Leila C. C.; Delmondes, Pedro H.; Valverde, Miguel A. G.; Jackowski, Marcel P.

    2017-03-01

    Content-based image retrieval (CBIR) aims at retrieving from a database objects that are similar to an object provided by a query, by taking into consideration a set of extracted features. While CBIR has been widely applied in the two-dimensional image domain, the retrieval of3D objects from medical image datasets using CBIR remains to be explored. In this context, the development of descriptors that can capture information specific to organs or structures is desirable. In this work, we focus on the retrieval of two anatomical structures commonly imaged by Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques, the left ventricle of the heart and blood vessels. Towards this aim, we developed the Area-Distance Local Descriptor (ADLD), a novel 3D local shape descriptor that employs mesh geometry information, namely facet area and distance from centroid to surface, to identify shape changes. Because ADLD only considers surface meshes extracted from volumetric medical images, it substantially diminishes the amount of data to be analyzed. A 90% precision rate was obtained when retrieving both convex (left ventricle) and non-convex structures (blood vessels), allowing for detection of abnormalities associated with changes in shape. Thus, ADLD has the potential to aid in the diagnosis of a wide range of vascular and cardiac diseases.

  13. Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network.

    Science.gov (United States)

    Sun, Xin; Qian, Huinan

    2016-01-01

    Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin.

  14. Dual-force ISOMAP: a new relevance feedback method for medical image retrieval.

    Science.gov (United States)

    Shen, Hualei; Tao, Dacheng; Ma, Dianfu

    2013-01-01

    With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user's preferences to bridge the "semantic gap" between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability.

  15. Optical multiple-image encryption based on multiplane phase retrieval and interference

    International Nuclear Information System (INIS)

    Chen, Wen; Chen, Xudong

    2011-01-01

    In this paper, we propose a new method for optical multiple-image encryption based on multiplane phase retrieval and interference. An optical encoding system is developed in the Fresnel domain. A phase-only map is iteratively extracted based on a multiplane phase retrieval algorithm, and multiple plaintexts are simultaneously encrypted. Subsequently, the extracted phase-only map is further encrypted into two phase-only masks based on a non-iterative interference algorithm. During image decryption, the advantages and security of the proposed optical cryptosystem are analyzed. Numerical results are presented to demonstrate the validity of the proposed optical multiple-image encryption method

  16. Context-based adaptive filtering of interest points in image retrieval

    DEFF Research Database (Denmark)

    Nguyen, Phuong Giang; Andersen, Hans Jørgen

    2009-01-01

    Interest points have been used as local features with success in many computer vision applications such as image/video retrieval and object recognition. However, a major issue when using this approach is a large number of interest points detected from each image and created a dense feature space...... a subset of features. Our approach differs from others in a fact that selected feature is based on the context of the given image. Our experimental results show a significant reduction rate of features while preserving the retrieval performance....

  17. Genetic Algorithm-Based Relevance Feedback for Image Retrieval Using Local Similarity Patterns.

    Science.gov (United States)

    Stejic, Zoran; Takama, Yasufumi; Hirota, Kaoru

    2003-01-01

    Proposes local similarity pattern (LSP) as a new method for computing digital image similarity. Topics include optimizing similarity computation based on genetic algorithm; relevance feedback; and an evaluation of LSP on five databases that showed an increase in retrieval precision over other methods for computing image similarity. (Author/LRW)

  18. Morphological image processing for quantitative shape analysis of biomedical structures: effective contrast enhancement

    International Nuclear Information System (INIS)

    Kimori, Yoshitaka

    2013-01-01

    A contrast enhancement approach utilizing a new type of mathematical morphology called rotational morphological processing is introduced. The method is quantitatively evaluated and then applied to some medical images. Image processing methods significantly contribute to visualization of images captured by biomedical modalities (such as mammography, X-ray computed tomography, magnetic resonance imaging, and light and electron microscopy). Quantitative interpretation of the deluge of complicated biomedical images, however, poses many research challenges, one of which is to enhance structural features that are scarcely perceptible to the human eye. This study introduces a contrast enhancement approach based on a new type of mathematical morphology called rotational morphological processing. The proposed method is applied to medical images for the enhancement of structural features. The effectiveness of the method is evaluated quantitatively by the contrast improvement ratio (CIR). The CIR of the proposed method is 12.1, versus 4.7 and 0.1 for two conventional contrast enhancement methods, clearly indicating the high contrasting capability of the method

  19. Morphological image processing for quantitative shape analysis of biomedical structures: effective contrast enhancement

    Energy Technology Data Exchange (ETDEWEB)

    Kimori, Yoshitaka, E-mail: kimori@orion.ac.jp [National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi 444-8787 (Japan)

    2013-11-01

    A contrast enhancement approach utilizing a new type of mathematical morphology called rotational morphological processing is introduced. The method is quantitatively evaluated and then applied to some medical images. Image processing methods significantly contribute to visualization of images captured by biomedical modalities (such as mammography, X-ray computed tomography, magnetic resonance imaging, and light and electron microscopy). Quantitative interpretation of the deluge of complicated biomedical images, however, poses many research challenges, one of which is to enhance structural features that are scarcely perceptible to the human eye. This study introduces a contrast enhancement approach based on a new type of mathematical morphology called rotational morphological processing. The proposed method is applied to medical images for the enhancement of structural features. The effectiveness of the method is evaluated quantitatively by the contrast improvement ratio (CIR). The CIR of the proposed method is 12.1, versus 4.7 and 0.1 for two conventional contrast enhancement methods, clearly indicating the high contrasting capability of the method.

  20. Medical imaging education in biomedical engineering curriculum: courseware development and application through a hybrid teaching model.

    Science.gov (United States)

    Zhao, Weizhao; Li, Xiping; Chen, Hairong; Manns, Fabrice

    2012-01-01

    Medical Imaging is a key training component in Biomedical Engineering programs. Medical imaging education is interdisciplinary training, involving physics, mathematics, chemistry, electrical engineering, computer engineering, and applications in biology and medicine. Seeking an efficient teaching method for instructors and an effective learning environment for students has long been a goal for medical imaging education. By the support of NSF grants, we developed the medical imaging teaching software (MITS) and associated dynamic assessment tracking system (DATS). The MITS/DATS system has been applied to junior and senior medical imaging classes through a hybrid teaching model. The results show that student's learning gain improved, particularly in concept understanding and simulation project completion. The results also indicate disparities in subjective perception between junior and senior classes. Three institutions are collaborating to expand the courseware system and plan to apply it to different class settings.

  1. Fabrication of a small animal restraint for synchrotron biomedical imaging using a rapid prototyper

    International Nuclear Information System (INIS)

    Zhu Ying; Zhang Honglin; McCrea, Richard; Bewer, Brian; Wiebe, Sheldon; Nichol, Helen; Ryan, Christopher; Wysokinski, Tomasz; Chapman, Dean

    2007-01-01

    Biomedical research at synchrotron facilities may involve imaging live animals that must remain motionless for extended periods of time to obtain quality images. Even breathing movements reduce image quality but on the other hand excessive restraint of animals increases morbidity and mortality. We describe a humane animal restraint designed to eliminate head movements while promoting animal survival. This paper describes how an animal restraint that conforms to the shape of an animal's head was fabricated by a 3D prototyper. The method used to translate medical computed tomography (CT) data to a 3D stereolithography format is described and images of its use at the Canadian Light Source (CLS) are shown. This type of restraint holds great promise in improving image quality and repeatability while reducing stress on experimental animals

  2. 3D UWB Magnitude-Combined Tomographic Imaging for Biomedical Applications. Algorithm Validation

    Directory of Open Access Journals (Sweden)

    S. Capdevila

    2011-06-01

    Full Text Available Biomedical microwave imaging is a topic of continuous research for its potential in different areas especially in breast cancer detection. In this paper, 3D UWB Magnitude-Combined tomographic algorithm is assessed for this recurrent application, but also for a more challenging one such as brain stroke detection. With the UWB Magnitude-Combined concept, the algorithm can take advantage of both the efficiency of Fourier Diffraction Theorem-based tomographic formulation and the robustness and image quality improvement provided by a multi-frequency combination.

  3. 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.

  4. A theoretical-experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods.

    Science.gov (United States)

    Leavesley, Silas J; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter; Rich, Thomas C

    2018-01-01

    Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. 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

  6. Three-dimensional imaging using phase retrieval with two focus planes

    Science.gov (United States)

    Ilovitsh, Tali; Ilovitsh, Asaf; Weiss, Aryeh; Meir, Rinat; Zalevsky, Zeev

    2016-03-01

    This work presents a technique for a full 3D imaging of biological samples tagged with gold-nanoparticles (GNPs) using only two images, rather than many images per volume as is currently needed for 3D optical sectioning microscopy. The proposed approach is based on the Gerchberg-Saxton (GS) phase retrieval algorithm. The reconstructed field is free space propagated to all other focus planes using post processing, and the 2D z-stack is merged to create a 3D image of the sample with high fidelity. Because we propose to apply the phase retrieving on nano particles, the regular ambiguities typical to the Gerchberg-Saxton algorithm, are eliminated. In addition, since the method requires the capturing of two images only, it can be suitable for 3D live cell imaging. The proposed concept is presented and validated both on simulated data as well as experimentally.

  7. 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.

  8. Algorithm for image retrieval based on edge gradient orientation statistical code.

    Science.gov (United States)

    Zeng, Jiexian; Zhao, Yonggang; Li, Weiye; Fu, Xiang

    2014-01-01

    Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.

  9. Multi-instance learning based on instance consistency for image retrieval

    Science.gov (United States)

    Zhang, Miao; Wu, Zhize; Wan, Shouhong; Yue, Lihua; Yin, Bangjie

    2017-07-01

    Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.

  10. Uncertainties in cloud phase and optical thickness retrievals from the Earth Polychromatic Imaging Camera (EPIC).

    Science.gov (United States)

    Meyer, Kerry; Yang, Yuekui; Platnick, Steven

    2016-01-01

    This paper presents an investigation of the expected uncertainties of a single channel cloud optical thickness (COT) retrieval technique, as well as a simple cloud temperature threshold based thermodynamic phase approach, in support of the Deep Space Climate Observatory (DSCOVR) mission. DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations in the ultraviolet and visible spectra. Since EPIC is not equipped with a spectral channel in the shortwave or mid-wave infrared that is sensitive to cloud effective radius (CER), COT will be inferred from a single visible channel with the assumption of appropriate CER values for liquid and ice phase clouds. One month of Aqua MODIS daytime granules from April 2005 is selected for investigating cloud phase sensitivity, and a subset of these granules that has similar EPIC sun-view geometry is selected for investigating COT uncertainties. EPIC COT retrievals are simulated with the same algorithm as the operational MODIS cloud products (MOD06), except using fixed phase-dependent CER values. Uncertainty estimates are derived by comparing the single channel COT retrievals with the baseline bi-spectral MODIS retrievals. Results show that a single channel COT retrieval is feasible for EPIC. For ice clouds, single channel retrieval errors are minimal (clouds the error is mostly limited to within 10%, although for thin clouds (COT cloud masking and cloud temperature retrievals are not considered in this study.

  11. Deformable Registration of Biomedical Images using 2D Hidden Markov Models.

    Science.gov (United States)

    Shenoy, Renuka; Shih, Min-Chi; Rose, Kenneth

    2016-07-18

    Robust registration of unimodal and multimodal images is a key task in biomedical image analysis, and is often utilized as an initial step on which subsequent analysis techniques critically depend. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model, namely the turbo hidden Markov model, to capture the deformation between pairs of images. The HMM is tailored to capture spatial transformations across images via state transitions, and modalityspecific data costs via emission probabilities. The method is derived for the unimodal setting (where simpler matching metrics may be used) as well as the multimodal setting, where different modalities may provide very different representations for a given class of objects, necessitating the use of advanced similarity measures. We utilize a rich model with hundreds of model parameters to describe the deformation relationships across such modalities. We also introduce a local edge-adaptive constraint to allow for varying degrees of smoothness between object boundaries and homogeneous regions. The parameters of the described method are estimated in a principled manner from training data via maximum likelihood learning, and the deformation is subsequently estimated using an efficient dynamic programming algorithm. Experimental results demonstrate the improved performance of the proposed approach over state-ofthe- art deformable registration techniques, on both unimodal and multimodal biomedical datasets.

  12. 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.

  13. Modeling multiple visual words assignment for bag-of-features based medical image retrieval

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-01-01

    In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

  14. Aspect-based Relevance Learning for Image Retrieval

    NARCIS (Netherlands)

    M.J. Huiskes (Mark)

    2005-01-01

    htmlabstractWe analyze the special structure of the relevance feedback learning problem, focusing particularly on the effects of image selection by partial relevance on the clustering behavior of feedback examples. We propose a scheme, aspect-based relevance learning, which guarantees that feedback

  15. Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

    Science.gov (United States)

    Taylor, Jaime; Rakoczy, John; Steincamp, James

    2003-01-01

    Phase retrieval requires calculation of the real-valued phase of the pupil fimction from the image intensity distribution and characteristics of an optical system. Genetic 'algorithms were used to solve two one-dimensional phase retrieval problem. A GA successfully estimated the coefficients of a polynomial expansion of the phase when the number of coefficients was correctly specified. A GA also successfully estimated the multiple p h e s of a segmented optical system analogous to the seven-mirror Systematic Image-Based Optical Alignment (SIBOA) testbed located at NASA s Marshall Space Flight Center. The SIBOA testbed was developed to investigate phase retrieval techniques. Tiphilt and piston motions of the mirrors accomplish phase corrections. A constant phase over each mirror can be achieved by an independent tip/tilt correction: the phase Conection term can then be factored out of the Discrete Fourier Tranform (DFT), greatly reducing computations.

  16. 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.

  17. An inventory of biomedical imaging physics elements-of-competence for diagnostic radiography education in Europe

    Energy Technology Data Exchange (ETDEWEB)

    Caruana, Carmel J. [University of Malta, Institute of Health Care, St Lukes Hospital, Gwardamangia (Malta)]. E-mail: carmel.j.caruana@um.edu.mt; Plasek, Jaromir [Charles University, Faculty of Mathematics and Physics, Institute of Physics, Division of Biophysics, Prague (Czech Republic)

    2006-08-15

    Purpose: To develop an inventory of biomedical physics elements-of-competence for diagnostic radiography education in Europe. Method: Research articles in the English literature and UK documentation pertinent to radiography education, competences and role development were subjected to a rigorous analysis of content from a functional and competence analysis perspective. Translations of radiography curricula from across Europe and relevant EU legislation were likewise analysed to ensure a pan-European perspective. Broad Subject Specific Competences for diagnostic radiography that included major biomedical physics components were singled out. These competences were in turn carefully deconstructed into specific elements-of-competence and those elements falling within the biomedical physics learning domain inventorised. A pilot version of the inventory was evaluated by participants during a meeting of the Higher Education Network for Radiography in Europe (HENRE), held in Marsascala, Malta, in November 2004. The inventory was further refined taking into consideration suggestions by HENRE members and scientific, professional and educational developments. Findings: The evaluation of the pilot inventory was very positive and indicated that the overall structure of the inventory was sensible, easily understood and acceptable - hence a good foundation for further development. Conclusions: Use of the inventory by radiography programme leaders and biomedical physics educators would guarantee that all necessary physics elements-of-competence underpinning the safe, effective and economical use of imaging devices are included within radiography curricula. It will also ensure the relevancy of physics content within radiography education. The inventory is designed to be a pragmatic tool for curriculum development across the entire range of radiography education up to doctorate level and irrespective of whether curriculum delivery is discipline-based or integrated, presentation

  18. An inventory of biomedical imaging physics elements-of-competence for diagnostic radiography education in Europe

    International Nuclear Information System (INIS)

    Caruana, Carmel J.; Plasek, Jaromir

    2006-01-01

    Purpose: To develop an inventory of biomedical physics elements-of-competence for diagnostic radiography education in Europe. Method: Research articles in the English literature and UK documentation pertinent to radiography education, competences and role development were subjected to a rigorous analysis of content from a functional and competence analysis perspective. Translations of radiography curricula from across Europe and relevant EU legislation were likewise analysed to ensure a pan-European perspective. Broad Subject Specific Competences for diagnostic radiography that included major biomedical physics components were singled out. These competences were in turn carefully deconstructed into specific elements-of-competence and those elements falling within the biomedical physics learning domain inventorised. A pilot version of the inventory was evaluated by participants during a meeting of the Higher Education Network for Radiography in Europe (HENRE), held in Marsascala, Malta, in November 2004. The inventory was further refined taking into consideration suggestions by HENRE members and scientific, professional and educational developments. Findings: The evaluation of the pilot inventory was very positive and indicated that the overall structure of the inventory was sensible, easily understood and acceptable - hence a good foundation for further development. Conclusions: Use of the inventory by radiography programme leaders and biomedical physics educators would guarantee that all necessary physics elements-of-competence underpinning the safe, effective and economical use of imaging devices are included within radiography curricula. It will also ensure the relevancy of physics content within radiography education. The inventory is designed to be a pragmatic tool for curriculum development across the entire range of radiography education up to doctorate level and irrespective of whether curriculum delivery is discipline-based or integrated, presentation

  19. 4D reconstruction of the past: the image retrieval and 3D model construction pipeline

    Science.gov (United States)

    Hadjiprocopis, Andreas; Ioannides, Marinos; Wenzel, Konrad; Rothermel, Mathias; Johnsons, Paul S.; Fritsch, Dieter; Doulamis, Anastasios; Protopapadakis, Eftychios; Kyriakaki, Georgia; Makantasis, Kostas; Weinlinger, Guenther; Klein, Michael; Fellner, Dieter; Stork, Andre; Santos, Pedro

    2014-08-01

    One of the main characteristics of the Internet era we are living in, is the free and online availability of a huge amount of data. This data is of varied reliability and accuracy and exists in various forms and formats. Often, it is cross-referenced and linked to other data, forming a nexus of text, images, animation and audio enabled by hypertext and, recently, by the Web3.0 standard. Our main goal is to enable historians, architects, archaeolo- gists, urban planners and affiliated professionals to reconstruct views of historical monuments from thousands of images floating around the web. This paper aims to provide an update of our progress in designing and imple- menting a pipeline for searching, filtering and retrieving photographs from Open Access Image Repositories and social media sites and using these images to build accurate 3D models of archaeological monuments as well as enriching multimedia of cultural / archaeological interest with metadata and harvesting the end products to EU- ROPEANA. We provide details of how our implemented software searches and retrieves images of archaeological sites from Flickr and Picasa repositories as well as strategies on how to filter the results, on two levels; a) based on their built-in metadata including geo-location information and b) based on image processing and clustering techniques. We also describe our implementation of a Structure from Motion pipeline designed for producing 3D models using the large collection of 2D input images (>1000) retrieved from Internet Repositories.

  20. Investigation of biomedical inner microstructures with hard X-ray phase-contrast imaging

    Energy Technology Data Exchange (ETDEWEB)

    Shu Hang [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); Graduate University of the Chinese Academy of Sciences, 100864 Beijing (China); Zhu Peiping [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); Chen Bo [Department of Physics, University of Science and Technology of China, Hefei 230026 (China); Liu Bo; Yin Hongxia [Capital University of Medical Sciences, 100054 Beijing (China); Li Enrong [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); Graduate University of the Chinese Academy of Sciences, 100864 Beijing (China); Liu Yijin [Department of Physics, University of Science and Technology of China, Hefei 230026 (China); Wang Junyue [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); Graduate University of the Chinese Academy of Sciences, 100864 Beijing (China); Yuan Qingxi; Huang Wanxia; Fang Shouxian [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); Wu Ziyu [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, CAS, 100049 Beijing (China); National Center for NanoScience and Technology, 100080 Beijing (China)], E-mail: wuzy@ihep.ac.cn

    2007-09-21

    Hard X-ray Phase-Contrast Imaging (HX-PCI) is a new and valuable method that may provide information of the inner parts of an opaque object. Previous reports demonstrated its applicability in soft and hard tissue imaging. Here we provide further evidence for improved image quality and the effective capability to distinguish inner microstructures in real biomedical systems such as cochlea. Experiments performed both at the 4W1A beamline of the Beijing Synchrotron Radiation Facility (BSRF) and at the Taiwan National Synchrotron Radiation Research Center (NSRRC) clearly show details of samples' inner microstructure with a resolution of a few microns. The improved spatial resolution is a relevant achievement for future improved understanding and clinical trials.

  1. Qualification of a Null Lens Using Image-Based Phase Retrieval

    Science.gov (United States)

    Bolcar, Matthew R.; Aronstein, David L.; Hill, Peter C.; Smith, J. Scott; Zielinski, Thomas P.

    2012-01-01

    In measuring the figure error of an aspheric optic using a null lens, the wavefront contribution from the null lens must be independently and accurately characterized in order to isolate the optical performance of the aspheric optic alone. Various techniques can be used to characterize such a null lens, including interferometry, profilometry and image-based methods. Only image-based methods, such as phase retrieval, can measure the null-lens wavefront in situ - in single-pass, and at the same conjugates and in the same alignment state in which the null lens will ultimately be used - with no additional optical components. Due to the intended purpose of a Dull lens (e.g., to null a large aspheric wavefront with a near-equal-but-opposite spherical wavefront), characterizing a null-lens wavefront presents several challenges to image-based phase retrieval: Large wavefront slopes and high-dynamic-range data decrease the capture range of phase-retrieval algorithms, increase the requirements on the fidelity of the forward model of the optical system, and make it difficult to extract diagnostic information (e.g., the system F/#) from the image data. In this paper, we present a study of these effects on phase-retrieval algorithms in the context of a null lens used in component development for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Approaches for mitigation are also discussed.

  2. The development of a human-centered object based image retrieval engine

    NARCIS (Netherlands)

    van Rikxoort, Eva M.; Kröse, B.J.A.; van den Broek, Egon; Bos, H.J.; Hendriks, E.A.; Schouten, Theo E.; Heijnsdijk, J.W.J.

    2005-01-01

    The development of a new object-based image retrieval (OBIR) engine is discussed. Its goal was to yield intuitive results for users by using human-based techniques. The engine utilizes a unique and efficient set of 15 features: 11 color categories and 4 texture features, derived from the color

  3. 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.

  4. 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

  5. Lensless coherent imaging of proteins and supramolecular assemblies: Efficient phase retrieval by the charge flippingalgorithm

    Czech Academy of Sciences Publication Activity Database

    Dumas, C.; Van der Lee, A.; Palatinus, Lukáš

    2013-01-01

    Roč. 182, č. 2 (2013), s. 106-116 ISSN 1047-8477 Institutional support: RVO:68378271 Keywords : coherent imaging * phase retrieval * single particle * charge flipping * X-ray free-electron lasers Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 3.369, year: 2013

  6. Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval

    Directory of Open Access Journals (Sweden)

    Liyan Chen

    2017-01-01

    Full Text Available Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease’s situation by observing patient’s tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge’s texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.

  7. A fast and effective model for wavelet subband histograms and its application in texture image retrieval.

    Science.gov (United States)

    Pi, Ming Hong; Tong, C S; Choy, Siu Kai; Zhang, Hong

    2006-10-01

    This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.

  8. Do physicians make their articles readable for their blind or low-vision patients? An analysis of current image processing practices in biomedical journals from the point of view of accessibility.

    Science.gov (United States)

    Splendiani, Bruno; Ribera, Mireia; Garcia, Roberto; Termens, Miquel

    2014-08-01

    Visual content in biomedical academic papers is a growing source of critical information, but it is not always fully readable for people with visual impairments. We aimed to assess current image processing practices, accessibility policies, and submission policies in a sample of 12 highly cited biomedical journals. We manually checked the application of text-based alternative image descriptions for every image in 12 articles (one for each journal). We determined whether the journals claimed to follow an accessibility policy and we reviewed their submission policy and their guidelines related to the visual content. We identified important features concerning the processing of images and the characteristics of the visual and the retrieval options of visual content offered by the publishers. The evaluation shows that the actual practices of textual image description in highly cited biomedical journals do not follow general guidelines on accessibility. The images within the articles analyzed lack alternative descriptions or have uninformative descriptions, even in the case of journals claiming to follow an accessibility policy. Consequently, the visual information of scientific articles is not accessible to people with severe visual disabilities. Instructions on image submission are heterogeneous and a declaration of accessibility guidelines was only found in two thirds of the sample of journals, with one third not explicitly following any accessibility policy, although they are required to by law.

  9. Color image encryption using random transforms, phase retrieval, chaotic maps, and diffusion

    Science.gov (United States)

    Annaby, M. H.; Rushdi, M. A.; Nehary, E. A.

    2018-04-01

    The recent tremendous proliferation of color imaging applications has been accompanied by growing research in data encryption to secure color images against adversary attacks. While recent color image encryption techniques perform reasonably well, they still exhibit vulnerabilities and deficiencies in terms of statistical security measures due to image data redundancy and inherent weaknesses. This paper proposes two encryption algorithms that largely treat these deficiencies and boost the security strength through novel integration of the random fractional Fourier transforms, phase retrieval algorithms, as well as chaotic scrambling and diffusion. We show through detailed experiments and statistical analysis that the proposed enhancements significantly improve security measures and immunity to attacks.

  10. 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.

  11. High sensitivity phase retrieval method in grating-based x-ray phase contrast imaging

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Zhao; Gao, Kun; Chen, Jian; Wang, Dajiang; Wang, Shenghao; Chen, Heng; Bao, Yuan; Shao, Qigang; Wang, Zhili, E-mail: wangnsrl@ustc.edu.cn [National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029 (China); Zhang, Kai [Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China); Zhu, Peiping; Wu, Ziyu, E-mail: wuzy@ustc.edu.cn [National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China and Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049 (China)

    2015-02-15

    Purpose: Grating-based x-ray phase contrast imaging is considered as one of the most promising techniques for future medical imaging. Many different methods have been developed to retrieve phase signal, among which the phase stepping (PS) method is widely used. However, further practical implementations are hindered, due to its complex scanning mode and high radiation dose. In contrast, the reverse projection (RP) method is a novel fast and low dose extraction approach. In this contribution, the authors present a quantitative analysis of the noise properties of the refraction signals retrieved by the two methods and compare their sensitivities. Methods: Using the error propagation formula, the authors analyze theoretically the signal-to-noise ratios (SNRs) of the refraction images retrieved by the two methods. Then, the sensitivities of the two extraction methods are compared under an identical exposure dose. Numerical experiments are performed to validate the theoretical results and provide some quantitative insight. Results: The SNRs of the two methods are both dependent on the system parameters, but in different ways. Comparison between their sensitivities reveals that for the refraction signal, the RP method possesses a higher sensitivity, especially in the case of high visibility and/or at the edge of the object. Conclusions: Compared with the PS method, the RP method has a superior sensitivity and provides refraction images with a higher SNR. Therefore, one can obtain highly sensitive refraction images in grating-based phase contrast imaging. This is very important for future preclinical and clinical implementations.

  12. High sensitivity phase retrieval method in grating-based x-ray phase contrast imaging

    International Nuclear Information System (INIS)

    Wu, Zhao; Gao, Kun; Chen, Jian; Wang, Dajiang; Wang, Shenghao; Chen, Heng; Bao, Yuan; Shao, Qigang; Wang, Zhili; Zhang, Kai; Zhu, Peiping; Wu, Ziyu

    2015-01-01

    Purpose: Grating-based x-ray phase contrast imaging is considered as one of the most promising techniques for future medical imaging. Many different methods have been developed to retrieve phase signal, among which the phase stepping (PS) method is widely used. However, further practical implementations are hindered, due to its complex scanning mode and high radiation dose. In contrast, the reverse projection (RP) method is a novel fast and low dose extraction approach. In this contribution, the authors present a quantitative analysis of the noise properties of the refraction signals retrieved by the two methods and compare their sensitivities. Methods: Using the error propagation formula, the authors analyze theoretically the signal-to-noise ratios (SNRs) of the refraction images retrieved by the two methods. Then, the sensitivities of the two extraction methods are compared under an identical exposure dose. Numerical experiments are performed to validate the theoretical results and provide some quantitative insight. Results: The SNRs of the two methods are both dependent on the system parameters, but in different ways. Comparison between their sensitivities reveals that for the refraction signal, the RP method possesses a higher sensitivity, especially in the case of high visibility and/or at the edge of the object. Conclusions: Compared with the PS method, the RP method has a superior sensitivity and provides refraction images with a higher SNR. Therefore, one can obtain highly sensitive refraction images in grating-based phase contrast imaging. This is very important for future preclinical and clinical implementations

  13. Parsed and fixed block representations of visual information for image retrieval

    Science.gov (United States)

    Bae, Soo Hyun; Juang, Biing-Hwang

    2009-02-01

    The theory of linguistics teaches us the existence of a hierarchical structure in linguistic expressions, from letter to word root, and on to word and sentences. By applying syntax and semantics beyond words, one can further recognize the grammatical relationship between among words and the meaning of a sequence of words. This layered view of a spoken language is useful for effective analysis and automated processing. Thus, it is interesting to ask if a similar hierarchy of representation of visual information does exist. A class of techniques that have a similar nature to the linguistic parsing is found in the Lempel-Ziv incremental parsing scheme. Based on a new class of multidimensional incremental parsing algorithms extended from the Lempel-Ziv incremental parsing, a new framework for image retrieval, which takes advantage of the source characterization property of the incremental parsing algorithm, was proposed recently. With the incremental parsing technique, a given image is decomposed into a number of patches, called a parsed representation. This representation can be thought of as a morphological interface between elementary pixel and a higher level representation. In this work, we examine the properties of two-dimensional parsed representation in the context of imagery information retrieval and in contrast to vector quantization; i.e. fixed square-block representations and minimum average distortion criteria. We implemented four image retrieval systems for the comparative study; three, called IPSILON image retrieval systems, use parsed representation with different perceptual distortion thresholds and one uses the convectional vector quantization for visual pattern analysis. We observe that different perceptual distortion in visual pattern matching does not have serious effects on the retrieval precision although allowing looser perceptual thresholds in image compression result poor reconstruction fidelity. We compare the effectiveness of the use of the

  14. 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.

  15. Optical image encryption based on phase retrieval combined with three-dimensional particle-like distribution

    International Nuclear Information System (INIS)

    Chen, Wen; Chen, Xudong; Sheppard, Colin J R

    2012-01-01

    We propose a new phase retrieval algorithm for optical image encryption in three-dimensional (3D) space. The two-dimensional (2D) plaintext is considered as a series of particles distributed in 3D space, and an iterative phase retrieval algorithm is developed to encrypt the series of particles into phase-only masks. The feasibility and effectiveness of the proposed method are demonstrated by a numerical experiment, and the advantages and security of the proposed optical cryptosystems are also analyzed and discussed. (paper)

  16. The effect of graphite sources on preparation of Photoluminescent graphene nano-sheets for biomedical imaging

    Directory of Open Access Journals (Sweden)

    Soroush Moasses Ghafary

    2017-07-01

    Full Text Available Objective(s: Graphene as two-dimensional (2D materials have attracted wide attention in different fields such as biomedical imaging. Ultra-small graphene nano-sheets (UGNSs have been designated as low dimensional graphene sheets with lateral dimensions less than few nanometres (≤ 500 nm in one, two or few layers. Several studies have proven that the process of acidic exfoliation and oxidation is one of the most effective methods to synthesize low dimensional graphene sheets. The band gap of graphene can be changed through changing the reaction temperature resulting in different photoluminescent colors. The aim of our study is synthesis of multi-color photoluminescent UGNSs for biomedical imaging.Materials and Methods: Two different UGNSs were synthesized from two different graphite sources via acidic treatment with a mixture of sulfuric and nitric acids. The prepared UGNSs were characterized by UV-Vis, photoluminescent, Raman spectroscopy and scanning electron microscopy (SEM. The photoluminescence colors of the prepared UGNSs were detected under excitation wavelength of 470 nm using optical filters.Results: The results showed that the graphite primary source is a determinant factor in the synthesis of different UGNSs. While altering reaction temperature didn't significantly change the emission wavelengths; however it affected their photoluminescent emission intensity.Conclusion: Overall, nontoxic UGNSs synthesized by simple acidic treatment of graphite with different photoluminescent colors (green, yellow and red can be a promising fluorescent probe for bioimaging.

  17. 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.

  18. 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/.

  19. A SIMPLE BUT EFFICIENT SCHEME FOR COLOUR IMAGE RETRIEVAL BASED ON STATISTICAL TESTS OF HYPOTHESIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2011-02-01

    Full Text Available This paper proposes a simple but efficient scheme for colour image retrieval, based on statistical tests of hypothesis, namely test for equality of variance, test for equality of mean. The test for equality of variance is performed to test the similarity of the query and target images. If the images pass the test, then the test for equality of mean is performed on the same images to examine whether the two images have the same attributes / characteristics. If the query and target images pass the tests then it is inferred that the two images belong to the same class i.e. both the images are same; otherwise, it is assumed that the images belong to different classes i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponding to the least value is identified as same / similar images. The proposed system is invariant for translation, scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is sample of other. The proposed scheme provides cent percent accuracy if the query and target images are same, whereas there is a slight variation for similar, transformed.

  20. Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

    Science.gov (United States)

    Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

    2018-04-01

    In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

  1. Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images.

    Science.gov (United States)

    Jenitta, A; Samson Ravindran, R

    2017-08-31

    In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP). The fusion of GLCM with the Local Mesh Pattern (LMeP) and the Local Vector Pattern (LVP) produces LMCoP and LVCoP respectively. The LMVCoP method has been investigated on the Open Access Series of Imaging Studies (OASIS): a Magnetic Resonance Imaging (MRI) brain image database. LMVCoP descriptor achieves 87.57% of ARP and 53.21% of ARR which are higher than the existing methods of LTCoP, PVEP, LBDP, LMeP and LVP. The LMVCoP method enhances the retrieval results of LMeP/LVP from 81.36%/83.52% to 87.57% in terms of ARP on OASIS MRI brain database.

  2. Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

    Science.gov (United States)

    Kauppi, Jukka-Pekka; Kandemir, Melih; Saarinen, Veli-Matti; Hirvenkari, Lotta; Parkkonen, Lauri; Klami, Arto; Hari, Riitta; Kaski, Samuel

    2015-05-15

    We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Context-based adaptive filtering of interest points in image retrieval

    DEFF Research Database (Denmark)

    Nguyen, Phuong Giang; Andersen, Hans Jørgen

    2009-01-01

    Interest points have been used as local features with success in many computer vision applications such as image/video retrieval and object recognition. However, a major issue when using this approach is a large number of interest points detected from each image and created a dense feature space....... This influences the processing speed in any runtime application. Selecting the most important features to reduce the size of the feature space will solve this problem. Thereby this raises a question of what makes a feature more important than the others? In this paper, we present a new technique to choose...... a subset of features. Our approach differs from others in a fact that selected feature is based on the context of the given image. Our experimental results show a significant reduction rate of features while preserving the retrieval performance....

  4. MARS Spectral Imaging: From High-Energy Physics to a Biomedical Business

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Abstract MARS spectral scanners provide colour X-Ray images. Current MARS pre-clinical scanners enable researchers and clinicians to measure biochemical and physiological processes in specimens, and animal models of disease. The scanners have developed from a 10 year scientific collaboration between New Zealand and CERN. In parallel a company, MARS Bioimaging Ltd, was founded to commercialise the technology by productising the scanner and selling it to biomedical users around the world. The New Zealand team is now more than 30 people including staff and students from the fields of physics, engineering, computing, maths, radiology, cardiology, biochemistry, oncology, and orthopaedics. Current work with pre-clinical scanners has concluded that the technology will be  useful in heart disease, stroke, arthritis, joint replacements, and cancer. In late 2014, the government announced funding for NZ to build a MARS scanner capable of imaging humans. Bio Professor Anthony Butler is a radiologist wit...

  5. 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.

  6. 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.

  7. Quantized embeddings: an efficient and universal nearest neighbor method for cloud-based image retrieval

    Science.gov (United States)

    Rane, Shantanu; Boufounos, Petros; Vetro, Anthony

    2013-09-01

    We propose a rate-efficient, feature-agnostic approach for encoding image features for cloud-based nearest neighbor search. We extract quantized random projections of the image features under consideration, transmit these to the cloud server, and perform matching in the space of the quantized projections. The advantage of this approach is that, once the underlying feature extraction algorithm is chosen for maximum discriminability and retrieval performance (e.g., SIFT, or eigen-features), the random projections guarantee a rate-efficient representation and fast server-based matching with negligible loss in accuracy. Using the Johnson-Lindenstrauss Lemma, we show that pair-wise distances between the underlying feature vectors are preserved in the corresponding quantized embeddings. We report experimental results of image retrieval on two image databases with different feature spaces; one using SIFT features and one using face features extracted using a variant of the Viola-Jones face recognition algorithm. For both feature spaces, quantized embeddings enable accurate image retrieval combined with improved bit-rate efficiency and speed of matching, when compared with the underlying feature spaces.

  8. 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.

  9. Conjunctive patches subspace learning with side information for collaborative image retrieval.

    Science.gov (United States)

    Zhang, Lining; Wang, Lipo; Lin, Weisi

    2012-08-01

    Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

  10. Retrieval of Sentence Sequences for an Image Stream via Coherence Recurrent Convolutional Networks.

    Science.gov (United States)

    Park, Cesc Chunseong; Kim, Youngjin; Kim, Gunhee

    2018-04-01

    We propose an approach for retrieving a sequence of natural sentences for an image stream. Since general users often take a series of pictures on their experiences, much online visual information exists in the form of image streams, for which it would better take into consideration of the whole image stream to produce natural language descriptions. While almost all previous studies have dealt with the relation between a single image and a single natural sentence, our work extends both input and output dimension to a sequence of images and a sequence of sentences. For retrieving a coherent flow of multiple sentences for a photo stream, we propose a multimodal neural architecture called coherence recurrent convolutional network (CRCN), which consists of convolutional neural networks, bidirectional long short-term memory (LSTM) networks, and an entity-based local coherence model. Our approach directly learns from vast user-generated resource of blog posts as text-image parallel training data. We collect more than 22 K unique blog posts with 170 K associated images for the travel topics of NYC, Disneyland , Australia, and Hawaii. We demonstrate that our approach outperforms other state-of-the-art image captioning methods for text sequence generation, using both quantitative measures and user studies via Amazon Mechanical Turk.

  11. Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases.

    Science.gov (United States)

    Dubey, Shiv Ram; Singh, Satish Kumar; Singh, Rajat Kumar

    2015-12-01

    A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.

  12. Image-Based Airborne LiDAR Point Cloud Encoding for 3d Building Model Retrieval

    Science.gov (United States)

    Chen, Yi-Chen; Lin, Chao-Hung

    2016-06-01

    With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority

  13. IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

    Directory of Open Access Journals (Sweden)

    Y.-C. Chen

    2016-06-01

    Full Text Available With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show

  14. A Simple and Universal Aerosol Retrieval Algorithm for Landsat Series Images Over Complex Surfaces

    Science.gov (United States)

    Wei, Jing; Huang, Bo; Sun, Lin; Zhang, Zhaoyang; Wang, Lunche; Bilal, Muhammad

    2017-12-01

    Operational aerosol optical depth (AOD) products are available at coarse spatial resolutions from several to tens of kilometers. These resolutions limit the application of these products for monitoring atmospheric pollutants at the city level. Therefore, a simple, universal, and high-resolution (30 m) Landsat aerosol retrieval algorithm over complex urban surfaces is developed. The surface reflectance is estimated from a combination of top of atmosphere reflectance at short-wave infrared (2.22 μm) and Landsat 4-7 surface reflectance climate data records over densely vegetated areas and bright areas. The aerosol type is determined using the historical aerosol optical properties derived from the local urban Aerosol Robotic Network (AERONET) site (Beijing). AERONET ground-based sun photometer AOD measurements from five sites located in urban and rural areas are obtained to validate the AOD retrievals. Terra MODerate resolution Imaging Spectrometer Collection (C) 6 AOD products (MOD04) including the dark target (DT), the deep blue (DB), and the combined DT and DB (DT&DB) retrievals at 10 km spatial resolution are obtained for comparison purposes. Validation results show that the Landsat AOD retrievals at a 30 m resolution are well correlated with the AERONET AOD measurements (R2 = 0.932) and that approximately 77.46% of the retrievals fall within the expected error with a low mean absolute error of 0.090 and a root-mean-square error of 0.126. Comparison results show that Landsat AOD retrievals are overall better and less biased than MOD04 AOD products, indicating that the new algorithm is robust and performs well in AOD retrieval over complex surfaces. The new algorithm can provide continuous and detailed spatial distributions of AOD during both low and high aerosol loadings.

  15. Uncertainties in Cloud Phase and Optical Thickness Retrievals from the Earth Polychromatic Imaging Camera (EPIC)

    Science.gov (United States)

    Meyer, Kerry; Yang, Yuekui; Platnick, Steven

    2016-01-01

    This paper presents an investigation of the expected uncertainties of a single channel cloud optical thickness (COT) retrieval technique, as well as a simple cloud-temperature-threshold-based thermodynamic phase approach, in support of the Deep Space Climate Observatory (DSCOVR) mission. DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations in the ultraviolet and visible spectra. Since EPIC is not equipped with a spectral channel in the shortwave or mid-wave infrared that is sensitive to cloud effective radius (CER), COT will be inferred from a single visible channel with the assumption of appropriate CER values for liquid and ice phase clouds. One month of Aqua MODIS daytime granules from April 2005 is selected for investigating cloud phase sensitivity, and a subset of these granules that has similar EPIC sun-view geometry is selected for investigating COT uncertainties. EPIC COT retrievals are simulated with the same algorithm as the operational MODIS cloud products (MOD06), except using fixed phase-dependent CER values. Uncertainty estimates are derived by comparing the single channel COT retrievals with the baseline bi-spectral MODIS retrievals. Results show that a single channel COT retrieval is feasible for EPIC. For ice clouds, single channel retrieval errors are minimal (less than 2 percent) due to the particle- size insensitivity of the assumed ice crystal (i.e., severely roughened aggregate of hexagonal columns) scattering properties at visible wavelengths, while for liquid clouds the error is mostly limited to within 10 percent, although for thin clouds (COT less than 2) the error can be higher. Potential uncertainties in EPIC cloud masking and cloud temperature retrievals are not considered in this study.

  16. Single-image phase retrieval using an edge illumination X-ray phase-contrast imaging setup

    International Nuclear Information System (INIS)

    Diemoz, Paul C.; Vittoria, Fabio A.; Hagen, Charlotte K.; Endrizzi, Marco; Coan, Paola; Brun, Emmanuel; Wagner, Ulrich H.; Rau, Christoph; Robinson, Ian K.; Bravin, Alberto; Olivo, Alessandro

    2015-01-01

    A method enabling the retrieval of thickness or projected electron density of a sample from a single input image is derived theoretically and successfully demonstrated on experimental data. A method is proposed which enables the retrieval of the thickness or of the projected electron density of a sample from a single input image acquired with an edge illumination phase-contrast imaging setup. The method assumes the case of a quasi-homogeneous sample, i.e. a sample with a constant ratio between the real and imaginary parts of its complex refractive index. Compared with current methods based on combining two edge illumination images acquired in different configurations of the setup, this new approach presents advantages in terms of simplicity of acquisition procedure and shorter data collection time, which are very important especially for applications such as computed tomography and dynamical imaging. Furthermore, the fact that phase information is directly extracted, instead of its derivative, can enable a simpler image interpretation and be beneficial for subsequent processing such as segmentation. The method is first theoretically derived and its conditions of applicability defined. Quantitative accuracy in the case of homogeneous objects as well as enhanced image quality for the imaging of complex biological samples are demonstrated through experiments at two synchrotron radiation facilities. The large range of applicability, the robustness against noise and the need for only one input image suggest a high potential for investigations in various research subjects

  17. Single-image phase retrieval using an edge illumination X-ray phase-contrast imaging setup

    Energy Technology Data Exchange (ETDEWEB)

    Diemoz, Paul C., E-mail: p.diemoz@ucl.ac.uk; Vittoria, Fabio A. [University College London, London WC1 E6BT (United Kingdom); Research Complex at Harwell, Oxford Harwell Campus, Didcot OX11 0FA (United Kingdom); Hagen, Charlotte K.; Endrizzi, Marco [University College London, London WC1 E6BT (United Kingdom); Coan, Paola [Ludwig-Maximilians-University, Munich 81377 (Germany); Ludwig-Maximilians-University, Garching 85748 (Germany); Brun, Emmanuel [Ludwig-Maximilians-University, Garching 85748 (Germany); European Synchrotron Radiation Facility, Grenoble 38043 (France); Wagner, Ulrich H.; Rau, Christoph [Diamond Light Source, Harwell Oxford Campus, Didcot OX11 0DE (United Kingdom); Robinson, Ian K. [Research Complex at Harwell, Oxford Harwell Campus, Didcot OX11 0FA (United Kingdom); London Centre for Nanotechnology, London WC1 H0AH (United Kingdom); Bravin, Alberto [European Synchrotron Radiation Facility, Grenoble 38043 (France); Olivo, Alessandro [University College London, London WC1 E6BT (United Kingdom); Research Complex at Harwell, Oxford Harwell Campus, Didcot OX11 0FA (United Kingdom)

    2015-06-25

    A method enabling the retrieval of thickness or projected electron density of a sample from a single input image is derived theoretically and successfully demonstrated on experimental data. A method is proposed which enables the retrieval of the thickness or of the projected electron density of a sample from a single input image acquired with an edge illumination phase-contrast imaging setup. The method assumes the case of a quasi-homogeneous sample, i.e. a sample with a constant ratio between the real and imaginary parts of its complex refractive index. Compared with current methods based on combining two edge illumination images acquired in different configurations of the setup, this new approach presents advantages in terms of simplicity of acquisition procedure and shorter data collection time, which are very important especially for applications such as computed tomography and dynamical imaging. Furthermore, the fact that phase information is directly extracted, instead of its derivative, can enable a simpler image interpretation and be beneficial for subsequent processing such as segmentation. The method is first theoretically derived and its conditions of applicability defined. Quantitative accuracy in the case of homogeneous objects as well as enhanced image quality for the imaging of complex biological samples are demonstrated through experiments at two synchrotron radiation facilities. The large range of applicability, the robustness against noise and the need for only one input image suggest a high potential for investigations in various research subjects.

  18. Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Assessment Using Dropsondes

    Science.gov (United States)

    Cecil, Daniel J.; Biswas, Sayak K.

    2018-01-01

    The Hurricane Imaging Radiometer (HIRAD) is an experimental C-band passive microwave radiometer designed to map the horizontal structure of surface wind speed fields in hurricanes. New data processing and customized retrieval approaches were developed after the 2015 Tropical Cyclone Intensity (TCI) experiment, which featured flights over Hurricanes Patricia, Joaquin, Marty, and the remnants of Tropical Storm Erika. These new approaches produced maps of surface wind speed that looked more realistic than those from previous campaigns. Dropsondes from the High Definition Sounding System (HDSS) that was flown with HIRAD on a WB-57 high altitude aircraft in TCI were used to assess the quality of the HIRAD wind speed retrievals. The root mean square difference between HIRAD-retrieved surface wind speeds and dropsonde-estimated surface wind speeds was 6.0 meters per second. The largest differences between HIRAD and dropsonde winds were from data points where storm motion during dropsonde descent compromised the validity of the comparisons. Accounting for this and for uncertainty in the dropsonde measurements themselves, we estimate the root mean square error for the HIRAD retrievals as around 4.7 meters per second. Prior to the 2015 TCI experiment, HIRAD had previously flown on the WB-57 for missions across Hurricanes Gonzalo (2014), Earl (2010), and Karl (2010). Configuration of the instrument was not identical to the 2015 flights, but the methods devised after the 2015 flights may be applied to that previous data in an attempt to improve retrievals from those cases.

  19. Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

    Science.gov (United States)

    Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

    2013-04-01

    Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

  20. Biomedical Applications of the Information-efficient Spectral Imaging Sensor (ISIS)

    Energy Technology Data Exchange (ETDEWEB)

    Gentry, S.M.; Levenson, R.

    1999-01-21

    The Information-efficient Spectral Imaging Sensor (ISIS) approach to spectral imaging seeks to bridge the gap between tuned multispectral and fixed hyperspectral imaging sensors. By allowing the definition of completely general spectral filter functions, truly optimal measurements can be made for a given task. These optimal measurements significantly improve signal-to-noise ratio (SNR) and speed, minimize data volume and data rate, while preserving classification accuracy. The following paper investigates the application of the ISIS sensing approach in two sample biomedical applications: prostate and colon cancer screening. It is shown that in these applications, two to three optimal measurements are sufficient to capture the majority of classification information for critical sample constituents. In the prostate cancer example, the optimal measurements allow 8% relative improvement in classification accuracy of critical cell constituents over a red, green, blue (RGB) sensor. In the colon cancer example, use of optimal measurements boost the classification accuracy of critical cell constituents by 28% relative to the RGB sensor. In both cases, optimal measurements match the performance achieved by the entire hyperspectral data set. The paper concludes that an ISIS style spectral imager can acquire these optimal spectral images directly, allowing improved classification accuracy over an RGB sensor. Compared to a hyperspectral sensor, the ISIS approach can achieve similar classification accuracy using a significantly lower number of spectral samples, thus minimizing overall sample classification time and cost.

  1. JAtlasView: a Java atlas-viewer for browsing biomedical 3D images and atlases.

    Science.gov (United States)

    Feng, Guangjie; Burton, Nick; Hill, Bill; Davidson, Duncan; Kerwin, Janet; Scott, Mark; Lindsay, Susan; Baldock, Richard

    2005-03-09

    Many three-dimensional (3D) images are routinely collected in biomedical research and a number of digital atlases with associated anatomical and other information have been published. A number of tools are available for viewing this data ranging from commercial visualization packages to freely available, typically system architecture dependent, solutions. Here we discuss an atlas viewer implemented to run on any workstation using the architecture neutral Java programming language. We report the development of a freely available Java based viewer for 3D image data, descibe the structure and functionality of the viewer and how automated tools can be developed to manage the Java Native Interface code. The viewer allows arbitrary re-sectioning of the data and interactive browsing through the volume. With appropriately formatted data, for example as provided for the Electronic Atlas of the Developing Human Brain, a 3D surface view and anatomical browsing is available. The interface is developed in Java with Java3D providing the 3D rendering. For efficiency the image data is manipulated using the Woolz image-processing library provided as a dynamically linked module for each machine architecture. We conclude that Java provides an appropriate environment for efficient development of these tools and techniques exist to allow computationally efficient image-processing libraries to be integrated relatively easily.

  2. Biomedical engineering and nanotechnology

    International Nuclear Information System (INIS)

    Pawar, S.H.; Khyalappa, R.J.; Yakhmi, J.V.

    2009-01-01

    This book is predominantly a compilation of papers presented in the conference which is focused on the development in biomedical materials, biomedical devises and instrumentation, biomedical effects of electromagnetic radiation, electrotherapy, radiotherapy, biosensors, biotechnology, bioengineering, tissue engineering, clinical engineering and surgical planning, medical imaging, hospital system management, biomedical education, biomedical industry and society, bioinformatics, structured nanomaterial for biomedical application, nano-composites, nano-medicine, synthesis of nanomaterial, nano science and technology development. The papers presented herein contain the scientific substance to suffice the academic directivity of the researchers from the field of biomedicine, biomedical engineering, material science and nanotechnology. Papers relevant to INIS are indexed separately

  3. Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

    Directory of Open Access Journals (Sweden)

    Lijuan Duan

    2017-01-01

    Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

  4. Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

    Science.gov (United States)

    Sahawneh, Saleem; Farrar, Spencer; Johnson, James; Jones, W. Linwood; Roberts, Jason; Biswas, Sayak; Cecil, Daniel

    2014-01-01

    Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

  5. A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

    Science.gov (United States)

    Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

    2010-01-01

    Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming

  6. NeuroTerrain--a client-server system for browsing 3D biomedical image data sets.

    Science.gov (United States)

    Gustafson, Carl; Bug, William J; Nissanov, Jonathan

    2007-02-05

    Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments. Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat) support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK), sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support development of non-interactive applications

  7. Coincident Aerosol and H2O Retrievals versus HSI Imager Field Campaign ReportH2O Retrievals versus HSI Imager Field Campaign Report

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Gail P. [National Oceanic and Atmospheric Administration (NOAA), Washington, DC (United States); Cipar, John [Air Force Research Lab. (AFRL), Wright-Patterson AFB, OH (United States); Armstrong, Peter S. [Air Force Research Lab. (AFRL), Wright-Patterson AFB, OH (United States); van den Bosch, J. [Air Force Research Lab. (AFRL), Wright-Patterson AFB, OH (United States)

    2016-05-01

    Two spectrally calibrated tarpaulins (tarps) were co-located at a fixed Global Positioning System (GPS) position on the gravel antenna field at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) site. Their placement was timed to coincide with the overflight of a new hyperspectral imaging satellite. The intention was to provide an analysis of the data obtained, including the measured and retrieved spectral albedos for the calibration tarps. Subsequently, a full suite of retrieved values of H2O column, and the aerosol overburden, were to be compared to those determined by alternate SGP ground truth assets. To the extent possible, the down-looking cloud images would be assessed against the all-sky images. Because cloud contamination above a certain level precludes the inversion processing of the satellite data, coupled with infrequent targeting opportunities, clear-sky conditions were imposed. The SGP site was chosen not only as a target of opportunity for satellite validation, but as perhaps the best coincident field measurement site, as established by DOE’s ARM Facility. The satellite team had every expectation of using the information obtained from the SGP to improve the inversion products for all subsequent satellite images, including the cloud and radiative models and parameterizations and, thereby, the performance assessment for subsequent and historic image collections. Coordinating with the SGP onsite team, four visits, all in 2009, to the Central Facility occurred: • June 6-8 (successful exploratory visit to plan tarp placements, etc.) • July 18-24 (canceled because of forecast for heavy clouds) • Sep 9-12 (ground tarps placed, onset of clouds) • Nov 7-9 (visit ultimately canceled because of weather predictions). As noted, in each instance, any significant overcast prediction precluded image collection from the satellite. Given the long task-scheduling procedures

  8. 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.

  9. Development of an Aerosol Opacity Retrieval Algorithm for Use with Multi-Angle Land Surface Images

    Science.gov (United States)

    Diner, D.; Paradise, S.; Martonchik, J.

    1994-01-01

    In 1998, the Multi-angle Imaging SpectroRadiometer (MISR) will fly aboard the EOS-AM1 spacecraft. MISR will enable unique methods for retrieving the properties of atmospheric aerosols, by providing global imagery of the Earth at nine viewing angles in four visible and near-IR spectral bands. As part of the MISR algorithm development, theoretical methods of analyzing multi-angle, multi-spectral data are being tested using images acquired by the airborne Advanced Solid-State Array Spectroradiometer (ASAS). In this paper we derive a method to be used over land surfaces for retrieving the change in opacity between spectral bands, which can then be used in conjunction with an aerosol model to derive a bound on absolute opacity.

  10. Optical image encryption using password key based on phase retrieval algorithm

    Science.gov (United States)

    Zhao, Tieyu; Ran, Qiwen; Yuan, Lin; Chi, Yingying; Ma, Jing

    2016-04-01

    A novel optical image encryption system is proposed using password key based on phase retrieval algorithm (PRA). In the encryption process, a shared image is taken as a symmetric key and the plaintext is encoded into the phase-only mask based on the iterative PRA. The linear relationship between the plaintext and ciphertext is broken using the password key, which can resist the known plaintext attack. The symmetric key and the retrieved phase are imported into the input plane and Fourier plane of 4f system during the decryption, respectively, so as to obtain the plaintext on the CCD. Finally, we analyse the key space of the password key, and the results show that the proposed scheme can resist a brute force attack due to the flexibility of the password key.

  11. Bio-degradable highly fluorescent conjugated polymer nanoparticles for bio-medical imaging applications.

    Science.gov (United States)

    Repenko, Tatjana; Rix, Anne; Ludwanowski, Simon; Go, Dennis; Kiessling, Fabian; Lederle, Wiltrud; Kuehne, Alexander J C

    2017-09-07

    Conjugated polymer nanoparticles exhibit strong fluorescence and have been applied for biological fluorescence imaging in cell culture and in small animals. However, conjugated polymer particles are hydrophobic and often chemically inert materials with diameters ranging from below 50 nm to several microns. As such, conjugated polymer nanoparticles cannot be excreted through the renal system. This drawback has prevented their application for clinical bio-medical imaging. Here, we present fully conjugated polymer nanoparticles based on imidazole units. These nanoparticles can be bio-degraded by activated macrophages. Reactive oxygen species induce scission of the conjugated polymer backbone at the imidazole unit, leading to complete decomposition of the particles into soluble low molecular weight fragments. Furthermore, the nanoparticles can be surface functionalized for directed targeting. The approach opens a wide range of opportunities for conjugated polymer particles in the fields of medical imaging, drug-delivery, and theranostics.Conjugated polymer nanoparticles have been applied for biological fluorescence imaging in cell culture and in small animals, but cannot readily be excreted through the renal system. Here the authors show fully conjugated polymer nanoparticles based on imidazole units that can be bio-degraded by activated macrophages.

  12. Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer

    Czech Academy of Sciences Publication Activity Database

    Malenovský, Z.; Homolová, L.; Zurita-Milla, R.; Lukeš, Petr; Kaplan, Věroslav; Hanuš, Jan; Gastellu-Etchegory, J.P.; Schaepman, M.E.

    2013-01-01

    Roč. 131, APR (2013), s. 85-102 ISSN 0034-4257 R&D Projects: GA MŠk(CZ) ED1.1.00/02.0073; GA MŠk(CZ) LM2010007 Institutional support: RVO:67179843 Keywords : Chlorophyll retrieval * Imaging spectroscopy * Continuum removal * Radiative transfer * PROSPECT * DART * Optical indices * Norway spruce * High spatial resolution * AISA Subject RIV: EH - Ecology, Behaviour Impact factor: 4.769, year: 2013

  13. Single-channel color image encryption using phase retrieve algorithm in fractional Fourier domain

    Science.gov (United States)

    Sui, Liansheng; Xin, Meiting; Tian, Ailing; Jin, Haiyan

    2013-12-01

    A single-channel color image encryption is proposed based on a phase retrieve algorithm and a two-coupled logistic map. Firstly, a gray scale image is constituted with three channels of the color image, and then permuted by a sequence of chaotic pairs generated by the two-coupled logistic map. Secondly, the permutation image is decomposed into three new components, where each component is encoded into a phase-only function in the fractional Fourier domain with a phase retrieve algorithm that is proposed based on the iterative fractional Fourier transform. Finally, an interim image is formed by the combination of these phase-only functions and encrypted into the final gray scale ciphertext with stationary white noise distribution by using chaotic diffusion, which has camouflage property to some extent. In the process of encryption and decryption, chaotic permutation and diffusion makes the resultant image nonlinear and disorder both in spatial domain and frequency domain, and the proposed phase iterative algorithm has faster convergent speed. Additionally, the encryption scheme enlarges the key space of the cryptosystem. Simulation results and security analysis verify the feasibility and effectiveness of this method.

  14. Hybrid phase retrieval algorithm for solving the twin image problem in in-line digital holography

    Science.gov (United States)

    Zhao, Jie; Wang, Dayong; Zhang, Fucai; Wang, Yunxin

    2010-10-01

    For the reconstruction in the in-line digital holography, there are three terms overlapping with each other on the image plane, named the zero order term, the real image and the twin image respectively. The unwanted twin image degrades the real image seriously. A hybrid phase retrieval algorithm is presented to address this problem, which combines the advantages of two popular phase retrieval algorithms. One is the improved version of the universal iterative algorithm (UIA), called the phase flipping-based UIA (PFB-UIA). The key point of this algorithm is to flip the phase of the object iteratively. It is proved that the PFB-UIA is able to find the support of the complicated object. Another one is the Fienup algorithm, which is a kind of well-developed algorithm and uses the support of the object as the constraint among the iteration procedure. Thus, by following the Fienup algorithm immediately after the PFB-UIA, it is possible to produce the amplitude and the phase distributions of the object with high fidelity. The primary simulated results showed that the proposed algorithm is powerful for solving the twin image problem in the in-line digital holography.

  15. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

    Directory of Open Access Journals (Sweden)

    Mei Yu

    2012-01-01

    Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

  16. Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

    Science.gov (United States)

    Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

    The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

  17. Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

    Directory of Open Access Journals (Sweden)

    Kompatsiaris Ioannis

    2004-01-01

    Full Text Available An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword and their relations in a human-centered fashion. When querying for a specific semantic object (or objects, the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.

  18. A preliminary assessment. Digital imaging storage and retrieval in the 1980s.

    Science.gov (United States)

    1984-01-01

    The current status of digital imaging storage and retrieval is described, as applied to both digitally created images and those converted from conventional films. Technologies that are beginning to play a role in digital image management--particularly, different configurations of Picture Archiving and Communications Systems (PACS)--are examined in terms of their stage of development, equipment, and operating costs. This assessment finds that the future success and diffusion of these systems will depend upon the diagnostic adequacy of digital images, improvements in image digitizing processes, and the availability of optical disk or other low-cost mass storage. In addition, the paper concludes that Medicare's prospective payment system will greatly influence the spread of this technology because of both the cost-saving incentives the system will place on health care professionals and the still-undetermined method of capital cost reimbursement.

  19. Phase retrieval for X-ray in-line phase contrast imaging

    International Nuclear Information System (INIS)

    Scattarella, F.; Bellotti, R.; Tangaro, S.; Gargano, G.; Giannini, C.

    2011-01-01

    A review article about phase retrieval problem in X-ray phase contrast imaging is presented. A simple theoretical framework of Fresnel diffraction imaging by X-rays is introduced. A review of the most important methods for phase retrieval in free-propagation-based X-ray imaging and a new method developed by our collaboration are shown. The proposed algorithm, Combined Mixed Approach (CMA) is based on a mixed transfer function and transport of intensity approach, and it requires at most an initial approximate estimate of the average phase shift introduced by the object as prior knowledge. The accuracy with which this initial estimate is known determines the convenience speed of algorithm. The new proposed algorithm is based on the retrieval of both the object phase and its complex conjugate. The results obtained by the algorithm on simulated data have shown that the obtained reconstructed phase maps are characterized by particularly low normalized mean square errors. The algorithm was also tested on noisy experimental phase contrast data, showing a good efficiency in recovering phase information and enhancing the visibility of details inside soft tissues.

  20. SOFTWARE FOR REGIONS OF INTEREST RETRIEVAL ON MEDICAL 3D IMAGES

    Directory of Open Access Journals (Sweden)

    G. G. Stromov

    2014-01-01

    Full Text Available Background. Implementation of software for areas of interest retrieval in 3D medical images is described in this article. It has been tested against large volume of model MRIs.Material and methods. We tested software against normal and pathological (severe multiple sclerosis model MRIs from tge BrainWeb resource. Technological stack is based on open-source cross-platform solutions. We implemented storage system on Maria DB (an open-sourced fork of MySQL with P/SQL extensions. Python 2.7 scripting was used for automatization of extract-transform-load operations. The computational core is written on Java 7 with Spring framework 3. MongoDB was used as a cache in the cluster of workstations. Maven 3 was chosen as a dependency manager and build system, the project is hosted at Github.Results. As testing on SSMU's LAN has showed, software has been developed is quite efficiently retrieves ROIs are matching for the morphological substratum on pathological MRIs.Conclusion. Automation of a diagnostic process using medical imaging allows to level down the subjective component in decision making and increase the availability of hi-tech medicine. Software has shown in the article is a complex solution for ROI retrieving and segmentation process on model medical images in full-automated mode.We would like to thank Robert Vincent for great help with consulting of usage the BrainWeb resource.

  1. STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

    Directory of Open Access Journals (Sweden)

    Muhammad Athoillah

    2015-03-01

    Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

  2. Unified modeling language and design of a case-based retrieval system in medical imaging.

    Science.gov (United States)

    LeBozec, C.; Jaulent, M. C.; Zapletal, E.; Degoulet, P.

    1998-01-01

    One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users. Images Figure 6 Figure 7 PMID:9929346

  3. PREFACE: 2nd International Conference and Young Scientist School ''Magnetic resonance imaging in biomedical research''

    Science.gov (United States)

    Naumova, A. V.; Khodanovich, M. Y.; Yarnykh, V. L.

    2016-02-01

    The Second International Conference and Young Scientist School ''Magnetic resonance imaging in biomedical research'' was held on the campus of the National Research Tomsk State University (Tomsk, Russia) on September 7-9, 2015. The conference was focused on magnetic resonance imaging (MRI) applications for biomedical research. The main goal was to bring together basic scientists, clinical researchers and developers of new MRI techniques to bridge the gap between clinical/research needs and advanced technological solutions. The conference fostered research and development in basic and clinical MR science and its application to health care. It also had an educational purpose to promote understanding of cutting-edge MR developments. The conference provided an opportunity for researchers and clinicians to present their recent theoretical developments, practical applications, and to discuss unsolved problems. The program of the conference was divided into three main topics. First day of the conference was devoted to educational lectures on the fundamentals of MRI physics and image acquisition/reconstruction techniques, including recent developments in quantitative MRI. The second day was focused on developments and applications of new contrast agents. Multinuclear and spectroscopic acquisitions as well as functional MRI were presented during the third day of the conference. We would like to highlight the main developments presented at the conference and introduce the prominent speakers. The keynote speaker of the conference Dr. Vasily Yarnykh (University of Washington, Seattle, USA) presented a recently developed MRI method, macromolecular proton fraction (MPF) mapping, as a unique tool for modifying image contrast and a unique tool for quantification of the myelin content in neural tissues. Professor Yury Pirogov (Lomonosov Moscow State University) described development of new fluorocarbon compounds and applications for biomedicine. Drs. Julia Velikina and Alexey

  4. Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging

    International Nuclear Information System (INIS)

    Esposito, M; Evans, P M; Wells, K; Anaxagoras, T; Konstantinidis, A C; Zheng, Y; Speller, R D; Allinson, N M

    2014-01-01

    Recently CMOS active pixels sensors (APSs) have become a valuable alternative to amorphous silicon and selenium flat panel imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However, despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ⩽1.9%. The uniformity of the image quality performance has been further investigated in a typical x-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practice. Finally, in order to compare the detection capability of this novel APS with the technology currently used (i.e. FPIs), theoretical evaluation of the detection quantum efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this

  5. Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging.

    Science.gov (United States)

    Esposito, M; Anaxagoras, T; Konstantinidis, A C; Zheng, Y; Speller, R D; Evans, P M; Allinson, N M; Wells, K

    2014-07-07

    Recently CMOS active pixels sensors (APSs) have become a valuable alternative to amorphous silicon and selenium flat panel imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However, despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ⩽1.9%. The uniformity of the image quality performance has been further investigated in a typical x-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practice. Finally, in order to compare the detection capability of this novel APS with the technology currently used (i.e. FPIs), theoretical evaluation of the detection quantum efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this

  6. Segmentation Technique for Image Indexing and Retrieval on Discrete Cosines Domain

    Directory of Open Access Journals (Sweden)

    Suhendro Yusuf Irianto

    2013-03-01

    Full Text Available This paper uses region growing segmentation technique to segment the Discrete Cosines (DC  image. The problem of content Based image retrieval (CBIR is the luck of accuracy in matching between image query and image in the database as it matches object and background in the same time.   This the reason previous CBIR techniques inaccurate and time consuming. The CBIR   based on the segmented region proposed in this work  separates object from background as CBIR need only match the object not the background.  By using region growing technique on DC image, it reduces the number of image       regions.    The proposed of recursive region growing is not new technique but its application on DC images to build    indexing keys is quite new and not yet presented by many     authors. The experimental results show  that the proposed methods on   segmented images present good precision which are higher than 0.60 on all classes . It can be concluded that  region growing segmented based CBIR more efficient    compare to DC images  in term of their precision 0.59 and 0.75, respectively. Moreover,  DC based CBIR  can save time and simplify algorithm compare to DCT images.

  7. A client/server system for Internet access to biomedical text/image databanks.

    Science.gov (United States)

    Thoma, G R; Long, L R; Berman, L E

    1996-01-01

    Internet access to mixed text/image databanks is finding application in the medical world. An example is a database of medical X-rays and associated data consisting of demographic, socioeconomic, physician's exam, medical laboratory and other information collected as part of a nationwide health survey conducted by the government. Another example is a collection of digitized cryosection images, CT and MR taken of cadavers as part of the National Library of Medicine's Visible Human Project. In both cases, the challenge is to provide access to both the image and the associated text for a wide end user community to create atlases, conduct epidemiological studies, to develop image-specific algorithms for compression, enhancement and other types of image processing, among many other applications. The databanks mentioned above are being created in prototype form. This paper describes the prototype system developed for the archiving of the data and the client software to enable a broad range of end users to access the archive, retrieve text and image data, display the data and manipulate the images. System design considerations include; data organization in a relational database management system with object-oriented extensions; a hierarchical organization of the image data by different resolution levels for different user classes; client design based on common hardware and software platforms incorporating SQL search capability, X Window, Motif and TAE (a development environment supporting rapid prototyping and management of graphic-oriented user interfaces); potential to include ultra high resolution display monitors as a user option; intuitive user interface paradigm for building complex queries; and contrast enhancement, magnification and mensuration tools for better viewing by the user.

  8. Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

    Science.gov (United States)

    Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

    2014-10-23

    A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

  9. Spectral unmixing algorithm for distributed endmembers with applications to biomedical imaging

    Science.gov (United States)

    Rahman, Sabbir A.

    1999-04-01

    Spectral unmixing algorithms tend to make the simplifying assumptions that each type of material in a spectral library may be represented by a single reference spectrum and that the mixing process is linear. While these assumptions are convenient in that they allow techniques of linear algebra to be used, they lack realism as each material type in a spectral image will in general emit a distribution of spectra while the mixing itself need not be linear. We describe a 'common sense' spectral unmixing algorithm for the general case where endmembers are described by arbitrary D-dimensional probability distribution and the mixing can be non-linear. As an application we outline an unsupervised procedure for deriving the fractional material content of every pixel in an image and identifying anomalies given no a priori knowledge. Accurate endmember distribution are obtained by first masking out impure pixels using locally normalized Sobel and Laplacian filters and then performing single-link hierarchical clustering on the pure pixels which remain. The most probable endmember decomposition for a given target spectrum is found by selecting an appropriate set of endmembers based on the target's immediate neighborhood, and performing a constrained maximum likelihood search over the space of fractional abundances. We also explain how the procedure may be applied to subpixel and anomaly detection. To illustrate our ideas the techniques described are applied to biomedical images throughout.

  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. Image formation mechanisms in scanning electron microscopy of carbon nanotubes, and retrieval of their intrinsic dimensions.

    Science.gov (United States)

    Jackman, H; Krakhmalev, P; Svensson, K

    2013-01-01

    We present a detailed analysis of the image formation mechanisms that are involved in the imaging of carbon nanotubes with scanning electron microscopy (SEM). We show how SEM images can be modelled by accounting for surface enhancement effects together with the absorption coefficient for secondary electrons, and the electron-probe shape. Images can then be deconvoluted, enabling retrieval of the intrinsic nanotube dimensions. Accurate estimates of their dimensions can thereby be obtained even for structures that are comparable to the electron-probe size (on the order of 2 nm). We also present a simple and robust model for obtaining the outer diameter of nanotubes without any detailed knowledge about the electron-probe shape. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. 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.

  13. 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.

  14. Unified modeling language and design of a case-based retrieval system in medical imaging.

    Science.gov (United States)

    LeBozec, C; Jaulent, M C; Zapletal, E; Degoulet, P

    1998-01-01

    One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users.

  15. Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

    Directory of Open Access Journals (Sweden)

    Weixun Zhou

    2017-05-01

    Full Text Available Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs for high-resolution remote sensing image retrieval (HRRSIR. To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

  16. Beamlines of the biomedical imaging and therapy facility at the Canadian light source - part 3

    Science.gov (United States)

    Wysokinski, Tomasz W.; Chapman, Dean; Adams, Gregg; Renier, Michel; Suortti, Pekka; Thomlinson, William

    2015-03-01

    The BioMedical Imaging and Therapy (BMIT) facility provides synchrotron-specific imaging and radiation therapy capabilities [1-4]. We describe here the Insertion Device (ID) beamline 05ID-2 with the beam terminated in the SOE-1 (Secondary Optical Enclosure) experimental hutch. This endstation is designed for imaging and therapy research primarily in animals ranging in size from mice to humans to horses, as well as tissue specimens including plants. Core research programs include human and animal reproduction, cancer imaging and therapy, spinal cord injury and repair, cardiovascular and lung imaging and disease, bone and cartilage growth and deterioration, mammography, developmental biology, gene expression research as well as the introduction of new imaging methods. The source for the ID beamline is a multi-pole superconducting 4.3 T wiggler [5]. The high field gives a critical energy over 20 keV. The high critical energy presents shielding challenges and great care must be taken to assess shielding requirements [6-9]. The optics in the POE-1 and POE-3 hutches [4,10] prepare a monochromatic beam that is 22 cm wide in the last experimental hutch SOE-1. The double crystal bent-Laue or Bragg monochromator, or the single-crystal K-edge subtraction (KES) monochromator provide an energy range appropriate for imaging studies in animals (20-100+ keV). SOE-1 (excluding the basement structure 4 m below the experimental floor) is 6 m wide, 5 m tall and 10 m long with a removable back wall to accommodate installation and removal of the Large Animal Positioning System (LAPS) capable of positioning and manipulating animals as large as a horse [11]. This end-station also includes a unique detector positioner with a vertical travel range of 4.9 m which is required for the KES imaging angle range of +12.3° to -7.3°. The detector positioner also includes moveable shielding integrated with the safety shutters. An update on the status of the other two end-stations at BMIT, described

  17. Beamlines of the biomedical imaging and therapy facility at the Canadian light source – part 3

    International Nuclear Information System (INIS)

    Wysokinski, Tomasz W.; Chapman, Dean; Adams, Gregg; Renier, Michel; Suortti, Pekka; Thomlinson, William

    2015-01-01

    The BioMedical Imaging and Therapy (BMIT) facility provides synchrotron-specific imaging and radiation therapy capabilities [1–4]. We describe here the Insertion Device (ID) beamline 05ID-2 with the beam terminated in the SOE-1 (Secondary Optical Enclosure) experimental hutch. This endstation is designed for imaging and therapy research primarily in animals ranging in size from mice to humans to horses, as well as tissue specimens including plants. Core research programs include human and animal reproduction, cancer imaging and therapy, spinal cord injury and repair, cardiovascular and lung imaging and disease, bone and cartilage growth and deterioration, mammography, developmental biology, gene expression research as well as the introduction of new imaging methods. The source for the ID beamline is a multi-pole superconducting 4.3 T wiggler [5]. The high field gives a critical energy over 20 keV. The high critical energy presents shielding challenges and great care must be taken to assess shielding requirements [6–9]. The optics in the POE-1 and POE-3 hutches [4,10] prepare a monochromatic beam that is 22 cm wide in the last experimental hutch SOE-1. The double crystal bent-Laue or Bragg monochromator, or the single-crystal K-edge subtraction (KES) monochromator provide an energy range appropriate for imaging studies in animals (20–100+ keV). SOE-1 (excluding the basement structure 4 m below the experimental floor) is 6 m wide, 5 m tall and 10 m long with a removable back wall to accommodate installation and removal of the Large Animal Positioning System (LAPS) capable of positioning and manipulating animals as large as a horse [11]. This end-station also includes a unique detector positioner with a vertical travel range of 4.9 m which is required for the KES imaging angle range of +12.3° to –7.3°. The detector positioner also includes moveable shielding integrated with the safety shutters. An update on the status of the other two end-stations at BMIT

  18. Beamlines of the biomedical imaging and therapy facility at the Canadian light source – part 3

    Energy Technology Data Exchange (ETDEWEB)

    Wysokinski, Tomasz W., E-mail: bmit@lightsource.ca [Canadian Light Source, Saskatoon, SK (Canada); Chapman, Dean [Anatomy and Cell Biology, University of Saskatchewan, Saskatoon, SK (Canada); Adams, Gregg [Western College of Veterinary Medicine, Saskatoon, SK (Canada); Renier, Michel [European Synchrotron Radiation Facility, Grenoble (France); Suortti, Pekka [Department of Physics, University of Helsinki (Finland); Thomlinson, William [Department of Physics, University of Saskatchewan, Saskatoon, SK (Canada)

    2015-03-01

    The BioMedical Imaging and Therapy (BMIT) facility provides synchrotron-specific imaging and radiation therapy capabilities [1–4]. We describe here the Insertion Device (ID) beamline 05ID-2 with the beam terminated in the SOE-1 (Secondary Optical Enclosure) experimental hutch. This endstation is designed for imaging and therapy research primarily in animals ranging in size from mice to humans to horses, as well as tissue specimens including plants. Core research programs include human and animal reproduction, cancer imaging and therapy, spinal cord injury and repair, cardiovascular and lung imaging and disease, bone and cartilage growth and deterioration, mammography, developmental biology, gene expression research as well as the introduction of new imaging methods. The source for the ID beamline is a multi-pole superconducting 4.3 T wiggler [5]. The high field gives a critical energy over 20 keV. The high critical energy presents shielding challenges and great care must be taken to assess shielding requirements [6–9]. The optics in the POE-1 and POE-3 hutches [4,10] prepare a monochromatic beam that is 22 cm wide in the last experimental hutch SOE-1. The double crystal bent-Laue or Bragg monochromator, or the single-crystal K-edge subtraction (KES) monochromator provide an energy range appropriate for imaging studies in animals (20–100+ keV). SOE-1 (excluding the basement structure 4 m below the experimental floor) is 6 m wide, 5 m tall and 10 m long with a removable back wall to accommodate installation and removal of the Large Animal Positioning System (LAPS) capable of positioning and manipulating animals as large as a horse [11]. This end-station also includes a unique detector positioner with a vertical travel range of 4.9 m which is required for the KES imaging angle range of +12.3° to –7.3°. The detector positioner also includes moveable shielding integrated with the safety shutters. An update on the status of the other two end-stations at BMIT

  19. Bag-of-features based medical image retrieval via multiple assignment and visual words weighting

    KAUST Repository

    Wang, Jingyan

    2011-11-01

    Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. © 2011 IEEE.

  20. 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.

  1. Biomedical nanotechnology.

    Science.gov (United States)

    Hurst, Sarah J

    2011-01-01

    This chapter summarizes the roles of nanomaterials in biomedical applications, focusing on those highlighted in this volume. A brief history of nanoscience and technology and a general introduction to the field are presented. Then, the chemical and physical properties of nanostructures that make them ideal for use in biomedical applications are highlighted. Examples of common applications, including sensing, imaging, and therapeutics, are given. Finally, the challenges associated with translating this field from the research laboratory to the clinic setting, in terms of the larger societal implications, are discussed.

  2. Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@ieee.org [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil); Giusti, Alessandro [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Pereira de Almeida, Andre; Parreira Nogueira, Liebert; Braz, Delson [Nuclear Engineering Program, Federal University of Rio de Janeiro, RJ (Brazil); Cely Barroso, Regina [Laboratory of Applied Physics on Biomedical Sciences, Physics Department, Rio de Janeiro State University, RJ (Brazil); Almeida, Carlos Eduardo de [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil)

    2011-12-21

    Synchrotron Radiation (SR) X-ray micro-Computed Tomography ({mu}CT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-{mu}CT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-{mu}CT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-{mu}CT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

  3. INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

    Directory of Open Access Journals (Sweden)

    H. Aasen

    2016-06-01

    Full Text Available Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

  4. Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval.

    Science.gov (United States)

    ElAdel, Asma; Zaied, Mourad; Amar, Chokri Ben

    2017-11-01

    Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization is still a challenging subject. In this paper, we propose a fast DCNN based on Fast Wavelet Transform (FWT), intelligent dropout and layer skipping. The proposed approach led to improve the image retrieval accuracy as well as the searching time. This was possible thanks to three key advantages: First, the rapid way to compute the features using FWT. Second, the proposed intelligent dropout method is based on whether or not a unit is efficiently and not randomly selected. Third, it is possible to classify the image using efficient units of earlier layer(s) and skipping all the subsequent hidden layers directly to the output layer. Our experiments were performed on CIFAR-10 and MNIST datasets and the obtained results are very promising. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Contributions on biomedical imaging, with a side-look at molecular imaging; Beitraege zur biomedizinischen Bildgebung mit einem Seitenblick auf Molecular Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Winkler, G. (ed.)

    2004-05-01

    This report is intended as a brief introduction to the emerging scientific field of biomedical imaging. The breadth of the subject is shown and future fields of research are indicated, which hopefully will serve as a guide to the identification of starting points for the research in 'Biomedical and/or Molecular Imaging' at the GSF-National Research Center for Environment and Health. The report starts with a brief sketch of the history. Then a - necessarily incomplete - list of research topics is presented. It is organized in two parts: the first one addresses medical imaging, and the second one is concerned with biological point aspects of the matter. (orig.) [German] In diesem Bericht sind einige Beitraege zum Gebiet 'Bildgebende Verfahren in Biologie und Medizin' zusammengestellt. Sie stammen saemtlich aus dem Institut fuer Biomathematik und Biometrie, IBB, am Forschungszentrum fuer Umwelt und Gesundheit, GSF, in Muenchen/Neuherberg, und seinem engeren Umfeld. Ziel war es, zu sichten, was in und um diesen Themenkreis herum an Wissen und sonstiger Kompetenz hier vorhanden ist. Einige am IBB etablierte Gebiete wie Roentgen-Mammographie oder funktionelle Magnetresonanztherapie wurden ausgeblendet. Der Grund ist die Fokussierung auf ein nicht exakt definierbares, neues Gebiet der Bildgebung, das unter dem Namen 'Molecular Imaging' kursiert und derzeit Furore macht macht. (orig.)

  6. Combining magnetic resonance imaging and ultrawideband radar: a new concept for multimodal biomedical imaging.

    Science.gov (United States)

    Thiel, F; Hein, M; Schwarz, U; Sachs, J; Seifert, F

    2009-01-01

    Due to the recent advances in ultrawideband (UWB) radar technologies, there has been widespread interest in the medical applications of this technology. We propose the multimodal combination of magnetic resonance (MR) and UWB radar for improved functional diagnosis and imaging. A demonstrator was established to prove the feasibility of the simultaneous acquisition of physiological events by magnetic resonance imaging and UWB radar. Furthermore, first in vivo experiments have been carried out, utilizing this new approach. Correlating the reconstructed UWB signals with physiological signatures acquired by simultaneous MR measurements, representing respiratory and myocardial displacements, gave encouraging results which can be improved by optimization of the MR data acquisition technique or the use of UWB antenna arrays to localize the motion in a focused area.

  7. Moderate Imaging Resolution Spectroradiometer (MODIS) Aerosol Optical Depth Retrieval for Aerosol Radiative Forcing

    Science.gov (United States)

    Asmat, A.; Jalal, K. A.; Ahmad, N.

    2018-02-01

    The present study uses the Aerosol Optical Depth (AOD) retrieved from Moderate Imaging Resolution Spectroradiometer (MODIS) data for the period from January 2011 until December 2015 over an urban area in Kuching, Sarawak. The results show the minimum AOD value retrieved from MODIS is -0.06 and the maximum value is 6.0. High aerosol loading with high AOD value observed during dry seasons and low AOD monitored during wet seasons. Multi plane regression technique used to retrieve AOD from MODIS (AODMODIS) and different statistics parameter is proposed by using relative absolute error for accuracy assessment in spatial and temporal averaging approach. The AODMODIS then compared with AOD derived from Aerosol Robotic Network (AERONET) Sunphotometer (AODAERONET) and the results shows high correlation coefficient (R2) for AODMODIS and AODAERONET with 0.93. AODMODIS used as an input parameters into Santa Barbara Discrete Ordinate Radiative Transfer (SBDART) model to estimate urban radiative forcing at Kuching. The observed hourly averaged for urban radiative forcing is -0.12 Wm-2 for top of atmosphere (TOA), -2.13 Wm-2 at the surface and 2.00 Wm-2 in the atmosphere. There is a moderate relationship observed between urban radiative forcing calculated using SBDART and AERONET which are 0.75 at the surface, 0.65 at TOA and 0.56 in atmosphere. Overall, variation in AOD tends to cause large bias in the estimated urban radiative forcing.

  8. Nonlinear approaches for phase retrieval in the Fresnel region for hard X-ray imaging

    International Nuclear Information System (INIS)

    Davidoiu, Valentina

    2013-01-01

    The development of highly coherent X-ray sources offers new possibilities to image biological structures at different scales exploiting the refraction of X-rays. The coherence properties of the third-generation synchrotron radiation sources enables efficient implementations of phase contrast techniques. One of the first measurements of the intensity variations due to phase contrast has been reported in 1995 at the European Synchrotron Radiation Facility (ESRF). Phase imaging coupled to tomography acquisition allows three dimensional imaging with an increased sensitivity compared to absorption CT. This technique is particularly attractive to image samples with low absorption constituents. Phase contrast has many applications, ranging from material science, paleontology, bone research to medicine and biology. Several methods to achieve X-ray phase contrast have been proposed during the last years. In propagation based phase contrast, the measurements are made at different sample-to-detector distances. While the intensity data can be acquired and recorded, the phase information of the signal has to be 'retrieved' from the modulus data only. Phase retrieval is thus an ill-posed nonlinear problem and regularization techniques including a priori knowledge are necessary to obtain stable solutions. Several phase recovery methods have been developed in recent years. These approaches generally formulate the phase retrieval problem as a linear one. Nonlinear treatments have not been much investigated. The main purpose of this work was to propose and evaluate new algorithms, in particularly taking into account the nonlinearity of the direct problem. In the first part of this work, we present a Landweber type nonlinear iterative scheme to solve the propagation based phase retrieval problem. This approach uses the analytic expression of the Frechet derivative of the phase-intensity relationship and of its adjoint, which are presented in detail. We also study the effect of

  9. Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

    Science.gov (United States)

    Yang, Mengzhao; Song, Wei; Mei, Haibin

    2017-07-23

    The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

  10. Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

    Directory of Open Access Journals (Sweden)

    Mengzhao Yang

    2017-07-01

    Full Text Available The rapid development of remote sensing (RS technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

  11. Three-dimensional fuse deposition modeling of tissue-simulating phantom for biomedical optical imaging

    Science.gov (United States)

    Dong, Erbao; Zhao, Zuhua; Wang, Minjie; Xie, Yanjun; Li, Shidi; Shao, Pengfei; Cheng, Liuquan; Xu, Ronald X.

    2015-12-01

    Biomedical optical devices are widely used for clinical detection of various tissue anomalies. However, optical measurements have limited accuracy and traceability, partially owing to the lack of effective calibration methods that simulate the actual tissue conditions. To facilitate standardized calibration and performance evaluation of medical optical devices, we develop a three-dimensional fuse deposition modeling (FDM) technique for freeform fabrication of tissue-simulating phantoms. The FDM system uses transparent gel wax as the base material, titanium dioxide (TiO2) powder as the scattering ingredient, and graphite powder as the absorption ingredient. The ingredients are preheated, mixed, and deposited at the designated ratios layer-by-layer to simulate tissue structural and optical heterogeneities. By printing the sections of human brain model based on magnetic resonance images, we demonstrate the capability for simulating tissue structural heterogeneities. By measuring optical properties of multilayered phantoms and comparing with numerical simulation, we demonstrate the feasibility for simulating tissue optical properties. By creating a rat head phantom with embedded vasculature, we demonstrate the potential for mimicking physiologic processes of a living system.

  12. 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.

  13. Evaluation of illumination system uniformity for wide-field biomedical hyperspectral imaging

    Science.gov (United States)

    Sawyer, Travis W.; Siri Luthman, A.; E Bohndiek, Sarah

    2017-04-01

    Hyperspectral imaging (HSI) systems collect both spatial (morphological) and spectral (chemical) information from a sample. HSI can provide sensitive analysis for biological and medical applications, for example, simultaneously measuring reflectance and fluorescence properties of a tissue, which together with structural information could improve early cancer detection and tumour characterisation. Illumination uniformity is a critical pre-condition for quantitative data extraction from an HSI system. Non-uniformity can cause glare, specular reflection and unwanted shading, which negatively impact statistical analysis procedures used to extract abundance of different chemical species. Here, we model and evaluate several illumination systems frequently used in wide-field biomedical imaging to test their potential for HSI. We use the software LightTools and FRED. The analysed systems include: a fibre ring light; a light emitting diode (LED) ring; and a diffuse scattering dome. Each system is characterised for spectral, spatial, and angular uniformity, as well as transfer efficiency. Furthermore, an approach to measure uniformity using the Kullback-Leibler divergence (KLD) is introduced. The KLD is generalisable to arbitrary illumination shapes, making it an attractive approach for characterising illumination distributions. Although the systems are quite comparable in their spatial and spectral uniformity, the most uniform angular distribution is achieved using a diffuse scattering dome, yielding a contrast of 0.503 and average deviation of 0.303 over a ±60° field of view with a 3.9% model error in the angular domain. Our results suggest that conventional illumination sources can be applied in HSI, but in the case of low light levels, bespoke illumination sources may offer improved performance.

  14. Skin Parameter Map Retrieval from a Dedicated Multispectral Imaging System Applied to Dermatology/Cosmetology

    Science.gov (United States)

    2013-01-01

    In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced. PMID:24159326

  15. Skin parameter map retrieval from a dedicated multispectral imaging system applied to dermatology/cosmetology.

    Science.gov (United States)

    Jolivot, Romuald; Benezeth, Yannick; Marzani, Franck

    2013-01-01

    In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced.

  16. Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows.

    Science.gov (United States)

    Depeursinge, Adrien; Vargas, Alejandro; Gaillard, Frédéric; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

    2012-01-01

    Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

  17. On Dimensionality Reduction for Indexing and Retrieval of Large-Scale Solar Image Data

    Science.gov (United States)

    Banda, J. M.; Angryk, R. A.; Martens, P. C. H.

    2013-03-01

    This work investigates the applicability of several dimensionality reduction techniques for large-scale solar data analysis. Using a solar benchmark dataset that contains images of multiple types of phenomena, we investigate linear and nonlinear dimensionality reduction methods in order to reduce our storage and processing costs and maintain a good representation of our data in a new vector space. We present a comparative analysis of several dimensionality reduction methods and different numbers of target dimensions by utilizing different classifiers in order to determine the degree of data dimensionality reduction that can be achieved with these methods, and to discover the method that is the most effective for solar images. After determining the optimal number of dimensions, we then present preliminary results on indexing and retrieval of the dimensionally reduced data.

  18. 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

  19. High-resolution fluorescence imaging for red and far-red SIF retrieval at leaf and canopy scales

    Science.gov (United States)

    Albert, L.; Alonso, L.; Cushman, K.; Kellner, J. R.

    2017-12-01

    New commercial-off-the-shelf imaging spectrometers promise the combination of high spatial and spectral resolution needed to retrieve solar induced fluorescence (SIF) at multiple wavelengths for individual plants and even individual leaves from low-altitude airborne or ground-based platforms. Data from these instruments could provide insight into the status of the photosynthetic apparatus at scales of space and time not observable from high-altitude and space-based platforms, and could support calibration and validation activities of current and forthcoming space missions to quantify SIF (OCO-2, OCO-3, FLEX, and GEOCARB). High-spectral resolution enables SIF retrieval from regions of strong telluric absorption by molecular oxygen, and also within numerous solar Fraunhofer lines in atmospheric windows not obscured by oxygen or water absorptions. Here we evaluate algorithms for SIF retrieval using a commercial-off-the-shelf diffraction-grating imaging spectrometer with a spectral sampling interval of 0.05 nm and a FWHM noise ratio using frame stacking and binning, and evaluate the consequences of these tradeoffs for SIF retrieval using three approaches: (1) oxygen-A and B retrieval; (2) retrieval based exclusively on solar Fraunhofer lines outside regions of telluric gas absorption; and (3) a retrieval based on the combination of these approaches. We evaluate the quality of these methods by comparison with coincident SIF spectra of leaves measured using a hand-held field spectrometer and short-pass filters that block incoming light at wavelengths > 650 or 700 nm. These filters enable a direct measurement of SIF emission > 650 or 700 nm that serves as a benchmark against which retrievals from reflectance spectra can be evaluated. We repeated this comparison between leaf-level SIF emission spectra and retrieved SIF emission spectra for leaves treated with drought stress and an herbicide (DCMU) that inhibits electron transfer from QA to QB of PSII.

  20. Preliminary results for X-ray phase contrast micro-tomography on the biomedical imaging beamline at SSRF

    International Nuclear Information System (INIS)

    Chen Rongchang; Du Guohao; Xie Honglan; Deng Biao; Tong Yajun; Hu Wen; Xue Yanling; Chen Can; Ren Yuqi; Zhou Guangzhao; Wang Yudan; Xiao Tiqiao; Xu Hongjie; Zhu Peiping

    2009-01-01

    With X-ray phase contrast micro-tomography(CT), one is able to obtain edge-enhanced image of internal structure of the samples. This allows visualization of the fine internal features for biology tissues, which is not able to resolve by conventional absorption CT. After preliminary modulation, monochromatic X-rays (8-72.5 keV) are available for experiments on the experimental station of the biomedical imaging beamline at Shanghai Synchrotron Radiation Facility(SSRF). In this paper, we report the in line phase contrast micro-tomography(IL-XPCT) of biology sample (locust) on the beamline. The reconstruct slice images and three dimensional rendering images of the locust were obtained, with clearly visible images of locus's wing, surface texture and internal tissue distribution. (authors)

  1. Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

    Science.gov (United States)

    Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

    Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

  2. Data Retrieval Algorithms for Validating the Optical Transient Detector and the Lightning Imaging Sensor

    Science.gov (United States)

    Koshak, W. J.; Blakeslee, R. J.; Bailey, J. C.

    2000-01-01

    A linear algebraic solution is provided for the problem of retrieving the location and time of occurrence of lightning ground strikes from an Advanced Lightning Direction Finder (ALDF) network. The ALDF network measures field strength, magnetic bearing, and arrival time of lightning radio emissions. Solutions for the plane (i.e., no earth curvature) are provided that implement all of these measurements. The accuracy of the retrieval method is tested using computer-simulated datasets, and the relative influence of bearing and arrival time data an the outcome of the final solution is formally demonstrated. The algorithm is sufficiently accurate to validate NASA:s Optical Transient Detector and Lightning Imaging Sensor. A quadratic planar solution that is useful when only three arrival time measurements are available is also introduced. The algebra of the quadratic root results are examined in detail to clarify what portions of the analysis region lead to fundamental ambiguities in sc)iirce location, Complex root results are shown to be associated with the presence of measurement errors when the lightning source lies near an outer sensor baseline of the ALDF network. For arbitrary noncollinear network geometries and in the absence of measurement errors, it is shown that the two quadratic roots are equivalent (no source location ambiguity) on the outer sensor baselines. The accuracy of the quadratic planar method is tested with computer-generated datasets, and the results are generally better than those obtained from the three-station linear planar method when bearing errors are about 2 deg.

  3. Learning binary code via PCA of angle projection for image retrieval

    Science.gov (United States)

    Yang, Fumeng; Ye, Zhiqiang; Wei, Xueqi; Wu, Congzhong

    2018-01-01

    With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.

  4. 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.

  5. 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.

  6. Rotation-robust math symbol recognition and retrieval using outer contours and image subsampling

    Science.gov (United States)

    Zhu, Siyu; Hu, Lei; Zanibbi, Richard

    2013-01-01

    This paper presents an unified recognition and retrieval system for isolated offline printed mathematical symbols for the first time. The system is based on nearest neighbor scheme and uses modified Turning Function and Grid Features to calculate the distance between two symbols based on Sum of Squared Difference. An unwrap process and an alignment process are applied to modify Turning Function to deal with the horizontal and vertical shift caused by the changing of staring point and rotation. This modified Turning Function make our system robust against rotation of the symbol image. The system obtains top-1 recognition rate of 96.90% and 47.27% Area Under Curve (AUC) of precision/recall plot on the InftyCDB-3 dataset. Experiment result shows that the system with modified Turning Function performs significantly better than the system with original Turning Function on the rotated InftyCDB-3 dataset.

  7. Hybridization of phase retrieval and off-axis digital holography for high resolution imaging of complex shape objects

    Science.gov (United States)

    Wang, Fengpeng; Wang, Dayong; Rong, Lu; Wang, Yunxin; Zhao, Jie

    2017-05-01

    In this paper, a hybrid method of phase retrieval and off-axis digital holography is proposed for imaging of the complex shape objects. Off-axis digital hologram and in-line hologram are recorded. The approximate phase distributions in the recording plane and object plane are obtained by constrained optimization approach from the off-axis hologram, and they are used as the initial value and the constraints in the phase retrieval for eliminating the twin image of in-line holography. Numerical simulations and optical experiments were carried out to validate the proposed method.

  8. First Results of AirMSPI Imaging Polarimetry at ORACLES 2016: Aerosol and Water Cloud Retrievals

    Science.gov (United States)

    van Harten, G.; Xu, F.; Diner, D. J.; Rheingans, B. E.; Tosca, M.; Seidel, F.; Bull, M. A.; Tkatcheva, I. N.; McDuffie, J. L.; Garay, M. J.; Jovanovic, V. M.; Cairns, B.; Alexandrov, M. D.; Hostetler, C. A.; Ferrare, R. A.; Burton, S. P.

    2017-12-01

    The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is a remote sensing instrument for the characterization of atmospheric aerosols and clouds. We will report on the successful deployment and resulting data products of AirMSPI in the 2016 field campaign as part of NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES). The goal of this five-year investigation is to study the impacts of African biomass burning aerosols on the radiative properties of the subtropical stratocumulus cloud deck over the southeast Atlantic Ocean. On board the NASA ER-2 high-altitude aircraft, AirMSPI collected over 4000 high-resolution images on 16 days. The observations are performed in two different modes: step-and-stare mode, in which a 10x10 km target is observed from 9 view angles at 10 m resolution, and sweep mode, where a 80-100 km along-track by 10-25 km across-track target is observed with continuously changing view angle between ±67° at 25 m resolution. This Level 1B2 calibrated and georectified imagery is publically available at the NASA Langley Atmospheric Science Data Center (ASDC)*. We will then describe the Level 2 water cloud products that will be made publically available, viz. optical depth and droplet size distribution, which are retrieved using a polarimetric algorithm. Finally, we will present the results of a recently developed research algorithm for the simultaneous retrieval of these cloud properties and above-cloud aerosols, and validations using collocated High Spectral Resolution Lidar-2 (HSRL-2) and Research Scanning Polarimeter (RSP) products. * https://eosweb.larc.nasa.gov/project/airmspi/airmspi_table

  9. 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.

  10. 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.

  11. 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.

  12. A new method for information retrieval in two-dimensional grating-based X-ray phase contrast imaging

    International Nuclear Information System (INIS)

    Wang Zhi-Li; Gao Kun; Chen Jian; Ge Xin; Tian Yang-Chao; Wu Zi-Yu; Zhu Pei-Ping

    2012-01-01

    Grating-based X-ray phase contrast imaging has been demonstrated to be an extremely powerful phase-sensitive imaging technique. By using two-dimensional (2D) gratings, the observable contrast is extended to two refraction directions. Recently, we have developed a novel reverse-projection (RP) method, which is capable of retrieving the object information efficiently with one-dimensional (1D) grating-based phase contrast imaging. In this contribution, we present its extension to the 2D grating-based X-ray phase contrast imaging, named the two-dimensional reverse-projection (2D-RP) method, for information retrieval. The method takes into account the nonlinear contributions of two refraction directions and allows the retrieval of the absorption, the horizontal and the vertical refraction images. The obtained information can be used for the reconstruction of the three-dimensional phase gradient field, and for an improved phase map retrieval and reconstruction. Numerical experiments are carried out, and the results confirm the validity of the 2D-RP method

  13. Computer-aided biomedical imaging and graphics physiological measurement and control. Proceedings of the Biological Engineering Society, 6th Nordic meeting, Aberdeen, July 1984

    International Nuclear Information System (INIS)

    Jordan, M.; Perkins, W.J.; Upton, J.; Markham, J.

    1984-01-01

    The proceedings of the Sixth Nordic Meeting of the Biological Engineering Society held in Aberdeen in July 1984 on computer-aided biomedical imaging and graphics and physiological measurement and control are presented. The summaries of the papers presented cover the use of computer imaging and graphics in ultrasonic imaging, nuclear medicine, radiology, biomedical radiography, tomography and NMR imaging. The papers on the use of computers in physiological measurement and control cover subject headings including computer-based instrumentation, transducers, monitoring and control, assessment and therapy, clinical measurement, blood flow and signal processing and analysis. (U.K.)

  14. Fractional Fourier domain optical image hiding using phase retrieval algorithm based on iterative nonlinear double random phase encoding.

    Science.gov (United States)

    Wang, Xiaogang; Chen, Wen; Chen, Xudong

    2014-09-22

    We present a novel image hiding method based on phase retrieval algorithm under the framework of nonlinear double random phase encoding in fractional Fourier domain. Two phase-only masks (POMs) are efficiently determined by using the phase retrieval algorithm, in which two cascaded phase-truncated fractional Fourier transforms (FrFTs) are involved. No undesired information disclosure, post-processing of the POMs or digital inverse computation appears in our proposed method. In order to achieve the reduction in key transmission, a modified image hiding method based on the modified phase retrieval algorithm and logistic map is further proposed in this paper, in which the fractional orders and the parameters with respect to the logistic map are regarded as encryption keys. Numerical results have demonstrated the feasibility and effectiveness of the proposed algorithms.

  15. Stress distribution retrieval in granular materials: A multi-scale model and digital image correlation measurements

    Science.gov (United States)

    Bruno, Luigi; Decuzzi, Paolo; Gentile, Francesco

    2016-01-01

    The promise of nanotechnology lies in the possibility of engineering matter on the nanoscale and creating technological interfaces that, because of their small scales, may directly interact with biological objects, creating new strategies for the treatment of pathologies that are otherwise beyond the reach of conventional medicine. Nanotechnology is inherently a multiscale, multiphenomena challenge. Fundamental understanding and highly accurate predictive methods are critical to successful manufacturing of nanostructured materials, bio/mechanical devices and systems. In biomedical engineering, and in the mechanical analysis of biological tissues, classical continuum approaches are routinely utilized, even if these disregard the discrete nature of tissues, that are an interpenetrating network of a matrix (the extra cellular matrix, ECM) and a generally large but finite number of cells with a size falling in the micrometer range. Here, we introduce a nano-mechanical theory that accounts for the-non continuum nature of bio systems and other discrete systems. This discrete field theory, doublet mechanics (DM), is a technique to model the mechanical behavior of materials over multiple scales, ranging from some millimeters down to few nanometers. In the paper, we use this theory to predict the response of a granular material to an external applied load. Such a representation is extremely attractive in modeling biological tissues which may be considered as a spatial set of a large number of particulate (cells) dispersed in an extracellular matrix. Possibly more important of this, using digital image correlation (DIC) optical methods, we provide an experimental verification of the model.

  16. Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela)

    Science.gov (United States)

    Yepez, Santiago; Laraque, Alain; Martinez, Jean-Michel; De Sa, Jose; Carrera, Juan Manuel; Castellanos, Bartolo; Gallay, Marjorie; Lopez, Jose L.

    2018-01-01

    In this study, 81 Landsat-8 scenes acquired from 2013 to 2015 were used to estimate the suspended sediment concentration (SSC) in the Orinoco River at its main hydrological station at Ciudad Bolivar, Venezuela. This gauging station monitors an upstream area corresponding to 89% of the total catchment area where the mean discharge is of 33,000 m3·s-1. SSC spatial and temporal variabilities were analyzed in relation to the hydrological cycle and to local geomorphological characteristics of the river mainstream. Three types of atmospheric correction models were evaluated to correct the Landsat-8 images: DOS, FLAASH, and L8SR. Surface reflectance was compared with monthly water sampling to calibrate a SSC retrieval model using a bootstrapping resampling. A regression model based on surface reflectance at the Near-Infrared wavelengths showed the best performance: R2 = 0.92 (N = 27) for the whole range of SSC (18 to 203 mg·l-1) measured at this station during the studied period. The method offers a simple new approach to estimate the SSC along the lower Orinoco River and demonstrates the feasibility and reliability of remote sensing images to map the spatiotemporal variability in sediment transport over large rivers.

  17. Phase retrieval in in-line x-ray phase contrast imaging based on total variation minimization

    NARCIS (Netherlands)

    Kostenko, A.; Batenburg, K.J.; Suhonen, H.; Offerman, S.E.; Van Vliet, L.J.

    2013-01-01

    State-of-the-art techniques for phase retrieval in propagation based X-ray phase-contrast imaging are aiming to solve an underdetermined linear system of equations. They commonly employ Tikhonov regularization – an L2-norm regularized deconvolution scheme – despite some of its limitations. We

  18. Encryption of QR code and grayscale image in interference-based scheme with high quality retrieval and silhouette problem removal

    Science.gov (United States)

    Qin, Yi; Wang, Hongjuan; Wang, Zhipeng; Gong, Qiong; Wang, Danchen

    2016-09-01

    In optical interference-based encryption (IBE) scheme, the currently available methods have to employ the iterative algorithms in order to encrypt two images and retrieve cross-talk free decrypted images. In this paper, we shall show that this goal can be achieved via an analytical process if one of the two images is QR code. For decryption, the QR code is decrypted in the conventional architecture and the decryption has a noisy appearance. Nevertheless, the robustness of QR code against noise enables the accurate acquisition of its content from the noisy retrieval, as a result of which the primary QR code can be exactly regenerated. Thereafter, a novel optical architecture is proposed to recover the grayscale image by aid of the QR code. In addition, the proposal has totally eliminated the silhouette problem existing in the previous IBE schemes, and its effectiveness and feasibility have been demonstrated by numerical simulations.

  19. Joseph F. Keithley Award For Advances in Measurement Science Lecture: Thermophotonic and Photoacoustic Radar Imaging Methods for Biomedical and Dental Imaging

    Science.gov (United States)

    Mandelis, Andreas

    2012-02-01

    In the first part of this presentation I will introduce thermophotonic radar imaging principles and techniques using chirped or binary-phase-coded modulation, methods which can break through the maximum detection depth/depth resolution limitations of conventional photothermal waves. Using matched-filter principles, a methodology enabling parabolic diffusion-wave energy fields to exhibit energy localization akin to propagating hyperbolic wave-fields has been developed. It allows for deconvolution of individual responses of superposed axially discrete sources, opening a new field: depth-resolved thermal coherence tomography. Several examples from dental enamel caries diagnostic imaging to metal subsurface defect thermographic imaging will be discussed. The second part will introduce the field of photoacoustic radar (or sonar) biomedical imaging. I will report the development of a novel biomedical imaging system that utilizes a continuous-wave laser source with a custom intensity modulation pattern, ultrasonic phased array for signal detection and processing coupled with a beamforming algorithm for reconstruction of photoacoustic correlation images. Utilization of specific chirped modulation waveforms (``waveform engineering'') achieves dramatic signal-to-noise-ratio increase and improved axial resolution over pulsed laser photoacoustics. The talk will conclude with aspects of instrumental sensitivity of the PA Radar to optical contrast using cancerous breast tissue-mimicking phantoms, super paramagnetic iron oxide nanoparticles as contrast enhancement agents and in-vivo tissue samples.

  20. 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.

  1. Coupled retrieval of aerosol properties and land surface reflection using the Airborne Multiangle SpectroPolarimetric Imager

    Science.gov (United States)

    Xu, Feng; van Harten, Gerard; Diner, David J.; Kalashnikova, Olga V.; Seidel, Felix C.; Bruegge, Carol J.; Dubovik, Oleg

    2017-07-01

    The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) has been flying aboard the NASA ER-2 high-altitude aircraft since October 2010. In step-and-stare operation mode, AirMSPI acquires radiance and polarization data in bands centered at 355, 380, 445, 470*, 555, 660*, 865*, and 935 nm (* denotes polarimetric bands). The imaged area covers about 10 km by 11 km and is typically observed from nine viewing angles between ±66° off nadir. For a simultaneous retrieval of aerosol properties and surface reflection using AirMSPI, an efficient and flexible retrieval algorithm has been developed. It imposes multiple types of physical constraints on spectral and spatial variations of aerosol properties as well as spectral and temporal variations of surface reflection. Retrieval uncertainty is formulated by accounting for both instrumental errors and physical constraints. A hybrid Markov-chain/adding-doubling radiative transfer (RT) model is developed to combine the computational strengths of these two methods in modeling polarized RT in vertically inhomogeneous and homogeneous media, respectively. Our retrieval approach is tested using 27 AirMSPI data sets with low to moderately high aerosol loadings, acquired during four NASA field campaigns plus one AirMSPI preengineering test flight. The retrieval results including aerosol optical depth, single-scattering albedo, aerosol size and refractive index are compared with Aerosol Robotic Network reference data. We identify the best angular combinations for 2, 3, 5, and 7 angle observations from the retrieval quality assessment of various angular combinations. We also explore the benefits of polarimetric and multiangular measurements and target revisits in constraining aerosol property and surface reflection retrieval.

  2. Enhanced x-ray imaging for a thin film cochlear implant with metal artefacts using phase retrieval tomography

    Energy Technology Data Exchange (ETDEWEB)

    Arhatari, B. D. [Department of Physics, La Trobe University, Victoria 3086 (Australia); ARC Centre of Excellence for Coherent X-ray Science, Melbourne (Australia); Harris, A. R.; Paolini, A. G. [School of Psychological Science, La Trobe University, Victoria 3086 (Australia); ARC Centre of Excellence for Electromaterials Science, Melbourne (Australia); Peele, A. G. [Department of Physics, La Trobe University, Victoria 3086 (Australia); ARC Centre of Excellence for Coherent X-ray Science, Melbourne (Australia); Australian Synchrotron, Victoria 3168 (Australia)

    2012-06-01

    Phase retrieval tomography has been successfully used to enhance imaging in systems that exhibit poor absorption contrast. However, when highly absorbing regions are present in a sample, so-called metal artefacts can appear in the tomographic reconstruction. We demonstrate that straightforward approaches for metal artefact reconstruction, developed in absorption contrast tomography, can be applied when using phase retrieval. Using a prototype thin film cochlear implant that has high and low absorption components made from iridium (or platinum) and plastic, respectively, we show that segmentation of the various components is possible and hence measurement of the electrode geometry and relative location to other regions of interest can be achieved.

  3. Lensless coherent imaging of proteins and supramolecular assemblies: Efficient phase retrieval by the charge flipping algorithm.

    Science.gov (United States)

    Dumas, Christian; van der Lee, Arie; Palatinus, Lukáš

    2013-05-01

    Diffractive imaging using the intense and coherent beam of X-ray free-electron lasers opens new perspectives for structural studies of single nanoparticles and biomolecules. Simulations were carried out to generate 3D oversampled diffraction patterns of non-crystalline biological samples, ranging from peptides and proteins to megadalton complex assemblies, and to recover their molecular structure from nanometer to near-atomic resolutions. Using these simulated data, we show here that iterative reconstruction methods based on standard and variant forms of the charge flipping algorithm, can efficiently solve the phase retrieval problem and extract a unique and reliable molecular structure. Contrary to the case of conventional algorithms, where the estimation and the use of a compact support is imposed, our approach does not require any prior information about the molecular assembly, and is amenable to a wide range of biological assemblies. Importantly, the robustness of this ab initio approach is illustrated by the fact that it tolerates experimental noise and incompleteness of the intensity data at the center of the speckle pattern. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry

    Science.gov (United States)

    Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

    2014-03-01

    This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

  5. 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

  6. Modern Trends in Imaging XI: Impedance Measurements in the Biomedical Sciences

    Directory of Open Access Journals (Sweden)

    Frederick D. Coffman

    2012-01-01

    Full Text Available Biological organisms and their component organs, tissues and cells have unique electrical impedance properties. Impedance properties often change with changes in structure, composition, and metabolism, and can be indicative of the onset and progression of disease states. Over the past 100 years, instruments and analytical methods have been developed to measure the impedance properties of biological specimens and to utilize these measurements in both clinical and basic science settings. This chapter will review the applications of impedance measurements in the biomedical sciences, from whole body analysis to impedance measurements of single cells and cell monolayers, and how cellular impedance measuring instruments can now be used in high throughput screening applications.

  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. A statistical retrieval of cloud parameters for the millimeter wave Ice Cloud Imager on board MetOp-SG

    Science.gov (United States)

    Prigent, Catherine; Wang, Die; Aires, Filipe; Jimenez, Carlos

    2017-04-01

    The meteorological observations from satellites in the microwave domain are currently limited to below 190 GHz. However, the next generation of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System-Second Generation-EPS-SG will carry an instrument, the Ice Cloud Imager (ICI), with frequencies up to 664 GHz, to improve the characterization of the cloud frozen phase. In this paper, a statistical retrieval of cloud parameters for ICI is developed, trained on a synthetic database derived from the coupling of a mesoscale cloud model and radiative transfer calculations. The hydrometeor profiles simulated with the Weather Research and Forecasting model (WRF) for twelve diverse European mid-latitude situations are used to simulate the brightness temperatures with the Atmospheric Radiative Transfer Simulator (ARTS) to prepare the retrieval database. The WRF+ARTS simulations have been compared to the Special Sensor Microwave Imager/Sounder (SSMIS) observations up to 190 GHz: this successful evaluation gives us confidence in the simulations at the ICI channels from 183 to 664 GHz. Statistical analyses have been performed on this simulated retrieval database, showing that it is not only physically realistic but also statistically satisfactory for retrieval purposes. A first Neural Network (NN) classifier is used to detect the cloud presence. A second NN is developed to retrieve the liquid and ice integrated cloud quantities over sea and land separately. The detection and retrieval of the hydrometeor quantities (i.e., ice, snow, graupel, rain, and liquid cloud) are performed with ICI-only, and with ICI combined with observations from the MicroWave Imager (MWI, with frequencies from 19 to 190 GHz, also on board MetOp-SG). The ICI channels have been optimized for the detection and quantification of the cloud frozen phases: adding the MWI channels improves the performance of the vertically integrated hydrometeor contents, especially for

  9. NeuroTerrain – a client-server system for browsing 3D biomedical image data sets

    Science.gov (United States)

    Gustafson, Carl; Bug, William J; Nissanov, Jonathan

    2007-01-01

    Background Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments. Results Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat) support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK), sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support development of non

  10. NeuroTerrain – a client-server system for browsing 3D biomedical image data sets

    Directory of Open Access Journals (Sweden)

    Nissanov Jonathan

    2007-02-01

    Full Text Available Abstract Background Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal. However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments. Results Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK, sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support

  11. 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.

  12. Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images – Year 3

    Data.gov (United States)

    National Aeronautics and Space Administration — To develop and utilize advanced image analysis techniques to maximize the resolution and utility of medical imaging methods being used during spaceflight. We have...

  13. Retrieval and Validation of Aerosol Optical Properties over East Asia from TANSO-Cloud and Aerosol Imager

    Science.gov (United States)

    Lee, Sanghee; Kim, Jhoon; Kim, Mijin; Choi, Myungje; Go, Sujung; Lim, HyunKwang; Ou, Mi-Lim; Goo, Tae-Young; Yokota, Tatsuya

    2015-04-01

    Aerosol is a significant component on air quality and climate change. In particular, spatial and temporal distribution of aerosol shows large variability over East Asia, thus has large effect in retrieving carbon dioxide from Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). An aerosol retrieval algorithm was developed from TANSO- Cloud and Aerosol Imager (CAI) onboard the GOSAT. The algorithm retrieves aerosol optical depth (AOD), size distribution of aerosol, and aerosol type in 0.1 degree grid resolution and surface reflectance was estimated using the clear sky composite method. To test aerosol absorptivity, the reflectance difference method was considered using channels of TANSO-CAI. In this study, the retrieved aerosol optical depth (AOD) was compared with those of Aerosol Robotic NETwork (AERONET) and MODerate resolution Imaging Sensor (MODIS) dataset from September 2011 and August 2014. Comparisons of AODs between AERONET and CAI show the reasonably good correlation with correlation coefficient of 0.77 and regression slope of 0.87 for the whole period. Moreover, those between MODIS and CAI for the same period show correlations with correlation coefficient of 0.7 ~ 0.9 and regression slope of 0.7 ~ 1.2, depending on season and comparison regions however, the largest error source in aerosol retrieval has been surface reflectance. Over ocean and some Land, surface reflectance tends to be overestimated, and thereby CAI-AOD tends to be underestimated. Based on the results with CAI algorithm developed, the algorithm is continuously improved for better performance.

  14. 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.

  15. A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

    Science.gov (United States)

    Marin, Carlo; Callegari, Mattia; Notarnicola, Claudia

    2016-10-01

    by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., "Retrieval of wet snow by means of multitemporal SAR data," in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., "Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions", EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., "A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., "Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images," in IEEE Transactions on

  16. Advances in Biomedical Imaging, Bioengineering, and Related Technologies for the Development of Biomarkers of Pancreatic Disease: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases and National Institute of Biomedical Imaging and Bioengineering Workshop.

    Science.gov (United States)

    Kelly, Kimberly A; Hollingsworth, Michael A; Brand, Randall E; Liu, Christina H; Singh, Vikesh K; Srivastava, Sudhir; Wasan, Ajay D; Yadav, Dhiraj; Andersen, Dana K

    2015-11-01

    A workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institute of Biomedical Imaging and Bioengineering focused on research gaps and opportunities in the development of new biomarkers of pancreatic disease. The session was held on July 22, 2015, and structured into 6 sessions: 1) Introduction and Overview; 2) Keynote Address; 3) New Approaches to the Diagnosis of Chronic Pancreatitis; 4) Biomarkers of Pain and Inflammation; 5) New Approaches to the Detection of Pancreatic Cancer; and 6) Shed Exosomes, Shed Cells, and Shed Proteins. Recent advances in the fields of pancreatic imaging, functional markers of pancreatic disease, proteomics, molecular and cellular imaging, and detection of circulating cancer cells and exosomes were reviewed. Knowledge gaps and research needs were highlighted. The development of new methods for the noninvasive determination of pancreatic pathology; the use of cellular markers of pancreatic function, inflammation, pain, and malignancy; and the refinement of methods to identify cells and cellular constituents of pancreatic cancer were discussed. The further refinement of sophisticated technical methods and the need for clinical studies to validate these new approaches in large-scale studies of patients at risk for the development of pancreatic disease were repeatedly emphasized.

  17. Asymmetric double-image encryption method by using iterative phase retrieval algorithm in fractional Fourier transform domain

    Science.gov (United States)

    Sui, Liansheng; Lu, Haiwei; Ning, Xiaojuan; Wang, Yinghui

    2014-02-01

    A double-image encryption scheme is proposed based on an asymmetric technique, in which the encryption and decryption processes are different and the encryption keys are not identical to the decryption ones. First, a phase-only function (POF) of each plain image is retrieved by using an iterative process and then encoded into an interim matrix. Two interim matrices are directly modulated into a complex image by using the convolution operation in the fractional Fourier transform (FrFT) domain. Second, the complex image is encrypted into the gray scale ciphertext with stationary white-noise distribution by using the FrFT. In the encryption process, three random phase functions are used as encryption keys to retrieve the POFs of plain images. Simultaneously, two decryption keys are generated in the encryption process, which make the optical implementation of the decryption process convenient and efficient. The proposed encryption scheme has high robustness to various attacks, such as brute-force attack, known plaintext attack, cipher-only attack, and specific attack. Numerical simulations demonstrate the validity and security of the proposed method.

  18. A Study on Retrieval Algorithm of Black Water Aggregation in Taihu Lake Based on HJ-1 Satellite Images

    International Nuclear Information System (INIS)

    Lei, Zou; Bing, Zhang; Junsheng, Li; Qian, Shen; Fangfang, Zhang; Ganlin, Wang

    2014-01-01

    The phenomenon of black water aggregation (BWA) occurs in inland water when massive algal bodies aggregate, die, and react with the toxic sludge in certain climate conditions to deprive the water of oxygen. This process results in the deterioration of water quality and damage to the ecosystem. Because charge coupled device (CCD) camera data from the Chinese HJ environmental satellite shows high potential in monitoring BWA, we acquired four HJ-CCD images of Taihu Lake captured during 2009 to 2011 to study this phenomenon. The first study site was selected near the Shore of Taihu Lake. We pre-processed the HJ-CCD images and analyzed the digital number (DN) gray values in the research area and in typical BWA areas. The results show that the DN values of visible bands in BWA areas are obviously lower than those in the research areas. Moreover, we developed an empirical retrieving algorithm of BWA based on the DN mean values and variances of research areas. Finally, we tested the accuracy of this empirical algorithm. The retrieving accuracies were89.9%, 58.1%, 73.4%, and 85.5%, respectively, which demonstrates the efficiency of empirical algorithm in retrieving the approximate distributions of BWA

  19. Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra

    Science.gov (United States)

    Jeong, Myeong-Jae; Hsu, N. Christina; Kwiatkowska, Ewa J.; Franz, Bryan A.; Meister, Gerhard; Salustro, Clare E.

    2012-01-01

    The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.

  20. Combined X-ray CT and mass spectrometry for biomedical imaging applications

    NARCIS (Netherlands)

    Schioppa Jr, E.; Ellis, S.; Bruinen, A.L.; Visser, J.; Heeren, R.M.A.; Uher, J.; Koffeman, E.

    2014-01-01

    Imaging technologies play a key role in many branches of science, especially in biology and medicine. They provide an invaluable insight into both internal structure and processes within a broad range of samples. There are many techniques that allow one to obtain images of an object. Different