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Sample records for hierarchical image feature

  1. Learning Hierarchical Feature Extractors for Image Recognition

    Science.gov (United States)

    2012-09-01

    Learning Hierarchical Feature Extractors For Image Recognition by Y-Lan Boureau A dissertation submitted in partial fulfillment of the requirements...DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Learning Hierarchical Feature Extractors For Image Recognition 5a. CONTRACT...pooling for all weighting schemes. With average pooling, weighting by the square root of the cluster weight performs best. P = 16 configuration space

  2. Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

    Science.gov (United States)

    Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

    2013-04-01

    In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

  3. Hierarchical Multi-modal Image Registration by Learning Common Feature Representations.

    Science.gov (United States)

    Ge, Hongkun; Wu, Guorong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    2015-10-05

    Mutual information (MI) has been widely used for registering images with different modalities. Since most inter-modality registration methods simply estimate deformations in a local scale, but optimizing MI from the entire image, the estimated deformations for certain structures could be dominated by the surrounding unrelated structures. Also, since there often exist multiple structures in each image, the intensity correlation between two images could be complex and highly nonlinear, which makes global MI unable to precisely guide local image deformation. To solve these issues, we propose a hierarchical inter-modality registration method by robust feature matching. Specifically, we first select a small set of key points at salient image locations to drive the entire image registration. Since the original image features computed from different modalities are often difficult for direct comparison, we propose to learn their common feature representations by projecting them from their native feature spaces to a common space, where the correlations between corresponding features are maximized. Due to the large heterogeneity between two high-dimension feature distributions, we employ Kernel CCA (Canonical Correlation Analysis) to reveal such non-linear feature mappings. Then, our registration method can take advantage of the learned common features to reliably establish correspondences for key points from different modality images by robust feature matching. As more and more key points take part in the registration, our hierarchical feature-based image registration method can efficiently estimate the deformation pathway between two inter-modality images in a global to local manner. We have applied our proposed registration method to prostate CT and MR images, as well as the infant MR brain images in the first year of life. Experimental results show that our method can achieve more accurate registration results, compared to other state-of-the-art image registration

  4. Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

    Science.gov (United States)

    Fesharaki, Nooshin Jafari; Pourghassem, Hossein

    2013-07-01

    Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

  5. Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching.

    Science.gov (United States)

    Kim, Minjeong; Wu, Guorong; Shen, Dinggang

    2012-01-01

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The

  6. Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

    Science.gov (United States)

    Tuia, Devis; Flamary, Rémi; Courty, Nicolas

    2015-07-01

    In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.

  7. Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement

    Science.gov (United States)

    Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan

    2017-03-01

    Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.

  8. Hierarchical image feature extraction by an irregular pyramid of polygonal partitions

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2008-01-01

    We present an algorithmic framework for hierarchical image segmentation and feature extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions of the original image. This multiscale hierarchy forms the basis for object-oriented image analysis. The framework incorporates the Gestalt principles of visual perception, such as proximity and closure, and exploits spectral and textural similarities of polygonal partitions, while iteratively grouping them until dissimilarity criteria are exceeded. Seed polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on the top of detected spectral discontinuities (such as edges), which form a network of constraints for the Delaunay triangulation. The image is then represented as a spatial network in the form of a graph with vertices corresponding to the polygonal partitions and edges reflecting their relations. The iterative agglomeration of partitions into object-oriented segments is formulated as Minimum Spanning Tree (MST) construction. An important characteristic of the approach is that the agglomeration of polygonal partitions is constrained by the detected edges; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects. The constructed partitions and their spatial relations are characterized using spectral, textural and structural features based on proximity graphs. The framework allows searching for object-oriented features of interest across multiple levels of details of the built hierarchy and can be generalized to the multi-criteria MST to account for multiple criteria important for an application.

  9. Feature Selection in Hierarchical Feature Spaces

    OpenAIRE

    Ristoski, Petar; Paulheim, Heiko

    2014-01-01

    Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hier...

  10. MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA

    Directory of Open Access Journals (Sweden)

    Z. Zhang

    2013-05-01

    Full Text Available Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method.

  11. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  12. Hierarchical image classification in the bioscience literature.

    Science.gov (United States)

    Kim, Daehyun; Yu, Hong

    2009-11-14

    Our previous work has shown that images appearing in bioscience articles can be classified into five types: Gel-Image, Image-of-Thing, Graph, Model, and Mix. For this paper, we explored and analyzed features strongly associated with each image type and developed a hierarchical image classification approach for classifying an image into one of the five types. First, we applied texture features to separate images into two groups: 1) a texture group comprising Gel Image, Image-of-Thing, and Mix, and 2) a non-texture group comprising Graph and Model. We then applied entropy, skewness, and uniformity for the first group, and edge difference, uniformity, and smoothness for the second group to classify images into specific types. Our results show that hierarchical image classification accurately divided images into the two groups during the initial classification and that the overall accuracy of the image classification was higher than that of our previous approach. In particular, the recall of hierarchical image classification was greatly improved due to the high accuracy of the initial classification.

  13. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  14. Image Search Reranking With Hierarchical Topic Awareness.

    Science.gov (United States)

    Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

    2015-10-01

    With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

  15. A hierarchical SVG image abstraction layer for medical imaging

    Science.gov (United States)

    Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

    2010-03-01

    As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

  16. A hierarchical classification scheme of psoriasis images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...

  17. Human Activity Recognition Using Hierarchically-Mined Feature Constellations

    NARCIS (Netherlands)

    Oikonomopoulos, A.; Pantic, Maja

    In this paper we address the problem of human activity modelling and recognition by means of a hierarchical representation of mined dense spatiotemporal features. At each level of the hierarchy, the proposed method selects feature constellations that are increasingly discriminative and

  18. Using PSO-Based Hierarchical Feature Selection Algorithm

    Directory of Open Access Journals (Sweden)

    Zhiwei Ji

    2014-01-01

    Full Text Available Hepatocellular carcinoma (HCC is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

  19. Hierarchical content-based image retrieval by dynamic indexing and guided search

    Science.gov (United States)

    You, Jane; Cheung, King H.; Liu, James; Guo, Linong

    2003-12-01

    This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

  20. Hierarchical imaging of the human knee

    Science.gov (United States)

    Schulz, Georg; Götz, Christian; Deyhle, Hans; Müller-Gerbl, Magdalena; Zanette, Irene; Zdora, Marie-Christine; Khimchenko, Anna; Thalmann, Peter; Rack, Alexander; Müller, Bert

    2016-10-01

    Among the clinically relevant imaging techniques, computed tomography (CT) reaches the best spatial resolution. Sub-millimeter voxel sizes are regularly obtained. For investigations on true micrometer level lab-based μCT has become gold standard. The aim of the present study is the hierarchical investigation of a human knee post mortem using hard X-ray μCT. After the visualization of the entire knee using a clinical CT with a spatial resolution on the sub-millimeter range, a hierarchical imaging study was performed using a laboratory μCT system nanotom m. Due to the size of the whole knee the pixel length could not be reduced below 65 μm. These first two data sets were directly compared after a rigid registration using a cross-correlation algorithm. The μCT data set allowed an investigation of the trabecular structures of the bones. The further reduction of the pixel length down to 25 μm could be achieved by removing the skin and soft tissues and measuring the tibia and the femur separately. True micrometer resolution could be achieved after extracting cylinders of several millimeters diameters from the two bones. The high resolution scans revealed the mineralized cartilage zone including the tide mark line as well as individual calcified chondrocytes. The visualization of soft tissues including cartilage, was arranged by X-ray grating interferometry (XGI) at ESRF and Diamond Light Source. Whereas the high-energy measurements at ESRF allowed the simultaneous visualization of soft and hard tissues, the low-energy results from Diamond Light Source made individual chondrocytes within the cartilage visual.

  1. Neutrosophic Features for Image Retrieval

    Directory of Open Access Journals (Sweden)

    A.A. Salama

    2016-12-01

    Full Text Available The goal of an Image Retrieval System is to retrieve images that are relevant to the user's request from a large image collection. In this paper, we present texture features for images embedded in the neutrosophic domain. The aim is to extract a set of features to represent the content of each image in the training database to be used for the purpose of retrieving images from the database similar to the image under consideration.

  2. Abdominal tuberculosis: Imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Jose M. [Department of Radiology, Hospital de S. Joao, Porto (Portugal)]. E-mail: jmpjesus@yahoo.com; Madureira, Antonio J. [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Vieira, Alberto [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Ramos, Isabel [Department of Radiology, Hospital de S. Joao, Porto (Portugal)

    2005-08-01

    Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis.

  3. Multimodal X-ray Imaging of Hierarchical Materials

    DEFF Research Database (Denmark)

    Birkbak, Mie Elholm

    2017-01-01

    Hierarchical materials – materials displaying distinct structural features on multiple different length scales - are found in abundance in nature. Bone is an excellent example of a hierarchical material, combining mineral platelets, water and organic molecules to an intricate 3D structure. Well-d...

  4. Fractal image perception provides novel insights into hierarchical cognition.

    Science.gov (United States)

    Martins, M J; Fischmeister, F P; Puig-Waldmüller, E; Oh, J; Geissler, A; Robinson, S; Fitch, W T; Beisteiner, R

    2014-08-01

    Hierarchical structures play a central role in many aspects of human cognition, prominently including both language and music. In this study we addressed hierarchy in the visual domain, using a novel paradigm based on fractal images. Fractals are self-similar patterns generated by repeating the same simple rule at multiple hierarchical levels. Our hypothesis was that the brain uses different resources for processing hierarchies depending on whether it applies a "fractal" or a "non-fractal" cognitive strategy. We analyzed the neural circuits activated by these complex hierarchical patterns in an event-related fMRI study of 40 healthy subjects. Brain activation was compared across three different tasks: a similarity task, and two hierarchical tasks in which subjects were asked to recognize the repetition of a rule operating transformations either within an existing hierarchical level, or generating new hierarchical levels. Similar hierarchical images were generated by both rules and target images were identical. We found that when processing visual hierarchies, engagement in both hierarchical tasks activated the visual dorsal stream (occipito-parietal cortex, intraparietal sulcus and dorsolateral prefrontal cortex). In addition, the level-generating task specifically activated circuits related to the integration of spatial and categorical information, and with the integration of items in contexts (posterior cingulate cortex, retrosplenial cortex, and medial, ventral and anterior regions of temporal cortex). These findings provide interesting new clues about the cognitive mechanisms involved in the generation of new hierarchical levels as required for fractals. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Hierarchical Factoring Based On Image Analysis And Orthoblique Rotations.

    Science.gov (United States)

    Stankov, L

    1979-07-01

    The procedure for hierarchical factoring suggested by Schmid and Leiman (1957) is applied within the framework of image analysis and orthoblique rotational procedures. It is shown that this approach necessarily leads to correlated higher order factors. Also, one can obtain a smaller number of factors than produced by typical hierarchical procedures.

  6. A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

    Science.gov (United States)

    An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

    2017-03-01

    Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

  7. Hierarchical clustering of RGB surface water images based on MIA ...

    African Journals Online (AJOL)

    Thus characterised images were partitioned into clusters of similar images using hierarchical clustering. The best defined clusters were obtained when the Ward's method was applied. Images were partitioned into the 2 main clusters in terms of similar colours of displayed objects. Each main cluster was further partitioned ...

  8. Imaging features of thalassemia

    Energy Technology Data Exchange (ETDEWEB)

    Tunaci, M.; Tunaci, A.; Engin, G.; Oezkorkmaz, B.; Acunas, G.; Acunas, B. [Dept. of Radiology, Istanbul Univ. (Turkey); Dincol, G. [Dept. of Internal Medicine, Istanbul Univ. (Turkey)

    1999-07-01

    Thalassemia is a kind of chronic, inherited, microcytic anemia characterized by defective hemoglobin synthesis and ineffective erythropoiesis. In all thalassemias clinical features that result from anemia, transfusional, and absorptive iron overload are similar but vary in severity. The radiographic features of {beta}-thalassemia are due in large part to marrow hyperplasia. Markedly expanded marrow space lead to various skeletal manifestations including spine, skull, facial bones, and ribs. Extramedullary hematopoiesis (ExmH), hemosiderosis, and cholelithiasis are among the non-skeletal manifestations of thalassemia. The skeletal X-ray findings show characteristics of chronic overactivity of the marrow. In this article both skeletal and non-skeletal manifestations of thalassemia are discussed with an overview of X-ray findings, including MRI and CT findings. (orig.)

  9. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

    Science.gov (United States)

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2014-11-01

    For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.

    Science.gov (United States)

    Zhao, Feng; Qiao, Lishan; Shi, Feng; Yap, Pew-Thian; Shen, Dinggang

    2016-08-17

    Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from different imaging modalities. Specifically, we integrate the Fisher discriminant criterion and local correlation information into the canonical correlation analysis (CCA) framework, giving a new feature fusion method, called Supervised Local CCA (SL-CCA), which caters specifically to local and global multimodal features. To alleviate the neighborhood selection problem associated with SL-CCA, we further propose a hierarchical SL-CCA (HSL-CCA), by performing SL-CCA with the gradually varying neighborhood sizes. Extensive experiments on the multimodal ABIDE database show that the proposed method achieves superior performance. In addition, based on feature weight analysis, we found that only a few specific brain regions play active roles in ASD diagnosis. These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD.

  11. Imaging features of aggressive angiomyxoma

    Energy Technology Data Exchange (ETDEWEB)

    Jeyadevan, N.N.; Sohaib, S.A.A.; Thomas, J.M.; Jeyarajah, A.; Shepherd, J.H.; Fisher, C

    2003-02-01

    AIM: To describe the imaging features of aggressive angiomyxoma in a rare benign mesenchymal tumour most frequently arising from the perineum in young female patients. MATERIALS AND METHODS: We reviewed the computed tomography (CT) and magnetic resonance (MR) imaging features of patients with aggressive angiomyxoma who were referred to our hospital. The imaging features were correlated with clinical information and pathology in all patients. RESULTS: Four CT and five MR studies were available for five patients (all women, mean age 39, range 24-55). Three patients had recurrent tumour at follow-up. CT and MR imaging demonstrated a well-defined mass-displacing adjacent structures. The tumour was of low attenuation relative to muscle on CT. On MR, the tumour was isointense relative to muscle on T1-weighted image, hyperintense on T2-weighted image and enhanced avidly after gadolinium contrast with a characteristic 'swirled' internal pattern. MR imaging demonstrates the extent of the tumour and its relation to the pelvic floor. Recurrent tumour has a similar appearance to the primary lesion. CONCLUSION: The MR appearances of aggressive angiomyxomas are characteristic, and the diagnosis should be considered in any young woman presenting with a well-defined mass arising from the perineum. Jeyadevan, N. N. etal. (2003). Clinical Radiology58, 157--162.

  12. Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex

    Science.gov (United States)

    Lu, Pei; Xu, Zhiyong; Yu, Huapeng; Chang, Yongxin; Fu, Chengyu; Shao, Jianxin

    2012-11-01

    According to models of object recognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then object recognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex.

  13. Architecture of the parallel hierarchical network for fast image recognition

    Science.gov (United States)

    Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

    2016-09-01

    Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

  14. Iris Image Classification Based on Hierarchical Visual Codebook.

    Science.gov (United States)

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

  15. Hierarchical mask creation for intelligent image coding using saliency maps

    Directory of Open Access Journals (Sweden)

    Radoslav Vargic

    2016-12-01

    Full Text Available In this paper we analyze basic mask creation methods for intelligent image coding using saliency maps. For saliency maps based image coding we use specific extension of SPIHT algorithm called SM SPIHT related to region of interest encoding but extending this approach further, ending with individual weight of importance for each pixel in image using the form of saliency map. This approach is proved to be effective. In this article we analyze impact of different basic hierarchical mask creation methods, which have impact on error separation between salient and not salient parts of the image. The results indicate that proposed mask creation method outperforms JPEG2000 based mask tree creation method.

  16. Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

    Directory of Open Access Journals (Sweden)

    Xiaobing Sun

    2017-01-01

    Full Text Available Program comprehension is an important task faced by developers during software maintenance. With the increasing complexity of evolving systems, program comprehension becomes more and more difficult. In practice, programmers are accustomed to getting a general view of the features in a software system and then finding the interesting or necessary files to start the understanding process. Given a system, developers may need a general view of the system. The traditional view of a system is shown in a package-class structure which is difficult to understand, especially for large systems. In this article, we focus on understanding the system in both feature view and file structure view. This article proposes an approach to generate a feature tree based on hierarchical Latent Dirichlet Allocation (hLDA, which includes two hierarchies, the feature hierarchy and file structure hierarchy. The feature hierarchy shows the features from abstract level to detailed level, while the file structure hierarchy shows the classes from whole to part. Empirical results show that the feature tree can produce a view for the features and files, and the clustering of classes in the package in our approach is better (in terms of recall than the other clustering approach, that is, hierarchical clustering.

  17. Hierarchical structural matching algorithms for registration of aerospace images

    Science.gov (United States)

    Lutsiv, Vadim R.; Malyshev, Igor A.; Potapov, Alexey

    2004-02-01

    The aim of investigation was developing the image registration algorithms dealing with the aerial and cosmic pictures taken in different seasons from differing view points, or formed by differing kinds of sensors (visible, IR, SAR). The task could not be solved using the traditional correlation based approaches, thus we chose the structural juxtaposition of the stable specific details of pictures as the general image matching technique. Structural matching was usually applied in the expert systems where the rather reliable results were based on the target specific algorithms, but our algorithms deal with the aerospace photographs of arbitrary contents for which the application specific approaches could not be used. The chosen form of structural descriptions should provide distinguishing between the similar simple elements in the huge multitudes of image contours, thus the descriptions were made hierarchical: we grouped the contour elements belonging to the separate compact image regions. The structural matching was carried out in two levels: matching the simple elements of every group in the first image with the ones of every group in the second image; matching the groups as the wholes. The top-down links were used to enhance the lower level matching using the higher level matching results.

  18. Infrared image mosaic using point feature operators

    Science.gov (United States)

    Huang, Zhen; Sun, Shaoyuan; Shen, Zhenyi; Hou, Junjie; Zhao, Haitao

    2016-10-01

    In this paper, we study infrared image mosaic around a single point of rotation, aiming at expanding the narrow view range of infrared images. We propose an infrared image mosaic method using point feature operators including image registration and image synthesis. Traditional mosaic algorithms usually use global image registration methods to extract the feature points in the global image, which cost too much time as well as considerable matching errors. To address this issue, we first roughly calculate the image shift amount using phase correlation and determine the overlap region between images, and then extract image features in overlap region, which shortens the registration time and increases the quality of feature points. We improve the traditional algorithm through increasing constraints of point matching based on prior knowledge of image shift amount based on which the weighted map is computed using fade in-out method. The experimental results verify that the proposed method has better real time performance and robustness.

  19. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

    Directory of Open Access Journals (Sweden)

    Jie Hu

    2018-01-01

    Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

  20. SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATURES

    OpenAIRE

    Nishant Singh; Shiv Ram Dubey; Pushkar Dixit; Jay Prakash Gupta

    2012-01-01

    In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar images, the need for databases, the semantic gap, and retrieving the desired images from huge collections are the keys to improve. CBIR system analyzes the image content for indexing, management, extraction and retrieval via low-level features such as color, texture and shape. To achieve higher semantic performance, recent system seeks to combine the low-level features of images with high-level...

  1. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  2. Unsupervised learning of electrocorticography motifs with binary descriptors of wavelet features and hierarchical clustering.

    Science.gov (United States)

    Pluta, Tim; Bernardo, Roman; Shin, Hae Won; Bernardo, D R

    2014-01-01

    We describe a novel method for data mining spectro-spatiotemporal network motifs from electrocorticographic (ECoG) data. The method utilizes wavelet feature extraction from ECoG data, generation of compact binary vectors from these features, and binary vector hierarchical clustering. The potential utility of this method in the discovery of recurring neural patterns is demonstrated in an example showing clustering of ictal and post-ictal gamma activity patterns. The method allows for the efficient and scalable retrieval and clustering of neural motifs occurring in massive amounts of neural data, such as in prolonged EEG/ECoG recordings and in brain computer interfaces.

  3. Categorizing biomedicine images using novel image features and sparse coding representation

    Science.gov (United States)

    2013-01-01

    Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are

  4. Multi-scale visual words for hierarchical medical image categorisation

    Science.gov (United States)

    Markonis, Dimitrios; Seco de Herrera, Alba G.; Eggel, Ivan; Müller, Henning

    2012-02-01

    The biomedical literature published regularly has increased strongly in past years and keeping updated even in narrow domains is difficult. Images represent essential information of their articles and can help to quicker browse through large volumes of articles in connection with keyword search. Content-based image retrieval is helping the retrieval of visual content. To facilitate retrieval of visual information, image categorisation can be an important first step. To represent scientific articles visually, medical images need to be separated from general images such as flowcharts or graphs to facilitate browsing, as graphs contain little information. Medical modality classification is a second step to focus search. The techniques described in this article first classify images into broad categories. In a second step the images are further classified into the exact medical modalities. The system combines the Scale-Invariant Feature Transform (SIFT) and density-based clustering (DENCLUE). Visual words are first created globally to differentiate broad categories and then within each category a new visual vocabulary is created for modality classification. The results show the difficulties to differentiate between some modalities by visual means alone. On the other hand the improvement of the accuracy of the two-step approach shows the usefulness of the method. The system is currently being integrated into the Goldminer image search engine of the ARRS (American Roentgen Ray Society) as a web service, allowing concentrating image search onto clinically relevant images automatically.

  5. Solving jigsaw puzzles using image features

    DEFF Research Database (Denmark)

    Nielsen, Ture R.; Drewsen, Peter; Hansen, Klaus

    2008-01-01

    In this article, we describe a method for automatic solving of the jigsaw puzzle problem based on using image features instead of the shape of the pieces. The image features are used for obtaining an accurate measure for edge similarity to be used in a new edge matching algorithm. The algorithm i...

  6. Wilson’s disease: Atypical imaging features

    Directory of Open Access Journals (Sweden)

    Venugopalan Y Vishnu

    2016-10-01

    Full Text Available Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present.

  7. Systematic feature analysis on timber defect images

    Directory of Open Access Journals (Sweden)

    Ummi Rabaah Hashim

    2017-07-01

    Full Text Available Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp. timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features.

  8. Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

    Science.gov (United States)

    Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan

    2015-12-01

    Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. Infrared image enhancement with learned features

    Science.gov (United States)

    Fan, Zunlin; Bi, Duyan; Ding, Wenshan

    2017-11-01

    Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

  10. Imaging features of iliopsoas bursitis

    Energy Technology Data Exchange (ETDEWEB)

    Wunderbaldinger, P. [Department of Radiology, University of Vienna (Austria); Center of Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA (United States); Bremer, C. [Department of Radiology, University of Muenster (Germany); Schellenberger, E. [Center of Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA (United States); Department of Radiology, Martin-Luther University of Halle-Wittenberg, Halle (Germany); Cejna, M.; Turetschek, K.; Kainberger, F. [Department of Radiology, University of Vienna (Austria)

    2002-02-01

    The aim of this study was firstly to describe the spectrum of imaging findings seen in iliopsoas bursitis, and secondly to compare cross-sectional imaging techniques in the demonstration of the extent, size and appearance of the iliopsoas bursitis as referenced by surgery. Imaging studies of 18 patients (13 women, 5 men; mean age 53 years) with surgically proven iliopsoas bursitis were reviewed. All patients received conventional radiographs of the pelvis and hip, US and MR imaging of the hip. The CT was performed in 5 of the 18 patients. Ultrasound, CT and MR all demonstrated enlarged iliopsoas bursae. The bursal wall was thin and well defined in 83% and thickened in 17% of all cases. The two cases with septations on US were not seen by CT and MRI. A communication between the bursa and the hip joint was seen, and surgically verified, in all 18 patients by MR imaging, whereas US and CT failed to demonstrate it in 44 and 40% of the cases, respectively. Hip joint effusion was seen and verified by surgery in 16 patients by MRI, whereas CT (4 of 5) and US (n=12) underestimated the number. The overall size of the bursa corresponded best between MRI and surgery, whereas CT and US tended to underestimate the size. Contrast enhancement of the bursal wall was seen in all cases. The imaging characteristics of iliopsoas bursitis are a well-defined, thin-walled cystic mass with a communication to the hip joint and peripheral contrast enhancement. The most accurate way to assess iliopsoas bursitis is with MR imaging; thus, it should be used for accurate therapy planning and follow-up studies. In order to initially prove an iliopsoas bursitis, US is the most cost-effective, easy-to-perform and fast alternative. (orig.)

  11. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation.

    Science.gov (United States)

    Cheng, Kai-Wen; Chen, Yie-Tarng; Fang, Wen-Hsien

    2015-12-01

    This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.

  12. Featured Image: Identifying Weird Galaxies

    Science.gov (United States)

    Kohler, Susanna

    2017-08-01

    Hoags Object, an example of a ring galaxy. [NASA/Hubble Heritage Team/Ray A. Lucas (STScI/AURA)]The above image (click for the full view) shows PanSTARRSobservationsof some of the 185 galaxies identified in a recent study as ring galaxies bizarre and rare irregular galaxies that exhibit stars and gas in a ring around a central nucleus. Ring galaxies could be formed in a number of ways; one theory is that some might form in a galaxy collision when a smaller galaxy punches through the center of a larger one, triggering star formation around the center. In a recent study, Ian Timmis and Lior Shamir of Lawrence Technological University in Michigan explore ways that we may be able to identify ring galaxies in the overwhelming number of images expected from large upcoming surveys. They develop a computer analysis method that automatically finds ring galaxy candidates based on their visual appearance, and they test their approach on the 3 million galaxy images from the first PanSTARRS data release. To see more of the remarkable galaxies the authors found and to learn more about their identification method, check out the paper below.CitationIan Timmis and Lior Shamir 2017 ApJS 231 2. doi:10.3847/1538-4365/aa78a3

  13. Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF.

    Science.gov (United States)

    Besbes, Bassem; Rogozan, Alexandrina; Rus, Adela-Maria; Bensrhair, Abdelaziz; Broggi, Alberto

    2015-04-13

    One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images.

  14. Extraction of Facial Features from Color Images

    Directory of Open Access Journals (Sweden)

    J. Pavlovicova

    2008-09-01

    Full Text Available In this paper, a method for localization and extraction of faces and characteristic facial features such as eyes, mouth and face boundaries from color image data is proposed. This approach exploits color properties of human skin to localize image regions – face candidates. The facial features extraction is performed only on preselected face-candidate regions. Likewise, for eyes and mouth localization color information and local contrast around eyes are used. The ellipse of face boundary is determined using gradient image and Hough transform. Algorithm was tested on image database Feret.

  15. Simultaneous hierarchical segmentation and vectorization of satellite images through combined data sampling and anisotropic triangulation

    Energy Technology Data Exchange (ETDEWEB)

    Grazzini, Jacopo [Los Alamos National Laboratory; Prasad, Lakshman [Los Alamos National Laboratory; Dillard, Scott [PNNL

    2010-10-21

    The automatic detection, recognition , and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding. However, it is a difficult task because of the high variability of satellite data. Indeed, the observed scenes usually exhibit a high degree of complexity, where complexity refers to the large variety of pictorial representations of objects with the same semantic meaning and also to the extensive amount of available det.ails. Therefore, there is still a strong demand for robust techniques for automatic information extraction and interpretation of satellite images. In parallel, there is a growing interest in techniques that can extract vector features directly from such imagery. In this paper, we investigate the problem of automatic hierarchical segmentation and vectorization of multispectral satellite images. We propose a new algorithm composed of the following steps: (i) a non-uniform sampling scheme extracting most salient pixels in the image, (ii) an anisotropic triangulation constrained by the sampled pixels taking into account both strength and directionality of local structures present in the image, (iii) a polygonal grouping scheme merging, through techniques based on perceptual information , the obtained segments to a smaller quantity of superior vectorial objects. Besides its computational efficiency, this approach provides a meaningful polygonal representation for subsequent image analysis and/or interpretation.

  16. MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

    Directory of Open Access Journals (Sweden)

    Dejan Tomaževič

    2012-03-01

    Full Text Available Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR and computed tomography (CT images. In addition, the measure was tested on positron emission tomography (PET and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

  17. MR imaging features of craniodiaphyseal dysplasia

    Energy Technology Data Exchange (ETDEWEB)

    Marden, Franklin A. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Wippold, Franz J. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Department of Radiology/Nuclear Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, MD 20814, Bethesda (United States)

    2004-02-01

    We report the magnetic resonance (MR) imaging findings in a 4-year-old girl with characteristic radiographic and computed tomography (CT) features of craniodiaphyseal dysplasia. MR imaging exquisitely depicted cranial nerve compression, small foramen magnum, hydrocephalus, and other intracranial complications of this syndrome. A syrinx of the cervical spinal cord was demonstrated. We suggest that MR imaging become a routine component of the evaluation of these patients. (orig.)

  18. Automatic extraction of planetary image features

    Science.gov (United States)

    LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

    2013-01-01

    A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

  19. Automatic Feature Extraction from Planetary Images

    Science.gov (United States)

    Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

    2010-01-01

    With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

  20. Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification.

    Science.gov (United States)

    Chen, Xiaobo; Zhang, Han; Lee, Seong-Whan; Shen, Dinggang

    2017-07-01

    Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.

  1. Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

    Science.gov (United States)

    Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

    2017-12-05

    Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017. Published by Elsevier B.V.

  2. Online Learning of Hierarchical Pitman-Yor Process Mixture of Generalized Dirichlet Distributions With Feature Selection.

    Science.gov (United States)

    Fan, Wentao; Sallay, Hassen; Bouguila, Nizar

    2017-09-01

    In this paper, a novel statistical generative model based on hierarchical Pitman-Yor process and generalized Dirichlet distributions (GDs) is presented. The proposed model allows us to perform joint clustering and feature selection thanks to the interesting properties of the GD distribution. We develop an online variational inference algorithm, formulated in terms of the minimization of a Kullback-Leibler divergence, of our resulting model that tackles the problem of learning from high-dimensional examples. This variational Bayes formulation allows simultaneously estimating the parameters, determining the model's complexity, and selecting the appropriate relevant features for the clustering structure. Moreover, the proposed online learning algorithm allows data instances to be processed in a sequential manner, which is critical for large-scale and real-time applications. Experiments conducted using challenging applications, namely, scene recognition and video segmentation, where our approach is viewed as an unsupervised technique for visual learning in high-dimensional spaces, showed that the proposed approach is suitable and promising.

  3. Angiomatous Meningioma: CT and MR Imaging Features

    Energy Technology Data Exchange (ETDEWEB)

    Ahn, Hee Yeon; Yu, In Kyu; Kim, Min Sun [Dept. of Radiology, Eulji University Hospital, Daejeon (Korea, Republic of); Kim, Seong Min; Kim, Han Kyu [Dept. of Neurosurgery, Eulji University Hospital, Daejeon (Korea, Republic of)

    2011-05-15

    To describe the computed tomography and magnetic resonance imaging features of angiomatous meningiomas. We reviewed the imaging findings of six patients with pathologically proven angiomatous meningiomas and characterized the location, margin, dura base, CT attenuation, MR signal intensity, intratumoral signal void, contrast enhancement, intratumoral cystic change, and peritumoral edema. Most tumors showed high signal intensity on T2-weighted images, and low signal intensity on diffusion-weighted images. After intravenous contrast administration, the tumor showed heterogeneous strong enhancement. Most tumors had a lobulated margin with prominent intratumoral signal voids. Four patients showed marked or small intratumoral cystic changes. Typically, angiomatous meningiomas were dura-based masses characterized by lobulated margins with high signal intensity on T2-weighted imaging (T2WI), low signal intensity on diffusion-weighted imaging (DWI), prominent intratumoral signal voids, intratumoral cystic changes, and marked enhancement after intravenous contrast administration.

  4. An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Science.gov (United States)

    Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

    2015-01-01

    Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

  5. ISee: perceptual features for image library navigation

    Science.gov (United States)

    Mojsilovic, Aleksandra; Gomes, Jose; Rogowitz, Bernice E.

    2002-06-01

    To develop more satisfying image navigation systems, we need tools to construct a semantic bridge between the user and the database. In this paper we present an image indexing scheme and a query language, which allow the user to introduce a cognitive dimension to the search. At an abstract level, this approach consists of: 1) learning the natural language that humans speak to communicate their semantic experience of images, 2) understand the relationships between this language and objective measurable image attributes, and then 3) develop the corresponding feature extraction schemes. In our previous work we have conducted a number of subjective experiments in which we asked human subjects to group images, and then explain verbally why they did so. The results of this study indicated that part of the abstraction involved in image interpretation is often driven by semantic categories, which can be broken into more tangible semantic entities, i.e. objective semantic indicators. By analyzing our experimental data, we identified some candidate semantic categories (i.e. portraits, people, crowds, cityscapes, landscapes, etc.), discovered their underlying semantic indicators (i.e. skin, sky, water, object, etc.), and derived important low-level image descriptors accounting for our perception of these indicators. In our recent work we have used these findings to develop a set of image features that match the way humans communicate image meaning, and a semantic-friendly query language for browsing and searching diverse collections of images. We have implemented our approach into an Internet search engine, ISee, and tested it on a large number of images. The results we obtained are very promising.

  6. Imaging features of unusual intracranial cystic meningiomas

    Energy Technology Data Exchange (ETDEWEB)

    Demir, M.K. [Trakya Univ. School of Medicine, Dept. of Radiology, Edirne (Turkey); Haydarpasa Numune Education and Research Hospital, Dept. of Radiology, Istanbul (Turkey)]. E-mail: demirkemal@superonline.com; Musluman, M. [Sisli Etfal Education and Research Hospital, Dept. of Neurosurgery, Istanbul (Turkey); Kilicoglu, G. [Haydarpasa Numune Education and Research Hospital, Dept. of Radiology, Istanbul (Turkey); Hakan, T. [Haydarpasa Numune Education and Research Hospital, Dept. of Neurosurgery, Istanbul (Turkey); Aker, F.V. [Haydarpasa Numune Education and Research Hospital, Dept. of Pathology, Istanbul (Turkey)

    2007-04-15

    To describe the imaging features of unusual intracranial cystic meningiomas in infants and adults. We retrospectively reviewed the magnetic resonance and computed tomography findings for 2 female patients and 3 male patients, ranging in age from 1 to 73 years (median 41 years), with histopathologically proven cystic meningioma. Although cystic meningiomas usually appear as solid and cystic masses, they may present as a mainly multicystic lesion. The wall of a cystic part of the meningioma may include both enhancing and unenhancing areas at imaging. The cystic portion of a meningioma is hypointense on diffusion-weighted images and markedly hyperintense on corresponding apparent diffusion coefficient maps. (author)

  7. Congenital infantile fibrosarcoma: review of imaging features.

    Science.gov (United States)

    Ainsworth, Kelly E; Chavhan, Govind B; Gupta, Abha A; Hopyan, Sevan; Taylor, Glenn

    2014-09-01

    Fibrosarcoma is a rare tumor in children with limited information on imaging features of these tumors in the literature. To retrospectively review the imaging features of histologically proven congenital infantile fibrosarcoma. The list of histologically confirmed congenital infantile fibrosarcomas between November 1999 and June 2013 was obtained from the oncology-pathology database. Imaging features and pathology reports of these tumors were reviewed. Patient charts were reviewed and clinical features, management and outcomes were recorded. During the study period, 13 children (9 girls and 4 boys; age range: 0 day-16 months, median age: 2.5 months) with congenital infantile fibrosarcomas were available for complete radiological review. The translocation (t[12;15]) was present in 11/13 (84.6%) and absent in 2/13. Eight/thirteen (61.5%) tumors were located in extremities (5 in lower and 3 in upper), 3/13 in thoracolumbar paraspinal regions, and one each in abdomen and sternocleidomastoid muscle. Imaging features included iso- to hyperintensity on T1-W, hyperintensity on T2-W as compared to skeletal muscles and heterogeneous enhancement. Six (37.5%) tumors showed hemorrhagic components and 2 (15.4%) showed low intensity foci. None of the patients had evidence of regional or distant metastases at diagnosis. Management included surgical resection only (1/13) and combined surgery and chemotherapy (10/13). Overall survival was 100% with a median follow-up of 49.3 months. Congenital infantile fibrosarcoma has nonspecific imaging characteristics but should be high on the differential diagnosis in a soft-tissue tumor presenting in infancy, located in an extremity and showing tumoral hemorrhage. Patients have a favorable outcome.

  8. Image Processing and Features Extraction of Fingerprint Images ...

    African Journals Online (AJOL)

    Several fingerprint matching algorithms have been developed for minutiae or template matching of fingerprint templates. The efficiency of these fingerprint matching algorithms depends on the success of the image processing and features extraction steps employed. Fingerprint image processing and analysis is hence an ...

  9. The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

    Science.gov (United States)

    Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

    2016-09-01

    The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

  10. Multispectral image fusion based on fractal features

    Science.gov (United States)

    Tian, Jie; Chen, Jie; Zhang, Chunhua

    2004-01-01

    Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the

  11. Barrett's esophagus: clinical features, obesity, and imaging.

    LENUS (Irish Health Repository)

    Quigley, Eamonn M M

    2011-09-01

    The following includes commentaries on clinical features and imaging of Barrett\\'s esophagus (BE); the clinical factors that influence the development of BE; the influence of body fat distribution and central obesity; the role of adipocytokines and proinflammatory markers in carcinogenesis; the role of body mass index (BMI) in healing of Barrett\\'s epithelium; the role of surgery in prevention of carcinogenesis in BE; the importance of double-contrast esophagography and cross-sectional images of the esophagus; and the value of positron emission tomography\\/computed tomography.

  12. Hierarchical clustering of RGB surface water images based on MIA ...

    African Journals Online (AJOL)

    2009-11-25

    Nov 25, 2009 ... found that the MIA-LSI approach complemented with a suitable clustering method is able to recognise the similar images of ... Keywords: multivariate image analysis (MIA), latent semantic indexing (LSI), RGB image, Ward's clustering, ..... for vision-based surveillance in heavy industry – convergence.

  13. Hierarchical clustering of RGB surface water images based on MIA ...

    African Journals Online (AJOL)

    This database, compiled from digital photographs of the various water levels and similar images of surface areas and vegetation, was transferred into an image matrix, and reorganised by means of principal component analysis (PCA) based on singular value decomposition (SVD). The high dimensionality of original images ...

  14. A Hierarchical Bayesian M/EEG Imaging Method Correcting for Incomplete Spatio-Temporal Priors

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke

    2013-01-01

    In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal ......In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio...

  15. Special feature on imaging systems and techniques

    Science.gov (United States)

    Yang, Wuqiang; Giakos, George

    2013-07-01

    The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

  16. Gestational Trophoblastic Disease: Clinical and Imaging Features.

    Science.gov (United States)

    Shaaban, Akram M; Rezvani, Maryam; Haroun, Reham R; Kennedy, Anne M; Elsayes, Khaled M; Olpin, Jeffrey D; Salama, Mohamed E; Foster, Bryan R; Menias, Christine O

    2017-01-01

    Gestational trophoblastic disease (GTD) is a spectrum of both benign and malignant gestational tumors, including hydatidiform mole (complete and partial), invasive mole, choriocarcinoma, placental site trophoblastic tumor, and epithelioid trophoblastic tumor. The latter four entities are referred to as gestational trophoblastic neoplasia (GTN). These conditions are aggressive with a propensity to widely metastasize. GTN can result in significant morbidity and mortality if left untreated. Early diagnosis of GTD is essential for prompt and successful management while preserving fertility. Initial diagnosis of GTD is based on a multifactorial approach consisting of clinical features, serial quantitative human chorionic gonadotropin (β-hCG) titers, and imaging findings. Ultrasonography (US) is the modality of choice for initial diagnosis of complete hydatidiform mole and can provide an invaluable means of local surveillance after treatment. The performance of US in diagnosing all molar pregnancies is surprisingly poor, predominantly due to the difficulty in differentiating partial hydatidiform mole from nonmolar abortion and retained products of conception. While GTN after a molar pregnancy is usually diagnosed with serial β-hCG titers, imaging plays an important role in evaluation of local extent of disease and systemic surveillance. Imaging also plays a crucial role in detection and management of complications, such as uterine and pulmonary arteriovenous fistulas. Familiarity with the pathogenesis, classification, imaging features, and treatment of these tumors can aid in radiologic diagnosis and guide appropriate management. (©)RSNA, 2017.

  17. Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

    Science.gov (United States)

    Tilton, James C.

    2003-01-01

    A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic

  18. Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

    Science.gov (United States)

    Noh, Myoung-Jong; Howat, Ian M.

    2018-02-01

    The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

  19. MR imaging features of hemispherical spondylosclerosis

    Energy Technology Data Exchange (ETDEWEB)

    Vicentini, Joao R.T.; Martinez-Salazar, Edgar L.; Chang, Connie Y.; Bredella, Miriam A.; Rosenthal, Daniel I.; Torriani, Martin [Massachusetts General Hospital and Harvard Medical School, Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Boston, MA (United States)

    2017-10-15

    Hemispherical spondylosclerosis (HS) is a rare degenerative entity characterized by dome-shaped sclerosis of a single vertebral body that may pose a diagnostic dilemma. The goal of this study was to describe the MR imaging features of HS. We identified spine radiographs and CT examinations of subjects with HS who also had MR imaging for correlation. Two musculoskeletal radiologists independently assessed sclerosis characteristics, presence of endplate erosions, marrow signal intensity, and disk degeneration (Pfirrmann scale). We identified 11 subjects (six males, five females, mean 48 ± 10 years) with radiographic/CT findings of HS. The most commonly affected vertebral body was L4 (6/11; 55%). On MR imaging, variable signal intensity was noted, being most commonly low on T1 (8/11, 73%) and high on fat-suppressed T2-weighted (8/11, 73%) images. In two subjects, diffuse post-contrast enhancement was seen in the lesion. Moderate disk degeneration and endplate bone erosions adjacent to sclerosis were present in all subjects. Erosions of the opposite endplate were present in two subjects (2/11, 18%). CT data from nine subjects showed the mean attenuation value of HS was 472 ± 96 HU. HS appearance on MR imaging is variable and may not correlate with the degree of sclerosis seen on radiographs or CT. Disk degenerative changes and asymmetric endplate erosions are consistent markers of HS. (orig.)

  20. Mass-like extramedullary hematopoiesis: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Ginzel, Andrew W. [Synergy Radiology Associates, Houston, TX (United States); Kransdorf, Mark J.; Peterson, Jeffrey J.; Garner, Hillary W. [Mayo Clinic, Department of Radiology, Jacksonville, FL (United States); Murphey, Mark D. [American Institute for Radiologic Pathology, Silver Spring, MD (United States)

    2012-08-15

    To report the imaging appearances of mass-like extramedullary hematopoiesis (EMH), to identify those features that are sufficiently characteristic to allow a confident diagnosis, and to recognize the clinical conditions associated with EMH and the relative incidence of mass-like disease. We retrospectively identified 44 patients with EMH; 12 of which (27%) had focal mass-like lesions and formed the study group. The study group consisted of 6 male and 6 female subjects with a mean age of 58 years (range 13-80 years). All 12 patients underwent CT imaging and 3 of the 12 patients had undergone additional MR imaging. The imaging characteristics of the extramedullary hematopoiesis lesions in the study group were analyzed and recorded. The patient's clinical presentation, including any condition associated with extramedullary hematopoiesis, was also recorded. Ten of the 12 (83%) patients had one or more masses located along the axial skeleton. Of the 10 patients with axial masses, 9 (90%) had multiple masses and 7 (70%) demonstrated internal fat. Eight patients (80%) had paraspinal masses and 4 patients (40%) had presacral masses. Seven patients (70%) had splenomegaly. Eleven of the 12 patients had a clinical history available for review. A predisposing condition for extramedullary hematopoiesis was present in 10 patients and included various anemias (5 cases; 45%), myelofibrosis/myelodysplastic syndrome (4 cases; 36%), and marrow proliferative disorder (1 case; 9%). One patient had no known predisposing condition. Mass-like extramedullary hematopoiesis most commonly presents as multiple, fat-containing lesions localized to the axial skeleton. When these imaging features are identified, extramedullary hematopoiesis should be strongly considered, particularly when occurring in the setting of a predisposing medical condition. (orig.)

  1. Classifications of Image Features: A Survey | Lichun | Discovery and ...

    African Journals Online (AJOL)

    An image feature is a descriptor of an image, which can avoid redundant data and reduce the effects of noise and variance. In computer imaging, feature selection is vital for researchers and processors. Feature extraction and image processing are based on the mathematical selection, computation and manipulation of ...

  2. [Hierarchical clustering analysis to detect associations between clinical and pathological features of gastric tumors and hypermethylation of suppressor genes].

    Science.gov (United States)

    Zavala G, Luis; Luengo J, Víctor; Ossandón C, Francisco; Riquelme S, Erick; Backhouse E, Claudia; Palma V, Mariana; Argandoña C, Jorge; Cumsille, Miguel Angel; Corvalán R, Alejandro

    2007-01-01

    Methylation is an inactivation mechanism for tumor suppressor genes, that can have important clinical implications. To analyze the methylation status of 11 tumor suppressor genes in pathological samples of diffuse gastric cancer. Eighty three patients with diffuse gastric cancer with information about survival and infection with Epstein Barr virus, were studied. DNA was extracted from pathological slides and the methylation status of genes p14, p15, p16, APC, p73, FHIT, E-cadherin, SEMA3B, BRCA-1, MINT-2 y MGMT, was studied using sodium bisulphite modification and polymerase chain reaction. Results were grouped according to the methylation index or Hierarchical clustering (TIGR MultiExperiment Viewer). Three genes had a high frequency of methylation (FHIT, BRCA1, APC), four had an intermediate frequency (p15, MGMT, p14, MINT2) and four had a low frequency (p16, p73, E-cadherin, SEMA3B). The methylation index had no association with clinical or pathological features of tumors or patients survival. Hierarchical clustering generated two clusters. One grouped clinical and pathological features with FHIT, BRCA1, and APC and the other grouped the other eight genes and Epstein Barr virus infection. Two significant associations were found, between APC and survival and p16/p14 and Epstein Barr virus infection. Hierarchical clustering is a tool that identifies associations between clinical and pathological features of tumors and methylation of tumor suppressor genes.

  3. BiOCl nanowire with hierarchical structure and its Raman features

    Energy Technology Data Exchange (ETDEWEB)

    Tian Ye [National Center for Nanoscience and Technology, No. 11, Beiyitiao, Zhongguancun, Beijing 100190 (China); Graduate School of Chinese Academy of Sciences, Beijing 100039 (China); Guo Chuanfei; Guo Yanjun; Wang Qi [National Center for Nanoscience and Technology, No. 11, Beiyitiao, Zhongguancun, Beijing 100190 (China); Liu Qian, E-mail: liuq@nanoctr.cn [National Center for Nanoscience and Technology, No. 11, Beiyitiao, Zhongguancun, Beijing 100190 (China)

    2012-01-01

    BiOCl is a promising V-VI-VII-compound semiconductor with excellent optical and electrical properties, and has great potential applications in photo-catalysis, photoelectric, etc. We successfully synthesize BiOCl nanowire with a hierarchical structure by combining wet etch (top-down) with liquid phase crystal growth (bottom-up) process, opening a novel method to construct ordered bismuth-based nanostructures. The morphology and lattice structures of Bi nanowires, {beta}-Bi{sub 2}O{sub 3} nanowires and BiOCl nanowires with the hierarchical structure are investigated by scanning electron microscope (SEM) and transition electron microscope (TEM). The formation mechanism of such ordered BiOCl hierarchical structure is considered to mainly originate from the highly preferred growth, which is governed by the lattice match between (1 1 0) facet of BiOCl and (2 2 0) or (0 0 2) facet of {beta}-Bi{sub 2}O{sub 3}. A schematic model is also illustrated to depict the formation process of the ordered BiOCl hierarchical structure. In addition, Raman properties of the BiOCl nanowire with the hierarchical structure are investigated deeply.

  4. Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI.

    Science.gov (United States)

    Qureshi, Muhammad Naveed Iqbal; Oh, Jooyoung; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

    2017-01-01

    Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

  5. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

    Directory of Open Access Journals (Sweden)

    L. Monika Moskal

    2013-10-01

    Full Text Available The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT in image segmentation, and to apply the object-based image analysis (OBIA approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

  6. Imaging features of foot osteoid osteoma

    Energy Technology Data Exchange (ETDEWEB)

    Shukla, Satyen; Clarke, Andrew W.; Saifuddin, Asif [Royal National Orthopaedic Hospital NHS Trust, Department of Radiology, Stanmore, Middlesex (United Kingdom)

    2010-07-15

    We performed a retrospective review of the imaging of nine patients with a diagnosis of foot osteoid osteoma (OO). Radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) had been performed in all patients. Radiographic features evaluated were the identification of a nidus and cortical thickening. CT features noted were nidus location (affected bone - intramedullary, intracortical, subarticular) and nidus calcification. MRI features noted were the presence of an identifiable nidus, presence and grade of bone oedema and whether a joint effusion was identified. Of the nine patients, three were female and six male, with a mean age of 21 years (range 11-39 years). Classical symptoms of OO (night pain, relief with aspirin) were identified in five of eight (62.5%) cases (in one case, the medical records could not be retrieved). In five patients the lesion was located in the hindfoot (four calcaneus, one talus), while four were in the mid- or forefoot (two metatarsal and two phalangeal). Radiographs were normal in all patients with hindfoot OO. CT identified the nidus in all cases (89%) except one terminal phalanx lesion, while MRI demonstrated a nidus in six of nine cases (67%). The nidus was of predominantly intermediate signal intensity on T1-weighted (T1W) sequences, with intermediate to high signal intensity on T2-weighted (T2W) sequences. High-grade bone marrow oedema, limited to the affected bone and adjacent soft tissue oedema was identified in all cases. In a young patient with chronic hindfoot pain and a normal radiograph, MRI features suggestive of possible OO include extensive bone marrow oedema limited to one bone, with a possible nidus demonstrated in two-thirds of cases. The presence or absence of a nidus should be confirmed with high-resolution CT. (orig.)

  7. Feature extraction & image processing for computer vision

    CERN Document Server

    Nixon, Mark

    2012-01-01

    This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, ""The main strength of the proposed book is the exemplar code of the algorithms."" Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filt

  8. Image Retrieval Based on Wavelet Features

    Science.gov (United States)

    Murtagh, F.

    2006-04-01

    A dominant (additive, stationary) Gaussian noise component in image data will ensure that wavelet coefficients are of Gaussian distribution, and in such a case Shannon entropy quantifies the wavelet transformed data well. But we find that both Gaussian and long tailed distributions may well hold in practice for wavelet coefficients. We investigate entropy-related features based on different wavelet transforms and the newly developed curvelet transform. Using a materials grading case study, we find that second, third, fourth order moments allow 100% successful test set discrimination.

  9. Automated Hierarchical Time Gain Compensation for In Vivo Ultrasound Imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hemmsen, Martin Christian; Martins, Bo

    2015-01-01

    Time gain compensation (TGC) is essential to ensure the optimal image quality of the clinical ultrasound scans. When large fluid collections are present within the scan plane, the attenuation distribution is changed drastically and TGC compensation becomes challenging. This paper presents an auto...

  10. Image segmentation by connectivity preserving relinking in hierarchical graph structures

    NARCIS (Netherlands)

    Nacken, P.F.M.

    1995-01-01

    The method of image segmentation by pyramid relinking is extended to the formalism of hierarchies of region adjacency graphs. This approach has a number of advantages: (1) resulting regions are connected; (2) the method is adaptive, and therefore artifacts caused by a regular grid are avoided; and

  11. Unsupervised feature learning for autonomous rock image classification

    Science.gov (United States)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  12. Body image features and interpersonal strategies in adolescents

    OpenAIRE

    Fadieieva Kseniia-Marharyta Olegivna

    2015-01-01

    This article describes the phenomenon of teenagers physical socialization in the context of extracurricular activities. The features of body image of dance, sports, travel and psychological courses participants. This article highlights typical stylistic features of teens body image.

  13. A Hierarchical Predictive Coding Model of Object Recognition in Natural Images.

    Science.gov (United States)

    Spratling, M W

    2017-01-01

    Predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article proposes a hierarchical neural network based on predictive coding for performing visual object recognition. This network is applied to the tasks of categorising hand-written digits, identifying faces, and locating cars in images of street scenes. It is shown that image recognition can be performed with tolerance to position, illumination, size, partial occlusion, and within-category variation. The current results, therefore, provide the first practical demonstration that predictive coding (at least the particular implementation of predictive coding used here; the PC/BC-DIM algorithm) is capable of performing accurate visual object recognition.

  14. Magnetic Resonance Imaging Features of Neuromyelitis Optica

    Energy Technology Data Exchange (ETDEWEB)

    You, Sun Kyung; Song, Chang June; Park, Woon Ju; Lee, In Ho; Son, Eun Hee [Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon (Korea, Republic of)

    2013-03-15

    To report the magnetic resonance (MR) imaging features of the spinal cord and brain in patients of neuromyelitis optica (NMO). Between January 2001 and March 2010, the MR images (spinal cord, brain, and orbit) and the clinical and serologic findings of 11 NMO patients were retrospectively reviewed. The contrast-enhancement of the spinal cord was performed (20/23). The presence and pattern of the contrast-enhancement in the spinal cord were classified into 5 types. Acute myelitis was monophasic in 8 patients (8/11, 72.7%); and optic neuritis preceded acute myelitis in most patients. Longitudinally extensive cord lesion (average, 7.3 vertebral segments) was involved. The most common type was the diffuse and subtle enhancement of the spinal cord with a multifocal nodular, linear or segmental intense enhancement (45%). Most of the brain lesions (5/11, 10 lesions) were located in the brain stem, thalamus and callososeptal interphase. Anti-Ro autoantibody was positive in 2 patients, and they showed a high relapse rate of acute myelitis. Anti-NMO IgG was positive in 4 patients (4/7, 66.7%). The imaging findings of acute myelitis in NMO may helpful in making an early diagnosis of NMO which can result in a severe damage to the spinal cord, and to make a differential diagnosis of multiple sclerosis and inflammatory diseases of the spinal cord such as toxocariasis.

  15. A fast and accurate feature-matching algorithm for minimally-invasive endoscopic images.

    Science.gov (United States)

    Puerto-Souza, Gustavo A; Mariottini, Gian-Luca

    2013-07-01

    The ability to find image similarities between two distinct endoscopic views is known as feature matching, and is essential in many robotic-assisted minimally-invasive surgery (MIS) applications. Differently from feature-tracking methods, feature matching does not make any restrictive assumption about the chronological order between the two images or about the organ motion, but first obtains a set of appearance-based image matches, and subsequently removes possible outliers based on geometric constraints. As a consequence, feature-matching algorithms can be used to recover the position of any image feature after unexpected camera events, such as complete occlusions, sudden endoscopic-camera retraction, or strong illumination changes. We introduce the hierarchical multi-affine (HMA) algorithm, which improves over existing feature-matching methods because of the larger number of image correspondences, the increased speed, and the higher accuracy and robustness. We tested HMA over a large (and annotated) dataset with more than 100 MIS image pairs obtained from real interventions, and containing many of the aforementioned sudden events. In all of these cases, HMA outperforms the existing state-of-the-art methods in terms of speed, accuracy, and robustness. In addition, HMA and the image database are made freely available on the internet.

  16. Toward Automated Feature Detection in UAVSAR Images

    Science.gov (United States)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  17. Dynamic stopping criteria of turbo codes for clustered set partitioning in hierarchical trees encoded image transmission

    Science.gov (United States)

    Fang, Jiunn-Tsair; Wu, Cheng-Shong

    2011-10-01

    Turbo codes adopt iterative decoding to increase the ability of error correction. However, the iterative method increases the decoding delay and power consumption. An effective approach is to decrease the number of iterations while tolerating slight performance degradation. We apply the clustered set partitioning in hierarchical trees for image coding. Different from other early stop criteria, we use the bit-error sensitivities from the image data. Then, the stop criterion is directly determined by the importance of image data. Simulation results show that our scheme can reduce more number of iterations with less degradation for peak-signal-to-noise ratio or structure similar performance.

  18. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

    Science.gov (United States)

    Sun, Kaiqiong; Udupa, Jayaram K; Odhner, Dewey; Tong, Yubing; Zhao, Liming; Torigian, Drew A

    2016-03-01

    In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed

  19. Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

    Directory of Open Access Journals (Sweden)

    Viral H. Borisagar

    2014-01-01

    Full Text Available A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair.

  20. Disparity map generation from illumination variant stereo images using efficient hierarchical dynamic programming.

    Science.gov (United States)

    Borisagar, Viral H; Zaveri, Mukesh A

    2014-01-01

    A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair.

  1. FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

    Directory of Open Access Journals (Sweden)

    M. Y. Yang

    2012-08-01

    Full Text Available The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  2. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

    Science.gov (United States)

    Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

    2017-01-01

    Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

  3. Image feature detectors and descriptors foundations and applications

    CERN Document Server

    Hassaballah, Mahmoud

    2016-01-01

    This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition. .

  4. UAV Image Registration Algorithm Using Color Invariant and AKAZE Feature

    Directory of Open Access Journals (Sweden)

    LIANG Huanqing

    2017-07-01

    Full Text Available Image matching based on feature was one of practical methods in UAV image matching. Since the conventional methods of image registration mainly used gray image as input that it could not take color features into account to distinguish the identical point. To address this problem, this paper designed a matching algorithm combined color invariant with AKAZE feature, which overcame the shortcoming of ignoring color information in traditional UAV image matching. Then gray level transformation was utilized to reduce the number of feature points and remain its reliability. Experimental results demonstrate that the proposed method can find the identical point accurately and enhance the efficiency.

  5. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    Full Text Available The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM classifier. We compared the performance of this classifier with that of a support vector machine (SVM and basic extreme learning machine (ELM for cortical MRI data from attention deficit/hyperactivity disorder (ADHD patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC, ADHD-inattentive (ADHD-I, and ADHD-combined (ADHD-C. We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM that enabled us to achieve good classification accuracy (60.78%. In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

  6. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

    Science.gov (United States)

    Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

    2016-01-01

    The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

  7. Image ratio features for facial expression recognition application.

    Science.gov (United States)

    Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

    2010-06-01

    Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

  8. Evaluation of image registration spatial accuracy using a Bayesian hierarchical model.

    Science.gov (United States)

    Liu, Suyu; Yuan, Ying; Castillo, Richard; Guerrero, Thomas; Johnson, Valen E

    2014-06-01

    To evaluate the utility of automated deformable image registration (DIR) algorithms, it is necessary to evaluate both the registration accuracy of the DIR algorithm itself, as well as the registration accuracy of the human readers from whom the "gold standard" is obtained. We propose a Bayesian hierarchical model to evaluate the spatial accuracy of human readers and automatic DIR methods based on multiple image registration data generated by human readers and automatic DIR methods. To fully account for the locations of landmarks in all images, we treat the true locations of landmarks as latent variables and impose a hierarchical structure on the magnitude of registration errors observed across image pairs. DIR registration errors are modeled using Gaussian processes with reference prior densities on prior parameters that determine the associated covariance matrices. We develop a Gibbs sampling algorithm to efficiently fit our models to high-dimensional data, and apply the proposed method to analyze an image dataset obtained from a 4D thoracic CT study. © 2014, The International Biometric Society.

  9. Defects' geometric feature recognition based on infrared image edge detection

    Science.gov (United States)

    Junyan, Liu; Qingju, Tang; Yang, Wang; Yumei, Lu; Zhiping, Zhang

    2014-11-01

    Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects' edges in infrared images effectively enables the identification of defects' geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects' edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects' geometric feature much more completely and clearly. The defects' diameters have been calculated based on the image edge detection results.

  10. Feature coding for image representation and recognition

    CERN Document Server

    Huang, Yongzhen

    2015-01-01

    This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) D

  11. Evaluation of textural features for multispectral images

    Science.gov (United States)

    Bayram, Ulya; Can, Gulcan; Duzgun, Sebnem; Yalabik, Nese

    2011-11-01

    Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

  12. Tracking image features with PCA-SURF descriptors

    CSIR Research Space (South Africa)

    Pancham, A

    2015-05-01

    Full Text Available The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper...

  13. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    Science.gov (United States)

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  14. Retinal image analysis: preprocessing and feature extraction

    Energy Technology Data Exchange (ETDEWEB)

    Marrugo, Andres G; Millan, Maria S, E-mail: andres.marrugo@upc.edu [Grup d' Optica Aplicada i Processament d' Imatge, Departament d' Optica i Optometria Univesitat Politecnica de Catalunya (Spain)

    2011-01-01

    Image processing, analysis and computer vision techniques are found today in all fields of medical science. These techniques are especially relevant to modern ophthalmology, a field heavily dependent on visual data. Retinal images are widely used for diagnostic purposes by ophthalmologists. However, these images often need visual enhancement prior to apply a digital analysis for pathological risk or damage detection. In this work we propose the use of an image enhancement technique for the compensation of non-uniform contrast and luminosity distribution in retinal images. We also explore optic nerve head segmentation by means of color mathematical morphology and the use of active contours.

  15. Feature representation of RGB-D images using joint spatial-depth feature pooling

    DEFF Research Database (Denmark)

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2016-01-01

    Recent development in depth imaging technology makes acquisition of depth information easier. With the additional depth cue, RGB-D cameras can provide effective support for many RGB-D perception tasks beyond traditional RGB information. However, current feature representation based on RGB-D images...... utilizes depth information only to extract local features, without considering it to improve robustness and discriminability of the feature representation by merging depth cues into feature pooling. Spatial pyramid model (SPM) has become the standard protocol to split a 2D image plane into sub......-regions for feature pooling of RGB-D images. We argue that SPM may not be the optimal pooling scheme for RGB-D images, as it only pools features spatially and completely discards their depth topological structures. Instead, we propose a novel joint spatial-depth pooling (JSDP) scheme which further partitions SPM...

  16. Enhancement of features in galaxy images

    Science.gov (United States)

    Djorgovski, S.

    1986-01-01

    Several image-enhancement techniques useful for morphological analysis of galactic or cometary images are described and compared. Such techniques can be used to search for, and investigate the properties of dust lanes, stellar disks or rings, jets, shells, tidal distortions, etc. Applications of the techniques are illustrated on CCD images of the peculiar galaxy Arp 230; this object has a rich morphology, indicative of a merger of two disk galaxies.

  17. Featured Image: Revealing Hidden Objects with Color

    Science.gov (United States)

    Kohler, Susanna

    2018-02-01

    Stunning color astronomical images can often be the motivation for astronomers to continue slogging through countless data files, calculations, and simulations as we seek to understand the mysteries of the universe. But sometimes the stunning images can, themselves, be the source of scientific discovery. This is the case with the below image of Lynds Dark Nebula 673, located in the Aquila constellation, that was captured with the Mayall 4-meter telescope at Kitt Peak National Observatory by a team of scientists led by Travis Rector (University of Alaska Anchorage). After creating the image with a novel color-composite imaging method that reveals faint H emission (visible in red in both images here), Rector and collaborators identified the presence of a dozen new Herbig-Haro objects small cloud patches that are caused when material is energetically flung out from newly born stars. The image adapted above shows three of the new objects, HH 118789, aligned with two previously known objects, HH 32 and 332 suggesting they are driven by the same source. For more beautiful images and insight into the authors discoveries, check out the article linked below!Full view of Lynds Dark Nebula 673. Click for the larger view this beautiful composite image deserves! [T.A. Rector (University of Alaska Anchorage) and H. Schweiker (WIYN and NOAO/AURA/NSF)]CitationT. A. Rector et al 2018 ApJ 852 13. doi:10.3847/1538-4357/aa9ce1

  18. Infrared image enhancement based on novel multiscale feature prior

    Science.gov (United States)

    Fan, Zunlin; Bi, Duyan; Xiong, Lei; Ding, Wenshan; Gao, Shan; Li, Cheng

    2017-04-01

    Infrared images have shortcomings of background noise, few details, and fuzzy edges. Therefore, noise suppression and detail enhancement play crucial roles in the infrared image technology field. To effectively enhance details and eliminate noises, an infrared image processing algorithm based on multiscale feature prior is proposed. First, the maximum a posterior model estimating optimal free-noise results is constructed and discussed. Second, based on the extended 16 high-order differential operators and multiscale features, we propose a structure feature prior that is immune to noises and depicts infrared image features more precisely. Third, with the noise-suppressed image, the final image is enhanced by the improved multiscale unsharp mask algorithm, which enhances details and edges adaptively. Finally, testing infrared images in different signal-to-noise ratio scenes, the effectiveness and robustness of the proposed approach is analyzed. Compared with other well-established methods, the proposed method shows the evident performance in terms of noise suppression and edge enhancement.

  19. Improving image retrieval effectiveness via query expansion using MeSH hierarchical structure.

    Science.gov (United States)

    Crespo Azcárate, Mariano; Mata Vázquez, Jacinto; Maña López, Manuel

    2013-01-01

    We explored two strategies for query expansion utilizing medical subject headings (MeSH) ontology to improve the effectiveness of medical image retrieval systems. In order to achieve greater effectiveness in the expansion, the search text was analyzed to identify which terms were most amenable to being expanded. To perform the expansions we utilized the hierarchical structure by which the MeSH descriptors are organized. Two strategies for selecting the terms to be expanded in each query were studied. The first consisted of identifying the medical concepts using the unified medical language system metathesaurus. In the second strategy the text of the query was divided into n-grams, resulting in sequences corresponding to MeSH descriptors. For the evaluation of the system, we used the collection made available by the ImageCLEF organization in its 2011 medical image retrieval task. The main measure of efficiency employed for evaluating the techniques developed was the mean average precision (MAP). Both strategies exceeded the average MAP score in the ImageCLEF 2011 competition (0.1644). The n-gram expansion strategy achieved a MAP of 0.2004, which represents an improvement of 21.89% over the average MAP score in the competition. On the other hand, the medical concepts expansion strategy scored 0.2172 in the MAP, representing a 32.11% improvement. This run won the text-based medical image retrieval task in 2011. Query expansion exploiting the hierarchical structure of the MeSH descriptors achieved a significant improvement in image retrieval systems.

  20. Family Perspectives on Siblings' Conflict Goals in Middle Childhood: Links to Hierarchical and Affective Features of Sibling Relationships.

    Science.gov (United States)

    Recchia, Holly E; Witwit, Ma-Ab

    2017-06-01

    This study examined parents' and children's descriptions of older and younger siblings' conflict goals in the late preschool and middle childhood years, and how these attributions were related to sibling relationship quality. Parents and 4- to 10-year-old children from 62 families were interviewed separately about siblings' motivations in two disputes and completed assessments of sibling relationship quality. Goal attributions varied across respondents in ways reflecting their family roles and positioning within disputes. Findings also revealed variations in the goals ascribed to older and younger siblings in line with hierarchical features of sibling relationships, such as younger siblings' conciliation/connectedness motives and older siblings' desires for autonomy/respect. Goal attributions were related to differences between families in the affective tenor of sibling relationships. Results demonstrate how power dynamics in sibling relationships play out in their everyday disputes, and underscore the importance of considering different family members' unique perspectives on children's sibling conflict experiences. © 2017 Wiley Periodicals, Inc.

  1. Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

    Science.gov (United States)

    Hoell, Simon; Omenzetter, Piotr

    2017-07-01

    Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

  2. A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

    Science.gov (United States)

    Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

    2010-09-01

    Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

  3. Segmentation of textured images based on multiple fractal feature combinations

    Science.gov (United States)

    Charalampidis, Dimitrios; Kasparis, Takis; Rolland, Jannick P.

    1998-07-01

    This paper describes an approach to segmentation of textured grayscale images using a technique based on image filtering and the fractal dimension (FD). Twelve FD features are computed based on twelve filtered versions of the original image using directional Gabor filters. Features are computed in a window and mapped to the central pixel of this window. An iterative K-means-based algorithm which includes feature smoothing and takes into consideration the boundaries between textures is used to segment an image into a desired number of clusters. This approach is partially supervised since the number of clusters has to be predefined. The fractal features are compared to Gabor energy features and the iterative K- means algorithm is compared to the original K-means clustering approach. The performance of segmentation for noisy images is also studied.

  4. Caroli's disease: magnetic resonance imaging features.

    Science.gov (United States)

    Guy, France; Cognet, François; Dranssart, Marie; Cercueil, Jean-Pierre; Conciatori, Laurent; Krausé, Denis

    2002-11-01

    Our objective was to describe the main aspects of MR imaging in Caroli's disease. Magnetic resonance cholangiography with a dynamic contrast-enhanced study was performed in nine patients with Caroli's disease. Bile duct abnormalities, lithiasis, dot signs, hepatic enhancement, renal abnormalities, and evidence of portal hypertension were evaluated. Three MR imaging patterns of Caroli's disease were found. In all but two patients, MR imaging findings were sufficient to confirm the diagnosis. Moreover, MR imaging provided information about the severity, location, and extent of liver involvement. This information was useful in planning the best therapeutic strategy. Magnetic resonance cholangiography with a dynamic contrast-enhanced study is a good screening tool for Caroli's disease. Direct cholangiography should be reserved for confirming doubtful cases.

  5. Detecting Image Splicing Using Merged Features in Chroma Space

    Directory of Open Access Journals (Sweden)

    Bo Xu

    2014-01-01

    Full Text Available Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature.

  6. Featured Image: A New Look at Fomalhaut

    Science.gov (United States)

    Kohler, Susanna

    2017-06-01

    ALMA continuum image overlaid as contours on the Hubble STIS image of Fomalhaut. [MacGregor et al. 2017]This stunning image of the Fomalhaut star system was taken by the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. This image maps the 1.3-mm continuum emission from the dust around the central star, revealing a ring that marks the outer edge of the planet-forming debris disk surrounding the star. In a new study, a team of scientists led by Meredith MacGregor (Harvard-Smithsonian Center for Astrophysics) examines these ALMA observations of Fomalhaut, which beautifully complement former Hubble images of the system. ALMAs images provide the first robust detection of apocenter glow the brightening of the ring at the point farthest away from the central star, a side effect of the rings large eccentricity. The authors use ALMAsobservations to measure properties of the disk, such as its span (roughly 136 x 14 AU), eccentricity (e 0.12), and inclination angle ( 66). They then explore the implications for Fomalhaut b, the planet located near the outer disk. To read more about the teams observations, check out the paper below.CitationMeredith A. MacGregor et al 2017 ApJ 842 8. doi:10.3847/1538-4357/aa71ae

  7. Feature analysis for detecting people from remotely sensed images

    OpenAIRE

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

    We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probabilit...

  8. Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images

    NARCIS (Netherlands)

    Giesen, R. J.; Huynen, A. L.; Aarnink, R. G.; de la Rosette, J. J.; Debruyne, F. M.; Wijkstra, H.

    1996-01-01

    A non-parametric algorithm is described for the construction of a binary decision tree classifier. This tree is used to correlate textural features, computed from ultrasonographic prostate images, with the histopathology of the imaged tissue. The algorithm consists of two parts; growing and pruning.

  9. Hierarchical Matching of Uncertain Building Models with Oblique View Airborne IR Image Sequences

    Science.gov (United States)

    Iwaszczuk, D.; Hoegner, L.; Schmitt, M.; Stilla, U.

    2012-07-01

    Thermal building textures can be used for detection of damaged and weak spots in the building structure. These textures can be extracted from airborne infrared (IR) image sequences by projecting the 3D building model into the images. However, the direct georeferencing is often not sufficiently accurate and the projected 3D model does not match the structures in the image. Thus we present a technique with the main goal to find the best fit between the existing 3D building model and the IR image sequence. For this purpose we developed a hierarchical approach consisting of two working stages. In the first stage we correct exterior orientation via line based matching. In the adjustment we consider both uncertainties: the ones of the model and the ones of the image. In the second stage we match each edge separately in its closest surrounding. Thanks to this approach a better fit between the 3D building model and the IR image was found. The originally unmodeled roof overlap was reconstructed.

  10. Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

    Directory of Open Access Journals (Sweden)

    Jian Cheng

    2015-05-01

    Full Text Available A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM for segmentation and support vector machine (SVM for classification. In the segmentation step, we propose an improved local binary pattern (LBP operator named the regional homogeneity local binary pattern (RHLBP to guarantee the regional homogeneity in PolSAR images. In the classification step, the color features extracted from false color images are applied to improve the classification accuracy. The RHLBP operator and color features can provide discriminative information to separate those pixels and regions with similar polarimetric features, which are from different classes. Extensive experimental comparison results with conventional methods on L-band PolSAR data demonstrate the effectiveness of our proposed method for PolSAR image classification.

  11. Adapting Local Features for Face Detection in Thermal Image.

    Science.gov (United States)

    Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

    2017-11-27

    A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  12. Image counter-forensics based on feature injection

    Science.gov (United States)

    Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

    2014-02-01

    Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

  13. NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    R. G. Villar

    2016-06-01

    Full Text Available Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc. in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification

  14. Non-Trivial Feature Derivation for Intensifying Feature Detection Using LIDAR Datasets Through Allometric Aggregation Data Analysis Applying Diffused Hierarchical Clustering for Discriminating Agricultural Land Cover in Portions of Northern Mindanao, Philippines

    Science.gov (United States)

    Villar, Ricardo G.; Pelayo, Jigg L.; Mozo, Ray Mari N.; Salig, James B., Jr.; Bantugan, Jojemar

    2016-06-01

    Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this

  15. Histology image retrieval in optimised multi-feature spaces.

    Science.gov (United States)

    Zhang, Qianni; Izquierdo, Ebroul

    2013-01-01

    Content based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in histology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to automatically combine heterogeneous visual features for histology image retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated to multiple query images. The core of this approach is a multi-objective learning method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimisation problem, and a multi-objective optimisation strategy is employed in order to handle potential contradictions in the query images associated to the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content based histology image retrieval by using an appropriately defined multi-feature fusion model, which takes careful consideration of the structure and distribution of visual features.

  16. Supratentorial cystic intracranial lesions: MR imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Young Joo; Son, Young Bo; Choi, Kyu Ho; Chun, Kyung Ah; Kim, Sung Hoon; Park, Seog Hee; Shinn, Kyung Sub [The Catholic University of Korea College of Medicine, Seoul (Korea, Republic of)

    1997-01-01

    To describe MR findings and differential points of supratentorial cystic intracranial lesions. We retrospectively reviewed and analyzed the MR findings of 59 patients with supratentorial cystic intracranial lesions, and classified them as follows : tumor-associated cyst, infectious cyst, ex-vacuo type cyst, and congentital/developmental cyst. Among 59 patients, 47 tumor-associated cysts were seen in 17, 42 infectious cysts in 13, 17 ex-vacuo type cysts in 10, and 19 congenital/developmental cysts in 19. In 44 of 47 tumor-associate cysts, increased or inhomogeneous internal signal intensity was seen on T1-weighted image, 37 of 47 showed thick uneven walls ; 37 of 47 had enhancing solid components and there was variable perifocal edema and mass effect. Infectious cysts were multiple (11 of 13). In cases of brain abscess, increased internal signal intensity on T1-weighted image, low signal intensity of abscess wall on T2-weighted image, thick even enhancing wall, and marked perifocal edema (4 of 4) were seen in all four cases. Cysts in cysticercosis were variable in appearance depending on the stage, but were smaller than other cystic lesions. Ex vacuo type cysts were of uniform CSF signal intensity in all pulse sequences and there was no identifiable wall or enhancement associated with enlarged adjacent ventricle and encephalomalacia (17 of 17). Congenital/developmental cysts showed a single lesion (19 of 19), a signal intensity similar to CSF in all pulse sequences (15 of 19), no identifiable wall (16 of 19), no enhancement (17 of 19), and no perifocal edema (19 of 19). MR was used to categorize supratentorial cystic intracranial lesions into four groups on the basis of their number, size, internal homogeneity of signal intensity on T1-weighted image, enhancing pattern, perifocal edema and mass effect, thereby improving diagnostic specificity and patient management.

  17. Simple Low Level Features for Image Analysis

    Science.gov (United States)

    Falcoz, Paolo

    As human beings, we perceive the world around us mainly through our eyes, and give what we see the status of “reality”; as such we historically tried to create ways of recording this reality so we could augment or extend our memory. From early attempts in photography like the image produced in 1826 by the French inventor Nicéphore Niépce (Figure 2.1) to the latest high definition camcorders, the number of recorded pieces of reality increased exponentially, posing the problem of managing all that information. Most of the raw video material produced today has lost its memory augmentation function, as it will hardly ever be viewed by any human; pervasive CCTVs are an example. They generate an enormous amount of data each day, but there is not enough “human processing power” to view them. Therefore the need for effective automatic image analysis tools is great, and a lot effort has been put in it, both from the academia and the industry. In this chapter, a review of some of the most important image analysis tools are presented.

  18. Featured Image: Active Cryovolcanism on Europa?

    Science.gov (United States)

    Kohler, Susanna

    2017-05-01

    Nighttime thermal image from the Galileo Photopolarimeter-Radiometer, revealing a thermal anomaly around the region where the plumes were observed. [Sparks et al. 2017]This image shows a 1320 900 km, high-resolution Galileo/Voyager USGS map of the surface of Europa, one of Jupiters moons. In March 2014, observations of Europa revealed a plume on its icy surface coming from somewhere within the green ellipse. In February 2016, another plume was observed, this time originating from somewhere within the cyan ellipse. In addition, a nighttime thermal image from the Galileo Photopolarimeter-Radiometer has revealed a thermal anomaly a region of unusually high temperature near the same location. In a recent study led by William Sparks (Space Telescope Science Institute), a team of scientists presents these observations and argues that they provide mounting evidence of active water-vapor venting from ongoing cryovolcanism beneath Europas icy surface. If this is true, then Europas surface is active and provides access to the liquid water at depth boosting the case for Europas potential habitability and certainly making for an interesting target point for future spacecraft exploration of this moon. For more information, check out the paper below!CitationW. B. Sparks et al 2017 ApJL 839 L18. doi:10.3847/2041-8213/aa67f8

  19. Automatic and hierarchical segmentation of the human skeleton in CT images

    Science.gov (United States)

    Fu, Yabo; Liu, Shi; Li, H. Harold; Yang, Deshan

    2017-04-01

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  20. World Wide Web Based Image Search Engine Using Text and Image Content Features

    Science.gov (United States)

    Luo, Bo; Wang, Xiaogang; Tang, Xiaoou

    2003-01-01

    Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image meta-search engine to retrieve images from the Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retrieve a broad coverage of images with a high recall rate and a relatively low precision. An image content based ordering is then performed on the initial image set. All the images are clustered into different folders based on the image content features. In addition, the images can be re-ranked by the content features according to the user feedback. Such a design makes it truly practical to use both text and image content for image retrieval over the Internet. Experimental results confirm the efficiency of the system.

  1. Lens designs with extreme image quality features

    Science.gov (United States)

    Shafer, David

    2013-02-01

    In order to best assess the importance of new technologies to optical design, it is useful to consider what the limits are to what can be done with `old' technologies. That may show where something new is needed to overcome the limitations of existing optical designs. This article will give a survey of some remarkable high-performance designs, some of which are extremely simple, and most of which only use technology that has already been around for decades. Each of these designs has some limitation that would be nice to overcome. One new technology that will probably revolutionize optical design will be curved surfaces on image chips.

  2. THE IDENTIFICATION OF PILL USING FEATURE EXTRACTION IN IMAGE MINING

    Directory of Open Access Journals (Sweden)

    A. Hema

    2015-02-01

    Full Text Available With the help of image mining techniques, an automatic pill identification system was investigated in this study for matching the images of the pills based on its several features like imprint, color, size and shape. Image mining is an inter-disciplinary task requiring expertise from various fields such as computer vision, image retrieval, image matching and pattern recognition. Image mining is the method in which the unusual patterns are detected so that both hidden and useful data images can only be stored in large database. It involves two different approaches for image matching. This research presents a drug identification, registration, detection and matching, Text, color and shape extraction of the image with image mining concept to identify the legal and illegal pills with more accuracy. Initially, the preprocessing process is carried out using novel interpolation algorithm. The main aim of this interpolation algorithm is to reduce the artifacts, blurring and jagged edges introduced during up-sampling. Then the registration process is proposed with two modules they are, feature extraction and corner detection. In feature extraction the noisy high frequency edges are discarded and relevant high frequency edges are selected. The corner detection approach detects the high frequency pixels in the intersection points. Through the overall performance gets improved. There is a need of segregate the dataset into groups based on the query image’s size, shape, color, text, etc. That process of segregating required information is called as feature extraction. The feature extraction is done using Geometrical Gradient feature transformation. Finally, color and shape feature extraction were performed using color histogram and geometrical gradient vector. Simulation results shows that the proposed techniques provide accurate retrieval results both in terms of time and accuracy when compared to conventional approaches.

  3. Feature Selection for Image Retrieval based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Kushwaha

    2016-12-01

    Full Text Available This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

  4. Featured Image: Waves in a Coronal Fan

    Science.gov (United States)

    Kohler, Susanna

    2017-09-01

    The inset in this Solar Dynamics Observatory image shows a close-up view of a stunning coronal fan extending above the Suns atmosphere. These sweeping loops were observed on 7 March 2012 by a number of observatories, revealing the first known evidence of standing slow magnetoacoustic waves in cool coronal fan loops. The oscillations of the loops, studied in a recent article led by Vaibhav Pant (Indian Institute of Astrophysics), were triggered by blast waves that were generated by X-class flares from the distant active region AR 11429 (marked withthe yellow box at left). The overplotted X-ray curve in the top right corner of the image (click for the full view) shows the evolution of the flares that perturbed the footpoints of the loops. You can check out the video of the action below, and follow the link to the original article to read more about what these oscillations tell us about the Suns activity. CitationV. Pant et al 2017 ApJL 847 L5. doi:10.3847/2041-8213/aa880f

  5. Featured Image: Structures in the Interstellar Medium

    Science.gov (United States)

    Kohler, Susanna

    2017-02-01

    This beautiful false-color image (which covers 57 degrees2; click for the full view!) reveals structures in the hydrogen gas that makes up the diffuse atomic interstellar medium at intermediate latitudes in our galaxy. The imagewas created by representing three velocity channels with colors red for gas moving at 7.59 km/s, green for 5.12 km/s, and blue for 2.64 km/s and it shows the dramatically turbulent and filamentary structure of this gas. This image is one of many stunning, high-resolution observations that came out of the DRAO HI Intermediate Galactic Latitude Survey, a program that used the Synthesis Telescope at the Dominion Radio Astrophysical Observatory in British Columbia to map faint hydrogen emission at intermediate latitudes in the Milky Way. The findings from the program were recently published in a study led by Kevin Blagrave (Canadian Institute for Theoretical Astrophysics, University of Toronto); to find out more about what they learned, check out the paper below!CitationK. Blagrave et al 2017 ApJ 834 126. doi:10.3847/1538-4357/834/2/126

  6. Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2014-01-01

    Full Text Available Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI, in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.

  7. Segmentation of MR images using multiple-feature vectors

    Science.gov (United States)

    Cole, Orlean I. B.; Daemi, Mohammad F.

    1996-04-01

    Segmentation is an important step in the analysis of MR images (MRI). Considerable progress has been made in this area, and numerous reports on 3D segmentation, volume measurement and visualization have been published in recent years. The main purpose of our study is to investigate the power and use of fractal techniques in extraction of features from MR images of the human brain. These features which are supplemented by other features are used for segmentation, and ultimately for the extraction of a known pathology, in our case multiple- sclerosis (MS) lesions. We are particularly interested in the progress of the lesions and occurrence of new lesions which in a typical case are scattered within the image and are sometimes difficult to identify visually. We propose a technique for multi-channel segmentation of MR images using multiple feature vectors. The channels are proton density, T1-weighted and T2-weighted images containing multiple-sclerosis (MS) lesions at various stages of development. We first represent each image as a set of feature vectors which are estimated using fractal techniques, and supplemented by micro-texture features and features from the gray-level co-occurrence matrix (GLCM). These feature vectors are then used in a feature selection algorithm to reduce the dimension of the feature space. The next stage is segmentation and clustering. The selected feature vectors now form the input to the segmentation and clustering routines and are used as the initial clustering parameters. For this purpose, we have used the classical K-means as the initial clustering method. The clustered image is then passed into a probabilistic classifier to further classify and validate each region, taking into account the spatial properties of the image. Initially, segmentation results were obtained using the fractal dimension features alone. Subsequently, a combination of the fractal dimension features and the supplementary features mentioned above were also obtained

  8. Featured Image: Star Clusters in M51

    Science.gov (United States)

    Kohler, Susanna

    2016-06-01

    This beautiful mosaic of images of the Whirlpool galaxy (M51) and its companion was taken with the Advanced Camera for Surveys on the Hubble Space Telescope. This nearby, grand-design spiral galaxy has a rich population of star clusters, making it both a stunning target for imagery and an excellent resource for learning about stellar formation and evolution. In a recent study, Rupali Chandar (University of Toledo) and collaborators cataloged over 3,800 compact star clusters within this galaxy. They then used this catalog to determine the distributions for the clusters ages, masses, and sizes, which can provide important clues as to how star clusters form, evolve, and are eventually disrupted. You can read more about their study and what they discovered in the paper below.CitationRupali Chandar et al 2016 ApJ 824 71. doi:10.3847/0004-637X/824/2/71

  9. Featured Image: The Birth of Spiral Arms

    Science.gov (United States)

    Kohler, Susanna

    2017-01-01

    In this figure, the top panels show three spiral galaxies in the Virgo cluster, imaged with the Sloan Digital Sky Survey. The bottom panels provide a comparison with three morphologically similar galaxies generated insimulations. The simulations run by Marcin Semczuk, Ewa okas, and Andrs del Pino (Nicolaus Copernicus Astronomical Center, Poland) were designed to examine how the spiral arms of galaxies like the Milky Way may have formed. In particular, the group exploredthe possibility that so-called grand-design spiral arms are caused by tidal effects as a Milky-Way-like galaxy orbits a cluster of galaxies. The authors show that the gravitational potential of the cluster can trigger the formation of two spiral arms each time the galaxy passes through the pericenter of its orbit around the cluster. Check out the original paper below for more information!CitationMarcin Semczuk et al 2017 ApJ 834 7. doi:10.3847/1538-4357/834/1/7

  10. Image processing tool for automatic feature recognition and quantification

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xing; Stoddard, Ryan J.

    2017-05-02

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

  11. Prostate cancer characterization on MR images using fractal features.

    Science.gov (United States)

    Lopes, R; Ayache, A; Makni, N; Puech, P; Villers, A; Mordon, S; Betrouni, N

    2011-01-01

    Computerized detection of prostate cancer on T2-weighted MR images. The authors combined fractal and multifractal features to perform textural analysis of the images. The fractal dimension was computed using the Variance method; the multifractal spectrum was estimated by an adaptation of a multifractional Brownian motion model. Voxels were labeled as tumor/nontumor via nonlinear supervised classification. Two classification algorithms were tested: Support vector machine (SVM) and AdaBoost. Experiments were performed on images from 17 patients. Ground truth was available from histological images. Detection and classification results (sensitivity, specificity) were (83%, 91%) and (85%, 93%) for SVM and AdaBoost, respectively. Classification using the authors' model combining fractal and multifractal features was more accurate than classification using classical texture features (such as Haralick, wavelet, and Gabor filters). Moreover, the method was more robust against signal intensity variations. Although the method was only applied to T2 images, it could be extended to multispectral MR.

  12. Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis.

    Science.gov (United States)

    Chen, Chiao-Min; Chen, Chih-Cheng; Wu, Ming-Chi; Horng, Gwoboa; Wu, Hsien-Chu; Hsueh, Shih-Hua; Ho, His-Yun

    Parkinson's disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

  13. Contributions to the study of the images of hierarchs in the catholicon of Studenica Monastery

    Directory of Open Access Journals (Sweden)

    Živković Miloš

    2014-01-01

    Full Text Available The paper is devoted to selected images of the saint hierarchs in the Church of the Virgin in Studenica Monastery. The attention is first of all focused on the figures of four bishops who were partially covered by the high iconostasis from the XIX century. These are standing figures of St. Achillius and an unidentified saint, and busts of St. Parthenius and St. Charalambos. In adittion to mentioned, some other figures are also analyzed - three saints named Gregory in the prothesis, St. Cyril of Alexandria in the apse, a bust of St. Clement of Rome on the west wall of the nave, as well as the bust of eponymous saint - probably Clement, bishop of Sardis - on the eastern arch below the dome. [Projekat Ministarstva nauke Republike Srbije, br. 177032: Tradicija, inovacija i identitet u vizantijskom svetu

  14. The analysis of image feature robustness using cometcloud

    Directory of Open Access Journals (Sweden)

    Xin Qi

    2012-01-01

    Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

  15. A comparison of image features for registering LWIR and visual images

    CSIR Research Space (South Africa)

    Cronje, J

    2012-11-01

    Full Text Available . Equivalent results for red channels are 86% through 91%. Brumby et al. [8] investigate the supervised evolution of feature extraction kernels by combining primitive image pro- cessing operations in order to extract the desired features (such as roads.... Scale-Invariant Feature Transform The SIFT [4] detector searches for stable features across multiple scales by searching for local extrema features over a set of Difference-of-Gaussian (DoG) images. An orientation histogram is constructed by sampling...

  16. Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

    Science.gov (United States)

    Al-Khafaji, Suhad Lateef; Zhou, Jun; Zia, Ali; Liew, Alan Wee-Chung

    2017-09-04

    Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named Spectral-Spatial Scale Invariant Feature Transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

  17. Featured Image: The Cosmic Velocity Web

    Science.gov (United States)

    Kohler, Susanna

    2017-09-01

    You may have heard of the cosmic web, a network of filaments, clusters and voids that describes the three-dimensional distribution of matter in our universe. But have you ever considered the idea of a cosmic velocity web? In a new study led by Daniel Pomarde (IRFU CEA-Saclay, France), a team of scientists has built a detailed 3D view of the flows in our universe, showing in particular motions along filaments and in collapsing knots. In the image above (click for the full view), surfaces of knots (red) are embedded within surfaces of filaments (grey). The rainbow lines show the flow motion, revealing acceleration (redder tones) toward knots and retardation (bluer tones) beyond them. You can learn more about Pomarde and collaborators work and see their unusual and intriguing visualizationsin the video they produced, below. Check out the original paper for more information.CitationDaniel Pomarde et al 2017 ApJ 845 55. doi:10.3847/1538-4357/aa7f78

  18. Featured Image: Making Dust in the Lab

    Science.gov (United States)

    Kohler, Susanna

    2017-12-01

    This remarkable photograph (which spans only 10 m across; click for a full view) reveals what happens when you form dust grains in a laboratory under conditions similar to those of interstellar space. The cosmic life cycle of dust grains is not well understood we know that in the interstellar medium (ISM), dust is destroyed at a higher rate than it is produced by stellar sources. Since the amount of dust in the ISM stays constant, however, there must be additional sources of dust production besides stars. A team of scientists led by Daniele Fulvio (Pontifical Catholic University of Rio de Janeiro and the Max Planck Institute for Astronomy at the Friedrich Schiller University Jena) have now studied formation mechanisms of dust grains in the lab by mimicking low-temperature ISM conditions and exploring how, under these conditions, carbonaceous materials condense from gas phase to form dust grains. To read more about their results and see additional images, check out the paper below.CitationDaniele Fulvio et al 2017 ApJS 233 14. doi:10.3847/1538-4365/aa9224

  19. Featured Image: Experimental Simulation of Melting Meteoroids

    Science.gov (United States)

    Kohler, Susanna

    2017-03-01

    Ever wonder what experimental astronomy looks like? Some days, it looks like this piece of rock in a wind tunnel (click for a betterlook!). In this photo, a piece of agrillite (a terrestrial rock) is exposed to conditions in a plasma wind tunnel as a team of scientists led by Stefan Loehle (Stuttgart University) simulate what happens to a meteoroid as it hurtles through Earths atmosphere. With these experiments, the scientists hope to better understand meteoroid ablation the process by which meteoroids are heated, melt, and evaporateas they pass through our atmosphere so that we can learn more from the meteorite fragments that make it to the ground. In the scientists experiment, the rock samples were exposed to plasma flow until they disintegrated, and this process was simultaneously studied via photography, video, high-speed imaging, thermography, and Echelle emission spectroscopy. To find out what the team learned from these experiments, you can check out the original article below.CitationStefan Loehle et al 2017 ApJ 837 112. doi:10.3847/1538-4357/aa5cb5

  20. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

    Directory of Open Access Journals (Sweden)

    Yuanshen Zhao

    2016-01-01

    Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

  1. Disorders of the pediatric pancreas: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Nijs, Els [University Hospital Gasthuisberg, Department of Radiology, Leuven (Belgium); Callahan, Michael J.; Taylor, George A. [Boston Children' s Hospital, Department of Radiology, Boston, MA (United States)

    2005-04-01

    The purpose of this manuscript is to provide an overview of the normal development of the pancreas as well as pancreatic pathology in children. Diagnostic imaging plays a major role in the evaluation of the pancreas in infants and children. Familiarity with the range of normal appearance and the diseases that commonly affect this gland is important for the accurate and timely diagnosis of pancreatic disorders in the pediatric population. Normal embryology is discussed, as are the most common congenital anomalies that occur as a result of aberrant development during embryology. These include pancreas divisum, annular pancreas, agenesis of the dorsal pancreatic anlagen and ectopic pancreatic tissue. Syndromes that can manifest pancreatic pathology include: Beckwith Wiedemann syndrome, von Hippel-Lindau disease and autosomal dominant polycystic kidney disease. Children and adults with cystic fibrosis and Shwachman-Diamond syndrome frequently present with pancreatic insufficiency. Trauma is the most common cause of pancreatitis in children. In younger children, unexplained pancreatic injury must always alert the radiologist to potential child abuse. Pancreatic pseudocysts are a complication of trauma, but can also be seen in the setting of acute or chronic pancreatitis from other causes. Primary pancreatic neoplasms are rare in children and are divided into exocrine tumors such as pancreatoblastoma and adenocarcinoma and into endocrine or islet cell tumors. Islet cell tumors are classified as functioning (insulinoma, gastrinoma, VIPoma and glucagonoma) and nonfunctioning tumors. Solid-cystic papillary tumor is probably the most common pancreatic tumor in Asian children. Although quite rare, secondary tumors of the pancreas can be associated with certain primary malignancies. (orig.)

  2. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

    Science.gov (United States)

    Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H; J Zahra, Sophia

    2016-01-01

    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

  3. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

    Directory of Open Access Journals (Sweden)

    Seyed Mostafa Mousavi Kahaki

    Full Text Available An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

  4. Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

    Directory of Open Access Journals (Sweden)

    Laercio B. Gonçalves

    2010-01-01

    Full Text Available We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses, basalt (four subclasses, diabase (five subclasses, and rhyolite (five subclasses. These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.

  5. Associations Between Spondyloarthritis Features and Magnetic Resonance Imaging Findings

    DEFF Research Database (Denmark)

    Arnbak, Bodil; Grethe Jurik, Anne; Hørslev-Petersen, Kim

    2016-01-01

    were 1) to estimate the prevalence of magnetic resonance imaging (MRI) findings and clinical features included in the ASAS criteria for SpA and 2) to explore the associations between MRI findings and clinical features. METHODS: We included patients ages 18-40 years with persistent low back pain who had...... of 1.1 per year). CONCLUSION: In this population, 53% had at least 1 clinical feature included in the ASAS criteria for SpA, and 21% had sacroiliitis according to the ASAS definition; furthermore, the associations between the clinical and imaging domains were inconsistent. The results indicate a need...

  6. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Directory of Open Access Journals (Sweden)

    Liangji Zhou

    2017-01-01

    Full Text Available As a typical deep-learning model, Convolutional Neural Networks (CNNs can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

  7. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  8. Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

    Science.gov (United States)

    Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

    2016-01-01

    In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

  9. Effective feature selection for image steganalysis using extreme learning machine

    Science.gov (United States)

    Feng, Guorui; Zhang, Haiyan; Zhang, Xinpeng

    2014-11-01

    Image steganography delivers secret data by slight modifications of the cover. To detect these data, steganalysis tries to create some features to embody the discrepancy between the cover and steganographic images. Therefore, the urgent problem is how to design an effective classification architecture for given feature vectors extracted from the images. We propose an approach to automatically select effective features based on the well-known JPEG steganographic methods. This approach, referred to as extreme learning machine revisited feature selection (ELM-RFS), can tune input weights in terms of the importance of input features. This idea is derived from cross-validation learning and one-dimensional (1-D) search. While updating input weights, we seek the energy decreasing direction using the leave-one-out (LOO) selection. Furthermore, we optimize the 1-D energy function instead of directly discarding the least significant feature. Since recent Liu features can gain considerable low detection errors compared to a previous JPEG steganalysis, the experimental results demonstrate that the new approach results in less classification error than other classifiers such as SVM, Kodovsky ensemble classifier, direct ELM-LOO learning, kernel ELM, and conventional ELM in Liu features. Furthermore, ELM-RFS achieves a similar performance with a deep Boltzmann machine using less training time.

  10. Hierarchical design of a polymeric nanovehicle for efficient tumor regression and imaging

    Science.gov (United States)

    An, Jinxia; Guo, Qianqian; Zhang, Peng; Sinclair, Andrew; Zhao, Yu; Zhang, Xinge; Wu, Kan; Sun, Fang; Hung, Hsiang-Chieh; Li, Chaoxing; Jiang, Shaoyi

    2016-04-01

    Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake, enhanced tumor cell internalization, pH-controlled drug release and simultaneous imaging. This co-assembled nanoparticle showed exceptional stability in complex biological media. Benefiting from the synergistic effects of zwitterionic and multivalent galactose polymers, drug-loaded nanoparticles were selectively internalized by cancer cells rather than normal tissue cells. In addition, the pH-responsive core retained their cargo within their polymeric coating through hydrophobic interaction and released it under slightly acidic conditions. In vivo pharmacokinetic studies in mice showed minimal uptake of nanoparticles by the mononuclear phagocyte system and excellent blood circulation half-lives of 14.4 h. As a result, tumor growth was completely inhibited and no damage was observed for normal organ tissues. This newly developed drug nanovehicle has great potential in cancer therapy, and the hierarchical design principle should provide valuable information for the development of the next generation of drug delivery systems.Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake

  11. Feature-Based Retinal Image Registration Using D-Saddle Feature.

    Science.gov (United States)

    Ramli, Roziana; Idris, Mohd Yamani Idna; Hasikin, Khairunnisa; A Karim, Noor Khairiah; Abdul Wahab, Ainuddin Wahid; Ahmedy, Ismail; Ahmedy, Fatimah; Kadri, Nahrizul Adib; Arof, Hamzah

    2017-01-01

    Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

  12. Feature-Based Retinal Image Registration Using D-Saddle Feature

    Directory of Open Access Journals (Sweden)

    Roziana Ramli

    2017-01-01

    Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

  13. Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

    Science.gov (United States)

    Kim, Deok-Hwan

    As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

  14. Semi-Supervised Feature Transformation for Tissue Image Classification.

    Directory of Open Access Journals (Sweden)

    Kenji Watanabe

    Full Text Available Various systems have been proposed to support biological image analysis, with the intent of decreasing false annotations and reducing the heavy burden on biologists. These systems generally comprise a feature extraction method and a classification method. Task-oriented methods for feature extraction leverage characteristic images for each problem, and they are very effective at improving the classification accuracy. However, it is difficult to utilize such feature extraction methods for versatile task in practice, because few biologists specialize in Computer Vision and/or Pattern Recognition to design the task-oriented methods. Thus, in order to improve the usability of these supporting systems, it will be useful to develop a method that can automatically transform the image features of general propose into the effective form toward the task of their interest. In this paper, we propose a semi-supervised feature transformation method, which is formulated as a natural coupling of principal component analysis (PCA and linear discriminant analysis (LDA in the framework of graph-embedding. Compared with other feature transformation methods, our method showed favorable classification performance in biological image analysis.

  15. Multimodal Image Alignment via Linear Mapping between Feature Modalities

    Directory of Open Access Journals (Sweden)

    Yanyun Jiang

    2017-01-01

    Full Text Available We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

  16. Nonrigid Registration of Lung CT Images Based on Tissue Features

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2013-01-01

    Full Text Available Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

  17. 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.

  18. Improved image retrieval based on fuzzy colour feature vector

    Science.gov (United States)

    Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

    2013-03-01

    One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

  19. HDR IMAGING FOR FEATURE DETECTION ON DETAILED ARCHITECTURAL SCENES

    Directory of Open Access Journals (Sweden)

    G. Kontogianni

    2015-02-01

    Full Text Available 3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  20. RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

    Directory of Open Access Journals (Sweden)

    Z. Wang

    2017-09-01

    Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  1. Feature detection in satellite images using neural network technology

    Science.gov (United States)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  2. Research on Forest Flame Recognition Algorithm Based on Image Feature

    Science.gov (United States)

    Wang, Z.; Liu, P.; Cui, T.

    2017-09-01

    In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  3. An image-processing methodology for extracting bloodstain pattern features.

    Science.gov (United States)

    Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

    2017-08-01

    There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Magnetic resonance imaging of anterior temporal lobe cysts in children: discriminating special imaging features in a particular group of diseases

    Energy Technology Data Exchange (ETDEWEB)

    Hoffmann Nunes, Renato; Torres Pacheco, Felipe; Rocha, Antonio Jose da [Fleury Medicina e Saude, Division of Neuroradiology, Sao Paulo (Brazil); Servico de Diagnostico por Imagem, Division of Neuroradiology, Santa Casa de Misericordia de Sao Paulo Paulo, Sao Paulo (Brazil)

    2014-07-15

    We hypothesized that disorders with anterior temporal lobe (ATL) cysts might exhibit common peculiarities and distinguishable imaging features that could be useful for diagnosis. We reviewed a series of patients for neuroimaging contributions to specific diagnoses. A literature search was conducted, and institutional imaging files were reviewed to identify MR examinations with ATL cysts in children. Patients were divided according to head size, calcifications, white matter and cortical abnormalities. Unsupervised hierarchical clustering of patients on the basis of their MR and CT items was performed. We identified 23 patients in our database in whom MR revealed ATL cysts. Our series included five patients with congenital muscular dystrophy (05/23 = 21.7 %), six with megalencephalic leukoencephalopathy with subcortical cysts (06/23 = 26.1 %), three with non-megalencephalic leukoencephalopathy with subcortical cysts (03/23 = 13.1 %), seven with congenital cytomegalovirus disease (07/23 = 30.4 %) and two with Aicardi-Goutieres syndrome (02/23 = 8.7 %). After analysis, 11 clusters resulted in the highest discriminative indices. Thereafter, patients' clusters were linked to their underlying diseases. The features that best discriminated between clusters included brainstem abnormalities, cerebral calcifications and some peculiar grey and white matter abnormalities. A flow chart was drafted to guide the radiologist in these diagnoses. The authors encourage the combined interpretation of these features in the herein proposed approach that confidently predicted the final diagnosis in this particular group of disorders associated with ATL cysts. (orig.)

  5. Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features.

    Science.gov (United States)

    Lee, Susan C; Endo, Yoshimi; Potter, Hollis G

    Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Clinical review. Level 4. MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C.

  6. Feature Recognition of Froth Images Based on Energy Distribution Characteristics

    Directory of Open Access Journals (Sweden)

    WU Yanpeng

    2014-09-01

    Full Text Available This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition.

  7. 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.

  8. Imaging features of poorly controlled congenital adrenal hyperplasia in adults

    Science.gov (United States)

    Sherlock, M; Healy, N A; Doody, O; Govender, P; Torreggiani, W C

    2015-01-01

    Congenital adrenal hyperplasia (CAH) is a genetic autosomal recessive condition most frequently as a result of a mutation in the 21-hydroxylase enzyme gene. Patients with poorly controlled CAH can manifest characteristic imaging findings as a result of adrenocorticotrophic hormone stimulation or the effects of cortisol precursor excess on various target organs. We present a spectrum of imaging findings encountered in adult patients with poorly treated CAH, with an emphasis on radiological features and their clinical relevance. PMID:26133223

  9. Estimating signal features from noisy images with stochastic backgrounds

    Science.gov (United States)

    Whitaker, Meredith Kathryn

    Imaging is often used in scientific applications as a measurement tool. The location of a target, brightness of a star, and size of a tumor are all examples of object features that are sought after in various imaging applications. A perfect measurement of these quantities from image data is impossible because of, most notably, detector noise fluctuations, finite resolution, sensitivity of the imaging instrument, and obscuration by undesirable object structures. For these reasons, sophisticated image-processing techniques are designed to treat images as random variables. Quantities calculated from an image are subject to error and fluctuation; implied by calling them estimates of object features. This research focuses on estimator error for tasks common to imaging applications. Computer simulations of imaging systems are employed to compare the estimates to the true values. These computations allow for algorithm performance tests and subsequent development. Estimating the location, size, and strength of a signal embedded in a background structure from noisy image data is the basic task of interest. The estimation task's degree of difficulty is adjusted to discover the simplest data-processing necessary to yield successful estimates. Even when using an idealized imaging model, linear Wiener estimation was found to be insufficient for estimating signal location and shape. These results motivated the investigation of more complex data processing. A new method (named the scanning-linear estimator because it maximizes a linear functional) is successful in cases where linear estimation fails. This method has also demonstrated positive results when tested in realistic simulations of tomographic SPECT imaging systems. A comparison to a model of current clinical estimation practices found that the scanning-linear method offers substantial gains in performance.

  10. Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.

    Science.gov (United States)

    Udupa, Jayaram K; Odhner, Dewey; Zhao, Liming; Tong, Yubing; Matsumoto, Monica M S; Ciesielski, Krzysztof C; Falcao, Alexandre X; Vaideeswaran, Pavithra; Ciesielski, Victoria; Saboury, Babak; Mohammadianrasanani, Syedmehrdad; Sin, Sanghun; Arens, Raanan; Torigian, Drew A

    2014-07-01

    To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and

  11. Feature analysis for detecting people from remotely sensed images

    Science.gov (United States)

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

    We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algorithm in real-life mass events.

  12. Scene classification of infrared images based on texture feature

    Science.gov (United States)

    Zhang, Xiao; Bai, Tingzhu; Shang, Fei

    2008-12-01

    Scene Classification refers to as assigning a physical scene into one of a set of predefined categories. Utilizing the method texture feature is good for providing the approach to classify scenes. Texture can be considered to be repeating patterns of local variation of pixel intensities. And texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity. Texture describes the structural information of images, so it provides another data to classify comparing to the spectrum. Now, infrared thermal imagers are used in different kinds of fields. Since infrared images of the objects reflect their own thermal radiation, there are some shortcomings of infrared images: the poor contrast between the objectives and background, the effects of blurs edges, much noise and so on. Because of these shortcomings, it is difficult to extract to the texture feature of infrared images. In this paper we have developed an infrared image texture feature-based algorithm to classify scenes of infrared images. This paper researches texture extraction using Gabor wavelet transform. The transformation of Gabor has excellent capability in analysis the frequency and direction of the partial district. Gabor wavelets is chosen for its biological relevance and technical properties In the first place, after introducing the Gabor wavelet transform and the texture analysis methods, the infrared images are extracted texture feature by Gabor wavelet transform. It is utilized the multi-scale property of Gabor filter. In the second place, we take multi-dimensional means and standard deviation with different scales and directions as texture parameters. The last stage is classification of scene texture parameters with least squares support vector machine (LS-SVM) algorithm. SVM is based on the principle of structural risk minimization (SRM). Compared with SVM, LS-SVM has overcome the shortcoming of

  13. A Feature Set for Cytometry on Digitized Microscopic Images

    Directory of Open Access Journals (Sweden)

    Karsten Rodenacker

    2003-01-01

    Full Text Available Feature extraction is a crucial step in most cytometry studies. In this paper a systematic approach to feature extraction is presented. The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated. The feature sets described are divided into morphometric, densitometric, textural and structural features. The latter group is used to describe the eu‐ and hetero‐chromatin in a way complementing the textural methods. The main goal of the paper is to bring attention to the need of a common and well defined description of features used in cyto‐ and histometrical studies. The application of the sets of features is shown in an overview of projects from different fields. Finally some rules of thumb for the design of studies in this field are proposed. Colour figures can be viewed on http://www.esacp.org/acp/2003/25‐1/rodenacker.htm.

  14. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Characterizing mammographic images by using generic texture features.

    Science.gov (United States)

    Häberle, Lothar; Wagner, Florian; Fasching, Peter A; Jud, Sebastian M; Heusinger, Katharina; Loehberg, Christian R; Hein, Alexander; Bayer, Christian M; Hack, Carolin C; Lux, Michael P; Binder, Katja; Elter, Matthias; Münzenmayer, Christian; Schulz-Wendtland, Rüdiger; Meier-Meitinger, Martina; Adamietz, Boris R; Uder, Michael; Beckmann, Matthias W; Wittenberg, Thomas

    2012-04-10

    Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.

  16. MR Imaging Features of Obturator Internus Bursa of the Hip

    Science.gov (United States)

    Lee, Sun Wha; Kim, Jong Oh

    2008-01-01

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the "boomerang"-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium. PMID:18682677

  17. MR Imaging Features of Obturator Internus Bursa of the Hip

    OpenAIRE

    Hwang, Ji Young; Lee, Sun Wha; Kim, Jong Oh

    2008-01-01

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the "boomerang"-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium.

  18. MR Imaging Features of Obturator Internus Bursa of the Hip

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Ji Young; Lee, Sun Wha; Kim, Jong Oh [School of Medicine, Ewha Womans University, Seoul (Korea, Republic of)

    2008-08-15

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the 'boomerang'-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium

  19. MR imaging features of obturator internus bursa of the hip.

    Science.gov (United States)

    Hwang, Ji Young; Lee, Sun Wha; Kim, Jong Oh

    2008-01-01

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the "boomerang"-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium.

  20. Identification and Quantification Soil Redoximorphic Features by Digital Image Processing

    Science.gov (United States)

    Soil redoximorphic features (SRFs) have provided scientists and land managers with insight into relative soil moisture for approximately 60 years. The overall objective of this study was to develop a new method of SRF identification and quantification from soil cores using a digital camera and imag...

  1. Detection of fungal damaged popcorn using image property covariance features

    Science.gov (United States)

    Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that cause a symptom called “blue-eye.” This infection of popcorn kernels causes economic losses because of their poor appearance and the frequently disagreeable flavor of the popped kernels. Images of ker...

  2. Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

    Directory of Open Access Journals (Sweden)

    A F M Saifuddin Saif

    Full Text Available Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA. Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

  3. HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    L. Xie

    2017-05-01

    Full Text Available Despite the success of multi-view stereo (MVS reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

  4. Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps

    Directory of Open Access Journals (Sweden)

    Torsten Mattfeldt

    2002-01-01

    Full Text Available Comparative genomic hybridization (CGH is an established genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i normal or minimally affected by losses and gains, (ii regions with many losses and few gains and (iii regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses, and that the regions 9q and 20q belonged to the same cluster (associated with gains. For the incidental cancers such clear correlations could not be demonstrated.

  5. THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

    OpenAIRE

    Akcay, O.; Avsar, E. O.

    2017-01-01

    A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical re...

  6. Visual Pattern Analysis in Histopathology Images Using Bag of Features

    Science.gov (United States)

    Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.

    This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.

  7. THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

    Directory of Open Access Journals (Sweden)

    O. Akcay

    2017-05-01

    Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

  8. The Effect of Image Enhancement Methods during Feature Detection and Matching of Thermal Images

    Science.gov (United States)

    Akcay, O.; Avsar, E. O.

    2017-05-01

    A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

  9. Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection

    Directory of Open Access Journals (Sweden)

    Jiajing Wang

    2014-01-01

    Full Text Available Unsupervised synthetic aperture radar (SAR image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U. To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data.

  10. Salient feature region: a new method for retinal image registration.

    Science.gov (United States)

    Zheng, Jian; Tian, Jie; Deng, Kexin; Dai, Xiaoqian; Zhang, Xing; Xu, Min

    2011-03-01

    Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration.

  11. EVALUATION OF SELECTED FEATURES FOR CAR DETECTION IN AERIAL IMAGES

    Directory of Open Access Journals (Sweden)

    S. Tuermer

    2012-09-01

    Full Text Available The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.

  12. Evaluation of Selected Features for CAR Detection in Aerial Images

    Science.gov (United States)

    Tuermer, S.; Leitloff, J.; Reinartz, P.; Stilla, U.

    2011-09-01

    The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.

  13. Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation

    Directory of Open Access Journals (Sweden)

    Maowei He

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC, for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

  14. Estimating perception of scene layout properties from global image features.

    Science.gov (United States)

    Ross, Michael G; Oliva, Aude

    2010-01-08

    The relationship between image features and scene structure is central to the study of human visual perception and computer vision, but many of the specifics of real-world layout perception remain unknown. We do not know which image features are relevant to perceiving layout properties, or whether those features provide the same information for every type of image. Furthermore, we do not know the spatial resolutions required for perceiving different properties. This paper describes an experiment and a computational model that provides new insights on these issues. Humans perceive the global spatial layout properties such as dominant depth, openness, and perspective, from a single image. This work describes an algorithm that reliably predicts human layout judgments. This model's predictions are general, not specific to the observers it trained on. Analysis reveals that the optimal spatial resolutions for determining layout vary with the content of the space and the property being estimated. Openness is best estimated at high resolution, depth is best estimated at medium resolution, and perspective is best estimated at low resolution. Given the reliability and simplicity of estimating the global layout of real-world environments, this model could help resolve perceptual ambiguities encountered by more detailed scene reconstruction schemas.

  15. Feature-aided multiple target tracking in the image plane

    Science.gov (United States)

    Brown, Andrew P.; Sullivan, Kevin J.; Miller, David J.

    2006-05-01

    Vast quantities of EO and IR data are collected on airborne platforms (manned and unmanned) and terrestrial platforms (including fixed installations, e.g., at street intersections), and can be exploited to aid in the global war on terrorism. However, intelligent preprocessing is required to enable operator efficiency and to provide commanders with actionable target information. To this end, we have developed an image plane tracker which automatically detects and tracks multiple targets in image sequences using both motion and feature information. The effects of platform and camera motion are compensated via image registration, and a novel change detection algorithm is applied for accurate moving target detection. The contiguous pixel blob on each moving target is segmented for use in target feature extraction and model learning. Feature-based target location measurements are used for tracking through move-stop-move maneuvers, close target spacing, and occlusion. Effective clutter suppression is achieved using joint probabilistic data association (JPDA), and confirmed target tracks are indicated for further processing or operator review. In this paper we describe the algorithms implemented in the image plane tracker and present performance results obtained with video clips from the DARPA VIVID program data collection and from a miniature unmanned aerial vehicle (UAV) flight.

  16. Central nervous system lymphoma: magnetic resonance imaging features at presentation

    Directory of Open Access Journals (Sweden)

    Ricardo Schwingel

    2012-02-01

    Full Text Available OBJECTIVE: This paper aimed at studying presentations of the central nervous system (CNS lymphoma using structural images obtained by magnetic resonance imaging (MRI. METHODS: The MRI features at presentation of 15 patients diagnosed with CNS lymphoma in a university hospital, between January 1999 and March 2011, were analyzed by frequency and cross tabulation. RESULTS: All patients had supratentorial lesions; and four had infra- and supratentorial lesions. The signal intensity on T1 and T2 weighted images was predominantly hypo- or isointense. In the T2 weighted images, single lesions were associated with a hypointense signal component. Six patients presented necrosis, all of them showed perilesional abnormal white matter, nine had meningeal involvement, and five had subependymal spread. Subependymal spread and meningeal involvement tended to occur in younger patients. CONCLUSION: Presentations of lymphoma are very pleomorphic, but some of them should point to this diagnostic possibility.

  17. Large Scale Hierarchical K-Means Based Image Retrieval With MapReduce

    Science.gov (United States)

    2014-03-27

    version of Hadoop at the time of this research. Hadoop is the open-source implementation of MapReduce and HDFS. • OpenCV [10]: C++ library for Computer...Boost.Python[1]: Allows for wrapping C++ code to create Python modules. Exposes full C++ OpenCV libraries, as well as native speeds for computation intensive...Yield (′0′, (ImageName, f eature)) end for end function Figure 3.3: MapReduce Feature Extraction Algorithm C++ using the OpenCV libraby. The C

  18. A comparison of image features for registering LWIR and visual images

    CSIR Research Space (South Africa)

    Cronje, J

    2012-11-01

    Full Text Available Features SURF [7] was inspired by SIFT [4], with the main goal to improve the execution speed of the detector and descriptor. SURF depends mainly on an integral image to approximate and speed-up the execution time. The detector relies... band). Other benefits may be found such as the haze mitigation of visual images via incorporating a Near Infra-Red (NIR) channel [2]. B. Related Work Many examples of image feature detector/descriptors have been developed for matching features...

  19. [Analysis of fragmented images perception: local features and global description].

    Science.gov (United States)

    Shelepin, Iu E; Chikhman, V N; Foreman, N

    2008-07-01

    Analysis of experimental investigations of the perception of incomplete images is presented. It illustrates two different approaches to work of the brain mechanisms involved: one approach is based on the perception of whole images and another on local informative features. These approaches describe two different mechanisms, which are possibly used by brain systems for incomplete image recognition. Performance on the Gollin test (measuring recognition thresholds for fragmented line drawings of everyday objects and animals) depends upon recognition based on image informational-statistical characteristics. We suggest that recognition thresholds for Gollin stimuli in part reflect the extraction of signal from noise. The brain uses local informative features as an additional source of information about them. We have suggested that fragmented images in the Gollin-test are perceived as whole structures. This structure is compared with a template in memory which is extracted with the help of selective attention mechanism in accordance with a matched filtration model. The Gollin-test is a tool for differential diagnosis of a various forms of cognitive disorders.

  20. WAVELET BASED SEGMENTATION USING OPTIMAL STATISTICAL FEATURES ON BREAST IMAGES

    Directory of Open Access Journals (Sweden)

    A. Sindhuja

    2014-05-01

    Full Text Available Elastography is the emerging imaging modality that analyzes the stiffness of the tissue for detecting and classifying breast tumors. Computer-aided detection speeds up the diagnostic process of breast cancer improving the survival rate. A multiresolution approach using Discrete wavelet transform is employed on real time images, using the low-low (LL, low-high (LH, high-low (HL, and high-high (HH sub-bands of Daubechies family. Features are extracted, selected and then finally segmented by K-means clustering algorithm. The proposed work can be extended to Classification of the tumors.

  1. FRACTAL IMAGE FEATURE VECTORS WITH APPLICATIONS IN FRACTOGRAPHY

    Directory of Open Access Journals (Sweden)

    Hynek Lauschmann

    2011-05-01

    Full Text Available The morphology of fatigue fracture surface (caused by constant cycle loading is strictly related to crack growth rate. This relation may be expressed, among other methods, by means of fractal analysis. Fractal dimension as a single numerical value is not sufficient. Two types of fractal feature vectors are discussed: multifractal and multiparametric. For analysis of images, the box-counting method for 3D is applied with respect to the non-homogeneity of dimensions (two in space, one in brightness. Examples of application are shown: images of several fracture surfaces are analyzed and related to crack growth rate.

  2. Cervical spine injury in the elderly: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Ehara, S. [Dept. of Radiology, Iwate Medical University School of Medicine, Morioka (Japan); Shimamura, Tadashi [Dept. of Orthopedic Surgery, Iwate Medical University School of Medicine, Morioka (Japan)

    2001-01-01

    An increase in the elderly population has resulted in an increased incidence of cervical spine injury in this group. No specific type of cervical spine trauma is seen in the elderly, although dens fractures are reported to be common. Hyperextension injuries due to falling and the resultant central cord syndrome in the mid and lower cervical segments due to decreased elasticity as a result of spondylosis may be also characteristic. The imaging features of cervical spine injury are often modified by associated spondylosis deformans, DISH and other systemic disorders. The value of MR imaging in such cases is emphasized. (orig.)

  3. Desirable features of an infrared imaging system for aerodynamic research

    Science.gov (United States)

    Wright, Robert E., Jr.; Puram, Chith K.; Daryabeigi, Kamran

    1992-01-01

    Advantage of non-intrusiveness, capability for field measurement, and increased availability of IR imaging systems have resulted in their wider use for aerodynamic research. However, certain difficulties persist while using currently available systems for such applications. A critical evaluation of the IR imaging systems is presented on the basis of the state-of-the-art of IR imaging technology and experiences in wind tunnel and flight testing at NASA's Langley Research Center. The requirements for using IR thermography as a measurement tool in aerodynamic research are examined in terms of range, sensitivity, and accuracy of temperature measurement, temporal and spatial resolution, and features of target. Deficiencies of present IR imaging systems are identified, and user precautions to avoid such problems by proper selection and operation of these units are suggested. Different aspects of imager performance such as imager optics, video capabilities, and environmental tolerance are discussed. Electronic data recording and image processing hardware and software requirements are evaluated. Slit response tests and spatial resolution are discussed with the objective of obtaining reliable, accurate, and meaningful information from IR thermography measurements for aerodynamic studies.

  4. MR imaging features of peritoneal adenomatoid mesothelioma: a case report

    Energy Technology Data Exchange (ETDEWEB)

    Lins, Cynthia Maria Coelho; Elias Junior, Jorge; Muglia, Valdair Francisco; Monteiro, Carlos Ribeiro [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Internal Medicine], e-mail: jejunior@fmrp.usp.br; Cunha, Adilson Ferreira [School of Medicine of Sao Jose do Rio Preto (FAMERP), SP (Brazil). Dept. of Gynecology and Obstetrics; Valeri, Fabio V. [Victorio Valeri Institute of Medical Diagnosis, Ribeirao Preto, SP (Brazil); Feres, Omar [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Surgery and Anatomy

    2009-07-01

    Adenomatoid mesothelioma of the peritoneum (AMP) is a rare benign tumor originating from mesothelial cells.1 Most frequently, AMP occurs between 26 and 55 years of age, at a mean age of 41 years. In contrast to diffuse malignant mesothelioma, which has been linked to asbestos exposure, the etiology of AMP has not been established. Only a minority of patients have symptoms related to the tumor. AMP may present local recurrence, but it has no potential for malignant transformation. Although there are many case reports of abdominal mesotheliomas, to date, there have been no reports of MR imaging features of AMP. In this article, we present the MR imaging features of a case of AMP with histopathological correlation. (author)

  5. Hyperspectral image classification based on NMF Features Selection Method

    Science.gov (United States)

    Abe, Bolanle T.; Jordaan, J. A.

    2013-12-01

    Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

  6. Local features in natural images via singularity theory

    CERN Document Server

    Damon, James; Haslinger, Gareth

    2016-01-01

    This monograph considers a basic problem in the computer analysis of natural images, which are images of scenes involving multiple objects that are obtained by a camera lens or a viewer’s eye. The goal is to detect geometric features of objects in the image and to separate regions of the objects with distinct visual properties. When the scene is illuminated by a single principal light source, we further include the visual clues resulting from the interaction of the geometric features of objects, the shade/shadow regions on the objects, and the “apparent contours”. We do so by a mathematical analysis using a repertoire of methods in singularity theory. This is applied for generic light directions of both the “stable configurations” for these interactions, whose features remain unchanged under small viewer movement, and the generic changes which occur under changes of view directions. These may then be used to differentiate between objects and determine their shapes and positions.

  7. Spinal cord ischemia: aetiology, clinical syndromes and imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Weidauer, Stefan [Frankfurt Univ., Sankt Katharinen Hospital Teaching Hospital, Frankfurt am Main (Germany). Dept. of Neurology; Hattingen, Elke; Berkefeld, Joachim [Frankfurt Univ., Frankfurt am Main (Germany). Inst. of Neuroradiology; Nichtweiss, Michael

    2015-03-01

    The purpose of this study was to analyse MR imaging features and lesion patterns as defined by compromised vascular territories, correlating them to different clinical syndromes and aetiological aspects. In a 19.8-year period, clinical records and magnetic resonance imaging (MRI) features of 55 consecutive patients suffering from spinal cord ischemia were evaluated. Aetiologies of infarcts were arteriosclerosis of the aorta and vertebral arteries (23.6 %), aortic surgery or interventional aneurysm repair (11 %) and aortic and vertebral artery dissection (11 %), and in 23.6 %, aetiology remained unclear. Infarcts occurred in 38.2 % at the cervical and thoracic level, respectively, and 49 % of patients suffered from centromedullar syndrome caused by anterior spinal artery ischemia. MRI disclosed hyperintense pencil-like lesion pattern on T2WI in 98.2 %, cord swelling in 40 %, enhancement on post-contrast T1WI in 42.9 % and always hyperintense signal on diffusion-weighted imaging (DWI) when acquired. The most common clinical feature in spinal cord ischemia is a centromedullar syndrome, and in contrast to anterior spinal artery ischemia, infarcts in the posterior spinal artery territory are rare. The exclusively cervical location of the spinal sulcal artery syndrome seems to be a likely consequence of anterior spinal artery duplication which is observed preferentially here. (orig.)

  8. Level set method coupled with Energy Image features for brain MR image segmentation.

    Science.gov (United States)

    Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminita

    2014-06-01

    Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

  9. Visual features underlying perceived brightness as revealed by classification images.

    Directory of Open Access Journals (Sweden)

    Ilmari Kurki

    Full Text Available Along with physical luminance, the perceived brightness is known to depend on the spatial structure of the stimulus. Often it is assumed that neural computation of the brightness is based on the analysis of luminance borders of the stimulus. However, this has not been tested directly. We introduce a new variant of the psychophysical reverse-correlation or classification image method to estimate and localize the physical features of the stimuli which correlate with the perceived brightness, using a brightness-matching task. We derive classification images for the illusory Craik-O'Brien-Cornsweet stimulus and a "real" uniform step stimulus. For both stimuli, classification images reveal a positive peak at the stimulus border, along with a negative peak at the background, but are flat at the center of the stimulus, suggesting that brightness is determined solely by the border information. Features in the perceptually completed area in the Craik-O'Brien-Cornsweet do not contribute to its brightness, nor could we see low-frequency boosting, which has been offered as an explanation for the illusion. Tuning of the classification image profiles changes remarkably little with stimulus size. This supports the idea that only certain spatial scales are used for computing the brightness of a surface.

  10. Unusual acute encephalitis involving the thalamus: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sam Soo [Kangwon National University Hospital, Chuncheon (Korea, Republic of); Chang, Kee Hyun; Kim, Kyung Won; Han Moon Hee [Seoul National University College of Medicine, Seoul (Korea, Republic of); Park, Sung Ho; Nam, Hyun Woo [Seoul City Boramae Hospital, Seoul (Korea, Republic of); Choi, Kyu Ho [Kangnam St. Mary' s Hospital, Seoul (Korea, Republic of); Cho, Woo Ho [Sanggyo Paik Hospital, Seoul (Korea, Republic of)

    2001-06-01

    To describe the brain CT and MR imaging findings of unusual acute encephalitis involving the thalamus. We retrospectively reviewed the medical records and CT and/or MR imaging findings of six patients with acute encephalitis involving the thalamus. CT (n=6) and MR imaging (n=6) were performed during the acute and/or convalescent stage of the illness. Brain CT showed brain swelling (n=2), low attenuation of both thalami (n=1) or normal findings (n=3). Initial MR imaging indicated that in all patients the thalamus was involved either bilaterally (n=5) or unilaterally (n=1). Lesions were also present in the midbrain (n=5), medial temporal lobe (n=4), pons (n=3), both hippocampi (n=3) the insular cortex (n=2), medulla (n=2), lateral temporal lobe cortex (n=1), both cingulate gyri (n=1), both basal ganglia (n=1), and the left hemispheric cortex (n=1). These CT or MR imaging findings of acute encephalitis of unknown etiology were similar to a combination of those of Japanese encephalitis and herpes simplex encephalitis. In order to document the specific causative agents which lead to the appearance of these imaging features, further investigation is required.

  11. Textural features and neural network for image classification

    Science.gov (United States)

    Haddadi, Souad; Fernandez, C.; Abdelnour, F.

    1996-03-01

    In this paper, we present a neural network approach for scene analysis: detection of human beings in images. To solve this problem, a precise classification system is required, with adaptation systems based on data processing. These systems must be largely parallel, which is why neural networks have been chosen. The first part of this text is a brief introduction to neural networks and their applications. The second part is a description of the image base composed for experiments and the low-level processing used, then we detail the method used to extract the texture feature of images. The third part describes the Bayesian method and its application to our problem. Part four shows the association of these texture processes with the neural network for identification of human beings. Finally, we conclude with the validity of the method and its future applications.

  12. Multispectral image feature fusion for detecting land mines

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Fields, D.J.; Sherwood, R.J. [Lawrence Livermore National Lab., CA (United States)] [and others

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  13. CFA-aware features for steganalysis of color images

    Science.gov (United States)

    Goljan, Miroslav; Fridrich, Jessica

    2015-03-01

    Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.

  14. The imaging features of neurologic complications of left atrial myxomas

    Energy Technology Data Exchange (ETDEWEB)

    Liao, Wei-Hua; Ramkalawan, Divya; Liu, Jian-Ling; Shi, Wei [Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Zee, Chi-Shing [Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033 (United States); Yang, Xiao-Su; Li, Guo-Liang; Li, Jing [Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Wang, Xiao-Yi, E-mail: cjr.wangxiaoyi@vip.163.com [Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China)

    2015-05-15

    Background: Neurologic complications may be the first symptoms of atrial myxomas. Understanding the imaging features of neurologic complications of atrial myxomas can be helpful for the prompt diagnosis. Objective: To identify neuroimaging features for patients with neurologic complications attributed to atrial myxoma. Methods: We retrospectively reviewed the medical records of 103 patients with pathologically confirmed atrial myxoma at Xiangya Hospital from January 2009 to January 2014. The neuroimaging data for patients with neurologic complications were analyzed. Results: Eight patients with atrial myxomas (7.77%) presented with neurologic manifestations, which constituted the initial symptoms for seven patients (87.5%). Neuroimaging showed five cases of cerebral infarctions and three cases of aneurysms. The main patterns of the infarctions were multiplicity (100.0%) and involvement of the middle cerebral artery territory (80.0%). The aneurysms were fusiform in shape, multiple in number (100.0%) and located in the distal middle cerebral artery (100.0%). More specifically, high-density in the vicinity of the aneurysms was observed on CT for two patients (66.7%), and homogenous enhancement surrounding the aneurysms was detected in the enhanced imaging for two patients (66.7%). Conclusion: Neurologic complications secondary to atrial myxoma consist of cerebral infarctions and aneurysms, which show certain characteristic features in neuroimaging. Echocardiography should be performed in patients with multiple cerebral infarctions, and multiple aneurysms, especially when aneurysms are distal in location. More importantly, greater attention should be paid to the imaging changes surrounding the aneurysms when myxomatous aneurysms are suspected and these are going to be the relevant features in our article.

  15. Electrophysiological features and multimodal imaging in ritonavir-related maculopathy.

    Science.gov (United States)

    Faure, Céline; Paques, Michel; Audo, Isabelle

    2017-12-01

    The purpose of this study is to report a case of ritonavir-related retinal toxicity followed over a year. Electrophysiological features and multimodal imaging, including adaptive optics, are provided and discussed. Electrophysiological recordings and multimodal imaging were performed and repeated over 1 year. Fundus examination revealed crystalline maculopathy in conjunction with pigment disruption. Spectral domain optical coherence tomography displayed thinning of the macula without cysts. Autofluorescence imaging revealed a mixed pattern of complete loss of the autofluorescence in the area of retinal pigment deposit and an increased transmission of the autofluorescence in the area of retinal thinning. Fluorescein angiography ruled out parafoveal telangiectasia. Indocyanine green angiography was not contributive. Increased spacing of the macular cone mosaic, crystal deposits and pigment migrations were seen with adaptive optics. Full-field electroretinogram was slightly reduced for both eyes, especially in the light-adapted responses, and mfERG confirmed bilateral maculopathy. Functional and structural abnormalities did not change with follow-up besides constant pigmentary changes monitored with adaptive optics. Ritonavir-related retinal toxicity is a maculopathy with peculiar features including crystalline and pigment migration associated with central or temporofoveolar thinning and inconstant macular telangiectasia. Despite drug cessation, retinal remodelling continues to progress.

  16. Feature selection from hyperspectral imaging for guava fruit defects detection

    Science.gov (United States)

    Mat Jafri, Mohd. Zubir; Tan, Sou Ching

    2017-06-01

    Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.

  17. Novel feature extraction method for hyperspectral remote sensing image

    Science.gov (United States)

    Liu, Chunhong; Zhao, Huijie

    2007-11-01

    In order to reduce high dimensions of hyperspectral remote sensing image and concentrate optimal information to reduced bands, this paper proposed a new method of feature extraction. The new method has two steps. The first step is to reduce the high dimensions by selecting high informative and low correlative bands according to the indexes calculated by a smart band selection method. The criterions that SBS method complied are: (1) The selected bands have the most information; (2) The selected bands have the smallest correlation with other bands. The second step is to decompose the selected bands by a novel second generation wavelet, predicting and updating subimages on rectangle and quincunx grids by Neville filters, finally using variance weighting as fusion weight. A 126-band HYMAP hyperspectral data was experimented in order to test the effect of the new method. The results showed classification accuracy is increased by using the novel feature extraction method.

  18. Nonhemorrhagic Adrenal Infarction With Magnetic Resonance Imaging Features During Pregnancy.

    Science.gov (United States)

    Guenette, Jeffrey P; Tatli, Servet

    2015-10-01

    Adrenal infarction is an infrequent cause of severe abdominal pain during pregnancy. The magnetic resonance imaging (MRI) features of adrenal infarction have not previously been thoroughly described. A 20-year-old woman, gravida 1 para 0, presented at 27 4/7 weeks of gestation with sudden-onset right upper quadrant and flank pain. A 29-year-old woman, gravida 2 para 1, presented at 17 5/7 weeks of gestation with sudden-onset right abdominal and flank pain and again at 35 5/7 weeks of gestation with sudden-onset severe left flank and upper quadrant pain. In both patients, unilateral adrenal infarction was diagnosed on contrast-enhanced computed tomography after initial nondiagnostic ultrasonography and MRI. Clinical presentation and MRI features of nonhemorrhagic adrenal infarction are described. Nonhemorrhagic adrenal infarction may be an underdiagnosed cause of acute abdominal pain during pregnancy and can be diagnosed with MRI.

  19. Imaging features of constrictive pericarditis: beyond pericardial thickening

    Energy Technology Data Exchange (ETDEWEB)

    Napolitano, G.; Pressacco, J.; Paquet, E. [Dept. of Radiology, Montreal Heart Inst., Montreal, Quebec (Canada)], E-mail: napolitanog@hotmail.com

    2009-02-15

    Constrictive pericarditis is caused by adhesions between the visceral and parietal layers of the pericardium and progressive pericardial fibrosis that restricts diastolic filling of the heart. Later on, the thickened pericardium may calcify. Despite a better understanding of the pathophysiologic basis of the imaging findings in constrictive pericarditis and the recent advent of magnetic resonance imaging (MRI) technology, which has dramatically improved the visualization of the pericardium, the diagnosis of constrictive pericarditis remains a challenge in many cases. In patients with clinical suspicion of underlying constrictive pericarditis, the most important radiologic diagnostic feature is abnormal pericardial thickening, which can be shown readily by computed tomography (CT) and especially by MRI, and is highly suggestive of constrictive pericarditis. Nevertheless, a thickened pericardium does not always indicate constrictive pericarditis. Furthermore, constrictive pericarditis can occur without pericardial thickening. (author)

  20. Featured Image: New Detail in the Toothbrush Cluster

    Science.gov (United States)

    Kohler, Susanna

    2018-01-01

    This spectacular composite (click here for the full image) reveals the galaxy cluster 1RXS J0603.3+4214, known as the Toothbrush cluster due to the shape of its most prominent radio relic. Featured in a recent publication led by Kamlesh Rajpurohit (Thuringian State Observatory, Germany), this image contains new Very Large Array (VLA) 1.5-GHz observations (red) showing the radio emission within the cluster. This is composited with a Chandra view of the X-ray emitting gas of the cluster (blue) and an optical image of the background from Subaru data. The new deep VLA data totaling 26 hours of observations provides a detailed look at the complex structure within the Toothbrush relic, revealing enigmatic filaments and twists (see below). This new data will help us to explore the possible merger history of this cluster, which is theorized to have caused the unusual shapes we see today. For more information, check out the original article linked below.High resolution VLA 12 GHz image of the Toothbrush showing the complex, often filamentary structures. [Rajpurohit et al. 2018]CitationK. Rajpurohit et al 2018 ApJ 852 65. doi:10.3847/1538-4357/aa9f13

  1. Feature Extraction in IR Images Via Synchronous Video Detection

    Science.gov (United States)

    Shepard, Steven M.; Sass, David T.

    1989-03-01

    IR video images acquired by scanning imaging radiometers are subject to several problems which make measurement of small temperature differences difficult. Among these problems are 1) aliasing, which occurs When events at frequencies higher than the video frame rate are observed, 2) limited temperature resolution imposed by the 3-bit digitization available in existing commercial systems, and 3) susceptibility to noise and background clutter. Bandwidth narrowing devices (e.g. lock-in amplifiers or boxcar averagers) are routinely used to achieve a high degree of signal to noise improvement for time-varying 1-dimensional signals. We will describe techniques which allow similar S/N improvement for 2-dimensional imagery acquired with an off the shelf scanning imaging radiometer system. These techniques are iplemented in near-real-time, utilizing a microcomputer and specially developed hardware and software . We will also discuss the application of the system to feature extraction in cluttered images, and to acquisition of events which vary faster than the frame rate.

  2. Automatic evaluation of skin histopathological images for melanocytic features

    Science.gov (United States)

    Koosha, Mohaddeseh; Hoseini Alinodehi, S. Pourya; Nicolescu, Mircea; Safaei Naraghi, Zahra

    2017-03-01

    Successfully detecting melanocyte cells in the skin epidermis has great significance in skin histopathology. Because of the existence of cells with similar appearance to melanocytes in hematoxylin and eosin (HE) images of the epidermis, detecting melanocytes becomes a challenging task. This paper proposes a novel technique for the detection of melanocytes in HE images of the epidermis, based on the melanocyte color features, in the HSI color domain. Initially, an effective soft morphological filter is applied to the HE images in the HSI color domain to remove noise. Then a novel threshold-based technique is applied to distinguish the candidate melanocytes' nuclei. Similarly, the method is applied to find the candidate surrounding halos of the melanocytes. The candidate nuclei are associated with their surrounding halos using the suggested logical and statistical inferences. Finally, a fuzzy inference system is proposed, based on the HSI color information of a typical melanocyte in the epidermis, to calculate the similarity ratio of each candidate cell to a melanocyte. As our review on the literature shows, this is the first method evaluating epidermis cells for melanocyte similarity ratio. Experimental results on various images with different zooming factors show that the proposed method improves the results of previous works.

  3. Heterotopic Pancreas: Histopathologic Features, Imaging Findings, and Complications.

    Science.gov (United States)

    Rezvani, Maryam; Menias, Christine; Sandrasegaran, Kumaresan; Olpin, Jeffrey D; Elsayes, Khaled M; Shaaban, Akram M

    2017-01-01

    Heterotopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separate from the main gland. The most common locations of this displacement include the upper gastrointestinal tract-specifically, the stomach, duodenum, and proximal jejunum. Less common sites are the esophagus, ileum, Meckel diverticulum, biliary tree, mesentery, and spleen. Uncomplicated heterotopic pancreas is typically asymptomatic, with the lesion being discovered incidentally during an unrelated surgery, during an imaging examination, or at autopsy. The most common computed tomographic appearance of heterotopic pancreas is that of a small oval intramural mass with microlobulated margins and an endoluminal growth pattern. The attenuation and enhancement characteristics of these lesions parallel their histologic composition. Acinus-dominant lesions demonstrate avid homogeneous enhancement after intravenous contrast material administration, whereas duct-dominant lesions are hypovascular and heterogeneous. At magnetic resonance imaging, the heterotopic pancreas is isointense to the orthotopic pancreas, with characteristic T1 hyperintensity and early avid enhancement after intravenous gadolinium-based contrast material administration. Heterotopic pancreatic tissue has a rudimentary ductal system in which an orifice is sometimes visible at imaging as a central umbilication of the lesion. Complications of heterotopic pancreas include pancreatitis, pseudocyst formation, malignant degeneration, gastrointestinal bleeding, bowel obstruction, and intussusception. Certain complications may be erroneously diagnosed as malignancy. Paraduodenal pancreatitis is thought to be due to cystic degeneration of heterotopic pancreatic tissue in the medial wall of the duodenum. Recognizing the characteristic imaging features of heterotopic pancreas aids in differentiating it from cancer and thus in avoiding unnecessary surgery. © RSNA, 2017.

  4. CT and MR imaging features in patients with intracranial dolichoectasia

    Energy Technology Data Exchange (ETDEWEB)

    Tien, Kuang Lung; Yu, In Kyu; Yoon, Sook Ja; Yoon, Yong Kyu [Eulji College of Medicine, Eulji Hospital, Seoul (Korea, Republic of)

    2000-02-01

    To describe the CT and MR imaging features in patients with intracranial dolichoectasia. The CT (n=3D21), MR (n=3D20) and MRA (n=3D11) imaging features seen in 28 patients (M:F=3D12:16 aged between 65 and 82 (mean, 65) years) with intracranial dolichoectasia were retrospectively reviewed with regard to involved sites, arterial changes (maximum diameter, wall calcification, high signal intensity in the involved artery, as seen on T1-weighted MR images), infarction, hemorrhagic lesion, compression of brain parenchyma or cranial nerves, hydrocephalus and brain atrophy. Involved sites were classified as either type 1 (involvement of only the posterior circulation), type 2 (only the anterior circulation), or type 3 (both). In order of frequency, involved sites were type 1 (43%), type 3 (36%) and type 2 (22%). Dolichoectasia was more frequently seen in the posterior circulation (79%) than in the anterior (57%). Arterial changes as seen on T1-weighted MR images, included dolichoectasia (mean maximum diameter 7.4 mm in the distal internal carotid artery, and 6.7 mm in the basilar artery), wall calcification (100% in involved arteries) and high signal intensity in involved. Cerebral infarction in the territory of the involved artery was found in all patients, and a moderate degree of infarct was 87%. Hemorrhagic lesions were found in 19 patients (68%); these were either lobar (53%), petechial (37%), or subarachnoid (16%), and three patients showed intracranial aneurysms, including one case of dissecting aneurysm. In 19 patients (68%), lesions were compressed lesions by the dolichoectatic arteries, and were found-in order of descending frequency-in the medulla, pons, thalamus, and cerebellopontine angle cistern. Obstructive hydrocephalus was found in two patients (7%), and 23 (82%) showed a moderate degree of brain atrophy. In patients with intracranial dolichoectasia, moderate degrees of cerebral infarction and brain atrophy in the territory of involved arteries, as well as

  5. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J Carlos Moreno

    2010-09-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  6. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Moreno J Carlos

    2010-12-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  7. Fusion of Deep Features and Weighted VLAD Vectors based on Multiple Features for Image Retrieval

    Directory of Open Access Journals (Sweden)

    Wang Yanhong.

    2017-01-01

    Full Text Available In traditional vector of locally aggregated descriptors (VLAD method, the final VLAD vector is reshaped by summing up the residuals between each descriptor and its corresponding visual word. The norm of the residuals varies significantly, and it can make “visual burst”. This is caused by a fact that the contribution of each descriptor to VLAD vector is not the same. To address this problem, we add a different weight to each residual such that the contribution of each descriptor to the VLAD vector becomes even to a certain degree. Also, traditional VLAD method only uses the local gradient features of images. Thus it has a low discrimination. In this paper, local color features are extracted and used to the VLAD method. Moreover, we fuse deep features and the multiple VLAD vectors based on local gradient and color information. Also, in order to reduce running time and improve retrieval accuracy, PCA and whitening operations are used for VLAD vectors. Our proposed method is evaluated on three benchmark datasets, i.e., Holidays, Ukbench and Oxford5k. Experimental results show that our proposed method achieves good performance.

  8. PERFORMANCE ANALYSIS OF SET PARTITIONING IN HIERARCHICAL TREES (SPIHT ALGORITHM FOR A FAMILY OF WAVELETS USED IN COLOR IMAGE COMPRESSION

    Directory of Open Access Journals (Sweden)

    A. Sreenivasa Murthy

    2014-11-01

    Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.

  9. Cystic synovial sarcomas: imaging features with clinical and histopathologic correlation

    Energy Technology Data Exchange (ETDEWEB)

    Nakanishi, Hirofumi; Araki, Nobuhito [Department of Orthopedic Surgery, Osaka Medical Center for Cancer and Cardiovascular Diseases, 1-3-3, Nakamichi, Higashinari-Ku, 537-8511, Osaka (Japan); Sawai, Yuka [Department of Radiology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka (Japan); Kudawara, Ikuo [Department of Orthopedic Surgery, Osaka National Hospital, Osaka (Japan); Mano, Masayuki; Ishiguro, Shingo [Department of Pathology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka (Japan); Ueda, Takafumi; Yoshikawa, Hideki [Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka (Japan)

    2003-12-01

    To characterize the radiological and clinicopathologic features of cystic synovial sarcoma. Seven patients with primary cystic synovial sarcoma were evaluated. Computed tomography (CT) and magnetic resonance (MR) imaging were undertaken at the first presentation. The diagnosis of synovial sarcoma was made on the basis of histological examinations followed by molecular analysis. Radiological and clinicopathologic findings were reviewed. CT showed well-defined soft tissue mass without cortical bone erosion and invasion. Calcification was seen at the periphery of the mass in three cases. T2-weighted MR images showed multilocular inhomogeneous intensity mass in all cases, five of which showed fluid-fluid levels. On gross appearance, old and/or fresh hematomas were detected in six cases. In the one remaining case, microscopic hemorrhage in the cystic lumen was proven. Four cases had poorly differentiated areas. In five cases prominent hemangiopericytomatous vasculature was observed. Histologic grade was intermediate in one tumor and high in six. One case had a history of misdiagnosis for tarsal tunnel syndrome, one for lymphadenopathy, two for sciatica and two for hematoma. All cystic synovial sarcomas demonstrated multilocularity with well-circumscribed walls and internal septae. Synovial sarcoma should be taken into consideration in patients with deeply situated multicystic mass with triple signal intensity on T2-weighted MR imaging. (orig.)

  10. Features of magnetic resonance imaging brain in eclampsia: clinicoradiologic correlation

    Directory of Open Access Journals (Sweden)

    Mubarak F

    2012-08-01

    Full Text Available Fatima Mubarak, Muhammad Idris, Quratulain HadiDepartment of Radiology, Aga Khan University Hospital, Karachi, PakistanObjective: Eclampsia is a gestational hypertensive condition that typically occurs after 20 weeks of pregnancy and is characterized by hypertension, peripheral edema, proteinuria, and seizures. Magnetic resonance imaging (MRI plays a vital role in the diagnosis and management of these patients, so it is essential to describe features of the brain MRI in these cases.Methods: MRI was performed on eleven consecutive patients with eclampsia. All patients underwent follow-up neurologic examinations until all symptoms resolved. Nine of those eleven patients underwent follow-up MRI. The clinical signs and symptoms were correlated with findings on initial and follow-up MRI.Results: MRI typically demonstrated bilateral hyperintense lesions on T2-weighted images and hypointense lesions on T1-weighted images without diffusion restriction. MRI abnormalities are most commonly located in the distribution of the posterior cerebral circulation mainly in occipital and parietal lobes, and are associated with visual disturbances and dizziness. Almost all lesions seen at MRI in patients with eclampsia were reversible in our series of patients.Conclusion: Involvement of the parietal and occipital lobes is common in patients with eclampsia, and the signal abnormalities on MRI are reversible if recognized and treated early.Keywords: pregnancy, seizures, hypertension, brain, MRI findings, reversible

  11. Feature-oriented multiple description wavelet-based image coding.

    Science.gov (United States)

    Liu, Yilong; Oraintara, Soontorn

    2007-01-01

    We address the problem of resilient image coding over error-prone networks where packet losses occur. Recent literature highlights the multiple description coding (MDC) as a promising approach to solve this problem. In this paper, we introduce a novel wavelet-based multiple description image coder, referred to as the feature-oriented MDC (FO-MDC). The proposed multiple description (MD) coder exploits the statistics of the wavelet coefficients and identifies the subsets of samples that are sensitive to packet loss. A joint optimization between tree-pruning and quantizer selection in the rate-distortion sense is used in order to allocate more bits to these sensitive coefficients. When compared with the state-of-the-art MD scalar quantization coder, the proposed FO-MDC yields a more efficient central-side distortion tradeoff control mechanism. Furthermore, it proves to be more robust for image transmission even with high packet loss ratios, which makes it suitable for protecting multimedia streams over packet-erasure channels.

  12. Programming with Hierarchical Maps

    DEFF Research Database (Denmark)

    Ørbæk, Peter

    This report desribes the hierarchical maps used as a central data structure in the Corundum framework. We describe its most prominent features, ague for its usefulness and briefly describe some of the software prototypes implemented using the technology....

  13. Imaging Features of Pilocytic Astrocytoma in Cerebral Ventricles.

    Science.gov (United States)

    Xia, Jg; Yin, B; Liu, L; Lu, Yp; Geng, Dy; Tian, Wz

    2016-09-01

    Our aim was to identify imaging characteristics of pilocytic astrocytomas (PAs) in the cerebral ventricles to help radiologists distinguish PAs from other brain tumors preoperatively. Twelve postsurgery patients with a pathological PA diagnosis were included. Among them, 10 had submitted to surgery based on 3.0-T magnetic resonance imaging sequences and 7 because of computed tomography (CT) results. We analyzed their clinical and radiological records retrospectively. The 12 patients (7 were male) had 13 lesions (11 with a single focus, 1 with multiple foci). Average age was 26.5 years (range, 6-49 years). Clinical symptoms included headache, dizziness, vomiting, and unstable gait. Tumor locations were the lateral ventricle (4), fourth ventricle (7), or both ventricles (1, but multifocal). One tumor had disseminated. PA diameters were 18.7-63.0 mm (mean ± standard deviation, 36.5 ± 12.4 mm). Nine had a round margin, and four had irregular margins. Two were cystic lesions. Eleven were mixed cystic and solid. CT showed the tumors as low-density masses. Two had calcifications. Their cystic portions showed low signal intensity (SI) on T1-weighted imaging (T1WI) and high SI on T2-weighted imaging (T2WI). The cystic walls and solid portions of the PAs showed slightly low SI on T1WI and slightly high SI on T2WI. After gadopentetate dimeglumine administration, the solid portion showed heterogeneous enhancement, whereas the cystic portion showed no enhancement. Radiological features of intraventricular and extraventricular PAs were similar to typical ones, including enhanced nodules within cysts. Radiological findings can usually diagnose PAs correctly.

  14. Imaging features of lower limb malformations above the foot.

    Science.gov (United States)

    Bergère, A; Amzallag-Bellenger, E; Lefebvre, G; Dieux-Coeslier, A; Mezel, A; Herbaux, B; Boutry, N

    2015-09-01

    Lower limb malformations are generally isolated or sporadic events. However, they are sometimes associated with other anomalies of the bones and/or viscera in patients with constitutional syndromes or disorders of the skeleton. This paper reviews the main imaging features of these abnormalities, which generally exhibit a broad spectrum. This paper focuses on several different bone malformations: proximal focal femoral deficiency, congenital short femur and femoral duplication for the femur, tibial hemimelia (aplasia/hypoplasia of the tibia) and congenital bowing for the tibia, fibular hemimelia (aplasia/hypoplasia) for the fibula, and aplasia, hypoplasia and congenital dislocation for the patella. Copyright © 2015 Éditions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

  15. Imaging trace element distributions in single organelles and subcellular features

    Science.gov (United States)

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

    2016-02-01

    The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. It could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.

  16. Clinical characteristics and MR imaging features of nonalcoholic Wernicke encephalopathy.

    Science.gov (United States)

    Fei, G-Q; Zhong, C; Jin, L; Wang, J; Zhang, Yuhao; Zheng, X; Zhang, Yuwen; Hong, Z

    2008-01-01

    Nonalcoholic Wernicke encephalopathy (WE) is prone to be underestimated in clinical practice. The purpose of this study was to improve its awareness and early accurate diagnosis. We conducted a retrospective review of the cases of 12 patients with nonalcoholic WE, consisting of clinical characteristics and MR imaging features as well as follow-up after administration of thiamine. Patients with mild coma or lethargy (7/12) exhibited typical MR features of symmetric brain paraventricular damage. Patients without disturbances of consciousness or who only had drowsiness (3/12) exhibited a lesion of the periaqueductal area only. In addition to typical MR manifestations, symmetric cortical involvement was observed in 2 of 12 patients with deep coma. Gadolinium enhancement of the mammillary bodies was observed in 2 of 3 patients. No atrophy of the mammillary bodies and cerebellar vermis was found in any patients. Of 10 patients without deep coma and cortical damage, 2 missed the follow-up and 8, who recovered clinically, also showed accordant resolution of abnormal hyperintense signal intensity on T2-weighted and fluid-attenuated inversion recovery images within 2 weeks to 1 year after thiamine supplementation. Two patients with deep coma and cortical damage showed a poor prognosis:1 patient died 15 days after being diagnosed with WE, and the other entered a persistent vegetative state during a follow-up of 2 years. Typical symmetric damage of the mammillary bodies and brain paraventricular regions may permit a specific diagnosis of nonalcoholic WE. In all patients, no atrophy of the mammillary bodies and cerebellar vermis was found. Cortical involvement in patients with nonalcoholic WE may be indicative of irreversible lesions and a poor prognosis.

  17. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.

    Science.gov (United States)

    Chen, Lei; Zhang, Yu-Hang; Huang, Guohua; Pan, Xiaoyong; Wang, ShaoPeng; Huang, Tao; Cai, Yu-Dong

    2017-09-14

    As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important.

  18. Hemicrania Continua: Functional Imaging and Clinical Features With Diagnostic Implications.

    Science.gov (United States)

    Sahler, Kristen

    2013-04-10

    This review focuses on summarizing 2 pivotal articles in the clinical and pathophysiologic understanding of hemicrania continua (HC). The first article, a functional imaging project, identifies both the dorsal rostral pons (a region associated with the generation of migraines) and the posterior hypothalamus (a region associated with the generation of cluster and short-lasting unilateral neuralgiform headache with conjunctival injection and tearing [SUNCT]) as active during HC. The second article is a summary of the clinical features seen in a prospective cohort of HC patients that carry significant diagnostic implications. In particular, they identify a wider range of autonomic signs than what is currently included in the International Headache Society criteria (including an absence of autonomic signs in a small percentage of patients), a high frequency of migrainous features, and the presence of aggravation and/or restlessness during attacks. Wide variations in exacerbation length, frequency, pain description, and pain location (including side-switching pain) are also noted. Thus, a case is made for widening and modifying the clinical diagnostic criteria used to identify patients with HC. © 2013 American Headache Society.

  19. Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

    Science.gov (United States)

    Liang, Yu-Li

    Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

  20. Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

    Science.gov (United States)

    Danala, Gopichandh; Thai, Theresa; Gunderson, Camille C; Moxley, Katherine M; Moore, Kathleen; Mannel, Robert S; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2017-10-01

    The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients. A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared. The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P imaging markers and RECIST, respectively. This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  1. 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization.

    Science.gov (United States)

    Noblet, Vincent; Heinrich, Christian; Heitz, Fabrice; Armspach, Jean-Paul

    2005-05-01

    This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

  2. Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China

    Directory of Open Access Journals (Sweden)

    Jing Sun

    2016-11-01

    Full Text Available Object-based image analysis (OBIA is an emerging technique for analyzing remote sensing image based on object properties including spectral, geometry, contextual and texture information. To reduce the computational cost of this comprehensive OBIA and make it more feasible in disaster responses, we developed a unique approach – distributed and hierarchical OBIA approach for damage assessment. This study demonstrated a completed classification of YingXiu town, heavily devastated by the 2008 Wenchuan earthquake using Quickbrid imagery. Two distinctive areas, mountainous areas and urban, were analyzed separately. This approach does not require substantial processing power and large amounts of available memory because image of a large disaster-affected area was split in smaller pieces. Two or more computers could be used in parallel to process and analyze these sub-images based on different requirements. The approach can be applicable in other cases whereas the established set of rules can be adopted in similar study areas. More experiments will be carried out in future studies to prove its feasibility.

  3. A feature-preserving hair removal algorithm for dermoscopy images.

    Science.gov (United States)

    Abbas, Qaisar; Garcia, Irene Fondón; Emre Celebi, M; Ahmad, Waqar

    2013-02-01

    Accurate segmentation and repair of hair-occluded information from dermoscopy images are challenging tasks for computer-aided detection (CAD) of melanoma. Currently, many hair-restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesion's pattern. In this article, a novel hair-restoration algorithm is presented, which has a capability to preserve the skin lesion features such as color and texture and able to segment both dark and light hairs. Our algorithm is based on three major steps: the rough hairs are segmented using a matched filtering with first derivative of gaussian (MF-FDOG) with thresholding that generate strong responses for both dark and light hairs, refinement of hairs by morphological edge-based techniques, which are repaired through a fast marching inpainting method. Diagnostic accuracy (DA) and texture-quality measure (TQM) metrics are utilized based on dermatologist-drawn manual hair masks that were used as a ground truth to evaluate the performance of the system. The hair-restoration algorithm is tested on 100 dermoscopy images. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation (PDE), and (iii) exemplar-based repairing techniques. Among different hair detection and removal techniques, our proposed algorithm obtained the highest value of DA: 93.3% and TQM: 90%. The experimental results indicate that the proposed algorithm is highly accurate, robust and able to restore hair pixels without damaging the lesion texture. This method is fully automatic and can be easily integrated into a CAD system. © 2011 John Wiley & Sons A/S.

  4. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    Science.gov (United States)

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

    2015-01-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

  5. Magnetic resonance imaging features of asymptomatic bipartite patella

    Energy Technology Data Exchange (ETDEWEB)

    O' Brien, J., E-mail: juliemobrien@gmail.com [Department of Radiology, Adelaide and Meath Incorporating National Children' s Hospital, Tallaght, Dublin 24 (Ireland); Murphy, C.; Halpenny, D.; McNeill, G.; Torreggiani, W.C. [Department of Radiology, Adelaide and Meath Incorporating National Children' s Hospital, Tallaght, Dublin 24 (Ireland)

    2011-06-15

    Objective: The purpose of our study was to describe the magnetic resonance imaging (MRI) features of bipartite patella in asymptomatic patients. Materials and methods: The study was prospective in type and performed following institutional ethical committees approval. In total, 25 subjects were recruited into the study and informed consent obtained in each case. The local radiology database was utilised in conjunction with a clinical questionnaire to identify patients who had asymptomatic bipartite patella. Any patient with a history of trauma or symptomatic disease was excluded from the study. MRI imaging was performed in each case on a 1.5 T system using a dedicated knee coil and a standardised knee protocol. The images obtained were then analysed by two musculoskeletal radiologists in consensus. Results: Of the 25 subjects, there were 8 females and 17 males. The mean age was 34.6 years. All but one of the bipartite fragments were located on the superolateral aspect of the patella. In 23 cases, one fragment was identified. The average transverse diameter of the fragment was 12.8 mm. The average distance between the fragment and the adjacent patella in the axial plane was 1.46 mm. In addition, the cartilage overlying the patella and accessory fragment was intact in all cases. The average thickness of the patella cartilage at its border to the fragment was 2.4 mm with an average ratio of the cartilage thickness of the fragment as compared with the cartilage thickness of the patella of 0.72. There was no evidence of high signal or bone marrow oedema on fluid sensitive sequences within either the patella or the fragment in any of the patients. Fluid was identified in the cleft between the patella and the fragment in the majority of cases. Conclusions: Asymptomatic bipartite patella is characterised by intact but thinned cartilage along the border between the patella and the fragment, fluid between the cleft and a lack of any bone marrow oedema or high signal within

  6. Robust ellipse detection based on hierarchical image pyramid and Hough transform.

    Science.gov (United States)

    Chien, Chung-Fang; Cheng, Yu-Che; Lin, Ta-Te

    2011-04-01

    In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%. © 2011 Optical Society of America

  7. Postmortem imaging: MDCT features of postmortem change and decomposition.

    Science.gov (United States)

    Levy, Angela D; Harcke, Howard Theodore; Mallak, Craig T

    2010-03-01

    Multidetector computed tomography (MDCT) has emerged as an effective imaging technique to augment forensic autopsy. Postmortem change and decomposition are always present at autopsy and on postmortem MDCT because they begin to occur immediately upon death. Consequently, postmortem change and decomposition on postmortem MDCT should be recognized and not mistaken for a pathologic process or injury. Livor mortis increases the attenuation of vasculature and dependent tissues on MDCT. It may also produce a hematocrit effect with fluid levels in the large caliber blood vessels and cardiac chambers from dependent layering erythrocytes. Rigor mortis and algor mortis have no specific MDCT features. In contrast, decomposition through autolysis, putrefaction, and insect and animal predation produce dramatic alterations in the appearance of the body on MDCT. Autolysis alters the attenuation of organs. The most dramatic autolytic changes on MDCT are seen in the brain where cerebral sulci and ventricles are effaced and gray-white matter differentiation is lost almost immediately after death. Putrefaction produces a pattern of gas that begins with intravascular gas and proceeds to gaseous distension of all anatomic spaces, organs, and soft tissues. Knowledge of the spectrum of postmortem change and decomposition is an important component of postmortem MDCT interpretation.

  8. Spectral images browsing using principal component analysis and set partitioning in hierarchical tree

    Science.gov (United States)

    Ma, Long; Zhao, Deping

    2011-12-01

    Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new area requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems are important because they offer improved color reproduction quality not only for a standard observer under a particular illuminantion, but for any other individual exhibiting normal color vision capability under another illuminantion. A possibility for browsing of the archives is needed. In this paper, the authors present a new spectral image browsing architecture. The architecture for browsing is expressed as follow: (1) The spectral domain of the spectral image is reduced with the PCA transform. As a result of the PCA transform the eigenvectors and the eigenimages are obtained. (2) We quantize the eigenimages with the original bit depth of spectral image (e.g. if spectral image is originally 8bit, then quantize eigenimage to 8bit), and use 32bit floating numbers for the eigenvectors. (3) The first eigenimage is lossless compressed by JPEG-LS, the other eigenimages were lossy compressed by wavelet based SPIHT algorithm. For experimental evalution, the following measures were used. We used PSNR as the measurement for spectral accuracy. And for the evaluation of color reproducibility, ΔE was used.here standard D65 was used as a light source. To test the proposed method, we used FOREST and CORAL spectral image databases contrain 12 and 10 spectral images, respectively. The images were acquired in the range of 403-696nm. The size of the images were 128*128, the number of bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method is suitable for browsing, i.e., for visual purpose.

  9. Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun [Dept. of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-03-15

    The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process.

  10. Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

    Directory of Open Access Journals (Sweden)

    Keranmu Xielifuguli

    2014-01-01

    Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

  11. Magnetic resonance imaging features of leukoaraiosis in elderly dogs.

    Science.gov (United States)

    Scarpante, Elena; Cherubini, Giunio Bruto; de Stefani, Alberta; Taeymans, Olivier

    2017-07-01

    Leukoaraiosis is a descriptive term used to designate bilateral, symmetrical, white matter lesions identified in brains of elderly human patients. These lesions are isointense to normal in magnetic resonance imaging (MRI) T1-weighted pulse sequences, non-contrast enhancing, and hyperintense in T2-weighted and FLAIR pulse sequences. Pathophysiologic mechanisms for leukoaraiosis remain incompletely understood; however, an ischemic origin is currently being favored. Age-related changes, such as brain atrophy, ventricular enlargement, and well-demarcated sulci, have also been previously described in dogs over 9 years of age. Objectives of this retrospective case series study were to describe MRI features of leukoaraiosis and brain atrophy in a group of elderly dogs. The Dick White Referrals MRI database between October 2009 and April 2016 was reviewed. Dogs with bilaterally symmetrical periventricular areas of T2 and FLAIR hyperintensity compatible with leukoaraiosis, and older than 9 years, were included. Fourteen dogs met the inclusion criteria, with a total of 18 MRI studies available for review. Median age for sampled dogs was 13 years. Ten dogs had MRI signs of concurrent brain atrophy; one of them had signs of brain atrophy before leukoaraiotic changes could be identified. In those cases where serial MRIs were available, progressive reduction of interthalamic adhesion thickness was observed. The current study introduces leukoaraiosis as a descriptive term for the MRI sign of bilaterally symmetrical, periventricular T2, and FLAIR hyperintensities in brains of elderly dogs. Future studies are needed to determine pathophysiologic mechanisms for this MRI sign. © 2017 American College of Veterinary Radiology.

  12. Neuroimaging feature terminology: A controlled terminology for the annotation of brain imaging features

    NARCIS (Netherlands)

    Iyappan, A. (Anandhi); Younesi, E. (Erfan); Redolfi, A. (Alberto); H.A. Vrooman (Henri); Khanna, S. (Shashank); G.B. Frisoni (Giovanni B.); M. Hofmann-Apitius (Martin)

    2017-01-01

    textabstractOntologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging

  13. Using Structural Features to Detect Buildings in Panchromatic Satellite Images

    OpenAIRE

    Sirmacek, Beril; Unsalan, Cem

    2011-01-01

    Detecting buildings from very high resolution aerial and satellite images is very important for mapping, urban planning, and land use analysis. Although it is possible to manually locate buildings from these very high resolution images; this operation may not be robust and fast. Therefore, automated systems to detect buildings from very high resolution aerial and satellite images are needed. Unfortunately, solution is not straightforward due to diverse characteristics and uncontrolled imaging...

  14. Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

    Directory of Open Access Journals (Sweden)

    Hui Huang

    2017-01-01

    Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

  15. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  16. Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.

    Directory of Open Access Journals (Sweden)

    Feng Jin

    Full Text Available Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS. The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume.

  17. Image search engine with selective filtering and feature-element-based classification

    Science.gov (United States)

    Li, Qing; Zhang, Yujin; Dai, Shengyang

    2001-12-01

    With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

  18. Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms.

    Science.gov (United States)

    Jaccard, N; Szita, N; Griffin, L D

    2017-09-03

    Phase contrast microscopy (PCM) is routinely used for the inspection of adherent cell cultures in all fields of biology and biomedicine. Key decisions for experimental protocols are often taken by an operator based on typically qualitative observations. However, automated processing and analysis of PCM images remain challenging due to the low contrast between foreground objects (cells) and background as well as various imaging artefacts. We propose a trainable pixel-wise segmentation approach whereby image structures and symmetries are encoded in the form of multi-scale Basic Image Features local histograms, and classification of them is learned by random decision trees. This approach was validated for segmentation of cell versus background, and discrimination between two different cell types. Performance close to that of state-of-the-art specialised algorithms was achieved despite the general nature of the method. The low processing time ( < 4 s per 1280 × 960 pixel images) is suitable for batch processing of experimental data as well as for interactive segmentation applications.

  19. Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (Open Access)

    Science.gov (United States)

    2016-02-18

    shared among images in the same geographic region. In the fields of image retrieval, there is a large body of lit- erature on feature selection and... images covering the Pittsburgh (U.S.) area. These images contain 8 overlap- ping perspective views extracted from the spherical panora- mas in two...

  20. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.

    Science.gov (United States)

    Xu, Yan; Jia, Zhipeng; Wang, Liang-Bo; Ai, Yuqing; Zhang, Fang; Lai, Maode; Chang, Eric I-Chao

    2017-05-26

    Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

  1. Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors

    OpenAIRE

    Mousavi, Hojjat Seyed; Srinivas, Umamahesh; Monga, Vishal; Suo, Yuanming; Dao, Minh; Tran, Trac D.

    2015-01-01

    Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \\emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phen...

  2. Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

    Science.gov (United States)

    Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

    2017-03-01

    This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

  3. Local feature representation based on linear filtering with feature pooling and divisive normalization for remote sensing image classification

    Science.gov (United States)

    Wan, Lihong; Liu, Na; Guo, Yiyou; Huo, Hong; Fang, Tao

    2017-01-01

    We propose a local feature representation based on two types of linear filtering, feature pooling, and nonlinear divisive normalization for remote sensing image classification. First, images are decomposed using a bank of log-Gabor and Gaussian derivative filters to obtain filtering responses that are robust to changes in various lighting conditions. Second, the filtering responses computed using the same filter at nearby locations are pooled together to enhance position invariance and compact representation. Third, divisive normalization with channel-wise strategy, in which each pooled feature is divided by a common factor plus the sum of the neighboring features to reduce dependencies among nearby locations, is introduced to extract divisive normalization features (DNFs). Power-law transformation and principal component analysis are applied to make DNF significantly distinguishable, followed by feature fusion to enhance local description. Finally, feature encoding is used to aggregate DNFs into a global representation. Experiments on 21-class land use and 19-class satellite scene datasets demonstrate the effectiveness of the channel-wise divisive normalization compared with standard normalization across channels and the fusion of the two types of linear filtering in improving classification accuracy. The experiments also illustrate that the proposed method is competitive with state-of-the-art approaches.

  4. Road Detection From Aerial Images Using Color Features

    OpenAIRE

    Sirmacek, Beril; Unsalan, Cem

    2011-01-01

    Urban regions are dynamic environments. Especially their road maps change by the expansion of the urban region. Therefore, automatic detection of roads from very high resolution aerial and satellite images is a very important research field. Unfortunately, the solution is not straightforward by using basic image processing and computer vision algorithms. Therefore, advanced methods are needed for road network detection from aerial and satellite images. In this study, we propose a nov...

  5. Imaging features of intradural spinal paragonimiasis: a case report.

    Science.gov (United States)

    Kim, M K; Cho, B M; Yoon, D Y; Nam, E S

    2011-04-01

    Spinal paragonimiasis is a rare form of ectopic infestation caused by Paragonimus westermani. We report a case of pathologically proven intradural paragonimiasis associated with concurrent intracranial involvement. MRI revealed multiple well-defined intradural masses that were markedly hypointense on T(2) weighted images and hypointense with a peripheral hyperintense rim on T(1) weighted images. Contrast-enhanced T(1) weighted images showed slight peripheral rim enhancement.

  6. Imaging features of non-traumatic vascular liver emergencies.

    Science.gov (United States)

    Onur, Mehmet Ruhi; Karaosmanoglu, Ali Devrim; Akca, Onur; Ocal, Osman; Akpinar, Erhan; Karcaaltincaba, Musturay

    2017-05-01

    Acute non-traumatic liver disorders can originate from abnormalities of the hepatic artery, portal vein and hepatic veins. Ultrasonography and computed tomography can be used in non-traumatic acute vascular liver disorders according to patient status, indication and appropriateness of imaging modality. Awareness of the imaging findings, in the appropriate clinical context, is crucial for prompt and correct diagnosis, as delay may cause severe consequences with significant morbidity and mortality. This review article will discuss imaging algorithms, and multimodality imaging findings for suspected acute vascular disorders of the liver.

  7. An Adequate Approach to Image Retrieval Based on Local Level Feature Extraction

    Directory of Open Access Journals (Sweden)

    Sumaira Muhammad Hayat Khan

    2010-10-01

    Full Text Available Image retrieval based on text annotation has become obsolete and is no longer interesting for scientists because of its high time complexity and low precision in results. Alternatively, increase in the amount of digital images has generated an excessive need for an accurate and efficient retrieval system. This paper proposes content based image retrieval technique at a local level incorporating all the rudimentary features. Image undergoes the segmentation process initially and each segment is then directed to the feature extraction process. The proposed technique is also based on image?s content which primarily includes texture, shape and color. Besides these three basic features, FD (Fourier Descriptors and edge histogram descriptors are also calculated to enhance the feature extraction process by taking hold of information at the boundary. Performance of the proposed method is found to be quite adequate when compared with the results from one of the best local level CBIR (Content Based Image Retrieval techniques.

  8. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  9. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

    2015-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  10. 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.

  11. Characterizing mammographic images by using generic texture features

    National Research Council Canada - National Science Library

    Häberle, Lothar; Wagner, Florian; Fasching, Peter A; Jud, Sebastian M; Heusinger, Katharina; Loehberg, Christian R; Hein, Alexander; Bayer, Christian M; Hack, Carolin C; Lux, Michael P; Binder, Katja; Elter, Matthias; Münzenmayer, Christian; Schulz-Wendtland, Rüdiger; Meier-Meitinger, Martina; Adamietz, Boris R; Uder, Michael; Beckmann, Matthias W; Wittenberg, Thomas

    2012-01-01

    .... The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD...

  12. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    Science.gov (United States)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  13. Essential features of Chiari II malformation in MR imaging : an interobserver reliability study-part 1

    NARCIS (Netherlands)

    Geerdink, Niels; van der Vliet, Ton; Rotteveel, Jan J.; Feuth, Ton; Roeleveld, Nel; Mullaart, Reinier A.

    Brain MR imaging is essential in the assessment of Chiari II malformation in clinical and research settings concerning spina bifida. However, the interpretation of morphological features of the malformation on MR images may not always be straightforward. In an attempt to select those features that

  14. Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting.

    Science.gov (United States)

    Yan, Bo; Tan, Weimin; Li, Ke; Tian, Qi

    2017-05-01

    Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k data sets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.

  15. 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.

  16. Caroli's disease: magnetic resonance imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Guy, France; Cognet, Francois; Dranssart, Marie; Cercueil, Jean-Pierre; Conciatori, Laurent; Krause, Denis [Department of Radiology and Imaging, Dijon Le Bocage University Hospital, 2 Blvd. Marechal de Lattre de Tassigny, BP 1542, 21034 Dijon Cedex (France)

    2002-11-01

    Our objective was to describe the main aspects of MR imaging in Caroli's disease. Magnetic resonance cholangiography with a dynamic contrast-enhanced study was performed in nine patients with Caroli's disease. Bile duct abnormalities, lithiasis, dot signs, hepatic enhancement, renal abnormalities, and evidence of portal hypertension were evaluated. Three MR imaging patterns of Caroli's disease were found. In all but two patients, MR imaging findings were sufficient to confirm the diagnosis. Moreover, MR imaging provided information about the severity, location, and extent of liver involvement. This information was useful in planning the best therapeutic strategy. Magnetic resonance cholangiography with a dynamic contrast-enhanced study is a good screening tool for Caroli's disease. Direct cholangiography should be reserved for confirming doubtful cases. (orig.)

  17. Iris image enhancement for feature recognition and extraction

    CSIR Research Space (South Africa)

    Mabuza, GP

    2012-10-01

    Full Text Available domain by using three enhancement techniques namely: (i) local histogram equalisation, (ii) global histogram equalisation and (iii) partial contrast, to improve the quality of iris images from three different iris databases....

  18. Physical features of visual images affect macaque monkey’s preference for these images

    Directory of Open Access Journals (Sweden)

    Shintaro Funahashi

    2016-11-01

    Full Text Available Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database as original stimuli. Four macaque monkeys performed a simple choice task, in which 2 stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful, and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey’s preference and that the complexity, clarity, and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment.

  19. Physical Features of Visual Images Affect Macaque Monkey's Preference for These Images.

    Science.gov (United States)

    Funahashi, Shintaro

    2016-01-01

    Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey's preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment.

  20. Physical Features of Visual Images Affect Macaque Monkey’s Preference for These Images

    Science.gov (United States)

    Funahashi, Shintaro

    2016-01-01

    Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey’s preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment. PMID:27853424

  1. Interpretation of archaeological small-scale features in spectral images

    DEFF Research Database (Denmark)

    Grøn, Ole; Palmer, Susanna; Stylegar, Frans-Arne

    2011-01-01

    The paper's focus is the use of spectral images for the distinction of small archaeological anomalies on the basis of the authors work. Special attention is given to the ground-truthing perspective in the discussion of a number of cases from Norway. Different approaches to pattern-recognition...... are considered in the light of the increasing availability of hyper-spectral images that are difficult to analyse using visual inspection alone....

  2. Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

    Directory of Open Access Journals (Sweden)

    Salam Shuleenda Devi

    2016-12-01

    Full Text Available Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the non- infected and malaria infected erythrocyte have been reviewed.

  3. Neural Networks for Medical Image Processing: A Study of Feature Identification

    OpenAIRE

    Dayhoff, Ruth E.; Dayhoff, Judith E.

    1988-01-01

    Neural networks, a parallel computing architecture modelled on living nervous systems, are able to “learn” by example. The ability of a simulated neural network to distinguish among simulated microscopic amoebae nuclei images was studied. The neural network was successfully shown to organize feature detectors without the intermediate step of manual identification of salient features. The feature detectors were mapped onto the image format and the issue of redundancy was examined.

  4. Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

    Science.gov (United States)

    Eken, S.; Aydın, E.; Sayar, A.

    2017-11-01

    In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  5. DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    S. Eken

    2017-11-01

    Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  6. Joint analysis of histopathology image features and gene expression in breast cancer.

    Science.gov (United States)

    Popovici, Vlad; Budinská, Eva; Čápková, Lenka; Schwarz, Daniel; Dušek, Ladislav; Feit, Josef; Jaggi, Rolf

    2016-05-11

    Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival.

  7. New feature of the neutron color image intensifier

    Science.gov (United States)

    Nittoh, Koichi; Konagai, Chikara; Noji, Takashi; Miyabe, Keisuke

    2009-06-01

    We developed prototype neutron color image intensifiers with high-sensitivity, wide dynamic range and long-life characteristics. In the prototype intensifier (Gd-Type 1), a terbium-activated Gd 2O 2S is used as the input-screen phosphor. In the upgraded model (Gd-Type 2), Gd 2O 3 and CsI:Na are vacuum deposited to form the phosphor layer, which improved the sensitivity and the spatial uniformity. A europium-activated Y 2O 2S multi-color scintillator, emitting red, green and blue photons with different intensities, is utilized as the output screen of the intensifier. By combining this image intensifier with a suitably tuned high-sensitive color CCD camera, higher sensitivity and wider dynamic range could be simultaneously attained than that of the conventional P20-phosphor-type image intensifier. The results of experiments at the JRR-3M neutron radiography irradiation port (flux: 1.5×10 8 n/cm 2/s) showed that these neutron color image intensifiers can clearly image dynamic phenomena with a 30 frame/s video picture. It is expected that the color image intensifier will be used as a new two-dimensional neutron sensor in new application fields.

  8. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

    DEFF Research Database (Denmark)

    Galavis, P.E.; Hollensen, Christian; Jallow, N.

    2010-01-01

    Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes...... reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were...... classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small...

  9. Using Hierarchical Clustering in Order to Increase Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions for Flood Estimation

    Directory of Open Access Journals (Sweden)

    F. Farsadnia

    2017-01-01

    Full Text Available Introduction: Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self Organizing feature Map (SOM method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. Materials and Methods: SOM approximates the probability density function of input data through an unsupervised learning algorithm, and is not only an effective method for clustering, but also for the visualization and abstraction of complex data. The algorithm has properties of neighborhood preservation and local resolution of the input space proportional to the data distribution. A SOM consists of two layers: an input layer formed by a set of nodes and an output layer formed by nodes arranged in a two-dimensional grid. In this study we used SOM for visualization and clustering of watersheds based on physiographical data in North and Razavi Khorasan provinces. In the next step, SOM weight vectors were used to classify the units by Ward’s Agglomerative hierarchical clustering (Ward methods. Ward’s algorithm is a frequently used technique for regionalization studies in hydrology and climatology. It is based on the assumption that if two clusters are merged, the resulting loss of information, or change in the value of objective function, will depend only on the relationship between the two merged clusters and not on the relationships with any other clusters. After the formation of clusters by SOM and Ward, the most frequently applied tests of regional homogeneity based on the theory of L

  10. Infrared image segmentation using HOG feature and kernel extreme learning machine

    Science.gov (United States)

    Liang, Ying; Wang, Luping; Zhang, Luping

    2015-10-01

    Image segmentation is an important application in computer vision. Nowadays, image segmentation of infrared image has not gain as much attention as image segmentation of visible light image. But this application is very useful. For example, searching and tracking targets with infrared search and track system (IRST) has been widely used these days due to its special passive mode. So it can be used as a kind of supplementary equipment for radar. Infrared image segmentation can help computers identify backgrounds of the image, and help it automatically adjust the related parameters for the next work, such as targets recognition or targets detection. Our work proposed a new image segmentation method for infrared image using histogram of oriented gradients (HOG) feature and kernel extreme learning machine (kernel ELM). HOG are feature descriptors which can be used in computer vision and image processing for the purpose of object detection. In this paper, we extract HOG feature of infrared image, and use this feature as the basis for classification. After having feature, we use kernel extreme learning machine to do the segmentation. Kernel extreme learning machine has shown many excellent characteristics in classification. By testing our algorithm proposed in our paper, we demonstrated that our algorithm is effective and feasible.

  11. Linear Features’ Detection in SAR Images Using Fuzzy Edge Detector (FED)

    Science.gov (United States)

    2000-10-01

    together with dissimilarity measure D4 constitute two promising edge detection • ,jI tools for SAR images, as they incorporate despeckling properties...UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADPO10840 TITLE: Linear Features’ Detection in SAR Images Using Fuzzy Edge...report: ADPO10816 thru ADPO10842 UNCLASSIFIED 49-1 Linear features’ detection in SAR images using Fuzzy Edge Detector (FED) Alexandros Dimoun’ 2

  12. Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features.

    Science.gov (United States)

    Zhang, Yi-Ze; Shen, Hong-Bin

    2017-04-24

    Signal peptides play key roles in targeting and translocation of integral membrane proteins and secretory proteins. However, signal peptides present several challenges for automatic prediction methods. One challenge is that it is difficult to discriminate signal peptides from transmembrane helices, as both the H-region of the peptides and the transmembrane helices are hydrophobic. Another is that it is difficult to identify the cleavage site between signal peptides and mature proteins, as cleavage motifs or patterns are still unclear for most proteins. To solve these problems and further enhance automatic signal peptide recognition, we report a new Signal-3L 2.0 predictor. Our new model is constructed with a hierarchical protocol, where it first determines the existence of a signal peptide. For this, we propose a new residue-domain cross-level feature-driven approach, and we demonstrate that protein functional domain information is particularly useful for discriminating between the transmembrane helices and signal peptides as they perform different functions. Next, in order to accurately identify the unique signal peptide cleavage sites along the sequence, we designed a top-down approach where a subset of potential cleavage sites are screened using statistical learning rules, and then a final unique site is selected according to its evolution conservation score. Because this mixed approach utilizes both statistical learning and evolution analysis, it shows a strong capacity for recognizing cleavage sites. Signal-3L 2.0 has been benchmarked on multiple data sets, and the experimental results have demonstrated its accuracy. The online server is available at www.csbio.sjtu.edu.cn/bioinf/Signal-3L/ .

  13. A Hierarchical Structure of Classification based on Trainable Bayesian Classifier for Logo Detection and Recognition in Document Image A Hierarchical Structure of Classification based on Trainable Bayesian Classifier for Logo Detection and Recognition in Document Image

    OpenAIRE

    Hossein Pourghassem

    2010-01-01

    The ever-increasing number of logo (trademark) in official automation systems for information management, archiving and retrieval application has created greater demand for an automatic detection and recognition logo. In this paper, a classification hierarchical structure based on Bayesian classifier is proposed to logo detection and recognition. In this hierarchical structure, using two measures false accept and false reject, a novel and straightforward training scheme is presented to extrac...

  14. Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features.

    Science.gov (United States)

    Murphy, James M; Le Moigne, Jacqueline; Harding, David J

    2016-03-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

  15. Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

    Science.gov (United States)

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2017-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

  16. Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features

    Science.gov (United States)

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2015-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

  17. MR imaging features of foot involvement in patients with psoriasis

    Energy Technology Data Exchange (ETDEWEB)

    Erdem, C. Zuhal [Department of Radiology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey)], E-mail: sunarerdem@yahoo.com; Tekin, Nilgun Solak [Department of Dermatology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey); Sarikaya, Selda [Department of Physical Therapy and Rehabilitation, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey); Erdem, L. Oktay; Gulec, Sezen [Department of Radiology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey)

    2008-09-15

    Objective: To determine alterations of the soft tissues, tendons, cartilage, joint spaces, and bones of the foot using magnetic resonance (MR) imaging in patients with psoriasis. Materials and methods: Clinical and MR examination of the foot was performed in 26 consecutive patients (52 ft) with psoriasis. As a control group, 10 healthy volunteers (20 ft) were also studied. Joint effusion/synovitis, retrocalcaneal bursitis, retroachilles bursitis, Achilles tendonitis, soft-tissue edema, para-articular enthesophytes, bone marrow edema, sinus tarsi syndrome, enthesopathy at the Achilles attachment and at the plantar fascia attachment, plantar fasciitis, tenosynovitis, subchondral cysts, and bone erosions, joint space narrowing, subchondral signal changes, osteolysis, luxation, and sub-luxation were examined. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in none of the patients while frequency of involvement was 92% (24/26) by MR imaging. The most common MR imaging findings were Achilles tendonitis (acute and peritendinitis) (57%), retrocalcaneal bursitis (50%), joint effusion/synovitis (46%), soft-tissue edema (46%), and para-articular enthesophytes (38%). The most commonly involved anatomical region was the hindfoot (73%). Conclusion: Our data showed that the incidence of foot involvement was very high in asymptomatic patients with psoriasis on MR imaging. Further MR studies are needed to confirm these data. We conclude that MR imaging may be of importance especially in early diagnosis and treatment of inflammatory changes in the foot.

  18. Blind image quality assessment based on aesthetic and statistical quality-aware features

    Science.gov (United States)

    Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

    2017-07-01

    The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

  19. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part II. Extracellular agents, hepatobiliary agents, and ancillary imaging features.

    Science.gov (United States)

    Choi, Jin-Young; Lee, Jeong-Min; Sirlin, Claude B

    2014-10-01

    Computed tomography (CT) and magnetic resonance (MR) imaging play critical roles in the diagnosis and staging of hepatocellular carcinoma (HCC). The second article of this two-part review discusses basic concepts of diagnosis and staging, reviews the diagnostic performance of CT and MR imaging with extracellular contrast agents and of MR imaging with hepatobiliary contrast agents, and examines in depth the major and ancillary imaging features used in the diagnosis and characterization of HCC. © RSNA, 2014.

  20. Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT

    Directory of Open Access Journals (Sweden)

    Boris Jutzi

    2011-09-01

    Full Text Available The real and imaginary parts are proposed as an alternative to the usual Polar representation of complex-valued images. It is proven that the transformation from Polar to Cartesian representation contributes to decreased mutual information, and hence to greater distinctiveness. The Complex Scale-Invariant Feature Transform (ℂSIFT detects distinctive features in complex-valued images. An evaluation method for estimating the uniformity of feature distributions in complex-valued images derived from intensity-range images is proposed. In order to experimentally evaluate the proposed methodology on intensity-range images, three different kinds of active sensing systems were used: Range Imaging, Laser Scanning, and Structured Light Projection devices (PMD CamCube 2.0, Z+F IMAGER 5003, Microsoft Kinect.

  1. Spectral feature variations in x-ray diffraction imaging systems

    Science.gov (United States)

    Wolter, Scott D.; Greenberg, Joel A.

    2016-05-01

    Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

  2. The relationship study between image features and detection probability based on psychology experiments

    Science.gov (United States)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  3. A new method to extract stable feature points based on self-generated simulation images

    Science.gov (United States)

    Long, Fei; Zhou, Bin; Ming, Delie; Tian, Jinwen

    2015-10-01

    Recently, image processing has got a lot of attention in the field of photogrammetry, medical image processing, etc. Matching two or more images of the same scene taken at different times, by different cameras, or from different viewpoints, is a popular and important problem. Feature extraction plays an important part in image matching. Traditional SIFT detectors reject the unstable points by eliminating the low contrast and edge response points. The disadvantage is the need to set the threshold manually. The main idea of this paper is to get the stable extremums by machine learning algorithm. Firstly we use ASIFT approach coupled with the light changes and blur to generate multi-view simulated images, which make up the set of the simulated images of the original image. According to the way of generating simulated images set, affine transformation of each generated image is also known. Instead of the traditional matching process which contain the unstable RANSAC method to get the affine transformation, this approach is more stable and accurate. Secondly we calculate the stability value of the feature points by the set of image with its affine transformation. Then we get the different feature properties of the feature point, such as DOG features, scales, edge point density, etc. Those two form the training set while stability value is the dependent variable and feature property is the independent variable. At last, a process of training by Rank-SVM is taken. We will get a weight vector. In use, based on the feature properties of each points and weight vector calculated by training, we get the sort value of each feature point which refers to the stability value, then we sort the feature points. In conclusion, we applied our algorithm and the original SIFT detectors to test as a comparison. While in different view changes, blurs, illuminations, it comes as no surprise that experimental results show that our algorithm is more efficient.

  4. Multimodal Imaging Features in Acute Exudative Paraneoplastic Polymorphous Vitelliform Maculopathy.

    Science.gov (United States)

    Li, Daniel Q; Golding, John; Glittenberg, Carl; Choudhry, Netan

    2016-12-01

    An 85-year-old woman with stage IV breast cancer was referred for gradually progressive blurred vision. Dilated fundus examination revealed unifocal, yellow, round vitelliform lesions in the macular region of both eyes. The diagnosis of acute exudative paraneoplastic polymorphous vitelliform maculopathy (AEPPVM) was confirmed with swept-source optical coherence tomography (SS-OCT), fundus autofluorescence, and fluorescein angiography. SS-OCT angiography revealed normal vascular findings in both eyes. Multimodal imaging is useful in the diagnosis and monitoring of AEPPVM and may further the understanding of its pathophysiology. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:1143-1146.]. Copyright 2016, SLACK Incorporated.

  5. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

    Science.gov (United States)

    Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

    2016-02-01

    Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

    OpenAIRE

    Youmaran, R.; Adler, A.

    2012-01-01

    This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates ...

  7. Extraction of Wavelet Based Features for Classification of T2-Weighted MRI Brain Images

    OpenAIRE

    Ms. Yogita K.Dubey; Mushrif, Milind M.

    2012-01-01

    Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, ...

  8. A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

    Science.gov (United States)

    Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

    2005-04-01

    Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

  9. Global image feature extraction using slope pattern spectra

    CSIR Research Space (South Africa)

    Toudjeu, IT

    2008-06-01

    Full Text Available of coffee beans. Granulometries were also used to estimate the dominant width of the white patterns in the X-ray images of welds [7]. Due to the computational load associated with the calculation of granulometries, Vincent [6], building on the work...

  10. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  11. MR imaging features of foot involvement in ankylosing spondylitis

    Energy Technology Data Exchange (ETDEWEB)

    Erdem, C. Zuhal E-mail: sunarerdem@yahoo.com; Sarikaya, Selda; Erdem, L. Oktay; Ozdolap, Senay; Gundogdu, Sadi

    2005-01-01

    Objective: To determine alterations of the soft tissue, tendon, cartilage, joint space, and bone of the foot using magnetic resonance (MR) imaging in ankylosing spondylitis (AS) patients. Materials and Method: Clinical and MR examination of the foot was performed in 23 AS patients (46 feet). Ten asymptomatic volunteers (20 feet) were studied on MR imaging, as a control group. MR imaging protocol included; T1-weighted spin-echo, T2-weighted fast-field echo (FFE) and fat-suppressed short tau inversion recovery (STIR) sequences in sagittal, sagittal oblique, and coronal planes using a head coil. Specifically, we examined: bone erosions, tendinitis (acute and chronic), para-articular enthesophyte, joint effusion, plantar fasciitis, joint space narrowing, soft tissue edema, bone marrow edema, enthesopathy in the Achilles tendon and plantar fascia attachment, subchondral signal intensity abnormalities (edema and sclerosis), tenosynovitis, retrocalcaneal bursitis, subchondral cysts, subchondral fissures, and bony ankylosis. Midfoot, hindfoot, and ankle were included in examined anatomic regions. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in 3 (13%) of the patients while frequency of involvement was 21 (91%) with MR imaging assessment. The MR imaging findings were bone erosions (65%), Achilles tendinitis (acute and chronic) (61%), para-articular enthesophyte (48%), joint effusion (43%), plantar fasciitis (40%), joint space narrowing (40%), subchondral sclerosis (35%), soft tissue edema (30%), bone marrow edema (30%), enthesopathy of the Achilles attachment (30%), subchondral edema (26%), enthesopathy in the plantar fascia attachment (22%), retrocalcaneal bursitis (22%), subchondral cysts (17%), subchondral fissures (17%), tendinitis and enthesopathy of the plantar ligament (13%), and bony ankylosis (9%). The most common involved anatomical region was the hindfoot (83%) following by midfoot (69% ) and ankle (22

  12. A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

    Directory of Open Access Journals (Sweden)

    Pattichis Marios S

    2007-11-01

    Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

  13. 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.

  14. 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

  15. Finding trajectories of feature points in a monocular image sequence.

    Science.gov (United States)

    Sethi, I K; Jain, R

    1987-01-01

    Identifying the same physical point in more than one image, the correspondence problem, is vital in motion analysis. Most research for establishing correspondence uses only two frames of a sequence to solve this problem. By using a sequence of frames, it is possible to exploit the fact that due to inertia the motion of an object cannot change instantaneously. By using smoothness of motion, it is possible to solve the correspondence problem for arbitrary motion of several nonrigid objects in a scene. We formulate the correspondence problem as an optimization problem and propose an iterative algorithm to find trajectories of points in a monocular image sequence. A modified form of this algorithm is useful in case of occlusion also. We demonstrate the efficacy of this approach considering synthetic, laboratory, and real scenes.

  16. Atypical magnetic resonance imaging features in subacute sclerosing panencephalitis

    Directory of Open Access Journals (Sweden)

    Biplab Das

    2016-01-01

    Full Text Available Objectives: Subacute sclerosing panencephalitis (SSPE is rare chronic, progressive encephalitis that affects primarily children and young adults, caused by a persistent infection with measles virus. No cure for SSPE exists, but the condition can be managed by medication if treatment is started at an early stage. Methods and Results: Heterogeneity of imaging findings in SSPE is not very uncommon. But pial and gyral enhancements are very rarely noticed. Significant asymmetric onset as well as pial-gyral enhancements is not reported. Herein we present a case of 16 years adolescent of SSPE having remarkable asymmetric pial-gyral enhancements, which were misinterpreted as tubercular infection. Conclusion: Early diagnosis and treatment is encouraging in SSPE, although it is not curable with current therapy. Clinico-radiological and electrophysiological correlation is very important in diagnosis of SSPE, more gravely in patients having atypical image findings as in our index case.

  17. Atypical magnetic resonance imaging features in subacute sclerosing panencephalitis.

    Science.gov (United States)

    Das, Biplab; Goyal, Manoj Kumar; Modi, Manish; Mehta, Sahil; Chakravarthi, Sudheer; Lal, Vivek; Vyas, Sameer

    2016-01-01

    Subacute sclerosing panencephalitis (SSPE) is rare chronic, progressive encephalitis that affects primarily children and young adults, caused by a persistent infection with measles virus. No cure for SSPE exists, but the condition can be managed by medication if treatment is started at an early stage. Heterogeneity of imaging findings in SSPE is not very uncommon. But pial and gyral enhancements are very rarely noticed. Significant asymmetric onset as well as pial-gyral enhancements is not reported. Herein we present a case of 16 years adolescent of SSPE having remarkable asymmetric pial-gyral enhancements, which were misinterpreted as tubercular infection. Early diagnosis and treatment is encouraging in SSPE, although it is not curable with current therapy. Clinico-radiological and electrophysiological correlation is very important in diagnosis of SSPE, more gravely in patients having atypical image findings as in our index case.

  18. Spinal dural arteriovenous fistula: Imaging features and its mimics

    Energy Technology Data Exchange (ETDEWEB)

    Jeog, Ying; Ting, David Yen; Hsu, Hui Ling; Huang, Yen Lin; Chen, Chi Jen; Tseng, Ting Chi [Dept. of Radiology, aipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan (China)

    2015-10-15

    Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular malformation, however it is still rare and underdiagnosed. Magnetic resonance imaging findings such as spinal cord edema and dilated and tortuous perimedullary veins play a pivotal role in the confirmation of the diagnosis. However, spinal angiography remains the gold standard in the diagnosis of SDAVF. Classic angiographic findings of SDAVF are early filling of radicular veins, delayed venous return, and an extensive network of dilated perimedullary venous plexus. A series of angiograms of SDAVF at different locations along the spinal column, and mimics of serpentine perimedullary venous plexus on MR images, are demonstrated. Thorough knowledge of SDAVF aids correct diagnosis and prevents irreversible complications.

  19. Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

    Science.gov (United States)

    Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

    2017-08-01

    Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

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

    Directory of Open Access Journals (Sweden)

    Sudeep Thepade

    2014-01-01

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

  1. Featured Image: Central Black Holes in Late-Type Galaxies

    Science.gov (United States)

    Kohler, Susanna

    2017-07-01

    The images above show just 8 of 51 different nearby, late-type galaxies found to host X-ray cores near their centers. The main images are optical views and the insets show Chandra X-ray images of the same galaxies. The cross marks identify the near-infrared/optical nucleus of each galaxy, and the green ellipses show the source regions for the X-rays. A recent publication led by Rui She (Tsinghua University, China) presents a search for low-mass (106 solar masses) black holes lurking in the centers of nearby late-type, low-mass galaxies. Many of the 51 X-ray cores discovered represent such hidden black holes. The authors use the statistics of this sample to estimate that at least 21% of late-type galaxies likethose studied here host low-mass black holes at their centers. You can view the full set of X-ray core hosts below; for more information, check out the paper linked at the bottom of the page.All 51 X-ray cores (displayed in 3 sets); see the article below for the originals.CitationRui She et al 2017 ApJ 842 131. doi:10.3847/1538-4357/aa7634

  2. Angioleiomyoma: magnetic resonance imaging features in ten cases

    Energy Technology Data Exchange (ETDEWEB)

    Gupte, Cynthia; Butt, Sajid H.; Saifuddin, Asif [Royal National Orthopaedic Hospital, Department of Radiology, Middlesex (United Kingdom); Tirabosco, Roberto [Royal National Orthopaedic Hospital, Department of Histopathology, Middlesex (United Kingdom)

    2008-11-15

    Angioleiomyoma is a rare, benign smooth muscle tumour arising from the tunica media of small veins and arteries and can occur anywhere in the body. The histological appearances are well documented, but there are relatively few descriptions of the magnetic resonance imaging (MRI) findings. A retrospective study of the clinical presentation, MRI appearances and histological findings of ten angioleiomyomas presenting as extremity soft tissue masses. MRI typically demonstrated a well-defined, oval mass located superficial to the fascia with the commonest sites being the hand (three cases) and ankle/foot (five cases). The lesion was isointense to muscle on T1-weighted spin echo images with heterogeneous increased internal T2W/short tau inversion recovery (STIR) signal intensity, which commonly appeared as multiple linear or branching areas of hyperintensity. Enhancement after IV gadolinium ranged from diffuse to heterogeneous. In a single case, central fat signal intensity was seen, while a further case showed marked T2W/STIR hypointensity due to diffuse hyalinisation within the lesion. This is the largest reported MRI series of extremity musculoskeletal angioleiomyoma. Angioleiomyoma should be considered in the differential diagnosis of a superficial mass in the hand or foot, particularly when characteristic linear or branching hyperintensity is seen on T2W or STIR images. (orig.)

  3. Optimization-Based Approaches To Feature Extraction from Aerial Images

    Science.gov (United States)

    Fua, Pascal; Gruen, Armin; Li, Haihong

    Extracting cartographic objects from images is a difficult task because aerial images are inherently noisy, complex, and ambiguous. Using models of the objects of interest to guide the search has proved to be an effective approach that yields good results. In such an approach, the problem becomes one of fitting the models to the image data, which we phrase as an optimization problem. The appropriate optimization technique to use depends on the exact nature of the model. In this paper, we review and contrast some of the approaches we have developed for extracting cartographic objects and present the key aspects of their implementation. Using these techniques, rough initial sketches of 2-D and 3-D objects can automatically be refined, resulting in accurate models that can be guaranteed to be consistent with one another. We believe that such capabilities will prove indispensable to automating the generation of complex object databases from imagery, such as the ones required for high-resolution mapping, realistic simulations or intelligence analysis.LNES 95, p. 190 ff.

  4. Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition.

    Science.gov (United States)

    Pang, Wenbo; Jiang, Huiyan; Li, Siqi

    2017-01-01

    Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition.

  5. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

    Science.gov (United States)

    Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

    2017-07-01

    Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

  6. Spectral embedding-based multiview features fusion for content-based image retrieval

    Science.gov (United States)

    Feng, Lin; Yu, Laihang; Zhu, Hai

    2017-09-01

    In many computer vision applications, an object can be described by multiple features from different views. For instance, to characterize an image well, a variety of visual features is exploited to represent color, texture, and shape information and encode each feature into a vector. Recently, we have witnessed a surge of interests of combining multiview features for image recognition and classification. However, these features are always located in different high-dimensional spaces, which challenge the features fusion, and many conventional methods fail to integrate compatible and complementary information from multiple views. To address the above issues, multifeatures fusion framework is proposed, which utilizes multiview spectral embedding and a unified distance metric to integrate features, the alternating optimization is reconstructed by learning the complementarities between different views. This method exploits complementary property of different views and obtains a low-dimensional embedding wherein the different dimensional subspace. Various experiments on several benchmark datasets have verified the excellent performance of the proposed method.

  7. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Sun Xun

    2016-12-01

    Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

  8. Helical CT of calcaneal fractures: technique and imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Wechsler, R.J.; Schweitzer, M.E.; Karasick, D.; Deely, D.M.; Morrison, W. [Thomas Jefferson University Hospital, Department of Radiology, 111 South 11th Street, Philadelphia, PA 19107 (United States)

    1998-01-01

    Since the degree of comminution, fracture alignment, and articular congruity of intra-articular calcaneal fractures are important determinants in surgical treatment and patient prognosis, we review helical computed tomographic (CT) technique and features for detecting and assessing the extent of acute calcaneal fractures. Helical CT can be used to classify these fractures and facilitate the surgeon`s understanding of the anatomy and position of the fracture components in all orthogonal planes independently of the patient`s condition, foot placement in the CT gantry, or other injuries. (orig.) With 13 figs., 13 refs.

  9. Magnetic resonance imaging features of an epidural spinal haemangioma

    Energy Technology Data Exchange (ETDEWEB)

    Gupta Sanjay; Sunil Kumar; Gujral Ratni [Sanjay Gandhi Postgraduate Inst. of Medical Sciences, Lucknow (India). Depts. of Radiology, Neurosurgery and Pathology

    1996-08-01

    Isolated spinal epidural haemangioma without vertebral involvement is rare. A case is reported, of an epidural capillary haemangioma in the thoracic region and present the magnetic resonance (MRI) findings, including contrast-enhanced features. MRI, performed on 1.5 T -Magnetrom, using T1, proton density (PD) and T2-weighted spin-echo sequences, revealed a well defined bilobed mass in the left half of the spinal canal, extending from D8 to D10 vertebral bodies. It showed and intense homogenous enhancement following intravenous administration of Gd-DTPA at dose of 0.1 mmol/kg bodyweight. 7 refs., 3 figs.

  10. Fibroatheroma identification in Intravascular Optical Coherence Tomography images using deep features.

    Science.gov (United States)

    Mengdi Xu; Jun Cheng; Annan Li; Lee, Jimmy Addison; Wong, Damon Wing Kee; Taruya, Akira; Tanaka, Atsushi; Foin, Nicolas; Wong, Philip

    2017-07-01

    Identifying vulnerable plaque is important in coronary heart disease diagnosis. Recent emerged imaging modality, Intravascular Optical Coherence Tomography (IVOCT), has been proved to be able to characterize the appearance of vulnerable plaques. Comparing with the manual method, automated fibroatheroma identification would be more efficient and objective. Deep convolutional neural networks have been adopted in many medical image analysis tasks. In this paper, we introduce deep features to resolve fibroatheroma identification problem. Deep features which extracted using four deep convolutional neural networks, AlexNet, GoogLeNet, VGG-16 and VGG-19, are studied. And a dataset of 360 IVOCT images from 18 pullbacks are constructed to evaluate these features. Within these 360 images, 180 images are normal IVOCT images and the rest 180 images are IVOCT images with fibroatheroma. Here, one pullback belongs to one patient; leave-one-patient-out cross-validation is employed for evaluation. Data augmentation is applied on training set for each classification scheme. Linear support vector machine is conducted to classify the normal IVOCT image and IVOCT image with fibroatheroma. The experimental results show that deep features could achieve relatively high accuracy in fibroatheroma identification.

  11. Perceptual quality prediction on authentically distorted images using a bag of features approach

    Science.gov (United States)

    Ghadiyaram, Deepti; Bovik, Alan C.

    2017-01-01

    Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417

  12. NOTE: Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    Science.gov (United States)

    Mohamed, S. S.; Salama, M. M. A.; Kamel, M.; El-Saadany, E. F.; Rizkalla, K.; Chin, J.

    2005-08-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN.

  13. Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    Energy Technology Data Exchange (ETDEWEB)

    Mohamed, S S [Electrical and Computer Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 (Canada); Salama, M M A [Electrical and Computer Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 (Canada); Kamel, M [Electrical and Computer Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 (Canada); El-Saadany, E F [Electrical and Computer Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1 (Canada); Rizkalla, K [University of Western Ontario, 1151 Richmond Street, Suite 2, London, Ontario N6A 5B8 (Canada); Chin, J [University of Western Ontario, 1151 Richmond Street, Suite 2, London, Ontario N6A 5B8 (Canada)

    2005-08-07

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

  14. Hyperspectral remote sensing image retrieval system using spectral and texture features.

    Science.gov (United States)

    Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

    2017-06-01

    Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

  15. Imaging Features of Idiopathic Intracranial Hypertension in Children.

    Science.gov (United States)

    Hartmann, Alexander J P W; Soares, Bruno P; Bruce, Beau B; Saindane, Amit M; Newman, Nancy J; Biousse, Valérie; Peragallo, Jason H

    2017-01-01

    Magnetic resonance imaging (MRI) signs of elevated intracranial pressure and idiopathic intracranial hypertension have been well characterized in adults but not in children. The MRIs of 50 children with idiopathic intracranial hypertension and 46 adults with idiopathic intracranial hypertension were reviewed for optic nerve head protrusion, optic nerve head enhancement, posterior scleral flattening, increased perioptic cerebrospinal fluid, optic nerve tortuosity, empty or partially empty sella, tonsillar herniation, enlargement of Meckel's cave meningoceles, and transverse venous sinus stenosis(TSS). Compared to adolescents (11-17 years, n = 40) and adults (>17 years, n = 46), prepubescent children (intracranial hypertension have similar MRI findings as adults, but they are less frequent in prepubescent children.

  16. Imaging features of ductal plate malformations in adults

    Energy Technology Data Exchange (ETDEWEB)

    Venkatanarasimha, N., E-mail: nandashettykv@yahoo.com [Department of Radiology, Derriford Hospital, Plymouth (United Kingdom); Thomas, R.; Armstrong, E.M.; Shirley, J.F.; Fox, B.M.; Jackson, S.A. [Department of Radiology, Derriford Hospital, Plymouth (United Kingdom)

    2011-11-15

    Ductal plate malformations, also known as fibrocystic liver diseases, are a group of congenital disorders resulting from abnormal embryogenesis of the biliary ductal system. The abnormalities include choledochal cyst, Caroli's disease and Caroli's syndrome, adult autosomal dominant polycystic liver disease, and biliary hamartoma. The hepatic lesions can be associated with renal anomalies such as autosomal recessive polycystic kidney disease (ARPKD), medullary sponge kidney, and nephronophthisis. A clear knowledge of the embryology and pathogenesis of the ductal plate is central to the understanding of the characteristic imaging appearances of these complex disorders. Accurate diagnosis of ductal plate malformations is important to direct appropriate clinical management and prevent misdiagnosis.

  17. Automatic extraction of disease-specific features from Doppler images

    Science.gov (United States)

    Negahdar, Mohammadreza; Moradi, Mehdi; Parajuli, Nripesh; Syeda-Mahmood, Tanveer

    2017-03-01

    Flow Doppler imaging is widely used by clinicians to detect diseases of the valves. In particular, continuous wave (CW) Doppler mode scan is routinely done during echocardiography and shows Doppler signal traces over multiple heart cycles. Traditionally, echocardiographers have manually traced such velocity envelopes to extract measurements such as decay time and pressure gradient which are then matched to normal and abnormal values based on clinical guidelines. In this paper, we present a fully automatic approach to deriving these measurements for aortic stenosis retrospectively from echocardiography videos. Comparison of our method with measurements made by echocardiographers shows large agreement as well as identification of new cases missed by echocardiographers.

  18. Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

    Directory of Open Access Journals (Sweden)

    Ya-Shuo Li

    2012-03-01

    Full Text Available Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

  19. Stress fractures: pathophysiology, clinical presentation, imaging features, and treatment options.

    Science.gov (United States)

    Matcuk, George R; Mahanty, Scott R; Skalski, Matthew R; Patel, Dakshesh B; White, Eric A; Gottsegen, Christopher J

    2016-08-01

    Stress fracture, in its most inclusive description, includes both fatigue and insufficiency fracture. Fatigue fractures, sometimes equated with the term "stress fractures," are most common in runners and other athletes and typically occur in the lower extremities. These fractures are the result of abnormal, cyclical loading on normal bone leading to local cortical resorption and fracture. Insufficiency fractures are common in elderly populations, secondary to osteoporosis, and are typically located in and around the pelvis. They are a result of normal or traumatic loading on abnormal bone. Subchondral insufficiency fractures of the hip or knee may cause acute pain that may present in the emergency setting. Medial tibial stress syndrome is a type of stress injury of the tibia related to activity and is a clinical syndrome encompassing a range of injuries from stress edema to frank-displaced fracture. Atypical subtrochanteric femoral fracture associated with long-term bisphosphonate therapy is also a recently discovered entity that needs early recognition to prevent progression to a complete fracture. Imaging recommendations for evaluation of stress fractures include initial plain radiographs followed, if necessary, by magnetic resonance imaging (MRI), which is preferred over computed tomography (CT) and bone scintigraphy. Radiographs are the first-line modality and may reveal linear sclerosis and periosteal reaction prior to the development of a frank fracture. MRI is highly sensitive with findings ranging from periosteal edema to bone marrow and intracortical signal abnormality. Additionally, a brief description of relevant clinical management of stress fractures is included.

  20. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    NARCIS (Netherlands)

    Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin; Hariharan, A.; Vijayakumari, Anupa A.; Aarabi, Mohammad Hadi; Aballi, John; Nour, Abd Elazeim Abd Alla Mohamed; Abdelaziz, Mohammed; Abdolalizadeh, AmirHussein; Abdollahi, Mahsa; Abdul Aziz, Siti Aishah; Salam, Amritha Abdul; Abdulaziz, Nidhal; Abdulkadir, Ahmed; Abdullah, Sachal; Abdullah, Osama; Abrigo, Jill; Adachi, Noriaki; Adamson, Christopher; Adduru, Viraj; Adel, Tameem; Aderghal, Karim; Ades-Aron, Benjamin; Adeyosoye, Michael; Adlard, Paul; Srinivasa, Ag; Aganj, Iman; Agarwal, Ayush; Agarwal, Anupam; Agarwal, Anchit; Aguero, Cinthya; Aguiar, Pablo; Ahdidan, Jamila; Ahmad, Fayyaz; Ahmad, Rziwan; Ahmadi, Hessam; Ahmed, Nisar; Sid, Farid Ahmed; Ai, Edward; Ai, Qing; Aicha, Benyahia; Aitharaju, Sai; Aiyer, Aditya; Akkus, Zeynettin; Akodad, Sanae; Akramifard, Hamid; Aksman, Leon; Aktas, Said; Al-Janabi, Omar; Al-Nuaimi, Ali; AlAila, BahaaEddin; Alakwaa, Fadhl; Alam, Saruar; Alam, Fakhre; Alam Zaidi, Syed Farhan; Alan, Wiener; Alansari, Mukhtar; Alareqi, Ebrahim; Alberdi, Ane; Albsoul, Mohammad; Alderson, Thomas; Aleem, Hassan; Alex, Aishwarya; Alexander, Jacob; Alexopoulos, Panagiotis; Alfoldi, Jessica; Ali, Ayesha; Ali, Imdad; Alimoradian, Shirin; Aljabar, Paul; Aljabbouli, Hasan; Aljovic, Almir; Allen, Genevera; Alliende, Luz Maria; Almaguel, Frankis; Almgren, Hannes; Montes, Carmen Alonso; Alowaisheq, Tasneem; Alryalat, Saif Aldeen; Alsado, Majd; Alsaedi, Abdalrahman; Alshehri, Haifa; Altaf, Tooba; Altendahl, Marie; Altmann, Andre; Alvand, Ashkan; Filho, Manoel Alves; Alzubi, Raid; Amaral, Robert; Ambatipudi, Mythri; Amernath, Remya; Amlien, Inge; Amoroso, Nicola; Amri, Hakima; Anastasiou, Athanasios; Anbarasi, Jani; Anbarjafari, Gholamreza; Anderson, Wes; Anderson, Jeff; Anderson, Valerie; Anderson, Loretta; Andonov, Jovan; Andova, Vesna; Andreopoulou, Irene; Andrews, K. Abigail; Andrews, Cameron; Angeles, Michel; Anne-Laure, Aziz; Ansari, Ghulam Jillani; Ansari, Sharaf; Anstey, Kaarin; Antunes, Augusto; Aoshuang, Zhang; Aouf, Mazin; Aow Yong, Li Yew; Aporntewan, Chatchawit; Apostolova, Liana; Appiah, Frank; Apsvalka, Dace; Arab, Abazar; Araque Caballero, Miguel Ángel; Arbabyazd, Mohammad; Arbelaez, Pablo; Archer, Kellie; Ardekani, Babak; Aretouli, Eleni; Arfanakis, Konstantinos; Arisi, Ivan; Armentrout, Steven; Arnold, Matthias; Arnold, Steven; Arslan, Salim; Artacho-Perula, Emilio; Arthofer, Christoph; Aruchamy, Srinivasan; Arya, Zobair; Pizarro, Carlos Asensio; Ashford, Wes; Ashraf, Azhaar; Askland, Kathleen; Aslaksen, Per; Aslakson, Eric; Aso, Toshihiko; Astphan, Michele; Ataloglou, Dimitrios; Atay, Meltem; Athanas, Argus; Atri, Roozbeh; Au, April; Aurich, Maike; Avants, Brian; Awasthi, Niharika; Awate, Suyash; Ayaz, Aymen; Son, Yesim Aydin; Aydogan, Dogu Baran; Ayhan, Murat; Ayton, Scott; Aziz, Adel; Azmi, Mohd Hafrizal; Ba, Maowen; Bach, Kevin; Badea, Alexandra; Bag, Asim; Bagewadi, Shweta; Bai, Xiangqi; Bai, Zilong; Bai, Haoli; Baird, Geoffrey; Baiwen, Zhang; Baker, Elizabeth; Baker, John; Bakker, Arnold; Ball, Erika; Ballén Galindo, Miguel Ángel; Banaei, Amin; Bandyopadhyay, Dipankar; Bang, Ki Hun; Bangen, Katherine; Banks, Sarah; Banning, Leonie; Bao, Wan Yun; Barakat, Rita; Barbará, Eduardo; Barber, Philip; Barber, Robert; de Araujo, Flavia Roberta Barbosa; Barnes, Josephine; Barredo, Jennifer; Barret, Olivier; Barrett, Matthew; Barsamian, Barsam; Barsky, Andrey; Bartel, Fabian; Bartoszewicz, Jakub; Bartram-Shaw, David; Barwood, Caroline; Basavaraj, Suryakanth; Basavaraj, Arshitha; Basiouny, Ahmed; Baskaran, Bhuvaneshwari; Basu, Arindam; Baths, Veeky; Bathula, Deepti; Batmanghelich, Nematollah Kayhan; Bauer, Roman; Bauer, Corinna; Bawa, Vanshika; Bayley, Peter; Bayram, Ali; Bazi, Yakoub; Beach, Thomas; Beaudoin, Kristin; Beaulieu, Christian; Becker, Cassiano; Beckett, Laurel; Bedding, Alun; Beer, Simone; Beer, Joanne; Beg, Mirza Faisal; Behfar, Qumars; Behjat, Hamed; Behjat, Hamid; Behseta, Sam; Bekris, Lynn; Suresh, Mahanand Belathur; Belichenko, Nadia; Bellio, Maura; Belyaev, Mikhail; Bemiller, Shane; Ahmed, Olfa Ben; Ben Bouallègue, Fayçal; Benedikt, Michael; Benge, Jared; Benitez, Andreana; Benlloch, Jose María; Benn, Marianne; Benyoussef, El Mehdi; Bergeron, David; Bermudez, Elaine; Bessadok, Alaa; Betzel, Richard; Bezuidenhoudt, Mauritz; Bhagwat, Nikhil; Bhalerao, Shailesh; Bhandari, Anindya; Bhasin, Harsh; Bhati, Radhika; Bhatkoti, Pushkar; Bhatt, Priya; Bhattacharjee, Debotosh; Bhattacharyya, Sudeepa; Bi, Rui; Bi, Jinbo; Bi, Harvy; Biancardi, Alberto; Bidart, Rene; Bilgel, Murat; Billiet, Thibo; Binczyk, Franciszek; Bingsheng, Huang; Bird, Christopher; Bischof, Gérard; Bishnoi, Ram; Biswas, Shameek; Bjelke, David; Black, Sandra; Blackwood, Jennifer; Blaese, Elise; Blair, James; Blanchard, Gilles; Bloom, Toby; Blujus, Jenna; Blusztajn, Jan Krzysztof; Bo, Wu; Bo, Jun; Boda, Ravi; Boellaard, Ronald; Bogorodzki, Piotr; Bokde, Arun; Bolhasani, Ehsan; Bonakdarpour, Borna; Bonazzoli, Matthew; Bône, Alexandre; Borkowsky, Jennifer; Borrajo, Danielle; Bos, Isabelle; Bosco, Paolo; Bott, Nicholas; Rodrigues, Renato Botter Maio Lopes; Boughanmi, Amani; Bougias, Haralabos; Boulier, Thomas; Bourgeat, Pierrick; Bouyagoub, Samira; Bowes, Mike; Boyes, Richard; Bozoki, Andrea; Bradshaw, Tyler; Pereira, Joana Braga; Brahami, Yoann; Brambati, Simona Maria; Bras, Jose; Braskie, Meredith; Brecheisen, Ralph; Bregman, Noa; Brewer, James; Briassouli, Alexia; Brickman, Adam; Bridges, Robert; Brihmat, Nabila; Brinkmann, Benjamin; Britschgi, Markus; Broers, Thomas; Bron, Esther; Brown, Jesse; Brown, Matthew; Brown, Abel; Brown, Maria; Brunberg, James; Bu, Tao; Bubbico, Giovanna; Bubenik, Peter; Bubu, Omonigho; Buchanan, Daniel; Buchholz, Hans-Georg; Buchsbaum, Bradley; Buck, Katharina; Buckley, Rachel; Budgeon, Charley; Buhl, Derek; Sánchez, Manuel Buitrago; Bundela, Saurabh; Burciu, Irina; Burgos, Ninon; Burke, Shanna; Burn, Katherine; Burns, Jeffrey; Burns, Gully; Burzykowski, Tomasz; Bush, Sammie; Buss, Stephanie; Butcher, Bradley; Butt, Victoria; Buxbaum, Joseph; Sandeep, C. S.; Cabrera, Cristóbal; Cahyaningrum, Winda; Cai, Zhen-Nao; Cai, Siqi; Cai, Erik; Cajka, Tomas; Calamia, Matthew; Caligiuri, Maria Eugenia; Calixte, Christopher; Calon, Frederic; Cameron, Briana; Campbell, Roy; Lopez, Jose Antonio Campos; Cao, Hongliu; Cao, Jiguo; Cao, Guanqun; Cao, Bo; Capizzano, Aristides; Capon, Daniel; Carmasin, Jeremy; Carmichael, Owen; Carr, Sarah; Carrier, Jason; Carter, Greg; Carvalho, Luis; Carvalho, Janessa; Carvalho, Carolina; Casamitjana, Adrià; Casanova, Ramon; Casas, Josep R.; Cash, David; Castelluccio, Pete; Castiglioni, Isabella; Caswell, Carrie; Cattell, Liam; Cauda, Franco; Cepeda, Ileana; Çevik, Alper; Cha, Jungho; Chakrabarti, Shreya; Chakraborty, Shouvik; Chammam, Takwa; Chan, Christina; Chand, Ganesh; Chang, Catie; Chang, Yu-Ming; Chang, Rui; Chang, Hyunggi; Chang, Yu-Chuan; Chang, Ki Jung; Chang, Che-Wei; Chantrel, Steeve; Chao, Justin; Chao, Linda; Chapleau, Marianne; Charil, Arnaud; Chatterjee, Pratishtha; Chatterjee, Sambit; Chaudhry, Zainab; Chauhan, Harmanpreet; Chehade, Abdallah; Chekuri, Omkar; Cheloshkina, Kseniia; Chen, Jianhong; Chen, Gang; Chen, Geng; Chen, Ting-Huei; Chen, Yin Jie; Chen, Xi; Chen, Tzu-Chieh; Chen, Guojun; Chen, Shuzhong; Chen, Jerome; Chen, Fang; Chen, Kaifeng; Chen, Gennan; Chen, Jason; Chen, Guanhua; Chen, Ying-Hsiang; Chen, Ming-Hui; Chen, Chenbingyao; Chen, S. Y.; Chen, Hsu-Hsin; Chen, Xing; Chen, Kewei; Chen, Yuhan; Chen, Hugo; Chen, Rong; Chen, Ing-jou; Chen, Jun; Chen, Jean; Chen, Bo; Cheng, Danni; Cheng, Hewei; Cheng, Yong; Cheng, Yang; Cheng, Zhang; Cheng, Wai Ho; Chenhall, Tanya; Chepkoech, Joy-Loi; Cherukuri, Venkateswararao; Chhibber, Aparna; Chi, Haoyuan; Chi, Chih-Lin; Chiang, Gloria; Chiesa, Patrizia; Childress, Daniel Micah; Chilukuri, Yogitha; Fatt, Cherise Chin; Chincarini, Andrea; Ching, Christopher; Chiotis, Konstantinos; Cho, Soo Hyun; Cho, Yongrae; Cho, Sooyun; Choi, Jun-Sik; Choi, Hongyoon; Choi, Yeoreum; Choi, Sophia; Choi, Jaesik; Choi, Euna; Choo, I. L. Han; Chopra, Vishal; Chougrad, Hiba; Chouraki, Vincent; Christini, Amanda; Chu, Yufang; Chuang, Tzu-Chao; Chuanji, Luo; Chuanjian, Yu; Chun, Marvin; Chun, Sung; Chung, Ai; Chung, Yu-Min; Chung, Jung-Che; Chung, Ai Wern; Chung, Jaeeun; Chyzhyk, Darya; Ciarleglio, Adam; Cioli, Claudia; Cittanti, Corrado; Cives, Ana; Clark, Marissa; Clayton, David; Clement, Mark; Clifft, Daniel; Climer, Sharlee; Clouston, Sean; Clunie, David; Cohen, Phoebe; Cohen, Taco; Cole, Michael; Cole, James; Colletti, Patrick; Collingwood, Joanna; Comley, Robert; Conklin, Bryan; Conner, Lindsay; Conover, Joanne; Contardo-Berning, Ivona; Conway, Ronan; Copani, Agata; Coppola, Giovanni; Corbett, Syl; Corlier, Fabian; Correia, Rui; Cosman, Joshua; Costantino, Sebastian; Coubard, Olivier; Coulson, Elizabeth; Couser, Elizabeth; Cox, Kris; Coyle, Patrick; Cozzi, Brian; Craddock, Cameron; Crawford, Karen; Creese, Byron; Cribben, Ivor; Crisostomo-Wynne, Theodore; Crossley, Nicolas; Croteau, Etienne; Cruchaga, Carlos; Cuajungco, Math; Cui, Jing; Cui, Sue; Cullen, Nicholas; Cuneo, Daniel; Cutanda, Vicente; Cynader, Max; Binu, D.; D'Avossa, Giovanni; Dai, Tian; Dai, Peng; Dai, Hui; Davied Hong, Daivied Hong; Dakovic, Marko; Dalca, Adrian; Damiani, Stefano; Dammak, Mouna; Damoiseaux, Jessica; Dan, Zou; Dang, Xuan Hong; Dang, Shilpa; Daniel, Zinkert; Danjou, Fabrice; Darby, Eveleen; Darby, Ryan; Dardzinska, Agnieszka; Darst, Burcu; Darvesh, Sultan; Das, Kalyan; Das, Devsmita; Das, Sandhitsu; Das, Dulumani; Datta, Shounak; Dauvillier, Jérôme; Davatzikos, Christos; Davidson, Ian; de Boer, Renske; de Bruijne, Marleen; de Buhan, Maya; de Jager, Philip; de La Concha Vega, Nuño; de Lange, Siemon; de Luis Garcia, Rodrigo; de Marco, Matteo; de Sitter, Alexandra; Dean, Scott; Decarli, Charles; Decker, Summer; del Gaizo, John; Demir, Zeynep; Denby, Charles; Deng, Yanjia; Deng, Wanyu; Denisova, Kristina; Denney, William; Depue, Brendan; DeRamus, Thomas; Desikan, Rahul; Desplats, Paula; Desrosiers, Christian; Devadas, Vivek; Devanarayan, Viswanath; Devarajan, Sridharan; Devenyi, Gabriel; Dezhina, Zalina; Dhami, Devendra; Dharsee, Moyez; Dhillon, Permesh; Di, Xin; Di Mauro, Nicola; Diah, Kimberly; Diamond, Sara; Diaz-Asper, Catherine; Diciotti, Stefano; Dickerson, Bradford; Dickie, David Alexander; Dickinson, Philip; Dicks, Ellen; Diedrich, Karl; Dieumegarde, Louis; Dill, Vanderson; Dilliott, Allison; Ding, Zhaohua; Ding, Shanshan; Ding, Yanhui; Ding, Xiuhua; Ding, Xuemei; Dinov, Ivo; Dinu, Valentin; Diouf, Ibrahima; Dmitriev, Phillip; Dobromyslin, Vitaly; Dodge, Hiroko; Dolui, Sudipto; Dona, Olga; Dondelinger, Frank; Dong, Wen; Dong, Hao-Ming; Kehoe, Patricio Donnelly; Donohue, Michael; Dore, Vincent; Dougherty, Chase; Doughty, Mitchell; Dowling, N. Maritza; Doyle, Senan; Doyle, Andrew; Dragan, Matthew; Draganski, Bogdan; Draghici, Sorin; Dragomir, Andrei; Drake, Derek; Drake, Erin; Drd, Shilpa; Dronkers, Nina; Drozdowski, Madelyn; Du, Changde; Du, Yuhui; Du, Lei; Du, Guangwei; Du, Xingqi; Duan, Fang; Duan, Yuzhuo; Duan, Kuaikuai; Duchesne, Simon; Duggento, Andrea; Dukart, Juergen; Dumont, Matthieu; Dunn, Ruth; Duong, Vu; Duraisamy, Baskar; Duran, Tugce; Durrleman, Stanley; Dutta, Joyita; Dyrba, Martin; Dyvorne, Hadrien; R, Amulya E.; Eads, Jennifer; Eastman, Jennifer; Eaton, Susan; Edlund, Christopher; Edmonds, Emily; Edmondson, Mackenzie; Ehsan, Fatima; El-Gabalawy, Fady; Elander, Annie; Elango, Vidhya E.; Eldeeb, Ghaidaa; Elgamal, Fatmaelzahraa; Rodrigues, Yuri Elias; Elman, Jeremy; Elrakaiby, Nada; Emahazion, Tesfai; Emami, Behnaz; Embrechts, Jurriën; Emran Khan Emon, Mohammad Asif; Emrani, Saba; Emrani, Asieh; Emri, Miklós; Engelhardt, Barbara; Engle, Bob; Epstein, Noam; Er, Fusun; Erhardt, Erik; Eriksson, Oscar; Omay, Zeynep Erson; Escudero, Javier; Eshleman, Jason; Eskildsen, Simon; Espinosa, Luis; Essex, Ryan; Esteban, Oscar; Estrada, Karol; Ethell, Douglas; Ethridge, Kimberly; Ettehadi, Seyedrohollah; Eva, Bouguen; Evenden, Dave; Evtikheeve, Rina; Ewert, Siobhan; Fague, Scot; Fahmi, Rachid; Faizal, Sherin; Falahati, Farshad; Fan, Li; Fan, Zhen; Fan, Yong; Fan, Maohua; Fan, Yonghui; Fan, Sili; Fan, Ruzong; Fang, Chen; Fang, Xiaoling; Fanjul-Vélez, Félix; Fanti, Alessandro; Far, Bab; Farah, Martha; Farahani, Naemeh; Farahibozorg, Seyedehrezvan; Farahnak, Farhood; Farajpour, Maryam; Fardo, David; Farkhani, Sadaf; Farnsworth, Bryn; Farooq, Hamza; Farooq, Ammarah; Farouk, Yasmeen; Farrar, Danielle; Farrer, Lindsay; Fatemehh, Fatemeh; Fatemizadeh, Emad; Fatfat, Kim; Fatima, Shizza; Faux, Noel; Favan-Niven, Anne; Favary, Clélia; Fazlollahi, Amir; Fei, Gao; Feingold, Franklin; Feizi, Soheil; Félix, Eloy; Femminella, Grazia Daniela; Feng, Zijun; Feng, Ao; Feng, Brad; Feng, Xinyang; Feragen, Aasa; Fereidouni, Marzieh; Fernandes, Miguel; Fernández, Víctor; Ferrari, Ricardo; Ferraris, Sebastiano; Ferreira, Francisco; Ferreira, Luiz Kobuti; Ferreira, Hugo; Fiecas, Mark; Fieremans, Els; Fiford, Cassidy; Figurski, Michal; Filippi, Massimo; Filshtein, Teresa; Findley, Caleigh; Finger, Elizabeth; Firth, Nicholas; Fischer, Christopher; Fischer, Florian; Fitall, Simon; Fleet, Blair; Fleishman, Greg; Flokas, Lambros; Flores, Alberto; Focke, Niels; Fok, Wai Yan; Foldi, Nancy; Fôlego, Guilherme; Forero, Aura; Fornage, Myriam; Fos Guarinos, Belén; Founshtein, Gregory; Franc, Benjamin; Francois, Clement; Franke, Katja; Fraser, Mark; Frasier, Mark; Frederick, Blaise; Freitas, Fernandho; Escalin, Frency Jj; Freudenberg-Hua, Yun; Friedman, Brad; Friedmann, Theodore; Friedrich, Christoph M.; Frings, Lars; Frisoni, Giovanni; Fritzsche, Klaus; Frolov, Alexander; Frost, Robert; Fu, Ling; Fu, Zening; Fudao, Ke; Fuentes, Emmanuel; Fujishima, Motonobu; Fujiwara, Ken; Fukami, Tadanori; Funk, Cory; Furcila, Diana; Fuselier, Jessica; Nagarjuna Reddy, G.; Gaasterland, Terry; Gabelle, Audrey; Gahm, Jin; Gaiteri, Chris; Gajawelli, Niharika; Galantino, Alexis; Galarza Hernández, Javier; Galasko, Douglas; Galea, Liisa; Galisot, Gaetan; Sánchez, Antonio Javier Gallego; Gallins, Paul; Gamberger, Dragan; Gan, Hong Seng; Gan, Gavin; Ganapathi, Subha; Gancayco, Christina; Gangishetti, Umesh; Ganzetti, Marco; Gao, Fei; Gao, Jingjing; Gao, Linlin; Gao, Tianxiang; Gao, Yuanyuan; Gao, Xiaohong; Garani, Ranjini; Garbarino, Sara; Garcia, Ivan; Garcia, Xiadnai; Garcia, Jorge; Garcia, Tanya; Garcia Arias, Hernan Felipe; de La Garza, Angel Garcia; Gaig, Mireia Garcia; Novoa, Jorge Garcia; Valero, Mar Garcia; Garcia-Ojalvo, Jord; García-Polo, Pablo; Garg, Rahul; Garg, Gaurav; Garg, Divya; Garibotto, Valentina; Garvey, Matthew; Garza-Villarreal, Eduardo; Gaubert, Malo; Gauthier, Serge; Gavett, Brandon; Gavidia, Giovana; Gavtash, Barzin; Gawryluk, Jodie; Gbah, Messon; Ge, Tian; Geerts, Hugo; Geisser, Niklaus; Geng, Junxian; Gentili, Claudio; Gess, Felix; Ghaderi, Halleh; Ghahari, Shabnam; Ghanbari, Yaghoob; Ghazi-Saidi, Ladan; Ghodrati, Mojgan; Ghorbani, Behnaz; Ghoreishiamiri, Reyhaneh; Ghosal, Sayan; Ghosh, Sukanta; Ghosh, Saheb; Ghosh, Sreya; Ghoshal, Ankur; Giannicola, Galetta; Gibert, Karina; Gibson, Gary; Gieschke, Ronald; Gil Valencia, Jorge Mario; Gillen, Daniel; Giordani, Alessandro; Giraldo, Diana; Gispert, Juan D.; Gitelman, Darren; Giuffrida, Mario Valerio; Madhu, G. K.; Glass, Jesse; Glazier, Brad; Gleason, Carey; Glerean, Enrico; Glozman, Tanya; Godbey, Michael; Goettlich, Martin; Gogoi, Minakshi; Gola, Kelly; Golbabaei, Soroosh; Golden, Daniel; Goldstein, Felicia; Gomes, Carlos; de Olivera, Ramon Gomes Durães; Gomez, Isabel; Gomez Gonzalez, Juan Pablo; Gomez-Verdejo, Vanessa; Gong, Weikang; Gong, Enhao; Gong, Kuang; Gonneaud, Julie; Gonzalez, Clio; Gonzalez, Evelio; Gonzalez, Gerardo; Moreira, Eduardo Gonzalez; Goodman, James; Gopinath, Srinath; Gopu, Anusharani; Gordon, Brian; Gordon, David; Gordon, Mark; Gorriz, Juan Manuel; Gors, Dorothy; Göttler, Jens; Gounari, Xanthippi; Goyal, Devendra; Graf, John; Graff, Ariel; Graham, Leah; Graham, Jinko; Grajski, Kamil; Grami, Maziyar; Grand'Maison, Marilyn; Grant, Kiran; Grassi, Elena; Gray, Katherine; Grecchi, Elisabetta; Green, Robert; Green, Elaine; Greenberg, Jonathan; Greening, Steven; Greenwood, Bryson; Gregori, Johannes; Gregory, Michael; Greicius, Michael; Greve, Douglas; Griffin, Jason; Grill, Joshua; Grodner, Kelsey; Grolmusz, Vince; Groot, Perry; Groothuis, Irme; Gross, Alden; Grundstad, Arne; Grundy, Edward; Grzegorczyk, Tomasz; Nandith, G. S.; Gu, David; Gu, Jiena; Gu, Yun; Gu, Ginam; Guan, Sheng; Guan, Yuanfang; Guennel, Tobias; Guerin, Laurent; Guerrero, Ricardo; Guerrier, Laura; Guevara, Pamela; Guggari, Shankru; Roy, Abhijit Guha; Guidotti, Roberto; Guillon, Jérémy; Gulcher, Jeff; Gulia, Sarita; Gumedze, Freedom; Gunawardena, Nishan; Gunn, Roger; Guo, Michael; Guo, Xiao; Guo, Xingzhi; Guo, Yi; Kai, Zhang Guo; Zhao, Ma Guo; Gupta, Navin; Gupta, Anubha; Gupta, Ishaan; Guren, Onan; Gurnani, Ashita; Gurol, Mahmut Edip; Guzman, Gloria; Gyy, Gyy; Rajanna, Vanamala H.; Ha, Seongwook; Haacke, Ewart; Haaksma, Miriam; Habadi, Maryam; Habeck, Christian; Habes, Mohamad; Hackspiel Zarate, Maria Mercedes; Hadimani, Ravi; Hahn, William; Hahn, Tim; Haight, Thaddeus; Hair, Nicole; Haixing, Wang; Hajarolasvadi, Noushin; Hajjar, Ihab; Hajjo, Rima; Halchenko, Yaroslav; Hall, Anette; Hallock, Kevin; Hamdi, Shah Muhammad; Hameed, Farhan; Hamidian, Hajar; Han, Dong; Han, Yang; Han, Hio-Been; Han, Qingchang; Han, Beomsoo; Han, Duke; Han, Shizhong; Han, Xiaoxia; Han, Peipei; Han, Joo Yoon; Han, Dong-Sig; Handsaker, Robert; Hanna-Pladdy, Brenda; Hanseeuw, Bernard; Hansson, Björn; Hao, Yang; Hao, Jhon; Happ, Clara; Harischandra, Dilshan; Haritaoglu, Esin; Harris, Richard; Harris, Breanna; Hart, Brian; Hartzell, James; Harvey, Danielle; Hashimoto, Tsuyoshi; Hasooni, Hossein; Hassan, Moaied; Hassan, Mehdi; Hassanzadeh, Hamid Reza; Hassanzadeh, Oktie; Hatton, Sean; Hawchar, Jinan; Hayashi, Toshihiro; Hayashi, Norio; Hayes, Jasmeet; Hayete, Boris; Haynor, David; He, Linchen; He, Yan; He, Yao; He, Huiguang; Heegaard, Niels; Hefny, Mohamed; Heil, Julius; Heindel, William; Henderson, Samuel; Henf, Judith; Henriquez, Claudio; Herholz, Karl; Hermessi, Haithem; Hernandez, Monica; Herrera, Luis; Hibar, Derrek; Hidane, Moncef; Higuchi, Satomi; Hind, Jade; Hives, Florent; Hoang, Mimi; Hobel, Zachary; Hoffman, John; Hofmeister, Jeremy; Hohman, Timothy; Holder, Daniel; Holguin, Jess; Holmes, Robin; Hong, John; Hongliang, Zou; Hongyu, Guo; Hopkins, Paul; Hor, Soheil; Hornbeck, Russ; Horng, Andy; Horton, Wesley; Hosny, Khalid; Hosseini, Eghbal; Hosseini, Hadi; Hosseini, Zahra; Asl, Ehsan Hosseini; Hou, Beibei; Houghton, Richard; Houghton, Katherine; Householder, Erin; Howlett, James; Hsiao, John; Hsiao, Ing-Tsung; Hsu, Chih-Chin; Hu, Xixi; Hu, Lingjing; Hu, Nan; Hu, Kun; Hu, Tao; Hu, Li; Hu, Xiaolan; Hua, Fei; Huang, Marissa; Huang, Qi; Huang, Michelle; Huang, Chao; Huang, JunMing; Huang, Xingyuan; Huang, Yuhan; Huang, Sing-Hang; Huang, Shuai; Huang, Peiyu; Huang, Chun-Chao; Huang, Zhiyue; Huang, Meiyan; Huang, Zhiwen; Hubrich, Markus; Huestis, Michael; Huey, Edward; Hufton, Andrew; Huijbers, Willem; Huisman, Sjoerd; Hung, Joe; Hunsaker, Naomi; Hunt, Fostor; Huppertz, Hans-Jürgen; Huser, Vojtech; Hussain, Lal; Hutchison, R. Matthew; Hutton, Alexandre; Huyck, Els; Hwang, Jihye; Hyun, JungMoon; Iakovakis, Dimitris; Ibañez, Victoria; Ide, Kayoko; Igarashi, Takuma; Iglesias, Juan Eugenio; Muñoz, Laura Igual; Iidaka, Tetsuya; Ikeuchi, Takeshi; Ikhena, John; Ikuta, Toshikazu; Im, Hyung-Jun; Insausti, Ana; Insel, Philip; Invernizzi, Azzurra; Iosif, Ana-Maria; Ip, Nancy; Irizarry, Sierra; Irmak, Emrah; Irwin, David; Isaza, Mariano; Ishii, Makoto; Ishii, Kenji; Islam, Jyoti; Israel, Ariel; Isufi, Elvin; Ito, Kaori; Ito, Masato; Izquierdo, Walter; Alphin, J.; Akhila, J. A.; Jaberzadeh, Amir; Jackowiak, Edward; Jackson, Eric; Jackson, Chris; Jackson, Jonathan; Jacob, Samson; Jacobsen, Nina; Jacobsen, Jörn; Jacquemont, Thomas; Jacques, Nerline; Jaeger, Ralf; Jafari, Tahere; Jafari-Khouzani, Kourosh; Jagadish, Akshay Kumar; Jagtap, Priti; Jagust, William; Jahr, Joseph; Jain, Shubhankar; Jain, Shubham; Jaiswal, Ayush; Jaiswal, Akshay; Jait, Amine; Jakkoju, Chetan; Jakobsson, Andreas; James, Olga; James, Oliver; Jamlai, Maedeh; Jammeh, Emmanuel; Janardhana, Lajavanthi; Jang, Jinseong; Jang, Jae-Won; Jang, Jinhee; Jang, Hyesue; Janghel, Rekh Ram; Jawahar, Shasvat; Jean, Kharne; Jean-Baptiste, Schiratti; Jedynak, Bruno; Jefferson, Angela; Jennings, Danna; Jennings, Dominique; Jeon, Seun; Jeong, Yong; Jester, Charles; Jethwa, Ketan; Jha, Debesh; Ji, Gong-Jun; Ji, Chong; Ji, Jin; Jia, Bowen; Jiacheng, Lee; Jiajia, Guo; Jian, Weijian; Jiang, Shan; Jiang, Chunxiang; Jianhua, Gao; Jiao, Zhuqing; Jiao, Zeyu; Jiao, Du; Jimenez Alaniz, Juan Ramon; Gomez, Carolina Jimenez; Jiménez-Huete, Adolfo; Jimura, Koji; Jin, Yan; Jin, Zhu; Jogia, Jigar; Johansson, Per; John, Kimberley; Johnsen, Stian; Johnson, Leonard; Johnson, Sterling; Johnson, Kent; Johnston, Jane; Johnston, Stephen; Jomeiri, Alireza; Jonas, Katherine; Jones, Richard; Jones-Davis, Dorothy; Jönsson, Linus; Joseph, Jane; Joshi, Himanshu; Joshi, Shantanu; Joshi, Abhinay; Joyce, Katherine; Juengling, Freimut; Jung, Youngjin; Junker, Viv; Junwei, Ding; Jyothi, Singaraju; Jyotiyana, Monika; Sarthaj, K.; Kachouane, Mouloud; Kadian, Amit; Kaewaramsri, Yothin; Kaicheng, Li; Kaiser, Marcus; Kakinami, Lisa; Kalra, Sanjay; Kam, Hye Jin; Kamarudin, Nur Shazwani; Kaminker, Josh; Kandel, Benjamin; Kandiah, Nagaendran; Kaneko, Tomoki; Kang, Yun Seok; Kang, Ju Hee; Kang, Hakmook; Kang, Jian; Kansal, Anuraag; Kaouache, Mohammed; Kaplan, Adam; Kottaram, Akhil Karazhma; Karim, Faizan; Karimi-Mostowfi, Nicki; Karjoo, Mahboobe; Karlin, Daniel; Karp, Juliana; Karray, Chiheb; Kartsonis, Nick; Karu, Naama; Kasa, Jaya; Kasiri, Keyvan; Katako, Audrey; Kato, Ryo; Katsonis, Panagiotis; Katti, Hkkatti; Kaur, Prabhjot; Kauwe, John; Kawaguchi, Atsushi; Kazemi, Samaneh; Kazemi, Yosra; Rijan, K. C.; Kechin, Andrey; Kelkhoff, Douglas; Kelleher, Thomas; Kellner-Weldon, Frauke; Kennion, Oliver; Kerr, Daniel; Kesler, Shelli; Kesselman, Carl; Kessler, Daniel; Keuken, Max; Keyvanfard, Farzaneh; Khademi, April; Khajehnejad, Moein; Khan, Wasim; Khan, Tabrej; Khan, Hikmat; Khan, Anzalee; Khan, Samreen; Khanmohammadi, Sina; Khasanova, Tatiana; Khazaee, Ali; Khazan, Lenny; Kherif, Ferath; Khl, Aym; KHlif, Mohamed Salah; Khondoker, Mizanur; Khoo, Sok Kean; Khosrowabadi, Reza; Khurshid, Kiran; Kianfard, Reihaneh; Kida, Satoshi; Kiddle, Steven; Kikuchi, Masashi; Killiany, Ron; Kim, Jeongchul; Kim, Jong Hun; Kim, Hyunwoo; Kim, Jongin; Kim, Yeo Jin; Kim, Jung-Jae; Kim, Hang-Rai; Kim, Jaeyeol; Kim, Ki Hwan; Kim, Joseph; Kim, Younghoon; Kim, Mijung; Kim, Jeongsik; Kim, Bohyun; Kim, Taehyun; Kim, Heeyoung; Kim, Seonjik; Kim, Nakyoung; Kim, Byeongnam; Kim, ChanMi; Kim, Jeonghun; Kim, Seong Yoon; Kim, Sunhee; Kingery, Lisle; Kinnunen, Kirsi; Kinomes, Marie; Kirchner, Jan Hendrik; Caldwell, Jessica Kirkland; Kirwan, Brock; Kitamura, Chiemi; Kitty, Kitty; Kiviat, David; Kiyasova, Vera; Klein, Richard; Klein, Alison; Klein, Gregory; Klein, Jan; Kleinman, Aaron; Kling, Mitchel; Klinger, Joern; Klinger, Rebecca; Klink, Katharina; Kocaturk, Mustafa; Koch, Philipp Johannes; Kochova, Elena; Koenig, Loren; Koh, Natalie; Köhler, Jens Erik; Koikkalainen, Juha; Koini, Marisa; Kolachalama, Vijaya; Koncz, Rebecca; Kong, Xiang-Zhen; Kong, Vincent; Kong, Xiangzhen; Kong, Dehan; Kong, Linglong; Konukoglu, Ender; Kopeinigg, Daniel; Kopera, Krzysztof; Koppers, Simon; Korb, Matheus; Korfiatis, Panagiotis; Korolev, Igor; Korolev, Sergey; Korostyshevskiy, Valeriy; Koshiya, Heena; Kost, James; Kotari, Vikas; Koutra, Danai; Koychev, Ivan; Kruthika, K. R.; Krahnke, Tillmann; Krause, Matthew; Kraybill, Matt; Kriebel, Martin; Hari Krishna, M.; Krohn, Stephan; Kruggel, Frithjof; Kuceyeski, Amy; Kuhl, Donald; Kulshreshtha, Devang; Kumar, Santosh; Kumar, Sambath; Kumar, Kuldeep; Kumar, Anil; Kumar, Abhishek; Kumar, A.; Kumar, Saurabh; Kumar, Ashwani; Kumar, Ambar; Kumar, Dinesh; Kumar, Rishab; Kumarasinghe, Janaka; Kundu, Suprateek; Kung, Te-Han; Kuo, Li-Wei; Kuo, Phillip; Channappa, Usha Kuppe; Kuriakose, Elmy; Kurian, P.; Kwan, Kenneth; Kwasigroch, Arkadiusz; Kwon, Young Hye; Kyeong, Sunghyon; Fleur, Claire La; Wungo, Supriyadi La; Labbe, Tomas; Lacombe, Daniel; Lad, Meher; Lahoti, Geet; Lai, Ying Liang; Lai, Catherine; Lai, Dongbing; Laird, Dillon; Lakatos, Anita; Lam, Alice; Lama, Ramesh; Lambert, Christian; Landau, Susan; Landman, Bennett; Landre, Victor; Lane, Elizabeth; Lange, Catharina; Langenieux, Alexandre; Lareau, Caleb; Larson, Katelyn; Latif, Ghazanfar; Lauber, Ross; Lawliet, Z. H.; Lawrence, Emma; Lazar, Anca; Le, Ngan; Le, Thi Khuyen; Le, Matthieu; Guen, Yann Le; Scouiller, Stephanie Le; Leandrou, Stephanos; Leatherday, Christopher; Leavitt, Mackenzie; Ledbetter, Christina; Lee, Hyekyoung; Lee, Wook; Lee, Annie; Lee, Jaehong; Lee, Dongyoung; Lee, Joel; Lee, Song-Ting; Lee, Kuo-Jung; Lee, Subin; Lee, Jaeho; Lee, Catherine; Lee, Gyungtae; Lee, Suzee; Lee, Erik; Lee, Yunseong; Lee, Sang-Gil; Lee, Seonjoo; Lee, Peng Jung; Lee, Hyunna; Lee, Cheng-Hsien; Lee, Hengtong; Lee, Mi Ri; Lee, Ilgu; Lee, Qixiang; Lefterov, Iliya; Leger, Charlie; Lehallier, Benoit; Lei, B.; Lei, Shi; Lei, Hongxing; Lei, Haoyun; Leong, Tze Yun; Leong, Sharlene; Leoutsakos, Jeannie-Marie; Lepore, Natasha; Lerch, Ondrej; Leung, Yip Sang; Leung, Yuk Yee; Leung, Shuyu; Leung, Hoi-Chung; Leung, Ming-Ying; Levakov, Gidon; Levine, Abraham; Li, Chawn; Li, Miranda; Li, Huijie; Li, Junning; Li, Xiaofeng; Li, Yi; Li, Jinchao; Li, Tianhong; Li, Yongming; Li, Xiangrui; Li, Tieqiang; Li, Yan; Li, Fuhai; Li, Feijiang; Li, Shuyang; Li, Zhi; Li, Xing; Li, Rongjian; Li, Rui; Li, Y. U.; Li, Kang; Li, Zhenzhen; Li, Qingqin; Li, Wenjun; Li, Yang; Li, Jialu; Li, Guangyu; Li, Michelle; Li, Yibai; Li, Yupeng; Li, Tao; Li, Zhujun; Li, Yafen; Li, Muwei; Li, Xuan; Li, Yi-Ju; Li, Cen Sing; Li, X. W.; Li, Yingjie; Li, Lin; Li, Yihan Jessie; Li, Yaqing; Li, Xiantao; Li, Xingfeng; Li, Chenxi; Li, Chao; Li, Jicong; Li, Jiewei; Li, Tengfei; Li, Wei; Li, Xinzhong; Li, Nannan; Li, Chunfei; Li, Yeshu; Liang, Chen; Liang, Nanying; Liang, Jingjing; Liang, Shengxiang; Liang, Xiaoyun; Liang, Xia; Liang, Ying; Liberman, Sofia; Libon, David; Liébana, Sergio; Liedes, Hilkka; Lim, Wee Keong; Lim, Yen Ying; Lin, Yenching; Lin, Katherine; Lin, Ming; Lin, Ai-Ling; Lin, Ching-Heng; Lin, Bing; Lin, Lin; Lin, Jyh-Miin; Lin, W. M.; Lin, Chien-Tong; Lin, Liyan; Lin, Jing; Lindberg, Olof; Linesch, Paul; Linn, Kristin; Lippert, Christoph; Litovka, Nikita; Little, Graham; Liu, Man-Yun; Liu, Jin; Liu, Chin-Fu; Liu, Zhaowen; Liu, Eulanca; Liu, Weixiang; Liu, K. E.; Liu, Hao Chen; Liu, Jia; Liu, Richann; Liu, Dongbo; Liu, Victor; Liu, Wenjie; Liu, Tao; Liu, Xiaoli; Liu, Yong; Liu, Lin; Liu, Dan; Liu, Xiuwen; Liu, Mengmeng; Liu, Chia-Shang; Liu, Ying; Liu, Yan; Liu, Xueqing; Liu, Han; Liu, Chien-Liang; Liu, Sidong; Liu, Jundong; Liu, Yang; Liu, Tianming; Liu, Tingshan; Liu, Ning; Liu, Lan; Liuyu, Liuyu; Lizarraga, Gabriel; Llido, Jerome; Lobach, Iryna; Lockhart, Samuel; Loft, Henrik; Lohr, Kelly; Lon, Hoi Kei; Lone, Kashif Javed; Long, Ziyi; Long, Xiaojing; Longo, Frank; Alves, Isadora Lopes; Lopez, Guadalupe; Lorenzi, Marco; Lotan, Eyal; Louie, Gregory; Louis, Maxime; Loukas, Andreas; Love, Seth; Lowe, Deborah; Lu, Bin; Lu, Chia-Feng; Lu, Zixiang; Lu, Lijun; Lu, Pascal; Lu, Shen; Lu, Qing; Lu, Zheshen; Lu, Chuan; Lu, Patty; Lu, Hangquan; Lu, Bo; Luktuke, Yadnyesh; Luo, Wei; Luo, Suhuai; Luo, Sheng; Luo, Shaojun; Luo, Peggy; Luo, Shan; Luo, Weidong; Luo, Liao; Luo, Xiao; Lupton, Michelle; Lutz, Michael; Lv, Eric; Lyu, Juan; Angshul, M.; Radha, M. R.; Dinesh, M. S.; Ma, Xiangyu; Ma, Chao; Ma, Li; Ma, Yu; Ma, Qianli; MacArthur, Daniel; Macey, Paul; Mach, Eric; MacPhee, Imola; Madadi, Mahboubeh; Madan, Christopher; Madan, Bharat; Madero, Giovanny; Madhavan, Radhika; Madhyastha, Tara; Maeno, Nobuhisa; Magsood, Hamzah; Mah, Linda; Mahdavi, Shirin; Mahdavi, Asef; Mahmoud, Abeer; Mahmoud, Hentati; Mahmoud, Kariman; Mahmoudi, Ahmad; Dehkordi, Siamak Mahmoudian; Mahor, Monika; Mahseredjian, Taleen; Mai, Cha; Maia, Rui; Maiti, Taps; Maj, Carlo; Maji, Pradipta; Majidpour, Jafar; Makhlouf, Laouchedi; Makino, Satoshi; Makrievski, Stefan; Makse, Hernan; Malagi, Archana; Malakhova, Katerina; Malamon, John; Malashenkova, Irina; Malchano, Zach; Maleki-Balajoo, Somayeh; Malik, Sadia; Malik, Tamoor; Mallik, Abhirup; Malm, Tarja; Malpas, Charles; Malpica, Norberto; Malviya, Meenakshi; Mamandi, A.; Manandhar, Abinash; Mandal, Pravat; Mandali, Alekhya; Mane, Prajakta; Manning, Emily; Manoufali, Mohamed; Manser, Paul; Mantini, Dante; Mantri, Ninad; Manyakov, Nikolay; Manzak, Dİlek; Mao, Shuai; Maoyu, Tian; Maple Grødem, Jodi; Maravilla, Kenneth; Marco, Simonetti; Marcus, Daniel; Margetis, John; Margolin, Richard; Mariano, Laura; Marinescu, Razvan Valentin; Markett, Sebastian; Markiewicz, Pawel; Marnane, Michael; Maroof, Asif; Marple, Laura; Marques, Cristiane; Marrakchi, Linda; Marshall, Gad; Märtens, Kaspar; Mårtensson, Gustav; Marti, Cristian; Martin, Harold; Martinaud, Olivier; Martinez, Victor; Martinez, Oliver; Martinez, Jesus; Martinez, Carlos; Abadías, Neus Martinez; Torteya, Antonio Martinez; Martini, Jean-Baptiste; Martins, Samuel; Masciotra, Viviane; Masmoudi, Ahmed; Masny, Aliaksandr; Shah, Pir Masoom; Massaro, Tyler; Masumoto, Jun; Matan, Cristy; Mate, Karen; Mateus, Pedro; Mather, Mara; Mather, Karen; Mathew, Jesia; Mathias, Samuel; Mathiyalagan, Tamilalaghan; Matloff, Will; Matsubara, Keisuke; Matsubara, Takashi; Matsuda, Yukihisa; Matthews, Dawn; Mattis, Paul; May, Patrick; Mayburd, Anatoly; Mayo, Chantel; Mayordomo, Elvira; Mbuyi, Gaylord; McCallum, Colleen; McCann, Bryony; McCollough, Todd; McCormick, Shannon; McCurdy, Sean; McDonald, Carrie; McEligot, Archana; McEvoy, Linda; McGeown, William; McGinnis, Scott; McHugh, Thomas; McIntosh, Elissa; McIntosh, Randy; McKenzie, Andrew; McLaren, Donald; McMillan, Corey; McMillan, Alan; McPherson, Brent; McRae-McKee, Kevin; Zaini, Muhammad Hafiz Md; Meadowcroft, Mark; Mecca, Adam; Meda, Shashwath; Medikonda, Venkata Srinu; Meeker, Karin; Megherbi, Thinhinane; Mehmood, Anum; Mehrtash, Alireza; Meiberth, Dix; Meier, Dominik; Meijerman, Antoine; Mejia, Jose; Mekkayil, Lasitha; Meles, Sanne; Melie-Garcia, Lester; Melo, Hans; Melrose, Rebecca; Melzer, Corina; Mendes, Aline; Leon, Ricardo Antonio Mendoza; Gonzalez, Manuel Menendez; Meng, Dewen; Meng, Xianglai; Meng, Guilin; Mengel, David; Menon, Ramesh; Menon, Ravi; Mercado, Flavio; Messick, Viviana; Meyer, Pierre-Francois; Meyer, Carsten; Mezher, Adam; Mi, Liang; Miao, Hongyu; Michailovich, Oleg; Michels, Lars; Mickael, Guedj; Mikhail, Mark; Mikhno, Arthur; Milana, Diletta; Miller, Rachel; Miller, Brendan; Millikin, Colleen; Min, Byung Wook; Minadakis, George; Minghui, Hu; Chinh, Truong Minh; Minkova, Lora; Miranda, Michelle; Misevic, Dusan; Mishra, Amit; Mishra, Chetan; Mishra, Shiwangi; Mishra, Ashutosh; Mishra, Krishna; Misquitta, Karen; Mitchell, Brian; Mithawala, Keyur; Mitnitski, Arnold; Mitra, Sinjini; Mittal, Gaurav; Mittner, Matthias; Miyapuram, Krishna Prasad; Mlalazi, Rebaone; Mo, Daojun; Moghekar, Abhay; Moguilner, Sebastian; Moh, Heba; Mohabir, Mark; Mohajer, Bahram; Mohamed, Moataz; Mohammadi, Sadeq; Mohammadi-Nejad, Ali-Reza; Mohammady, Saed; Taqi, Arwa Mohammed; Mohan, Kishore Kumar; Mohy-Ud-Din, Hassan; Moitra, Dipanjan; Mojaradi, Mehdi; Mojtabavi, Alireza; Molina, Helena; Mollon, Jennifer; Molteni, Erika; Montajabi, Mohaddeseh; Montal, Victor; Montazami, Aram; Monté-Rubio, Gemma; Montembeault, Maxime; Montero-Odasso, Manuel; Montillo, Albert; Moon, Byung-Seung; Moon, Chan; Moon, Chooza; Moore, Archer; Morabito, Francesco C.; Moradi, Masoud; Moraes, Renato; Ballesteros, Orlando Morales; Morales-Henriquez, Daniela; Moratal, David; Moreno, Herman; Morihara, Ryuta; Mormino, Elizabeth; Morris, Jeffrey; Mortamet, Bénédicte; Morton, John; Moscato, Pablo; Rial, Alexis Moscoso; Mossa, Abdela Ahmed; Mottaghi, Setare; Mouelhi, Aymen; Moussavi, Arezou; Moustafa, Ahmed; Mowrey, Wenzhu; Mtetwa, Lungile; Muehlboeck, Sebastian; Mueller, Susanne; Mueller-Sarnowski, Felix; Mufidah, Ratna; Mukherjee, Rik; Mukherjee, Shubhabrata; Müller, Christian; Müller, Hans-Peter; Mullins, Paul; Mullins, Roger; Muncy, Nathan; Munir, Akhtar; Munirathinam, Ramesh; Munoz, David; Munro, Catherine; Muranevici, Gabriela; Rendon, Santiago Murillo; Murilo, Robson; Murphy, Sonya; Muscio, Cristina; Musso, Gabriel; Mustafa, Yasser; Myall, Daniel; Gayathri, N.; Nabavi, Shahab; Nabeel, Eman; Nagele, Robert; Naghshbandi, Hane; Naik, Shruti; Najmitabrizi, Neda; Nakawah, Mohammad Obadah; Nalls, Mike; Namboori, Krishnan; Nancy, Annie; Napolitano, Giulio; Narayan, Manjari; Narkhede, Atul; Naseri, Mahsa; Nasrallah, Ilya; Nasrallah, Fatima; Nassif, Rana; Nath, Sruthi R.; Nathoo, Farouk; Nation, Daniel; Naughton, Brian; Nault, Larry; Nautiyal, Deeksha; Nayak, Deepak Ranjan; Naz, Mufassra; Nazemian, Shayan; Nazeri, Arash; Neckoska, Emilija; Neelamegam, Malinee; Nehary, Ebrahim; Nelson, Peter; Nelson, Linda; Nematzadeh, Hosein; Nerur, Shubha; Nesteruk, Thomas; Neu, Scott; Ng, Yen-Bee; Nguyen, Tin; Nguyen, Thanh; Nguyen, Harrison; Nguyen, Nghi; Trung, Hieu Nguyen; Ni, Lucy; Nian, Yongjian; Nichols, Thomas; Nicodemus, Kristin; Nie, Yunlong; Nielsen, Casper; Nikolov, Robert; Nila, Jessica; Nishioka, Christopher; Njeh, Ines; Njie, Emalick; Nobakht, Samaneh; Noble, Andrew; Noda, Art; Noroozi, Ali; Norton, Derek; Nosarti, Chiara; Nosheny, Rachel; Notsu, Akifumi; Novak, Gerald; Nozadi, Seyed Hossein; Nu, Fen; Nudelman, Kelly; Nunes, Adonay; Nunes, Ana; Núñez, Christian; Nuno, Michelle; Nuriel, Tal; Nygaard, Haakon; Nyquist, Paul; O'Bott, Jacob; O'Charoen, Sirimon; O'Neill, William; O'Rawe, Jonathan; Obrzut, Grzegorz; Och, Ganzorig; Odaibo, David; Odry, Benjamin; Oehmichen, Axel; Ofori, Edward; Ogunsanmi, Abdulfatai; Oguz, Kaya; Oh, Jungsu; Oh, Minyoung; Oh, Hwamee; Ohigashi, Hironori; Oishi, Kenichi; Oishi, Naoya; Okhravi, Hamid; Okonkwo, Ozioma; Okyay, Savaş; Oliveira, Cyrill; Oliveira, João; Oliveira, Francisco; Oliver, Ruth; Olmos, Salvador; Olszowy, Wiktor; Oltra-Cucarella, Javier; Önen, Zehra; Ong, Rowena; Onoda, Keiichi; Onyike, Chiadi; Operto, Grégory; Oppedal, Ketil; Orejuela, Juan; Orhon, Atila; Orozco, Max; Ortuño, Juan; Osadebey, Michael; Osborn, Joseph; Osoba, Osonde; Ostadrahimi, Hamid; Ostovari, Parisa; Otis, Sarah; Overgaard, Shauna; Owen, Catrin Elin; Oxtoby, Neil; Öziç, Muhammet Üsame; Ozkaya, Gorkem; Okur, Ozlem Ozmen; Ozsolak, Fatih; Ozyildirim, Melis; Pa, Judy; Pacheco, Joe; Pack, Gary; Padilla, Daniel; Cerezo, Berizohar Padilla; Padovese, Bruno; Pae, Chongwon; Pagano, Gennaro; Pahuja, Gunjan; Pai, Shraddha; Pajavand, Shahryar; Pajula, Juha; Pak, Kyoungjune; Pakzad, Ashkan; Palaniappan, Mathiyalagan; Palanisamy, Sindhu; Palmqvist, Sebastian; Palsson, Frosti; Pan, Dan; Pan, Tiffany; Pan, Yuqing; Pan, Wei; Pan, Sun; Pan, Hongliang; Pan, Xiaoxi; Pandey, Lokesh; Pang, Qiaoyu; Pangilinan, Erin; Pannetier, Nicolas; Panpan, Xu; Panyavaraporn, Jantana; Pardini, Matteo; Paredes, José; Parikh, Jignesh; Park, Seongbeom; Park, Young Ho; Park, Min Tae; Park, Hyunjin; Park, Sejin; Park, JongSeong; Park, DooHyun; Park, Ji Eun; Park, Yuhyun; Park, Jiyong; Parker, Jason; Parker, Richard; Parodi, Alice; Bautista, Yohn Jairo Parra; Parrish, Marcus; Parthiban, Preethy; Pascariello, Guido; Pascual, Belen; Paskov, Hristo; Pasquini, Lorenzo; Tantaleán, Julio Sergio Eduardo Pastor; Pastur, Lucas; Patel, Raihaan; Patel, Sejal; Paterson, Ross; Paton, Bryan; Patriarche, Julia; Patriat, Rémi; Pattichis, Constantinos; Paul, Debashis; Pawar, Kuldeep; Pawlak, Mikolaj; Paz, Rotem; Pedroto, Maria; Pelekanos, Matthew; Péléraux, Annick; Peng, Dan; Peng, Jing; Pengfei, Tian; Perani, Daniela; Peraza, Luis; Pereira, Fabricio; Pereira, Francisco; Perkins, Diana; Perneczky, Robert; Persad, Umesh; Peter, Jessica; Peters, Mette; Peters, Ruth; Pether, Mark; Petrella, Jeffrey; Petrenko, Roman; Petrone, Paula; Petrov, Dmitry; Pezzatini, Daniele; Pfenning, Andreas; Pham, Chi-Tuan; Philipson, Pete; Phillips, Jeffrey; Phillips, Nicole; Phophalia, Ashish; Phuah, Chia-Ling; Pichai, Shanthi; Pichardo, Cesar; Binette, Alexa Pichet; Pietras, Olga; Pietrzyk, Mariusz; Pike, Kerryn; Pillai, Jagan; Piludu, Francesca; Pineda, Joanna; Ping, He; Pirraglia, Elizabeth; Pither, Richard; Piyush, Ranjan; Pizzi, Nick; Gonzalez, Luis Fernando Planella; Plassard, Andrew; Platero, Carlos; Plocharski, Maciej; Podhorski, Adam; Poggiali, Davide; Poghosyan, Mher; Pohl, Kilian; Poirier, Judes; Polakow, Jean Jacques; Politis, Marios; Poljak, Anne; Poloni, Katia Maria; Poole, Victoria; Poppenk, Jordan; Porsteinsson, Anton; Portelius, Erik; Posta, Filippo; Posthuma, Danielle; 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Nunes; Urrutia, Leandro; Usama, Ahmed; Ustun, Ali Alp; Uus, Alena; Uyar, Muharrem Umit; Visalatchi, V.; Rajinikanth, V.; Vafaei, Amin; Vairre, Darlene; Vaishnavi, Sanjeev; Vaithinathan, Krishnakumar; Vakorin, Vasily; Hernández, Maria Valdés; van Bokhoven, Pieter; Deerlin, Vivianna Van; van der Brug, Marcel; Dijk, Koene Van; van Duijn, Cornelia; van Erp, Theo; van Hooren, Roy; Leemput, Koen Van; van Loenhoud, Anita; Schependom, Jeroen Van; van Velden, Floris; van Westen, Danielle; Vandekar, Simon; Vandijck, Manu; Vanhoutte, Matthieu; Vannini, Patrizia; Vansteenkiste, Elias; Varatharajah, Yogatheesan; Vardarajan, Badri; Varey, Stephen; Vargas, Hernan; Varkey, Julia; Varma, Susheel; Varma, Vijay; Varma, Sudhr; Vasanthakumar, Aparna; Vashi, Tejal; Vasilchuk, Kseniia; Vassileva, Albena; Vatsalan, Dinusha; Vb, Nastaran; Veeramacheneni, Teja; Veeranah, Darvesh; Vejdani, Kaveh; Veldsman, Michele; Velgos, Stefanie; Veloso, Adriano; Vemuri, Prashanthi; Venero, Cesar; Venkataraman, Ashwin; Venkatasubramanian, Palamadai; Venkatraghavan, Vikram; Venugopal, Vinisha; Venugopalan, Janani; Verbeeck, Rudi; Verbel, David; Verbist, Bie; Verdoliva, Luisa; Verma, Ajay Kumar; Verma, Tarun; Verma, Ishan; Veronese, Mattia; Grabovetsky, Alejandro Vicente; Victor, Jonathan; Vieira, Domingos; Vijayaraj, Vinesh Raja; Vikas, Vinutha; Vilaplana, Veronica; Vilaplana, Eduard; Villar, José Ramón; Vincent, Fabrice; Vinkler, Mojmir; Viswanath, Satish; Viswanathan, Srikrishnan; Vitek, Michael; Viti, Mario; Vladutu, Liviu; Vlock, Daniel; Voineskos, Aristotle; Vora, Anvi; Vos, Stephanie; Voyle, Nicola; Vrenken, Hugo; Vu, Tien Duong; Vucetic, Zivjena; Vuksanovic, Vesna; Wachinger, Christian; Wada, Masataka; Wade, Sara; Wagstyl, Konrad; Wahba, Grace; Waldorf, Johannes; Walker, Douglas; Moore, Kim Poki Walker; Walsh, Dominic; Wan, Lin; Wang, Di; Wang, Jane-Ling; Wang, Yongmao; Wang, Huaming; Wang, Miao; Wang, Zi-Rui; Wang, Zheyu; Wang, Z. 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    2017-01-01

    Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions,

  1. Identification of natural images and computer-generated graphics based on statistical and textural features.

    Science.gov (United States)

    Peng, Fei; Li, Jiao-ting; Long, Min

    2015-03-01

    To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

  2. Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer.

    Science.gov (United States)

    Zheng, Feng-Yang; Lu, Qing; Huang, Bei-Jian; Xia, Han-Sheng; Yan, Li-Xia; Wang, Xi; Yuan, Wei; Wang, Wen-Ping

    2017-01-01

    To investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. We examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. By univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n=128) were retraction phenomenon (odds ratio [OR]=10.188), post-acoustic shadowing (OR=5.112), and echogenic halo (OR=3.263, Pimaging features, especially retraction phenomenon, have a strong correlation with the molecular subtypes, expanding the scope of ultrasound in identifying breast cancer subtypes with confidence. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  3. Classification of radiolarian images with hand-crafted and deep features

    Science.gov (United States)

    Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

    2017-12-01

    Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

  4. Imaging Features of Pediatric Pentastomiasis Infection: a Case Report

    Energy Technology Data Exchange (ETDEWEB)

    Lai, Can; Wang, Xi Qun; Lin, Long; Gao, De Chun; Zhang, Hong Xi; Zhang, Yi Ying; Zhou, Yin Bao [Zhejiang University School of Medicine, Zhejiang (China)

    2010-08-15

    We report here a case of pentastomiasis infection in a 3-year-old girl who had high fever, abdominal pain, abdominal tension and anemia. Ultrasound scanning of the abdomen revealed disseminated hyperechoic nodules in the liver and a small amount of ascites. Abdominal MRI showed marked hepatomegaly with disseminated miliary nodules of high signal intensity throughout the hepatic parenchyma on T2-weighted images; retroperitoneal lymphadenopathy and disseminated miliary nodules on the peritoneum were also noted. Chest CT showed scattered small hyperdense nodules on both sides of the lungs. The laparoscopy demonstrated diffuse white nodules on the liver surface and the peritoneum. After the small intestinal wall and peritoneal biopsy, histological examination revealed parenchymal tubercles containing several larvae of pentastomids and a large amount of inflammatory cell infiltration around them. The pathological diagnosis was parasitic granuloma from pentastomiasis infection

  5. Mathematical morphology-based approach to the enhancement of morphological features in medical images.

    Science.gov (United States)

    Kimori, Yoshitaka

    2011-12-16

    Medical image processing is essential in many fields of medical research and clinical practice because it greatly facilitates early and accurate detection and diagnosis of diseases. In particular, contrast enhancement is important for optimal image quality and visibility. This paper proposes a new image processing method for enhancing morphological features of masses and other abnormalities in medical images. The proposed method involves two steps: (1) selective extraction of target features by mathematical morphology and (2) enhancement of the extracted features by two contrast modification techniques. The goal of the proposed method is to enable enhancement of fine morphological features of a lesion region with high suppression of surrounding tissues. The effectiveness of the method was evaluated in quantitative terms of the contrast improvement ratio. The results clearly show that the method outperforms five conventional contrast enhancement methods. The effectiveness and usefulness of the proposed method were further demonstrated by application to three types of medical images: a mammographic image, a chest radiographic image, and a retinal image. The proposed method enables specific extraction and enhancement of mass lesions, which is essential for clinical diagnosis based on medical image analysis. Thus, the method can be expected to achieve automatic recognition of lesion location and quantitative analysis of legion morphology.

  6. A Feature Subtraction Method for Image Based Kinship Verification under Uncontrolled Environments

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    The most fundamental problem of local feature based kinship verification methods is that a local feature can capture the variations of environmental conditions and the differences between two persons having a kin relation, which can significantly decrease the performance. To address this problem...... the feature distance between face image pairs with kinship and maximize the distance between non-kinship pairs. Based on the subtracted feature, the verification is realized through a simple Gaussian based distance comparison method. Experiments on two public databases show that the feature subtraction method...... outperforms or is comparable to state-of-the-art kinship verification methods. Copyright ©2015 by IEEE....

  7. Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

    Directory of Open Access Journals (Sweden)

    Shibin Wu

    2013-01-01

    Full Text Available A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR, and contrast improvement index (CII.

  8. Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

    Science.gov (United States)

    Wu, Shibin; Xie, Yaoqin

    2013-01-01

    A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

  9. Medical Image Retrieval with Compact Binary Codes Generated in Frequency Domain Using Highly Reactive Convolutional Features.

    Science.gov (United States)

    Ahmad, Jamil; Muhammad, Khan; Baik, Sung Wook

    2017-12-19

    Efficient retrieval of relevant medical cases using semantically similar medical images from large scale repositories can assist medical experts in timely decision making and diagnosis. However, the ever-increasing volume of images hinder performance of image retrieval systems. Recently, features from deep convolutional neural networks (CNN) have yielded state-of-the-art performance in image retrieval. Further, locality sensitive hashing based approaches have become popular for their ability to allow efficient retrieval in large scale datasets. In this paper, we present a highly efficient method to compress selective convolutional features into sequence of bits using Fast Fourier Transform (FFT). Firstly, highly reactive convolutional feature maps from a pre-trained CNN are identified for medical images based on their neuronal responses using optimal subset selection algorithm. Then, layer-wise global mean activations of the selected feature maps are transformed into compact binary codes using binarization of its Fourier spectrum. The acquired hash codes are highly discriminative and can be obtained efficiently from the original feature vectors without any training. The proposed framework has been evaluated on two large datasets of radiology and endoscopy images. Experimental evaluations reveal that the proposed method significantly outperforms other features extraction and hashing schemes in both effectiveness and efficiency.

  10. Hierarchical photocatalysts.

    Science.gov (United States)

    Li, Xin; Yu, Jiaguo; Jaroniec, Mietek

    2016-05-07

    As a green and sustainable technology, semiconductor-based heterogeneous photocatalysis has received much attention in the last few decades because it has potential to solve both energy and environmental problems. To achieve efficient photocatalysts, various hierarchical semiconductors have been designed and fabricated at the micro/nanometer scale in recent years. This review presents a critical appraisal of fabrication methods, growth mechanisms and applications of advanced hierarchical photocatalysts. Especially, the different synthesis strategies such as two-step templating, in situ template-sacrificial dissolution, self-templating method, in situ template-free assembly, chemically induced self-transformation and post-synthesis treatment are highlighted. Finally, some important applications including photocatalytic degradation of pollutants, photocatalytic H2 production and photocatalytic CO2 reduction are reviewed. A thorough assessment of the progress made in photocatalysis may open new opportunities in designing highly effective hierarchical photocatalysts for advanced applications ranging from thermal catalysis, separation and purification processes to solar cells.

  11. UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

    Directory of Open Access Journals (Sweden)

    A. Kianisarkaleh

    2015-12-01

    Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

  12. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.

    Science.gov (United States)

    Zhang, Xi; Xu, Xiaopan; Tian, Qiang; Li, Baojuan; Wu, Yuxia; Yang, Zengyue; Liang, Zhengrong; Liu, Yang; Cui, Guangbin; Lu, Hongbing

    2017-11-01

    To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively. Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1281-1288. © 2017 International Society for Magnetic Resonance in Medicine.

  13. Profiles of US and CT imaging features with a high probability of appendicitis

    NARCIS (Netherlands)

    van Randen, A.; Laméris, W.; van Es, H.W.; ten Hove, W.; Bouma, W.H.; van Leeuwen, M.S.; van Keulen, E.M.; van der Hulst, V.P.M.; Henneman, O.D.; Bossuyt, P.M.; Boermeester, M.A.; Stoker, J.

    2010-01-01

    To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with

  14. Featured Image: A Slow-Spinning X-Ray Pulsar

    Science.gov (United States)

    Kohler, Susanna

    2017-05-01

    This image (click for a closer look!) reveals the sky location of a new discovery: the slowest spinning X-ray pulsar a spinning, highly magnetized neutron star ever found in an extragalactic globular cluster. The pulsar, XB091D (circled in the bottom left inset), lies in the globular cluster B091D in the Andromeda galaxy. In a recent study led by Ivan Zolotukhin (University of Toulouse, Moscow State University, and Special Astrophysical Observatory of the Russian Academy of Sciences), a team of scientists details the importance of this discovery. This pulsar is gradually spinning faster and faster a process thats known as recycling, thought to occur as a pulsar accretes material from a donor star in a binary system. Zolotukhin and collaborators think that this particular pairing formed relatively recently, when the pulsar captured a passing star into a binary system. Were now seeing it in a unique stage of evolution where the pulsar is just starting to get recycled. For more information, check out the paper below!CitationIvan Yu. Zolotukhin et al 2017 ApJ 839 125. doi:10.3847/1538-4357/aa689d

  15. Comparison of clustering methods for tracking features in RGB-D images

    CSIR Research Space (South Africa)

    Pancham, Ardhisha

    2016-10-01

    Full Text Available difficult to track individually over an image sequence. Clustering techniques have been recommended and used to cluster image features to improve tracking results. New and affordable RGB-D cameras, provide both color and depth information. This paper...

  16. The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2014-09-01

    Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

  17. Essential features of Chiari II malformation in MR imaging: an interobserver reliability study--part 1.

    NARCIS (Netherlands)

    Geerdink, N.; Vliet, T. van der; Rotteveel, J.J.; Feuth, T.; Roeleveld, N.; Mullaart, R.A.

    2012-01-01

    PURPOSE: Brain MR imaging is essential in the assessment of Chiari II malformation in clinical and research settings concerning spina bifida. However, the interpretation of morphological features of the malformation on MR images may not always be straightforward. In an attempt to select those

  18. Learning effective color features for content based image retrieval in dermatology

    NARCIS (Netherlands)

    Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

    We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

  19. Review of Metaplastic Carcinoma of the Breast: Imaging Findings and Pathologic Features

    Directory of Open Access Journals (Sweden)

    Rebecca Leddy

    2012-01-01

    Full Text Available Metaplastic carcinoma (MPC, an uncommon but often aggressive breast cancer, can be challenging to differentiate from other types of breast cancer and even benign lesions based on the imaging appearance. It has a variable pathology classification system. These types of tumors are generally rapidly growing palpable masses. MPCs on imaging can present with imaging features similar to invasive ductal carcinoma and probably even benign lesions. The purpose of this article is to review MPC of the breast including the pathology subtypes, imaging features, and imaging pathology correlations. By understanding the clinical picture, pathology, and overlap in imaging characteristics of MPC with invasive ductal carcinoma and probably benign lesions can assist in diagnosing these difficult malignancies.

  20. A blur-invariant local feature for motion blurred image matching

    Science.gov (United States)

    Tong, Qiang; Aoki, Terumasa

    2017-07-01

    Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

  1. Scene Classification of Remote Sensing Image Based on Multi-scale Feature and Deep Neural Network

    Directory of Open Access Journals (Sweden)

    XU Suhui

    2016-07-01

    Full Text Available Aiming at low precision of remote sensing image scene classification owing to small sample sizes, a new classification approach is proposed based on multi-scale deep convolutional neural network (MS-DCNN, which is composed of nonsubsampled Contourlet transform (NSCT, deep convolutional neural network (DCNN, and multiple-kernel support vector machine (MKSVM. Firstly, remote sensing image multi-scale decomposition is conducted via NSCT. Secondly, the decomposing high frequency and low frequency subbands are trained by DCNN to obtain image features in different scales. Finally, MKSVM is adopted to integrate multi-scale image features and implement remote sensing image scene classification. The experiment results in the standard image classification data sets indicate that the proposed approach obtains great classification effect due to combining the recognition superiority to different scenes of low frequency and high frequency subbands.

  2. Parallel hierarchical radiosity rendering

    Energy Technology Data Exchange (ETDEWEB)

    Carter, Michael [Iowa State Univ., Ames, IA (United States)

    1993-07-01

    In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.

  3. The design and implementation of image query system based on color feature

    Science.gov (United States)

    Yao, Xu-Dong; Jia, Da-Chun; Li, Lin

    2013-07-01

    ASP.NET technology was used to construct the B/S mode image query system. The theory and technology of database design, color feature extraction from image, index and retrieval in the construction of the image repository were researched. The campus LAN and WAN environment were used to test the system. From the test results, the needs of user queries about related resources were achieved by system architecture design.

  4. Hierarchical automated clustering of cloud point set by ellipsoidal skeleton: application to organ geometric modeling from CT-scan images

    Science.gov (United States)

    Banegas, Frederic; Michelucci, Dominique; Roelens, Marc; Jaeger, Marc

    1999-05-01

    We present a robust method for automatically constructing an ellipsoidal skeleton (e-skeleton) from a set of 3D points taken from NMR or TDM images. To ensure steadiness and accuracy, all points of the objects are taken into account, including the inner ones, which is different from the existing techniques. This skeleton will be essentially useful for object characterization, for comparisons between various measurements and as a basis for deformable models. It also provides good initial guess for surface reconstruction algorithms. On output of the entire process, we obtain an analytical description of the chosen entity, semantically zoomable (local features only or reconstructed surfaces), with any level of detail (LOD) by discretization step control in voxel or polygon format. This capability allows us to handle objects at interactive frame rates once the e-skeleton is computed. Each e-skeleton is stored as a multiscale CSG implicit tree.

  5. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    OpenAIRE

    Dat Tien Nguyen; Ki Wan Kim; Hyung Gil Hong; Ja Hyung Koo; Min Cheol Kim; Kang Ryoung Park

    2017-01-01

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has ...

  6. Imaging features of nontumorous conditions involving the trachea and main-stem bronchi

    Energy Technology Data Exchange (ETDEWEB)

    Jeon, Kyung Nyeo; Kang, Duk Sik; Bae, Kyung Soo [Kyungpook National University Hospital, Taegu (Korea, Republic of)

    2002-09-01

    A number of nontumorous diseases may affect the trachea and main-stem bronchi, and their nonspecific symptoms may include coughing, dyspnea, wheezing and stridor. The clinical course is often long-term and a misdiagnosis of bronchial asthma is common. The imaging findings of these nontumorous conditions are, however, relatively characteristic, and diagnosis either without or in conjunction with clinical information is often possible. For specific diagnosis, recognition of their imaging features is therefore of prime importance. In this pictorial essay, we illustrate the imaging features of various nontumorous conditions involving the trachea and main-stem bronchi.

  7. Unexpected diagnosis of superficial neurofibroma in a lesion with imaging features of a vascular malformation

    Energy Technology Data Exchange (ETDEWEB)

    O' Keefe, Patrick; Reid, Janet; Morrison, Stuart [Cleveland Clinic Foundation, Department of Radiology, Cleveland, OH (United States); Vidimos, Allison [Cleveland Clinic Foundation, Department of Dermatology, Cleveland, OH (United States); DiFiore, John [Cleveland Clinic Foundation, Department of Pediatric Surgery, Cleveland, OH (United States)

    2005-12-01

    Plexiform neurofibroma is a pathognomonic, often disabling feature of neurofibromatosis type I. Although the target-like appearance of deep plexiform neurofibroma on T2-weighted MRI has been well-described, a second superficial form of plexiform neurofibroma has differing imaging features. We report a 15-year-old boy who presented with multiple cutaneous lesions exhibiting clinical and imaging characteristics of a venolymphatic malformation. These lesions were histologically proved to represent superficial plexiform neurofibromas. We wish to emphasize the unique MR findings of superficial plexiform neurofibromas; these findings are different from the imaging characteristics of the deep form and can be confused with a low-flow vascular malformation. (orig.)

  8. Profiles of US and CT imaging features with a high probability of appendicitis

    Energy Technology Data Exchange (ETDEWEB)

    Randen, A. van; Lameris, W. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands); University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Es, H.W. van [St Antonius Hospital, Department of Radiology, Nieuwegein (Netherlands); Hove, W. ten; Bouma, W.H. [Gelre Hospitals, Department of Surgery, Apeldoorn (Netherlands); Leeuwen, M.S. van [University Medical Centre, Department of Radiology, Utrecht (Netherlands); Keulen, E.M. van [Tergooi Hospitals, Department of Radiology, Hilversum (Netherlands); Hulst, V.P.M. van der [Onze Lieve Vrouwe Gasthuis, Department of Radiology, Amsterdam (Netherlands); Henneman, O.D. [Bronovo Hospital, Department of Radiology, The Hague (Netherlands); Bossuyt, P.M. [University of Amsterdam, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, Amsterdam (Netherlands); Boermeester, M.A. [University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Stoker, J. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands)

    2010-07-15

    To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

  9. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    Science.gov (United States)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  10. A comparative study of shape features for polyp detection in wireless capsule endoscopy images.

    Science.gov (United States)

    Li, Baopu; Meng, Max Q H; Xu, Lisheng

    2009-01-01

    Wireless capsule endoscopy (WCE) has been gradually employed in hospitals because it can directly view the entire small bowel of a human body for the first time. However, a troublesome problem related to this new technology is that too many images produced by WCE will take a lot of efforts for doctors to inspect. In this paper, we propose a comparative study of shape features aiming for intestinal polyp detection for WCE images. As polyps exhibit strong shape characteristics, also a powerful clue used by physicians, we investigate two kinds of shape features, MEPG-7 region-based shape descriptor and Zernike moments, in our study. With multi-layer perceptron neural network as the classifier, experiments on our present image data show that it is promising to employ both Zernike moments and MEPG-7 region-based shape descriptor as the shape features to recognize the intestinal polyp regions, and a better performance is obtained by the Zernike moments based shape features.

  11. A unified framework of image latent feature learning on Sina microblog

    Science.gov (United States)

    Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

    2015-10-01

    Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

  12. Segmentation of Clinical Endoscopic Images Based on the Classification of Topological Vector Features

    Directory of Open Access Journals (Sweden)

    O. A. Dunaeva

    2013-01-01

    Full Text Available In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.

  13. An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Yuanyuan Fu

    2017-03-01

    Full Text Available Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix. In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix. The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed

  14. Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

    Directory of Open Access Journals (Sweden)

    R. Youmaran

    2012-01-01

    Full Text Available This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal-Component Analysis- (PCA- and Independent-Component Analysis- (ICA- based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278 bits for PCA and 288 bits for ICA iris features using Masek's iris recognition scheme. This value approximately matches previous estimates of iris information content.

  15. Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

    Directory of Open Access Journals (Sweden)

    Ji Ming

    2008-03-01

    Full Text Available We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

  16. 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.

  17. A Methodology for Texture Feature-based Quality Assessment in Nucleus Segmentation of Histopathology Image.

    Science.gov (United States)

    Wen, Si; Kurc, Tahsin M; Gao, Yi; Zhao, Tianhao; Saltz, Joel H; Zhu, Wei

    2017-01-01

    Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently. Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images. We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch's label. The proposed methodology has been

  18. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  19. A deep bag-of-features model for the classification of melanomas in dermoscopy images.

    Science.gov (United States)

    Sabbaghi, S; Aldeen, M; Garnavi, R

    2016-08-01

    Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the field of medical image diagnosis, particularly in melanoma classification. In this paper, a novel application of deep learning (stacked sparse auto-encoders) is presented for skin lesion classification task. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities). These high-level features are subsequently fed into a classifier for classifying dermoscopy images. In addition, we proposed a new deep neural network architecture based on bag-of-features (BoF) model, which learns high-level image representation and maps images into BoF space. Then, we examine how using this deep representation of BoF, compared with pixel intensities of images, can improve the classification accuracy. The proposed method is evaluated on a test set of 244 skin images. To test the performance of the proposed method, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed method is found to achieve 95% accuracy.

  20. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Science.gov (United States)

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  1. Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort.

    Science.gov (United States)

    Pak, Linda M; Chakraborty, Jayasree; Gonen, Mithat; Chapman, William C; Do, Richard Kg; Koerkamp, Bas Groot; Verhoef, Kees; Lee, Ser Yee; Massani, Marco; van der Stok, Eric P; Simpson, Amber L

    2018-02-14

    Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of the present study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort. Patients were retrospectively identified from five high-volume academic centers that developed PHLI after major hepatectomy and were matched to control patients without PHLI (by extent of resection, pre-operative chemotherapy treatment, age (±5 years), and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. %RLV was defined as follows: (FLR volume)/(total liver volume)x100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression. From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p=0.018 and p=0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV. Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool. Copyright © 2018. Published by Elsevier Inc.

  2. Despeckle and geographical feature extraction in SAR images by wavelet transform

    Science.gov (United States)

    Gupta, Karunesh K.; Gupta, Rajiv

    This paper presents a method to despeckle Synthetic Aperture Radar (SAR) image, and then extract geographical features in it. In this research work, speckle is reduced by multiscale analysis in wavelet domain. In terms of geographical feature preservation the result shows that the method is better compared to spatial domain filters, such as Lee, Kuan, Frost, Ehfrost, Median, Gamma filters. The geographical features such as roads, airport runways, rivers and other ribbon-like shape structures are detected by the new wavelet-based method as proposed by Yuan Yan Tang. Experimental results show that the proposed method extracts geographical features of different width as well as different gray levels.

  3. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

    Science.gov (United States)

    Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

    2017-10-06

    The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

  4. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    Science.gov (United States)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  5. Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

    Directory of Open Access Journals (Sweden)

    Xu Cheng

    2016-01-01

    Full Text Available Aiming at determining the category of an image captured from mobile robots for intelligent application, classification with the bag-of-words model is proved effectively in near-duplicate/planar images. When it comes to images from mobile robots with complex background, does it still work well? In this paper, based on the merging criterion improvement, a method named hierarchical selective search is proposed hierarchically extracting complementary features to form a combined and environment-adaptable similarity measurement for segmentation resulting a small and high-quality regions set. Simultaneously those regions rather than a whole image are used for classification. As a result, it well improved the classification accuracy and make the bog-of-word model still work well on classification for mobile robots. The experiments on hierarchical selective search show its better performance than selective search on two task datasets for mobile robots. The experiments on classification shows the samples from regions are better than those original whole images. The advantage of less quantity and higher quality object regions from hierarchical selective search is more prominent when it comes to those special tasks for mobile robots with scarce data.

  6. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  7. An unsupervised feature learning framework for basal cell carcinoma image analysis.

    Science.gov (United States)

    Arevalo, John; Cruz-Roa, Angel; Arias, Viviana; Romero, Eduardo; González, Fabio A

    2015-06-01

    The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Discriminative feature representation for image classification via multimodal multitask deep neural networks

    Science.gov (United States)

    Mei, Shuang; Yang, Hua; Yin, Zhouping

    2017-01-01

    A good image feature representation is crucial for image classification tasks. Many traditional applications have attempted to design single-modal features for image classification; however, these may have difficulty extracting sufficient information, resulting in misjudgments for various categories. Recently, researchers have focused on designing multimodal features, which have been successfully employed in many situations. However, there are still some problems in this research area, including selecting efficient features for each modality, transforming them to the subspace feature domain, and removing the heterogeneities among different modalities. We propose an end-to-end multimodal deep neural network (MDNN) framework to automate the feature selection and transformation procedures for image classification. Furthermore, inspired by Fisher's theory of linear discriminant analysis, we improve the proposed MDNN by further proposing a multimodal multitask deep neural network (M2DNN) model. The motivation behind M2DNN is to improve the classification performance by incorporating an auxiliary discriminative constraint to the subspace representation. Experimental results on five representative datasets (NUS-WIDE, Scene-15, Texture-25, Indoor-67, and Caltech-101) demonstrate the effectiveness of the proposed MDNN and M2DNN models. In addition, experimental comparisons of the Fisher score criterion exhibit that M2DNN is more robust and has better discriminative power than other approaches.

  9. Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

    Directory of Open Access Journals (Sweden)

    Jie Zhao

    2013-01-01

    Full Text Available Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

  10. Visual pattern mining in histology image collections using bag of features.

    Science.gov (United States)

    Cruz-Roa, Angel; Caicedo, Juan C; González, Fabio A

    2011-06-01

    The paper addresses the problem of finding visual patterns in histology image collections. In particular, it proposes a method for correlating basic visual patterns with high-level concepts combining an appropriate image collection representation with state-of-the-art machine learning techniques. The proposed method starts by representing the visual content of the collection using a bag-of-features strategy. Then, two main visual mining tasks are performed: finding associations between visual-patterns and high-level concepts, and performing automatic image annotation. Associations are found using minimum-redundancy-maximum-relevance feature selection and co-clustering analysis. Annotation is done by applying a support-vector-machine classifier. Additionally, the proposed method includes an interpretation mechanism that associates concept annotations with corresponding image regions. The method was evaluated in two data sets: one comprising histology images from the different four fundamental tissues, and the other composed of histopathology images used for cancer diagnosis. Different visual-word representations and codebook sizes were tested. The performance in both concept association and image annotation tasks was qualitatively and quantitatively evaluated. The results show that the method is able to find highly discriminative visual features and to associate them to high-level concepts. In the annotation task the method showed a competitive performance: an increase of 21% in f-measure with respect to the baseline in the histopathology data set, and an increase of 47% in the histology data set. The experimental evidence suggests that the bag-of-features representation is a good alternative to represent visual content in histology images. The proposed method exploits this representation to perform visual pattern mining from a wider perspective where the focus is the image collection as a whole, rather than individual images. Copyright © 2011 Elsevier B.V. All

  11. Developing a visual sensitive image features based CAD scheme to assist classification of mammographic masses

    Science.gov (United States)

    Wang, Yunzhi; Aghaei, Faranak; Tan, Maxine; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2017-03-01

    Computer-aided diagnosis (CAD) schemes of mammograms have been previously developed and tested. However, due to using "black-box" approaches with a large number of complicated features, radiologists have lower confidence to accept or consider CAD-cued results. In order to help solve this issue, this study aims to develop and evaluate a new CAD scheme that uses visual sensitive image features to classify between malignant and benign mammographic masses. A dataset of 301 masses detected on both craniocaudal (CC) and mediolateraloblique (MLO) view images was retrospectively assembled. Among them, 152 were malignant and 149 were benign. An iterative region-growing algorithm was applied to the special Gaussian-kernel filtered images to segment mass regions. Total 13 Image features were computed to mimic 5 categories of visually sensitive features that are commonly used by radiologists in classifying suspicious mammographic masses namely, mass size, shape factor, contrast, homogeneity and spiculation. We then selected one optimal feature in each of 5 feature categories by using a student t-test, and applied two logistic regression classifiers using either CC or MLO view images to distinguish between malignant and benign masses. Last, a fusion method of combining two classification scores was applied and tested. By applying a 10-fold cross-validation method, the area under receiver operating characteristic curves was 0.806+/-0.025. This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual-aid" interface, CAD results are much more easily explainable to the observers and may increase their confidence to consider CAD-cued results.

  12. Remote Sensing Image Fusion Based on Enhancement of Edge Feature Information

    Directory of Open Access Journals (Sweden)

    Yang Song

    2014-03-01

    Full Text Available A new image fusion algorithm of the multispectral image and the panchromatic image is proposed by using the non-subsampled contourlet transform and the lab color space. The non- subsampled contourlet transform is used to decompose an image into a low frequency approximate component and several high frequency detail components, and an edge enhancement method is employed to extract features from a high resolution image. For keeping the spectral little changing when image fusion, the lab color space, which is a new color space that simulates the visual perception of human, is adopted in this paper. Experimental results indicate that this proposed algorithm can obtain a fusion image which has more rich details.

  13. Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

    Science.gov (United States)

    Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

    2008-03-01

    Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

  14. Using image quality measures and features to choose good images for classification of ISAR imagery

    CSIR Research Space (South Africa)

    Steyn, JM

    2014-10-01

    Full Text Available the quality measures and to determine the minimum dwell-time for ISAR image formation. Keywords—ISAR (inverse synthetic aperture radar), Dwell-time, Quality Measure, Image Contrast, Image Entropy, SNR (signal-to-noise ratio), Maritime Vessels ...

  15. Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors.

    Science.gov (United States)

    Eitz, M; Hildebrand, K; Boubekeur, T; Alexa, M

    2011-11-01

    We introduce a benchmark for evaluating the performance of large-scale sketch-based image retrieval systems. The necessary data are acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.

  16. The effects of TIS and MI on the texture features in ultrasonic fatty liver images

    Science.gov (United States)

    Zhao, Yuan; Cheng, Xinyao; Ding, Mingyue

    2017-03-01

    Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient's situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

  17. A CAD system for B-mode fatty liver ultrasound images using texture features.

    Science.gov (United States)

    Subramanya, M B; Kumar, Vinod; Mukherjee, Shaktidev; Saini, Manju

    2015-02-01

    The present study proposes a computer-aided diagnosis (CAD) system for the diagnosis of grades of fatty liver disease, namely mild, moderate and severe fatty liver along with normal liver tissue. Fifty-three B-mode ultrasound images consisting of 12 normal, 14 mild, 14 moderate and 13 severe fatty liver images are used. Based on the visual interpretations by the radiologists, region of interests (ROIs) from within the liver and one ROI from the diaphragm region are considered from each image. The texture features of these ROIs are combined in three ways to form ratio features, inverse ratio features and additive features. The sub-sets of optimal features are obtained by a differential evolution feature selection (DEFS) algorithm and a support vector machine (SVM) has been used for the classification task. The Laws ratio features have shown better performance with an average accuracy and standard deviation of 84.9±3.2. Hence, the CAD system could be useful to the radiologists in diagnosing grades of fatty liver disease.

  18. AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    E. Khesali

    2015-12-01

    Full Text Available This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1 feature extraction; 2 feature selection; and 3 ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  19. Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

    Science.gov (United States)

    Fu, Dongmei; Tong, Hejun; Zheng, Shuang; Luo, Ling; Gao, Fulin; Minar, Jiri

    2016-07-22

    Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

  20. Hierarchical XP

    OpenAIRE

    Jacobi, Carsten; Rumpe, Bernhard

    2014-01-01

    XP is a light-weight methodology suited particularly for small-sized teams that develop software which has only vague or rapidly changing requirements. The discipline of systems engineering knows it as approach of incremental system change or also of "muddling through". In this paper, we introduce three well known methods of reorganizing companies, namely, the holistic approach, the incremental approach, and the hierarchical approach. We show similarities between software engineering methods ...

  1. Nanoparticles for Cardiovascular Imaging and Therapeutic Delivery, Part 1: Compositions and Features

    Science.gov (United States)

    Stendahl, John C.; Sinusas, Albert J.

    2016-01-01

    Imaging agents made from nanoparticles are functionally versatile and have unique properties that may translate to clinical utility in several key cardiovascular imaging niches. Nanoparticles exhibit size-based circulation, biodistribution, and elimination properties different from those of small molecules and microparticles. In addition, nanoparticles provide versatile platforms that can be engineered to create both multimodal and multifunctional imaging agents with tunable properties. With these features, nanoparticulate imaging agents can facilitate fusion of high-sensitivity and high-resolution imaging modalities and selectively bind tissues for targeted molecular imaging and therapeutic delivery. Despite their intriguing attributes, nanoparticulate imaging agents have thus far achieved only limited clinical use. The reasons for this restricted advancement include an evolving scope of applications, the simplicity and effectiveness of existing small-molecule agents, pharmacokinetic limitations, safety concerns, and a complex regulatory environment. This review describes general features of nanoparticulate imaging agents and therapeutics and discusses challenges associated with clinical translation. A second, related review to appear in a subsequent issue of JNM highlights nuclear-based nanoparticulate probes in preclinical cardiovascular imaging. PMID:26272808

  2. DEVELOPING AN IMAGE PROCESSING APPLICATION THAT SUPPORTS NEW FEATURES OF JPEG2000 STANDARD

    Directory of Open Access Journals (Sweden)

    Evgin GÖÇERİ

    2007-03-01

    Full Text Available In recent years, developing technologies in multimedia brought the importance of image processing and compression. Images that are reduced in size using lossless and lossy compression techniques without degrading the quality of the image to an unacceptable level take up much less space in memory. This enables them to be sent and received over the Internet or mobile devices in much shorter time. The wavelet-based image compression standard JPEG2000 has been created by the Joint Photographic Experts Group (JPEG committee to superseding the former JPEG standard. Works on various additions to this standard are still under development. In this study, an Application has been developed in Visual C# 2005 which implies important image processing techniques such as edge detection and noise reduction. The important feature of this Application is to support JPEG2000 standard as well as supporting other image types, and the implementation does not only apply to two-dimensional images, but also to multi-dimensional images. Modern software development platforms that support image processing have also been compared and several features of the developed software have been identified.

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

    Science.gov (United States)

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

    2017-03-01

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

  4. Soft sensor design by multivariate fusion of image features and process measurements

    DEFF Research Database (Denmark)

    Lin, Bao; Jørgensen, Sten Bay

    2011-01-01

    This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine...... with a multivariate analysis technique from RGB pictures. The color information is also transformed to hue, saturation and intensity components. Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression. A dynamic PLS model...... the weight of each regressor, is achieved through multivariate regression. The framework is described and illustrated with applications to cement kiln systems that are characterized by off-line quality measurements and on-line analyzers with limited reliability. Image features are extracted...

  5. Selection of the best features for leukocytes classification in blood smear microscopic images

    Science.gov (United States)

    Sarrafzadeh, Omid; Rabbani, Hossein; Talebi, Ardeshir; Banaem, Hossein Usefi

    2014-03-01

    Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.

  6. Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain

    Directory of Open Access Journals (Sweden)

    Xiangzeng Liu

    2011-12-01

    Full Text Available This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images. First, a new robust SIFT descriptor is presented, which is invariant under affine transformation. Then, an adaptive similarity measure is developed according to the robust SIFT descriptor and the adaptive normalized cross correlation of feature point’s neighborhood. Finally, the corresponding feature points can be determined by the adaptive similarity measure in Steerable-Domain of the two input images, and the final refined transformation parameters determined by using gradual optimization are adopted to achieve the registration results. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large scale, rotation changes, and illumination contrast. The effectiveness of the proposed method is demonstrated by the experimental results.

  7. On Feature Relevance in Image-Based Prediction Models: An Empirical Study

    DEFF Research Database (Denmark)

    Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

    2013-01-01

    Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from...... the community. In this article, we present an empirical study on the relevant features produced by two recently developed discriminative learning algorithms: neighborhood approximation forests (NAF) and the relevance voxel machine (RVoxM). Specifically, we examine whether the sets of features these methods...... produce are exhaustive; that is whether the features that are not marked as relevant carry disease-related information. We perform experiments on three different problems: image-based regression on a synthetic dataset for which the set of relevant features is known, regression of subject age as well...

  8. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  9. Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

    Science.gov (United States)

    Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

    2017-01-01

    This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

  10. 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.

  11. An alternative to scale-space representation for extracting local features in image recognition

    DEFF Research Database (Denmark)

    Andersen, Hans Jørgen; Nguyen, Phuong Giang

    2012-01-01

    In image recognition, the common approach for extracting local features using a scale-space representation has usually three main steps; first interest points are extracted at different scales, next from a patch around each interest point the rotation is calculated with corresponding orientation...... and compensation, and finally a descriptor is computed for the derived patch (i.e. feature of the patch). To avoid the memory and computational intensive process of constructing the scale-space, we use a method where no scale-space is required This is done by dividing the given image into a number of triangles...

  12. Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    J. Del Rio Vera

    2009-01-01

    Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

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

    Directory of Open Access Journals (Sweden)

    B. Kuhlenkötter

    2006-01-01

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

  14. [The comparison between mental image manipulation and distinctive feature scan on recognition memory of faces].

    Science.gov (United States)

    Kihara, K; Yoshikawa, S

    2001-08-01

    The authors proposed "mental image manipulation of expression" as processing strategy for faces, and investigated whether this strategy facilitates memory for faces or not. In the Experiment, four groups of subjects were assigned to a combination of a task (mental image manipulation of expression or distinctive feature scan) and a retention interval (short-term latency or long-term latency). Each task was followed by an unexpected yes-no recognition test in which identical pictures of the target faces or the same person's expression-changed faces were randomly presented with distractor faces. The mental image manipulation group was better than distinctive feature scan group in long-term storage. This result is considered as a long-term effect of imagery encoding and a configurational encoding by mental image manipulation.

  15. Color Image Segmentation Based on Statistics of Location and Feature Similarity

    Science.gov (United States)

    Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

    The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

  16. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  17. Thermal feature extraction of servers in a datacenter using thermal image registration

    Science.gov (United States)

    Liu, Hang; Ran, Jian; Xie, Ting; Gao, Shan

    2017-09-01

    Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.

  18. 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

  19. Body image dissatisfaction: clinical features, and psychosocial disability in inflammatory bowel disease.

    Science.gov (United States)

    McDermott, Edel; Mullen, Georgina; Moloney, Jenny; Keegan, Denise; Byrne, Kathryn; Doherty, Glen A; Cullen, Garret; Malone, Kevin; Mulcahy, Hugh E

    2015-02-01

    Body image refers to a person's sense of their physical appearance and body function. A negative body image self-evaluation may result in psychosocial dysfunction. Crohn's disease and ulcerative colitis are associated with disabling features, and body image dissatisfaction is a concern for many patients with inflammatory bowel disease (IBD). However, no study has assessed body image and its comorbidities in patients with IBD using validated instruments. Our aim was to explore body image dissatisfaction in patients with IBD and assess its relationship with biological and psychosocial variables. We studied 330 patients (median age, 36 yr; range, 18-83; 169 men) using quantitative and qualitative methods. Patients completed a self-administered questionnaire that included a modified Hopwood Body Image Scale, the Cash Body Image Disturbance Questionnaire, and other validated instruments. Clinical and disease activity data were also collected. Body image dissatisfaction was associated with disease activity (P Body image dissatisfaction was also associated with low levels of general (P quality of life, self-esteem (P depression (P body image dissatisfaction, including steroid side effects and impaired work and social activities. Body image dissatisfaction is common in patients with IBD, relates to specific clinical variables and is associated with significant psychological dysfunction. Its measurement is warranted as part of a comprehensive patient-centered IBD assessment.

  20. Imaging Features of AlloDerm® Used in Postmastectomy Breast Reconstructions

    Directory of Open Access Journals (Sweden)

    Christine U Lee

    2014-01-01

    Full Text Available The purpose of this pictorial essay is to demonstrate the imaging features (ultrasound, mammogram, and magnetic resonance imaging (MRI of AlloDerm® (LifeCell Corp.; Branchburg, NJ, an acellular dermal matrix sometimes used in both primary and reconstructive breast surgeries. AlloDerm® is derived from cadaveric dermis and provides an immunologically inert scaffold in tissue reconstruction. Since there is little literature on the imaging of this substance, radiologists may be unfamiliar with its appearance in breast imaging. For this manuscript, ex vivo and in vivo images of AlloDerm® in postmastectomy patients were evaluated using different imaging modalities. The appearance of AlloDerm® can vary based on length of time postsurgery and incorporation into the host. AlloDerm® appears as an isodense to glandular tissue on a mammogram and isoechoic to glandular tissue on ultrasound imaging. On MRI, in comparison with normal breast parenchyma, AlloDerm® is hyperintense on T2-weighted imaging and isointense on T1-weighted imaging and demonstrates mild enhancement. To the best of the authors′ knowledge, this is the first multimodality imaging description of AlloDerm® used in postmastectomy patients. The conformation of AlloDerm® at surgical placement and the degree of host cell migration and neoangiogenesis are factors to take into consideration when performing diagnostic evaluations; and, familiarity with the various imaging appearances of AlloDerm® can be helpful to exclude residual or recurrent disease.

  1. Feature-specific imaging: Extensions to adaptive object recognition and active illumination based scene reconstruction

    Science.gov (United States)

    Baheti, Pawan K.

    Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination. We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented. Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image

  2. Classification of photographed document images based on deep-learning features

    Science.gov (United States)

    Zhong, Guoqiang; Yao, Hui; Liu, Yutong; Hong, Chen; Pham, Tuan

    2017-02-01

    In this paper, we propose two new problems related to classification of photographed document images, and based on deep learning methods, present the baseline solutions for these two problems. The first problem is that, for some photographed document images, which book do they belong to? The second one is, for some photographed document images, what is the type of the book they belong to? To address these two problems, we apply "AexNet" to the collected document images. Using the pre-trained "AlexNet" on the ImageNet data set directly, we obtain 92.57% accuracy for the book-name classification and 93.33% accuracy for the book-type one. After fine-tuning on the training set of the photographed document images, the accuracy of the book-name classification increases to 95.54% and that of the booktype one to 95.42%. To our best knowledge, although there exist many image classification algorithm, no previous work has targeted to these two challenging problems. In addition, the experiments demonstrate that deep-learning features outperform features extracted with traditional image descriptors on these two problems.

  3. Dengue encephalitis with predominant cerebellar involvement: Report of eight cases with MR and CT imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Hegde, Vinay; Bhat, Maya; Prasad, Chandrajit; Gupta, A.K.; Saini, Jitender [National Institute of Mental Health and Neurosciences, Department of Neuroimaging and Interventional Radiology, Bangalore, Karnataka (India); Aziz, Zarina [Sri Sathya Sai Institute of Medical Science, Department of Radiology, Bangalore (India); Kumar, Sharath [Apollo Hospital, Department of Neuroradiology, Bangalore (India); Netravathi, M. [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore (India)

    2014-11-01

    CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis. (orig.)

  4. Least-Squares Estimation of Imaging Parameters for an Ultrasonic Array Using Known Geometric Image Features

    NARCIS (Netherlands)

    Hunter, A.J.; Drinkwater, B.W.; Wilcox, P.D.

    2011-01-01

    Ultrasonic array images are adversely affected by errors in the assumed or measured imaging parameters. For non-destructive testing and evaluation, this can result in reduced defect detection and characterization performance. In this paper, an autofocus algorithm is presented for estimating and

  5. Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

    Science.gov (United States)

    Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

    2013-01-01

    We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

  6. Injectable facial fillers: imaging features, complications, and diagnostic pitfalls at MRI and PET CT.

    Science.gov (United States)

    Mundada, Pravin; Kohler, Romain; Boudabbous, Sana; Toutous Trellu, Laurence; Platon, Alexandra; Becker, Minerva

    2017-12-01

    Injectable fillers are widely used for facial rejuvenation, correction of disabling volumetric fat loss in HIV-associated facial lipoatrophy, Romberg disease, and post-traumatic facial disfiguring. The purpose of this article is to acquaint the reader with the anatomy of facial fat compartments, as well as with the properties and key imaging features of commonly used facial fillers, filler-related complications, interpretation pitfalls, and dermatologic conditions mimicking filler-related complications. The distribution of facial fillers is characteristic and depends on the anatomy of the superficial fat compartments. Silicone has signature MRI features, calcium hydroxyapatite has characteristic calcifications, whereas other injectable fillers have overlapping imaging features. Most fillers (hyaluronic acid, collagen, and polyalkylimide-polyacrylamide hydrogels) have signal intensity patterns compatible with high water content. On PET-CT, most fillers show physiologic high FDG uptake, which should not be confounded with pathology. Abscess, cellulitis, non-inflammatory nodules, and foreign body granulomas are the most common filler-related complications, and imaging can help in the differential diagnosis. Diffusion weighted imaging helps in detecting a malignant lesion masked by injected facial fillers. Awareness of imaging features of facial fillers and their complications helps to avoid misinterpretation of MRI, and PET-CT scans and facilitates therapeutic decisions in unclear clinical cases. • Facial fillers are common incidental findings on MRI and PET-CT scans. • They have a characteristic appearance and typical anatomic distribution • Although considered as safe, facial filler injections are associated with several complications • As they may mask malignancy, knowledge of typical imaging features is mandatory. • MRI is a problem-solving tool for unclear cases.

  7. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-01-01

    The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were provided to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC=0.793±0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography. PMID:25029964

  8. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

  9. [Study on identification the crack feature of fresh jujube using hyperspectral imaging].

    Science.gov (United States)

    Yu, Ke-Qiang; Zhao, Yan-Ru; Li, Xiao-Li; Zhang, Shu-Juan; He, Yong

    2014-02-01

    Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.

  10. Imaging Features of Radiofrequency Ablation with Heat-Deployed Liposomal Doxorubicin in Hepatic Tumors

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Cheng William, E-mail: williamhongcheng@gmail.com; Chow, Lucy, E-mail: lucychow282@gmail.com [National Institutes of Health, Center for Interventional Oncology, Clinical Center (United States); Turkbey, Evrim B., E-mail: evrimbengi@yahoo.com [National Institutes of Health, Radiology and Imaging Sciences, Clinical Center (United States); Lencioni, Riccardo, E-mail: riccardo.lencioni@med.unipi.it [Pisa University Hospital, Division of Diagnostic Imaging and Intervention, Department of Hepatology and Liver Transplantation (Italy); Libutti, Steven K., E-mail: slibutti@montefiore.org [Albert Einstein College of Medicine, Montefiore-Einstein Center for Cancer Care, Department of Surgery (United States); Wood, Bradford J., E-mail: bwood@nih.gov [National Institutes of Health, Center for Interventional Oncology, Clinical Center (United States)

    2016-03-15

    IntroductionThe imaging features of unresectable hepatic malignancies in patients who underwent radiofrequency ablation (RFA) in combination with lyso-thermosensitive liposomal doxorubicin (LTLD) were determined.Materials and MethodsA phase I dose escalation study combining RFA with LTLD was performed with peri- and post- procedural CT and MRI. Imaging features were analyzed and measured in terms of ablative zone size and surrounding penumbra size. The dynamic imaging appearance was described qualitatively immediately following the procedure and at 1-month follow-up. The control group receiving liver RFA without LTLD was compared to the study group in terms of imaging features and post-ablative zone size dynamics at follow-up.ResultsPost-treatment scans of hepatic lesions treated with RFA and LTLD have distinctive imaging characteristics when compared to those treated with RFA alone. The addition of LTLD resulted in a regular or smooth enhancing rim on T1W MRI which often correlated with increased attenuation on CT. The LTLD-treated ablation zones were stable or enlarged at follow-up four weeks later in 69 % of study subjects as opposed to conventional RFA where the ablation zone underwent involution compared to imaging acquired immediately after the procedure.ConclusionThe imaging features following RFA with LTLD were different from those after standard RFA and can mimic residual or recurrent tumor. Knowledge of the subtle findings between the two groups can help avoid misinterpretation and proper identification of treatment failure in this setting. Increased size of the LTLD-treated ablation zone after RFA suggests the ongoing drug-induced biological effects.

  11. Exact feature extraction using finite rate of innovation principles with an application to image super-resolution.

    Science.gov (United States)

    Baboulaz, Loïc; Dragotti, Pier Luigi

    2009-02-01

    The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image super-resolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.

  12. IMAGING DIAGNOSIS-MAGNETIC RESONANCE IMAGING FEATURES OF CRANIOMANDIBULAR OSTEOPATHY IN AN AIREDALE TERRIER.

    Science.gov (United States)

    Matiasovic, Matej; Caine, Abby; Scarpante, Elena; Cherubini, Giunio Bruto

    2016-05-01

    An Airedale Terrier was presented for evaluation of depression and reluctance to be touched on the head. Magnetic resonance (MR) imaging of the head was performed. The images revealed bone lesions affecting the calvarium at the level of the coronal suture and left mandibular ramus, with focal cortical destruction, expansion, and reactive new bone formation. Skull lesions were hypointense on T1-weighted sequences, hyperintense on T2-weighted sequences, and showed an intense and homogeneous enhancement after gadolinium administration. Reactive new bone formation and periosteal proliferation were confirmed histopathologically. The clinical signs, imaging findings, and histopathological examination were consistent with craniomandibular osteopathy. © 2015 American College of Veterinary Radiology.

  13. MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

    Directory of Open Access Journals (Sweden)

    Y. Di

    2017-05-01

    Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

  14. Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

    Science.gov (United States)

    Rezaeian, A.; Homayouni, S.; Safari, A.

    2015-12-01

    Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  15. SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    A. Rezaeian

    2015-12-01

    Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed met