Retrieval by content of medical images using texture for tissue identification

Felipe, Joaquim Cezar; Traina, Agma J. M.; Traina, Caetano

doi:10.1109/cbms.2003.1212785

Cited by 89 publications

(60 citation statements)

References 8 publications

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“…The Haar wavelets with two levels were extracted, generating seven subbands; for each subband, the gradient histogram equalized to 16 ranges of gray level was calculated, 39 i.e., 16 attributes; the feature vector concatenating the 7 gradient histograms was composed, resulting in 112 attributes; the relief attribute evaluation algorithm was applied to determine the most relevant attributes for discriminating the images as either normal or pathological. The free software Weka (University of Waikato, New Zealand) was used, and 16 attributes were selected; these 16 attributes were used to represent the image.…”

Section: Similarity Of Medical Images Based On Wavelets and Histogrammentioning

confidence: 99%

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

Felipe

Traina

2008

J Digit Imaging

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A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition of the AID family with L p distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR.KEY WORDS: Distance function, medical images, content-based image retrieval INTRODUCTION D igital images, which are present in the majority of medical systems, serve to support diagnostic activities. However, for the effective and suitable use of images, these systems must include tools for image management, including fast and effective comparison and retrieval. In the image analysis environment, the ability to compare images automatically is important because the number of stored images is usually very large, precluding a radiologist from making individual comparisons of the images in the entire database. Some groups of radiologists prefer to search in reference libraries, which are composed of sets of typical images. However, the possibility of retrieving images containing patient-related information from specific databases enables the analyst to make more in-depth analyses, for instance, to explore and study the patients from a particular geographic region.The core of content-based image retrieval (CBIR) is the use of intrinsic visual features, which are extracted automatically from the images to describe them while keeping their most relevant characteristics. Such features, usually consisting of numerical values obtained by image-processing algorithms, are used to compare and index images and are usually placed together in a feature vector. Each item of a feature vector is also called an image attribute. CBIR techniques take advantage of index structures that use the similarity of features to speed up the retrieval, doing this automatically. 1,2 In Figure 1, the user places a query, which is done typically by providing the system with an image (query image) ...

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Section: Similarity Of Medical Images Based On Wavelets and Histogrammentioning

confidence: 99%

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

Felipe

Traina

2008

J Digit Imaging

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“…In [19] wavelet transform based brain image retrieval is presented. Medical CT and MRI image retrieval using co-occurrence matrix is presented by Felipe et al [20]. Further, image retrieval of different body parts using color quantization and wavelet transform is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Local Differential Excitation Binary Co-occurrence Pattern (LDEBCoP): A New Descriptor for Texture and Bio-Medical Image Retrieval

Kumar¹,

Mohan²

2017

IOSR JCE

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“…A major focus of the existing research is to extract semantics around human objects from sports videos and attempt to add annotations to human object-related videos via classifications. 5,6,[8][9][10][11][12] As a result, human object segmentation plays crucial roles in all of these techniques, yet such segmentation itself is a difficult research problem, and existing work is primarily relying on low-level features and detections of their consistency within regions to complete segmentation. 13 Consequently, the accuracy of human object segmentation is limited.…”

Section: Introductionmentioning

confidence: 99%

“…At present, extensive research has been carried out and reported on video content analysis and various event detections. [1][2][3][4][5][6][7][8][9][10][11][12] Reference 1 reported a sports video classification technique via exploitation of the human vision system in perceiving some salient regions inside video frames, which are represented by regions of interests (ROI). The technique first extracts ROIs and then clusters these ROIs to extract color and texture features to classify sports videos.…”

Section: Introductionmentioning

confidence: 99%

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

et al. 2010

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract. We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications. C 2010 Society of Photo-Optical Instrumentation Engineers.Optical Engineering 49(12), 127003 (December 2010

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Retrieval by content of medical images using texture for tissue identification

Cited by 89 publications

References 8 publications

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

Local Differential Excitation Binary Co-occurrence Pattern (LDEBCoP): A New Descriptor for Texture and Bio-Medical Image Retrieval

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

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