Content-based image retrieval in mammography: using texture features for correlation with BI-RADS categories

Honda, Marcelo Ossamu; Marques, Paulo Mazzoncini de Azevedo; Rodrigues, José

doi:10.1007/978-3-642-59327-7_55

Cited by 10 publications

(6 citation statements)

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“…Working toward this goal, we have previously presented a CBIR-based CAD system for the detection and diagnosis of masses in screening mammograms. 19,20 In contrast to feature-based CBIR algorithms in mammography, [21][22][23][24][25][26][27] our system relies on information theoretic principles to assess image similarity. Specifically, the system uses the popular concept of mutual information ͑MI͒ to measure the similarity between a query image and those stored in the knowledge database.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

et al. 2006

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The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

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Section: Introductionmentioning

confidence: 99%

Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

et al. 2006

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“…Honda et al [5] developed a CBIR system for mammograms to aid the diagnosis of breast lesions. They used spatial gray level dependence matrices to extract textural features of mammograms from 136 clinical cases.…”

Section: Previous Studiesmentioning

confidence: 99%

<title>A general framework for content-based medical image retrieval with its application to mammograms</title>

Wei

Wilson

2005

SPIE Proceedings

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In the medical field, content-based image retrieval (CBIR) is used to aid radiologists in the retrieval of images with similar contents. CBIR methods are usually developed for specific features of images, so that those methods are not readily applicable across different kinds of medical images. This study proposes a sound methodology for CBIR of mammograms, which is applicable to various formats of medical image. The methodology is divided into two partsimage analysis and image retrieval. In the image analysis part, 19 abnormal regions of interest (ROI) and 20 normal ROIs are selected as samples for the whole ROI dataset. These two groups of ROIs are used to analyze 11 textural features based on gray level co-occurrence matrices. The multivariate t test is then applied to examine the significance of the differences for these 11 textural features from normal and abnormal ROIs. The discriminating features are incorporated into a feature descriptor for the ROI. This descriptor is embedded into the CBIR system. In the image retrieval part, a CBIR system for mammograms is developed. For normalization of feature vectors, a novel technique is proposed to clip the values of feature elements of the top 5%, and then project each image feature onto the unit sphere.To determine the similarity between query image and each ROI in the dataset, the 2 L norm is used to measure the similarity between two images. This system was designed by query-by-example (QBE). Query images were selected from different classes of abnormal ROIs. To evaluate the performance of the CBIR system, the precision and recall were measured. A maximum precision of 51% and recall of 19% were obtained using the gray level co-occurrence matrices and a distance of 5. The averages of precision and recall are 49% and 18% in this experiment.

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“…Some researchers have investigated the application of content-based image retrieval (CBIR) and data mining techniques to explore the richness present in databases of mammograms and patient information [123]. Honda et al [124] presented a CBIR system based on textural features and PCA: the authors reported a precision rate between 25% and 100%. Nakagawa et al [125] presented a technique for CBIR where mammographic ROIs containing masses were represented by autocorrelation measures.…”

Section: Indexed Atlases Data Mining and Content-based Retrievalmentioning

confidence: 99%

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Rangayyan

Ayres

Desautels

2007

Journal of the Franklin Institute

302

143

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Content-based image retrieval in mammography: using texture features for correlation with BI-RADS categories

Cited by 10 publications

References 1 publication

Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

<title>A general framework for content-based medical image retrieval with its application to mammograms</title>

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

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