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Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns

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Abstract

This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick’s texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.

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Acknowledgment

We thank the radiologists and faculty members of the Clinical Hospital of the Faculty of Medicine, University of São Paulo, Ribeirão Preto, SP, Brazil, for providing the mammograms and the related reports used in this work. We thank the State of São Paulo Research Foundation (FAPESP), the National Council for Scientific and Technological Development (CNPq), and the Foundation to Aid Teaching, Research, and Patient Care of the Clinical Hospital of Ribeirão Preto (FAEPA/HCRP) for financial support. This work was also supported by a Catalyst grant from Research Services, University of Calgary.

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Authors and Affiliations

  1. Image Science and Medical Physics Center, Internal Medicine Department, Faculty of Medicine of Ribeirao Preto, University of Sao Paulo, avenida dos Bandeirantes, 3900, 14048-900, Ribeirão Preto, São Paulo, Brazil

    Sérgio Koodi Kinoshita, Paulo Mazzoncini de Azevedo-Marques, Roberto Rodrigues Pereira Jr & Jośe Antônio Heisinger Rodrigues

  2. Department of Electrical Engineering, University of São Paulo, avenida do Trabalhador Sancarlense, 400, 13560-250, Sao Carlos, SP, Brazil

    Sérgio Koodi Kinoshita

  3. Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada, T2N 1N4

    Rangaraj Mandayam Rangayyan

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  1. Sérgio Koodi Kinoshita
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  2. Paulo Mazzoncini de Azevedo-Marques
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  3. Roberto Rodrigues Pereira Jr
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  4. Jośe Antônio Heisinger Rodrigues
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  5. Rangaraj Mandayam Rangayyan
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Correspondence to Paulo Mazzoncini de Azevedo-Marques.

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Kinoshita, S.K., de Azevedo-Marques, P.M., Pereira, R.R. et al. Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns. J Digit Imaging 20, 172–190 (2007). https://doi.org/10.1007/s10278-007-9004-0

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  • DOI: https://doi.org/10.1007/s10278-007-9004-0

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