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Proceedings Paper

Computer-aided diagnostics of screening mammography using content-based image retrieval
Author(s): Thomas M. Deserno; Michael Soiron; Júlia E. E. de Oliveira; Arnaldo de A. Araújo
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Paper Abstract

Breast cancer is one of the main causes of death among women in occidental countries. In the last years, screening mammography has been established worldwide for early detection of breast cancer, and computer-aided diagnostics (CAD) is being developed to assist physicians reading mammograms. A promising method for CAD is content-based image retrieval (CBIR). Recently, we have developed a classification scheme of suspicious tissue pattern based on the support vector machine (SVM). In this paper, we continue moving towards automatic CAD of screening mammography. The experiments are based on in total 10,509 radiographs that have been collected from different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotation of cancerous regions. In different experiments, this data is divided into 12 and 20 classes, distinguishing between four categories of tissue density, three categories of pathology and in the 20 class problem two categories of different types of lesions. Balancing the number of images in each class yields 233 and 45 images remaining in each of the 12 and 20 classes, respectively. Using a two-dimensional principal component analysis, features are extracted from small patches of 128 x 128 pixels and classified by means of a SVM. Overall, the accuracy of the raw classification was 61.6 % and 52.1 % for the 12 and the 20 class problem, respectively. The confusion matrices are assessed for detailed analysis. Furthermore, an implementation of a SVM-based CBIR system for CADx in screening mammography is presented. In conclusion, with a smarter patch extraction, the CBIR approach might reach precision rates that are helpful for the physicians. This, however, needs more comprehensive evaluation on clinical data.

Paper Details

Date Published: 23 February 2012
PDF: 9 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831527 (23 February 2012); doi: 10.1117/12.912392
Show Author Affiliations
Thomas M. Deserno, RWTH Aachen Univ. (Germany)
Michael Soiron, RWTH Aachen Univ. (Germany)
Júlia E. E. de Oliveira, Ctr. de Desenvolvimento da Tecnologia Nuclear (Brazil)
Arnaldo de A. Araújo, Univ. Federal de Minas Gerais (Brazil)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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