Effective shape-based retrieval and classification of mammograms

Authors:
Joaquim C. Felipe;Marcela X. Ribeiro;Elaine P. M. Sousa;Agma J. M. Traina;Caetano Jr Traina
Affiliations:
University of São Paulo at Ribeirão Preto - Brazil;University of São Paulo at São Carlos - Brazil;University of São Paulo at São Carlos - Brazil;University of São Paulo at São Carlos - Brazil;University of São Paulo at São Carlos - Brazil
Venue:
Proceedings of the 2006 ACM symposium on Applied computing
Year:
2006

Citing 6
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Abstract

This paper presents a new approach to support Computer-aided Diagnosis (CAD) aiming at assisting the task of classification and similarity retrieval of mammographic mass lesions, based on shape content. We have tested classical algorithms for automatic segmentation of this kind of image, but usually they are not precise enough to generate accurate contours to allow lesion classification based on shape analyses. Thus, in this work, we have used Zernike moments for invariant pattern recognition within regions of interest (ROIs), without previous segmentation of images. A new data mining algorithm that generates statistical-based association rules is used to identify representative features that discriminate the disease classes of images. In order to minimize the computational effort, an algorithm based on fractal theory is applied to reduce the dimension of feature vectors. K-nearest neighbor retrieval was applied to a database containing images excerpted from previously classified digitalized mammograms presenting breast lesions. The results reveal that our approach allows fast and effective feature extraction and is robust and suitable for analyzing this kind of image.