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Automated mammogram classification using a multiresolution pattern recognition approach

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2 Author(s)
Ferreira, C.B.R. ; Pontificia Univ. Catolica do Parana, Curitiba, Brazil ; Borges, D.L.

In order to fully achieve automated mammogram analysis one has to tackle two problems: classification of radial, circumscribed microcalcifications, and normal samples; and classification of benign, malignant, and normal samples. How to extract and select the best features from the images for classification is a very difficult task, since all of those classes are basically irregular textures with a wide visual variety inside each class. The authors propose a multiresolution pattern recognition approach for this problem, by transforming the data of the images in a wavelet basis, and then using special sets of the coefficients as the features tailored towards separating each of those classes. For the experiments, we have used samples of images labeled by physicians. Results shown are very promising, and the paper describes possible lines for future directions

Published in:

Computer Graphics and Image Processing, 2001 Proceedings of XIV Brazilian Symposium on

Date of Conference:

Oct 2001

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