A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Directional Analysis of Images with Gabor Wavelets
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Automated image analysis techniques for digital mammography
Automated image analysis techniques for digital mammography
Multiresolution mammogram analysis in multilevel decomposition
Pattern Recognition Letters
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
Computers in Biology and Medicine
Mammographic Mass Detection using Wavelets as Input to Neural Networks
Journal of Medical Systems
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
An evaluation of wavelet features subsets for mammogram classification
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Computers in Biology and Medicine
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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, malign, and normal ones. 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. Besides there is a lack of tested solutions for these problems in the literature. In this paper we propose to construct and evaluate a supervised classifier for these two problems, 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. We have realized that this is a suitable solution worth further exploration. 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.