Microcalcification Detection Using a Kernel Bayes Classifier

Authors:
Barbara Caputo;E. La Torre;Giovanni E. Gigante
Affiliations:
-;-;-
Venue:
ISMDA '02 Proceedings of the Third International Symposium on Medical Data Analysis
Year:
2002

Citing 4
Cited 1

Fundamentals of digital image processing

Fundamentals of digital image processing
Color indexing

International Journal of Computer Vision
Recognition without Correspondence using MultidimensionalReceptive Field Histograms

International Journal of Computer Vision
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition

Bayesian classifier with simplified learning phase for detecting microcalcifications in digital mammograms

Journal of Biomedical Imaging

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Abstract

Mammography associated with clinical breast examination is the only effective method for mass breast screening. Microcalcifications are one of the primary signs for early detection of breast cancer. In this paper we propose a new kernel method for classification of dificult to diagnose regions in mammographic images. It consists of a novel class of Markov Random Fields, using techniques developed within the context of statistical mechanics. This method is used for the classification of positive Region of Interest (ROI's) containing clustered microcalcifications and negative ROI's containing normal tissue. We benchmarked the new proposed method with a nearest neighbor classifier and with an artificial neural network, widely used in literature for computer-aided diagnosis. We obtained the best performance using the novel approach.