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International Journal of Biomedical Imaging
Volume 2009, Article ID 767805, 13 pages
Research Article

Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms

1Department of Electrical and Computer Engineering, Western Michigan University, MI 49008, USA
2Radiology Department, Bronson Methodist Hospital, Kalamazoo, MI 49007, USA

Received 24 April 2009; Accepted 13 October 2009

Academic Editor: Scott Pohlman

Copyright © 2009 Imad Zyout et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.