A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stochastic Model for Automated Detction of Calculations in Digital Mammograms
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
Adaptive Noise Equalization and Image Analysis in Mammography
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Microcalcification Detection Using a Kernel Bayes Classifier
ISMDA '02 Proceedings of the Third International Symposium on Medical Data Analysis
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Machine Graphics & Vision International Journal
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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.