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

Abstract

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.

Microcalcifications detection in mammograms based on Ant Colony Optimization and Markov Random Field

“…The first one describes the underlying image and serves as prior contextual information for making a decision. The second describes the observation at each pixel and/or the relation between feature vectors and pattern classes [12].…”

“…By this way, the MRF model provides an excellent tool for blending the information on local spatial interaction into a global framework [12].…”

“…As a result, X has a Gibbs distribution given by the system of equations (10): (10) Leading to have: (11) Where U(X) is the energy function is given by (12): (12) The minimization of the MAP corresponds, so, to the maximization of the energy function. Subsequently, the minimization of MAP leads to the optimal labeling of the entire image.…”

“…Due to the textural characteristics on mammographic images, the MRF model is plausible for pixels' labeling [12]. The process of minimization of the MAP ensures the optimal labeling of the mammogram pixels.…”

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

“…Some previous studies show that Computer Aided Diagnosis (CAD) can simplify the process of interpreting mammograms and giving more accurate result [7]. The output of CAD is used to help radiologist in the detection of breast cancer [8].…”