A genetic algorithm design for microcalcification detection and classification in digital mammograms.
In this paper, we propose a genetic algorithm design to automatically classify and detect micocalcification clusters in digital mammograms. The proposed GA technique is characterised by transforming input images into a feature domain, where each pixel is represented by its mean and standard deviation inside a surrounding window of size 9 x 9 pixel. In the feature domain, chromosomes are constructed to populate the initial generation and further features are extracted to enable the proposed GA to search for optimised classification and detection of microcalcification clusters via regions of 128 x 128 pixels. Extensive experiments show that the proposed GA design is able to achieve high performances in microcalcification classification and detection, which are measured by ROC curves, sensitivity against specificity, areas under ROC curves and benchmarked by existing representative techniques.
Jiang
J
J
University of Bradford, School of Informatics, Richmond Road, Bradford BD7 1DP, United Kingdom. j.jiang1@bradford.ac.uk
Yao
B
B
Wason
A M
AM
D000465:Algorithms; D002114:Calcinosis / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D006113:United Kingdom