University of Surrey

Test tubes in the lab Research in the ATI Dance Research

A genetic algorithm design for microcalcification detection and classification in digital mammograms.

Jiang, J, Yao, B and Wason, AM (2007) A genetic algorithm design for microcalcification detection and classification in digital mammograms. Comput Med Imaging Graph, 31 (1). pp. 49-61.

Full text not available from this repository.

Abstract

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.

Item Type: Article
Authors :
NameEmailORCID
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Yao, BUNSPECIFIEDUNSPECIFIED
Wason, AMUNSPECIFIEDUNSPECIFIED
Date : January 2007
Identification Number : 10.1016/j.compmedimag.2006.09.011
Uncontrolled Keywords : Algorithms, Calcinosis, Diagnosis, Computer-Assisted, Female, Great Britain, Humans, Mammography
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:24
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835189

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800