Title: A texture analysis method for detection of clustered microcalcifications on digital mammograms
Authors: M.C. Barretto; D.A. Kulkarni; G.R. Udupi
Addresses: Department of Information Technology, Padre Conceicao College of Engineering, Verna, 403712, Goa, India ' Department of Computer Science, Gogte Institute of Engineering, Belgaum, 590008, Karnataka, India ' Department of Electronics and Communications, VDRIT, Haliyal, 581329, Karnataka, India
Abstract: Breast cancer is one of the major causes of death among women. Early detection of breast cancer is possible by the detection of clustered microcalcifications on X-ray mammograms. Texture is an important characteristic used in identifying objects or region of interest in a digitised mammogram. This work focuses on a statistical texture analysis method called Surrounding Region Dependence Method (Kim and Park, 1999) - based on second order histogram in two surrounding regions. Six textural features are extracted and are used to classify region of interests into positive ROIs, containing clustered microcalcifications and negative ROIs composed of normal breast tissues. A 3-layer backpropagation neural network is used as a classifier. Results are evaluated using Receiver Operating Characteristics analysis.
Keywords: breast cancer; clustered micorcalcifications; neural networks; texture analysis; digital mammograms; early detection; cancer detection.
International Journal of Bioinformatics Research and Applications, 2012 Vol.8 No.5/6, pp.366 - 381
Published online: 10 Oct 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article