Title: Automatic mammogram segmentation and computer aided diagnoses for breast tissue density according to BIRADS dictionary
Authors: Mohamed Abu ElSoud; Ahmed M. Anter
Addresses: Faculty of Computer and Information, Computer Science Department, Mansoura University, 35516, Mansoura, Egypt. ' Communication and Information Technology Center (CITC), Mansoura University, 35516, Mansoura, Egypt
Abstract: It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Recent studies have shown that their sensitivity is significantly decreased as the density of the breast is increased. The internal density of the breast is a parameter that clearly affects the performance of segmentation algorithms in defining abnormal regions. In this study, the breast region was extracted from background, and pectoral muscle was suppressed. We review different methods for computing tissue density parameter. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. We also present and exhaustively evaluate algorithms using computer vision techniques. We obtained accuracy as high as 94% of automatic agreement classification.
Keywords: computer aided diagnosis; segmentation; mammography; breast tissue density; parenchyma pattern; BIRADS; classification; image enhancement; microcalcification; risk factors; breast cancer.
International Journal of Computer Aided Engineering and Technology, 2012 Vol.4 No.2, pp.165 - 180
Published online: 22 Feb 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article