Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function

• Published in: Journal of signal processing systems Vol. 55; no. 1-3; pp. 77 - 90
• Main Authors: de Oliveira Martins, Leonardo, Silva, Aristófanes Corrêa, de Paiva, Anselmo Cardoso, Gattass, Marcelo
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2009
• Subjects: Circuits and Systems; Computer Imaging; Electrical Engineering; Engineering; Image Processing and Computer Vision; Pattern Recognition; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Mammography; Ripley’s; Growing neural gas; Computer-aided detection; Texture; function
• ISSN: 1939-8018; 1939-8115
• DOI: 10.1007/s11265-008-0209-3

Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley’s K function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image.