Mammogram image analysis: Bio-inspired computational approach

Abstract

Breast cancer screening programs attempt to detect and eradicate cancer at the earliest possible stage to increase the rate of survival amongst women. The early detection of breast cancer greatly improves the prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. Various efforts have been made to improve the performance of the bio-inspired algorithms such as Genetic Algorithm(GA), Ant Colony Optimization (ACO), Particle Swarm Optimization(PSO) and Bee Colony Optimization(BCO) algorithms for classification in various domains. This paper introduces some novel methods on a biologically inspired adaptive models. Bio-inspired algorithms are more powerful for solving more complex optimization problems. In this paper, the extracted features from mammogram images are selected using ACO, GA and BCO algorithms. Fuzzy-C-Means algorithm has been employed for validation through classification. © 2012 Springer India Pvt. Ltd.