Medical image analysis for cancer management in natural computing framework

Abstract

Natural computing, through its repertoire of nature-inspired strategies, is playing a major role in the development of intelligent decision-making systems. The objective is to provide flexible, application-oriented solutions to current medical image analysis problems. It encompasses fuzzy sets, neural networks, genetic algorithms, rough sets, swarm intelligence, and a host of other paradigms, mimicking biological and physical processes from nature. Radiographic imaging modalities, like computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), help in providing improved diagnosis, prognosis and treatment planning for cancer. This survey highlights the role of natural computing, in efficiently analyzing radiographic medical images, for improved tumor management. We also provide a categorization of the segmentation, feature extraction and selection methods, based on different natural computing technologies, with reference to the application – involving malignancy of the brain, breast, prostate, skin, lung, and liver.