Local Features in Biomedical Image Clusters extracted with Independent Component Analysis

Abstract

A neural network model for the identification and classification of malign and benign skin lesions from ALA-induced fluorescence images is presented. A self-organizing feature map or generative topographic mapping is used to cluster images patches according to their inherent local features which then can be extracted with ICA. These components are used to distinguish skin cancer from benign lesions achieving an average classification rate of 70% so far.