Unsupervised segmentation for MR brain images

Abstract

As described herein, we propose an unsupervised method for segmentation of magnetic resonance (MR) brain images by hybridizing the self-mapping characteristics of 1-D Self-Organizing Maps (SOMs) and using incremental learning functions of fuzzy Adaptive Resonance Theory (ART). As the proposed method requires the appropriate parameters to segment tissues (such as cerebrospinal fluid, gray matter and white matter) that are necessary for brain atrophy diagnosis, first we derive the optimal parameter set through the preliminary experiments. The main contribution of this work is to evaluate the effectiveness of the proposed method, considering the conventional methods that are highly accurate in terms of usefulness as classification techniques. We focus on Fuzzy C-means (FCM) and Expectation Maximization Gaussian Mixture (EM-GM) with previous setting of the number of clusters, and then Mean Shift (MS) without previous setting of the number of clusters. Through the comparative experiments on the two metrics, we confirmed that our method could achieve higher accuracy than these conventional methods. Additionally, we propose a Computer-Aided Diagnosis (CAD) system for use with brain dock examinations based on case analyses of diagnostic reading. We construct a prototype system for reducing loads on diagnosticians during quantitative analysis of the degree of brain atrophy. Field tests of 193 examples of brain dock medical examinees reveal that the system efficiently supports diagnostic work in the clinical field: the alteration of brain atrophy attributable to aging can be quantified easily, irrespective of the diagnostician.