Unsupervised Segmentation of MR Images for Brain Dock Examinations

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). The proposed method requires no operator to specify the representative points. Nevertheless, it can segment tissues (such as cerebrospinal fluid, gray matter and white matter) that are necessary for brain atrophy diagnosis. 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.