Segmentation of human brain MR images using rule-based fuzzy logic inference

Abstract

The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes the segmentation as a preliminary stage for the visualization or the quantification of such data. In this paper, we present a fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The presented method consists of two main stages. The histogram analysis based on the S-function membership and Shannon's entropy function is the first step. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. After the segmentation is complete, attributes of different tissue classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. The implemented system provides many advanced 3D imaging tools, which enable visual exploration of segmented anatomical structures.