Rule-based labeling of CT head image

Abstract

A rule-based approach to the labeling of computed tomography (CT) head images containing intracerebral brain hemorrhage (ICH) is presented in this paper. Fully automated segmentation of CT image is achieved by the method composed of two components: an unsupervised fuzzy clustering and a rule-based labeling. The unsupervised fuzzy clustering algorithm outlines the regions in the input CT head image. Extracted regions are spatially localized and have uniform brightness. Region features and region-neighborhood relations are used to create the knowledge base for the rule-based system. The rule-based system performs the labeling of the segmented regions into one of the following labels: background, skull, brain, ICH, and calcifications. The rules are determined from the a priori knowledge about the relations between the CT image regions and their characteristics. The method has been applied to a number of real CT head images and has shown satisfactory results.