Hierarchical Segmentation of Ct Head

| Quantitative analysis of head images obtained by computed tomography (CT) requires accurate segment-ation. A new method for automatic segmentation of human spontaneous intracerebral brain hemorrhage (ICH) from digitized CT lms is presented in the paper. The proposed segmentation method has a two-level hierarchical structure. The segmentation at both levels is based on the unsuper-vised fuzzy C-means (UFCM) clustering algorithm but using diierent feature vectors. At the higher hierarchical level, clusters obtained by UFCM algorithm consist of several disconnected image regions. An image labeling algorithm labels each image region with one of the following labels: background, skull, brain, and ICH to obtain the global image segmentation. At the lower hierarchical level the brain region is further segmented using UFCM clustering to obtain the edema region and the ventricle region. The advantage of the method is that it is not sensitive to variability in image brightness. The method has been tested on real CT images and has performed correct segmentation.