CT Brain Image: Abnormalities Recognition and Segmentation

In order to develop an automated segmentation system for Computed Tomography (CT) brain images, a new approach which consists of several unsupervised segmentation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid (CSF), and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy c-means (FCM) methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions, In the second phase segmentation, the modified FCM with population-diameter independent (PDI) segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.