Multi-level segmentation method for serial computed tomography brain images

Abstract

This paper presents an automated computed tomography brain segmentation approach used to segment intracranial into brain matters and cerebrospinal fluid in order to detect any asymmetry present. Intracranial midline is used as reference axial where left and right segmented regions are subjectively compared. Two-level Otsu multi-thresholding method has been developed and applied to 213 abnormal cases of serial computed tomography brain images of thirty one patients. Prior to that, multilevel Fuzzy C-Means is used to extract the intracranial from background and skull. The segmented regions found to be very useful in providing information regarding normal and abnormal structures in the intracranial where any asymmetry detected would indicate high probability of abnormalities. This approach proved to effectively isolate important homogenous regions of computed tomography brain images from which extracted features would provide a strong basis in the application of content-based medical image retrieval.