Double segmentation method for brain region using FCM and graph cut for CT scan images

In the field of neuropsychiatric disorders, it is known that brain segmentation is important for both detection and diagnosis. The segmentation of the brain, which leads to the computation of brain volume proved to be vital in the detection of many brain pathology having Computed Tomography (CT) scan as the primary modality. Due to the fact that Fuzzy c-Means (FCM) proven to be robust, it is often used in data clustering and also in image segmentation. On the other hand, Graph cut is also a great segmentation algorithm for image segmentation as it allows the separation of the image into numerous partitions according to the similarity between each nodes in the image. In this paper, FCM was first used as global processing on CT scan images that separated the images into clusters based on pixel intensity. After that, local processing with graph cut algorithm was carried out on the automatically selected cluster from the FCM. Manual interaction is needed after the images were separated into partitions to select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.