Segmentation of brain MRI image based on clustering algorithm

Medical images are widely used by the physicians to find abnormalities in human bodies. Physicians use the findings to plan further treatment for the patient. However, the images are sometimes corrupted with a noise which normally exists or occurs during storage, or while transferring the image and sometimes while handling the devices. Therefore the need to enhance the image is crucial in order to improve the image quality. The segmentation technique for Magnetic Resonance Imaging (MRI) of the brain is one of the methods used by radiographers to detect any abnormalities specifically brain abnormalities. The method is used to identify important regions in the brain such as white matter (WM), grey matter (GM) and cerebrospinal fluid spaces(CSF). In this project, the image segmentation via the clustering method is used to cluster or segment the images into three different regions which represent the white matter (WM), grey matter (GM) and cerebrospinal fluid spaces (CSF), respectively. These regions are significant for the physician or radiographer to analyse and diagnose the disease. The clustering method known as Adaptive Fuzzy K-means (AFKM) is proposed to be used in this project as a tool to classify the three regions. The results are then compared with the fuzzy C-means clustering. The segmented image is analysed both qualitatively and quantitatively. The results demonstrate that the proposed method is suitable to be used as segmentation tools for MRI brain images using image segmentation.