Detecting lesions in MRI brain images combining pseudo-color segmentation with fuzzy C-Means clustering

ABSTRACT: As biomedical image analysis has been improved over the last decades, the widespread advancement of detection/estimation approaches has aided the rapid development of new technologies for monitoring and diagnosis, as well as, treatment of patients. Image segmentation plays a substantial role as part of the preprocessing in various biomedical applications. The segmentation technique is widely used by the radiologists to interpret the input medical image into meaningful data to be used for the extraction of the required features for further processing. Clustering as one of the widely used image segmentation techniques, which can be used in numerous biomedical applications, such as quantification of tissue volumes, diagnosis, study of anatomical structure, and computer-integrated surgery. There is a vast variety of imaging tools such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and ultra sound in which the segmentation can be utilized. In this thesis we propose a new approach for tumor detection in magnetic resonance imaging (MRI) brain images, which is utilized by using pseudo-color based segmentation with Fuzzy C-Means clustering (FCM) method. The key idea of pseudo-colored segmentation method with FCM is to segment the given MRI image by converting the prior gray-scale image into a pseudo-colored image and then identify the tumor tissue by using proposed clustering algorithm FCM. The proposed method contains an efficient clustering scheme which can be used in MRI applications. The application of this method in tumor detection and segmentation could assist pathologist to recognize tumor size and region successfully. The results obtained by the proposed FCM based approach are very competitive and better in most cases in comparison with the K-Means clustering method, which is one of the important approaches available in the literature for the same problem. FCM based system outperforms the K-Means based system with respect to final segmentation performance evaluated by sensitivity, precision, SSIM, PSNR and segmentation accuracy metrics. The superiority of the FCM based system over the K-Means based system has been verified with the obtained results. Keywords: Image segmentation, Clustering, K-Means, FCM, medical image processing, brain tumor detection, MRI analysis. …………………………………………………………………………………………………………………………