International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, Oct 2011 DOI : 10.5121/ijcsit.2011.3512 137          Indah Soesanti 1 , Adhi Susanto 1 , Thomas Sri Widodo 1 , Maesadji Tjokronagoro 2 1 Department of Electrical Engineering and Information Technology, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia indah@mti.ugm.ac.id 2 Faculty of Medicine, Gadjah Mada University, Yogyakarta, Indonesia ABSTRACT In this paper, an optimized fuzzy logic method for Magnetic Resonance Imaging (MRI) brain images segmentation is presented. The method is a technique based on a modified fuzzy c-means (FCM) clustering algorithm. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image. The advantages of the algorithm are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. Originality of this research is the methods applied on a normal MRI brain image and MRI brain images with tumor, and analyze the area of tumor from segmented images. The results show that the method effectively segmented MRI brain images with spatial information, and the segmented MRI normal brain image and MRI brain images with tumor can be analyzed for diagnosis purpose. In order to identify the area of abnormal mass of MRI brain images with tumor, it is resulted that the area is identified from 8.38 to 25.57 cm 2 . KEYWORDS Adaptive Image Segmentation, Optimized Fuzzy Logic, MRI Brain Image, Fuzzy Membership Function 1. INTRODUCTION Image segmentation is one of the most important tasks to extract information in image processing. To satisfy increasing requirement of image segmentation, a variety of segmentation methods have been developed over past several years. FCM (Fuzzy c-means) is unsupervised technique that has been successfully applied to future analysis, clustering, and classifier designs in the fields such as astronomy, geology, medical imaging, target recognition, and image segmentation. An image can be represented in various feature spaces, and the FCM method classifies the image by grouping similar data points in the feature space into clusters. There has been considerable interest recently in the use of fuzzy segmentation methods, which retain more information from the original image than hard segmentation methods (e.g. Bezdek et al. [1], Udupa et al. [2], Pham [3], Masoole and Moosavi [4]). The fuzzy C-means method (FCM), in particular, can be used to obtain a segmentation via fuzzy pixel classification. Unlike hard classification methods which force pixels to belong exclusively to one class, FCM allows pixels to belong to multiple classes with varying degrees of membership. This approach allows additional flexibility in many applications and has recently been used in processing of magnetic resonance image (MRI) [5]. For example, in their segmentation of MRI brain images, Pham et al. [6] thresholded the FCM memberships in order to extract pixels which a high confidence of correct classification. Xu et al. [7] used deformable surfaces that converged to the peaks of the memberships. The FCM method, however, does not address the intensity inhomogeneity artifact that occurs in nearly all MRI [8]-[9]. In case of tumor detection in MRI, Kekre et al. [10] used