Proceedings of The 19th Iranian conference on Biomedical Engineering (ICBME 2012), Tehran, Iran, 21-22 December 2012 Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation Abbas Biniaz,Ataollah Abbassi Computational Neuroscience Laboratory, Sahand University of Technology Tabriz, Iran CNLab@sut.ac.ir Asac-Conventional fuzzy c-means (FCM) algorithm is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper aims to develop a Gaussian spatial FCM (gsFCM) for segmentation of brain magnetic resonance (MR) images. The proposed algorithm uses fuzzy spatial information to update fuzzy membership with a Gaussian function. Proposed method has less sensitivity to noise speciically in tissue boundaries, angles, and borders than spatial FCM (sFCM). Furthermore by the proposed algorithm a pixel which is a distinct tissue from anatomically point of view for example a tumor in preliminary stages of its appearance, has more chance to be a unique cluster. The quantitative assessment of presented FCM techniques is evaluated by conventional validity functions. Experimental results show the eiciency of proposed algorithm in segmentation of MR images. ewos-comonen; Segmenaion; I; FM; saial infoaion. I. INTRODUCTION Magnetic resonance imaging (MRI) is most common imaging modalities employed as a diagnostic technique [1]. Segmentation of medical images infered to prtition pixels/voxels in an image into the number of 2D/3D tissues, each with unique features and similar properties. Segmentation process could be based on numerous features of input data. Therefore a vriety of edge based techniques has been developed in image segmentation. Here is a list of edge operators which commonly is used in the image segmentation rials: Sobel, Roberts, Prewitt, Cnny, Zero crossing,Laplacian,and Laplacian of Gaussian( LoG ) [2,3]. There re the lrge number of gray level based approaches for segmentation of medical images using both local and universal image intensity information. Thresholding is one of the image segmentation techniques and has two common types: Global thresholding ,nd Local thresholding [4]. Mousa Shamsi, Afshin Ebrahimi. Department of Elecrical Engineering, Sahand University of Technology Tabriz,Iran {shamsi,aebrahimi}@sut.ac.ir Region based approaches are popular segmentation procedures. A well-developed region based method is region growing. Based on some predeined criteria, a connected rea is porrayed by region growing. Disadvntageous of these methods re creation of holes and disconnectedness in segmented image [5, 6]. Other mehods like deformable models and active contours models (ACMs) or level set are applied as numerical methods for racking boundries and borders in an image [7]. Fuzzy clustering has many applications in medical image segmentation, because they cn preserve more information about original image using uzziness membership thn other mehods [8]. However standrd FCM doesn't exploit spatial information of neighborhood pixels in image segmentation. In order to develop a modiied FCM algorithm compred with sFCM approach [9], this paper presents a modiied sFCM algorithm based on Gaussin spatial information as gsFCM. New approach exracts tissue boundaries, borders, ngles, and small organisms successully. The rest of this paper is organized as follows: Section 2 inroduces mehodology of his paper. Section 3 describes quantitative validity unctions; and Section 4 presents experimental results. Section 5 smm arizes conclusions of this paper. II. M ETHODOLOGY A Fuy c-Means Clustering Fuzzy c-Mens clustering algorithms,developed in 1970s nd optimized later [10]. Let X = { i, 2, ... , xn} denotes an input vector with n number of elements to be ptitioned into c (2; Sn) clusters, nd ; denotes the feature value. The FCM algorithm is an iterative optimization process that minimizes the following cost unction: 978-1-4673-3130-2/12/$31.00 ©2012 IEEE 154