A Novel Segmentation Approach for Brain Tumor in MRI Yong Wei 1 , H. Keith Brown 2 , Junfeng Qu 3 1. Department of Computer Science & Information Systems University of North Georgia, Dahlonega, Georgia, U.S.A. ywei@ung.edu 2. Department of Bio-Medical Sciences Philadelphia College of Osteopathic Medicine-Georgia Campus, Suwanee, Georgia, U.S.A. keithbr@pcom.edu 3. Department of Computer Science & Information Technology Clayton State University, Clayton, Georgia, U.S.A. junfengqu@clayton.edu ABSTRACT Brain MRI image segmentation is one of the most important applications of image segmentation technique, and is an important part of clinical diagnostic tools. Segmented image can help physicians to identify tumor tissues in brain, and monitor effectiveness of chemotherapy treatments. However, manual segmentation of muscle regions is not only inaccurate, but also time consuming. In this work, Intensity Space Map (ISM) is used along with fuzzy c-means clustering algorithm to segment tumor regions in color MRI images. Experiments show the proposed ISM-based fuzzy c-means clustering brain MRI image segmentation yields promising results. KEYWORDS MRI Image; Segmentation; Intensity Space Map (ISM); Fuzzy C-means Clustering; Brain Tumor. 1 INTRODUCTION Image segmentation is to group pixels into regions for future process. In each partitioned region of an image, pixels have similar characteristics based on given criteria. Brain MRI image segmentation is one of the most important applications of image segmentation technique, and is an important part of clinical diagnostic tools. Physicians can use magnetic resonance images (MRI) to estimate volume of tumor tissues in brain before and after chemotherapy. The thickness of reconstruction of MRI slices is determined during scanning. If the size of tumor region in MRI can be measured, the volumetric estimation of tumor can be obtained by calculating the sum of the products of the slice thickness and the tumor region size of each MRI slice. Hence, segmenting the tumor region in MRI images becomes the key step in the procedure. There have been many research endeavors to segment brain tumor in MRI. Stadlbauer et al. used the Gaussian distribution of spatial distribution of choline-containing compounds (Cho), creatine (Cr) and N-acetyl-aspartate (NAA) in brain tumors as threshold in normal brain T2-weighted MRI [1]. The region growing segmentation methods are part of the region-based methods, and are the most commonly used for brain tumor segmentation [2]. Chong et al. [3] used region growing based algorithm to measure the tumor volume demonstrated on pretreatment T2-weighted magnetic resonance data sets. The k-means algorithm is the most commonly used clustering algorithm since it is easy to implement and found to be effective in many applications. The fuzzy version of k-means clustering (fuzzy c-means, FCM) is widely adopted for medical image segmentation Proceedings of the The Third International Conference on Digital Enterprise and Information Systems, Shenzhen, China, 2015 ISBN: 978-1 -941968-10-9 ©2015 SDIWC 98