International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1587 Brain Tumor Detection Using Clustering Algorithms in MRI Images Nikhita Biradar 1 , Prakash H. Unki 2 1 M.Tech Student, Dept. of Computer Science and Engineering, BLDEA’s V.P. Dr. P.G. Halakatti College of Engineering and Technology, Vijayapur, Karnataka, Indi a 2 Associate Professor, Dept. of Computer Science and Engineering, BLDEA’s V.P. Dr. P.G. Halakatti College of Engineering and Technology, Vijayapur, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Brain tumor is an accumulation of abnormal cells in the brain. Detection of tumor from magnetic resonance imaging (MRI) brain scan is one of the most promising research topics in medical image processing. This paper presents a novel tumor detection system in MRI images using k-means technique integrated with Fuzzy c-means (FCM) clustering algorithm and artificial neural network (ANN). ANN is used to classify the MRI images into two categories; normal and tumor image. The proposed system takes benefit of both integrated algorithms in the aspect of minimal computation time and accuracy. It accurately extracts the tumor region and calculates the tumor area. The accuracy is calculated by comparing results with the ground truth (GT) of processed image. Key Words: Magnetic resonance imaging (MRI), k-means clustering, fuzzy c-means (FCM) clustering, artificial neural network (ANN), ground truth (GT). 1. INTRODUCTION Brain tumors are formed by collection of abnormal cells that grows uncontrollable. Diagnosis of brain tumors is done by detection of the abnormal brain structure. The internal structure of brain can be viewed by magnetic resonance imaging (MRI) and computed tomography (CT) scans. Compared to CT scan, MRI scan is more efficient and it doesn’t affect the patient body as no radiations are used. MRI scanning is done by using radiofrequency and magnetic field [1]. MRI images are analyzed by the radiologists to diagnose the tumor. Segmentation of images is important as large numbers of images are generated during the scan and it is unlikely for clinical experts to manually divide these images in a reasonable time. Image segmentation refers to segregation of given image into multiple non-overlapping regions. Segmentation represents the image into sets of pixels that are more significant and easier for analysis. It is applied to approximately locate the boundaries or objects in an image and the resulting segments collectively cover the complete image [2]. The segmentation algorithms works on one of the two basic characteristics of image intensity; similarity and discontinuity [3]. In the former, segmentation technique is based on dividing an image into set of pixels that are similar to the some predefined criteria. The latter partitioning works on the changes in intensity of an image, such as corners and edges. Segmentation has a significant part in clinical diagnosis and can be useful in pre-surgical planning and computer assisted surgery. Therefore, numerous segmentation techniques are available which can be used widely, such as threshold based segmentation, histogram based methods, region-based (region growing, splitting and merging methods), edge-based and clustering methods (expectation maximization, k-means, FCM and mean shift) [4]-[6]. Clustering methods are most promising technique for processing the medical images. Cluster analysis can be set out as a pre-processing stage for other methods, namely classifiers that would then run on selected clusters [7]. Therefore in our system, we have used clustering segmentation techniques for diagnosis of tumor and calculating tumor area in MRI images. This paper presents an effective tumor detection system for MRI images by integrating k-means with FCM clustering techniques. This system gets benefit of the k-means in the aspect of minimal computation time and fuzzy c-means in the aspect of accuracy. k-means algorithm is to perform the initial segmentation. Then, on the criteria of updated membership set and exact cluster selection, an approximate segmented tumor is located from FCM technique. Even the minute changes in intensities of normal and tumor tissue is recognized by this method. ANN classifies MRI into two categories, normal image and image with tumor by preparing pertinent training, target data. Finally, the reliability of the system is calculated by comparing the result with GT of the processed image. Essential utilization of this technique is to get measure and location of tumor, which will help in organizing of treatment and surgery. 2. RELATED WORK Many researchers have suggested several methodologies and procedures for medical image segmentation and techniques for tumor detection. These include region growing, thresholding, mean shift methods, clustering and statistical model. H. Suzuki and J. Torwaki [8], developed an algorithm for automatic segmentation of head MRI images using thresholding techniques. The algorithm consists of three incremental steps; histogram analysis to locate the brain and next step is to create a mask using nonlinear anisotropic