1 Page 1-11 © MAT Journals 2018. All Rights Reserved Journal of VLSI Design and Signal Processing Volume 4 Issue 2 Using of Image Processing for Diagnostic the Brain Tumor by of Methods K-mean Clustering and C-mean Fuzzy Maysam Toghraee 1 , Mohammad Reza Toghraee 2 , Farhad Rad 3 1 Department of Software Computer Engineering, Science and Research, Islamic Azad University, Yasouj, Iran 2 Department of Industrial management, Sepahan Institute of Higher Education, Isfahan, Iran 3 Faculty of Engineering, Department of Computer Science, Islamic Azad University, Yasouj, Iran. Email: may.toghraee@gmail.com, mo.toghraee@gmail.com, fd_rad@gmail.com ABSTRACT Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different varieties and that they have totally different Characteristics and different treatment. As it is thought, brain tumor is inherently serious and serious due to its character within the restricted area of the intracranial cavity (space shaped within the skull).Most analysis in developed countries show that the number of individuals who have brain tumors were died because of the actual fact of inaccurate detection. Generally, CT scan or mri that's directed into intracranial cavity produces an entire image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumour. But this methodology of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided methodology for segmentation (detection) of brain tumour supported the combination of two algorithms. This technique permits the segmentation of tumor tissue with accuracy and reliability like manual segmentation. Additionally, it also reduces the time for analysis. At the top of the method the tumor is extracted from the mri image and its actual position and the form also determined. The stage of the tumor is displayed supported the quantity of space calculated from the cluster. Keywords-Abnormalities, Magnetic Resonance Imaging (MRI), Brain tumor, Pre-processing, K-means, Fuzzy Cmeans, Thresholding. INTRODUCTION This paper deals with the concept for automatic braintumor segmentation. Normally the anatomy of the Brain can be viewed by the MRI scan or CT scan. In this paper the MRI scanned image is taken for the entire process. The MRI scan is more comfortable than CT scan for diagnosis. It does not affect the human body because it doesn't use any radiation. It is based on the magnetic field and radio waves. There are different types of algorithmwere developed for brain tumor detection.But they mayhave some drawback in detection and extraction [1– 4]. In thispaper, two algorithms are used for segmentation. So it gives the accurate result for tumor segmentation. Tumor is due to the uncontrolled growth of the tissues in any part ofthe body. The tumor may be primary or secondary. If it is an origin, then it is called primary. If the part of the tumor is unfold to a different place and fully grown as its own then it is called secondary. Usually tumor affects CSF (Cerebral Spinal Fluid). It causes for strokes. The doctor offers the treatment for the strokes instead of the treatment for tumor. Thus detection of tumor is very important for that treatment. The period of time of the one who is suffering from the tumor can increase if it is detected at current stage. That will increase the life time about 1 to 2 years. Normally, tumor cells are of two brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by MAT Journals