Brain Tumor Detection by Using Mri Images

BRAIN TUMOR DETECTION BY USING MRI IMAGES

Miss. Hemangi S. Phalak, 2nd year ME student Mr. O. K. Firke, Assistant Professor

Department of Electronics and Telecommunication, J. T. M. College of Engineering, Faizpur.

Abstract to gray-level image, size of gray scale image as Brain tumor is one of the most life-threatening 256*256 which displays the image. diseases and it is an abnormal cell formation within the b) Resizing of image: The gray-level image is brain leading to brain cancer. Thus it is very important then resized to a suitable size of 200⨯ 200 for to detect brain tumor. This can be achieved by the providing uniformity. execution of automated tumor detection techniques on c) Median Filtering: Median filters are used to medical images. A brain tumor is detected manually by remove the noise from the images. experts from the MRI images. Pre-processing is Methods of Tumour Segmentation: Segmentation necessary to enhance the input MRI image. The main bridges the gap between low-level image processing thing behind the brain tumor detection from an MRI and high-level image processing. image is the image segmentation. Region growing is Region Growing Segmentation: In the region the segmentation based on similarity criterion and edge growing segmentation, the first aim is to determine the detection is the segmentation based on discontinuity. initial seed points. A seed point is the starting point Cellular automata provide clear and exact edge for region growing. Starting with a seed point, the detection. Simulation of the proposed work is done in region will grow by appending to each seed whose MATLAB.[8] neighbouring pixels have properties similar to the seed. Keywords- The following steps are performed to find the seed Brain tumor, MRI, Image Segmentation, Region point: growing, Cellular automata. (a) Convert the given coloured image into the gray Introduction image. (b) First of all count no pixels whose intensities are Imaging technology in Medicine made the greater than hundred and less than hundred and store doctors to see the interior portions of the body for easy them in separate variables. diagnosis. It also helped doctors to make keyhole (c) Find difference between both variables if difference surgeries for reaching the interior parts without really is small then go to step ‘d’ else convert the image into opening too much of the body. The body is made up of negative and go to step ‘d’ many cells which have their own special function (d) Convert the obtained gray image into the binary Abnormal growth of cells inside the skull is known as image. brain tumor.A tumor is basically a mass of tissue that (e) Find maximum length and breadth of the brain then grows out of control of the normal forces that regulates From the centre convert the pixels in the area of its growth. Brain tumor is a group of abnormal cells rectangle shape of size twenty rows and ten columns to that grows either inside the brain or around the brain. black colour. Tumors are generally classified based on the location (f) Find the sum of all rows individually and store it in of their origin and its malignancy. There are two types the array. of tumors based on the location of the origin of tumors. (g) Find the sum of all the columns and store the First one is primary brain tumor and second one is results in another array. metastatic brain tumor. Primary tumors originate in the (h) Find the intersection of row and column having brain cells and sometimes they spread to other parts of maximum sum .This is taken as seed point. . the brain or to the spine. But spreading to other organs i) Start by choosing an arbitrary seed pixel and occurs only rarely. compare it with neighbouring pixels. Proposed Methodology:- j) Region is growing from the seed pixel by adding in 1. Image Acquisition: The dataset of MRI neighbouring pixels that are similar, increasing the size Images are acquired from publicly available sources. of the region. The image used here is in .jpg format. k)When the growth of one region stops we simply 2. Pre-Processing Stage: choose another seed pixels which does not yet belongs Pre-processing stage consists of various steps as to any region and start again. following: l)This whole process is continued until all pixels a) Gray-level Conversion: The image which is belong to some region. read in .jpg format is first converted from RGB model Figure. Flowchart of the proposed technique. Cellular automata edge detection 2D cellular automata based on moore neighbourhood model is used. The rule number used here is rule “124”. In the rule “124”, depending on the number of neighbours, each cell undergoes any of the four situations such as loneliness, over population, happiness and reproduction. The rule can be stated as following: 1) Loneliness condition: Death of an alive cell (“0”) due to less than two neighbours. 2) Overpopulation: Death of an alive cell (“0”) due to more than neighbours. 3) Happiness condition: Continuing as an alive cell (“1”) when it has either 3, 4, 6 or 7 alive neighbours. 4) Reproduction: Transformation of a dead cell (”0”) to an alive cell (“1”) when it has exactly 5 neighbours. Original No of Pixels inNo of Pixels in theNos of Pixels in Nos of Pixels in Nos of Pixels in the image the Boundary ofBoundary of Brainthe Boundary ofthe Boundary ofBoundary of Brain Brain TumorTumor area usingBrain Tumor areaBrain TumorTumor area using area using sobelprewitt edge using roberts edge area using cannycellular automata edge edge edge detection.

179 pixels 168 pixels 202 pixels 223 pixels

132 pixels 121 pixels 170 pixels 158 pixels 265 pixels

193 pixels 207 pixels 280 pixels 265 pixels Table 1. Comparison table for different edge detection method

Result and Discussion: 5) Mukesh Kumar, Kamal K. Mehta “A Texture based Brain tumor detection using region growing Tumor detection and automatic Segmentation using Seeded Region and cellular automata edge detection is proposed. Brain Growing Method” Int. J. Comp. Tech. Appl.,Vol 2 (4), JULY- tumored area is detected by using region growing AUGUST 2011 pp-855-859. segmentation and Brain tumored boundry is detected 6) Sreeletha S.H.1, Keerthi M. Rajan2, Prof. (Dr.) M. Abdul by using cellular automata edge detection. Sobel Rahman3 “Single Tumor Growth Analysis: Cellular Automata Segmentation” International Journal For Advance Research In operator provides thick edges that provide clarity. Engineering And Technology Volume 3, Issue IX, Sep 2015 ISSN Robert’s operator result is similar to Sobel with similar 2320-6802, pp117-122. problems. Prewitt and Canny does not provide 7) Ms. Tanuja Pandurang Shewale, Dr. Shubhangi B. Patil, continuous edges. The output achieved after applying “Detection of Brain Tumor Based On Segmentation Using Region Cellular Automata concept has much clarity. Cellular Growing Method” International Journal of Engineering Innovation & automata edge detection will give exact and clear Research, Volume 5, Issue 2, ISSN: 2277 – 5668, pp 173-176. boundaries or edges. 8) Charutha S. , M. J. Jayashree “An Efficient Brain Tumor Detection By Integrating Modified Texture Based Region Growing Conclusion And Cellular Automata Edge Detection” 978-1-4799-4190- Brain tumor detection using region growing and 2/14/$31.00 ©2014 IEEE,pp-1193-1199. cellular automata edge detection is proposed. 9) S.U. Aswathy, Dr G. Glan Deva Dhas, Dr. S. S. Kumar Simulated results have shown that the proposed “A Survey on Detection of Brain Tumor from MRI Brain Images” method is an efficient brain tumor detection technique, International Conference on Control, Instrumentation, by using region growing and cellular automata both Communication and Computational Technologies, 978-1-4799-4190- segmentation techniques exact size and location of 2/14/$31.00 ©2014 IEEE, pp 871-877. tumor is detected. Also, it is found that cellular 10) Gopu M, Rajesh T, “Brain Tumor Segmentation based on Rough Set Theory for MR Images with CA Approach”, International automata provide clear and exact edges compred to Journal of Emerging Trends in Electrical and Electronics (IJETEE – other classical edge detection method. References ISSN: 2320-9569) Vol. 4, Issue. 1, June 2013,pp71-76. 1) Ganesh Madhikar, Prof. Sunita S. Lokhande, “Brain 11) Mr. Shital S. Agrawal, Prof. Dr. S. R. 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