Detection of Brain Tumor Using Self Organizing Map ... - IRD India

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International Journal on Advanced Computer Theory and Engineering (IJACTE)V. METHODOLOGYThe part of the image containing the tumornormally has more intensity then the other portion andwe can assume the area, shape and radius of the tumorin the image. We have used these basic conditions todetect tumor in our code and the code goes through thfollowing steps: we present a new efficient edge tracingmethod, which was developed to find characteristicvector for the black-and-white images. The previouslyproposed edge tracing methods have two maindisadvantages. These are: The edge detection of onlyone object in case of more than one object in anumerical image and investigating only outer contoursnot inner contours.VI. FIGURESVII. PREPROCESSINGIn processing the following different steps arefollowed:-THRESHOLD SEGMENTATION:Segmentation isdone on basis of a threshold, due to which whole imageis converted into binary image. Basic matlab commandsfor thresholding are used for this segmentation.WATERSHED SEGMENTATION:It is the best methodto segment an image to separate a tumor but it suffersfrom over and under segmentation, due to which wehave used it as a check to our output. We have not usedwatershed segmentation on our input, rather it is onlyused on our output to check of the result is correct or notand it give the correct answer every time as is shownbelow.Morphological Operators:After that some morphological operations areapplied on the image after converting it into binaryform. The basic purpose of the operations is to showonly that part of the image which has the tumor that isthe part of the image having more intensity and morearea then that specified in the strel command.The basic commands used in this step are strel, imerodeand imdilate.Imerode():It is used to erode an image.Imdilate():It is used to dilate an imageIX. OUTPUTS/RESULTSWe have mapped the resultant tumor image onto theoriginal grayscale image for presentation purposes124ISSN (Print) : 2319 – 2526, Volume-1, Issue-2, 2013
International Journal on Advanced Computer Theory and Engineering (IJACTE)[3] Metin Akay, Time Frequency and Wavelets inBiomedical Signal Processing, IEEE Press Serieson Biomedical Engineering.[4] Starčuk Z, Starčuk jr, Horký J., ‘Baseline’problems in very short echo-time proton MRspectroscopy of low molecular weightmetabolites in the brain, Measurment ScienceReview, Vol.1, No.1, 2001, pp.17-20.[5] Weber-Fahr W, Ende G, Braus D, Bachert P,Soher J, Henn F, Buchel C., A Fully AutomatedMethod for Tissue Segmentation and CSF-Correction of Proton MRSI MetabolitesCorroborates Abnormal Hippocampal NAA inSchizophrenia, NeuroImage, Vol.16, 2002,pp.49–60.[6] Axelson D, Bakken I, Gribbestad I, Ehrnholm B,Nilsen G, Aasly J., Applications of NeuralNetwork Analyses to In Vivo 1H MagneticResonance Spectroscopy of Parkinson DiseasePatients, Journal of Magnetic ResonanceImaging, Vol.16, 2002, pp.13–20.X. REFERENCES[1] Bonavita S, Di Salle F, Tedeschi D., ProtonMRS in neurological disorders, European Journalof Radiology, No.31, 1999, pp.30-125.[2] Baik H, Choe B, Lee H, Suh T, Son B, Lee J.,Metabolic Alterations in Parkinson’s Diseaseafter halamotomy, as Revealed by 1H MRSpectroscopy, Korean J Radiol, Vol.3, No.3,2002, pp.180-9.125ISSN (Print) : 2319 – 2526, Volume-1, Issue-2, 2013
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