Using of Image Processing for Diagnostic the Brain Tumor by of Methods K-mean Clustering and C-mean Fuzzy

Maysam Toghraee, Mohammad Reza Toghraee, Farhad Rad

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.

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References


[I]. M.H. Fazel Zarandia, M. Zarinbala, M. Izadi b(2011), "Systematicimage processing for diagnosing brain tumors: A Type-II fuzzyexpert system approach," Applied soft computing 11,285-294

. S.Mary Praveena ,Dr.I1aVennila , June 2010, "Optimization FusionApproach for [mage Segmentation Using K-Means Algorithm,"Interational Journal of Computer Applications (0975 - 8887)Volume 2 - NO.7.

. M. Masroor Ahmed & Dzulkiļ¬‚i Bin Mohammad(2010),"Segmentation of Brain MR [mages for Tumor Extraction byCombining Kmeans Clustering and Perona-Malik AnisotropicDiffusion Model," International Journal of Image Processing,Volume (2) : Issue(I) 27

. Manisha Bhagwatl, R.K.Krishna& V.E.Pise July-December 2010,"Image Segmentation by Improved Watershed Transformation inProgramming Environment MATLAB," Interational Journal ofComputer Science & Communication Vol. I, No. 2,pp. 17/-/74

. Tse-Wei Chen , Yi-Ling Chen , Shao-Yi Chien (2010), "Fast ImageSegmentation Based on K-Means Clustering with Histograms inHSV Color Space," Journal of Scientifc Research ISSN I452-2I6XVol. 44 No.2,pp.337-35I

. Anil Z Chitade(2010) , " Colour based imagesegmentation using kmeansclustering,"Interational Journal of Engineering Scienceand Technolog Vol. 2(10),5319-5325

. S. Zulaikha BeeviM, Mohamed Sathik(20IO). "An EffectiveApproach for Segmentation of MRI Images:Combining SpatialInformation with Fuzzy C-Means Clustering," European Joural ofScientifc Research, ISSN I450-2I6X Vol.41 No.3 pp.437-45I

. K.S. Ravichandran and 2B. Ananthi(2009), "Color SkinSegmentation Using K-Means Cluster," International Joural ofComputational and Applied Mathematics ISSN 1819-4966 Volume 4Number 2,pp. 153 -157

. A. Suman Tatiraju,july-2008, "Image Segmentation using k-means clustering, EM and Nonalized Cuts," Symposium of Discrete Algorithms

. J.M. Mendel, R.1. John, F. Liu(2006),"[nterval Type-2 fuzzy logicsystems made simple," IEEE Transactions on Fuzzy Systems 14808-821.

[II]. D. Van De Ville, M. Nachtegael, D. Van Der Weken, E.E. Kerre,W. Philips, I.Lemahieu(2003), "Noise reduction by fuzzy image fltering," IEEE Transactions on Fuzz Systems I I

. H.R. Tizhoosh, G. Krell, and B. Michaelis, (1997) "On fuzzyenhancement of megavoltage images in radiation therapy," in:Proceedings of the 6th IEEE International Conference on FuzzSystems 3, pp. 1398-1404.

. J.M. Mendel, R.I. John, (2002) "Type-2 fuzzy sets made simple,"IEEE Transactions on Fuzzy Systems 1 0117-127

. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman,& A. Y.Wu (2002) , "An efcient k-means clustering algorithm:Analysis and implementation", Proc. JEEE Con! Computer Visionand Pattern Recogition,pp.881-892.

. Toghraee M, rad F, parvin H,.(2016). THE impact of feature selection on meta heuristic algorithm to data mining methods. International journal of modern education and computer science. Volum 8; issue 10., page(33).

. Toghraee M, rad F, parvin H,.(2016). Evaluation of meta heuristic algorithm for stable feature selection. I.J.iformation technology and computer science (ijitcs),.volum8. issue:2074-9015; pp: 22-29.

. Toghraee M, rad F, parvin H,.(2016). Effect neural networks on selected feature by meta heuristic. i.j. mathematical science and computing (ijmcs).volum2.issue:2310- 9033.pp:41-48.

. Toghraee M, Esmaeili M , parvin H,.(2016).evaluation neural networks on selected feature by meta heuristic algorithms. Artificial intelligent system and machine learning.volum8.pp:108-115.

. Toghraee M, rad F, parvin H,.(2017). Evaluation average total data set learning machine on the meta heuristic algorithm. International journal of emerging trend& technology in computer science .volum6.issue:2278- 6856.page(7).

. Toghraee M, rad F, parvin H,.(2017). The Influence Select Feature on The Clustering Algorithm. Journal of Computer Science Engineering and Software Testing Volume 3 Issue 3.pp:1-10.

. R. Krishnapuram, J.M. Keller(1993), " A possibilistic approach toclustering," IEEE Transactionson Fuzzy SystemsI.


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