{"title":"Medical Image Segmentation Based On Vigorous Smoothing and Edge Detection Ideology","authors":"Jagadish H. Pujar, Pallavi S. Gurjal, Shambhavi D. S, Kiran S. Kunnur","volume":44,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1143,"pagesEnd":1150,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/14997","abstract":"
Medical image segmentation based on image smoothing followed by edge detection assumes a great degree of importance in the field of Image Processing. In this regard, this paper proposes a novel algorithm for medical image segmentation based on vigorous smoothening by identifying the type of noise and edge diction ideology which seems to be a boom in medical image diagnosis. The main objective of this algorithm is to consider a particular medical image as input and make the preprocessing to remove the noise content by employing suitable filter after identifying the type of noise and finally carrying out edge detection for image segmentation. The algorithm consists of three parts. First, identifying the type of noise present in the medical image as additive, multiplicative or impulsive by analysis of local histograms and denoising it by employing Median, Gaussian or Frost filter. Second, edge detection of the filtered medical image is carried out using Canny edge detection technique. And third part is about the segmentation of edge detected medical image by the method of Normalized Cut Eigen Vectors. The method is validated through experiments on real images. The proposed algorithm has been simulated on MATLAB platform. The results obtained by the simulation shows that the proposed algorithm is very effective which can deal with low quality or marginal vague images which has high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use of medical image diagnosis.<\/p>\r\n","references":"[1] Chen Tie Qi and Lu Yi,\" Color image segmentation- an innovative\r\napproach. Pattern recognition\", vol. 35, 2002, pp. 395-405. 134\r\n[2] Reginald L. Lagendijk and Jan Biemond ,\"Basic methods for Image\r\nRrestoration and Identification\", 15 February, 1999.\r\n[3] X.Z.Sun and Anastasios N. Venetsanopoulos, \"Adaptive Schemes for\r\nNoise Filtering and Edge Detection by Use of Local Statistics\", IEEE\r\ntransactions on circuits and systems,vol 35, no. 1, January 1988.\r\n[4] Bellon Olga Regina Pereira, Dhirene Alexandre Ibrahim et al. Edge\r\ndetection to guide image segmentation by clustering techniques,\r\nInternational conference on image processing, Vol. 2, pp. 725-729,\r\n1999.\r\n[5] Cheriet M., Said J.N. and Suen C.Y,\" A recursive thresholding technique\r\nfor image segmentation\", IEEE transactions on image processing, vol. 7,\r\nno.6, June 1998, pp. 918-921\r\n[6] Jianbo Shi and Jitendra Malik,\"Normalized Cuts and Image\r\nSegmentation\", IEEE transactions on pattern analysis and machine\r\nintelligence, vol. 22, no. 8, August 2000.\r\n[7] Neeta Nain, Gaurav Jindal, Ashish Garg and Anshul Jain,\" Dynamic\r\nThresholding Based Edge Detection\", Proceedings of the World\r\nCongress on Engineering 2008 Vol I,2008.\r\n[8] M. Kass and A.Witkin et al., \"Snakes - active contour models,\"IJCV,\r\nvol. 1, no. 4, pp. 321-331, 1987.\r\n[9] S. Osher and J. Sethian, \"Fronts propagation with curvature dependent\r\nspeed: Algorithms based on hamilton-jacobi formulations,\" J. of Comp.\r\nPhy., vol. 79, pp. 12-49, 1988.\r\n[10] F.R. Hansen and H. Elliott, \"Image segmentation using simple markov\r\nfield models,\" CGIP, vol. 20, no. 2, pp. 101-132, October 1982.\r\n[11] G.A.Baxes,\" Digital Image Processing Principles & Applications\",\r\nWiley & Sons, 1994.\r\n[12] Behrooz Ghandeharian, Hadi Sadoghi Yazdi,\"Modified Adaptive Center\r\nWeighted Median Filter for suppressing Impulse noise in images\",\r\nInternational Journal of Research and Reviews in Applied Sciences,\r\n2009.\r\n[13] Kevin Liu.\" An Implementation of the Median Filter and Its\r\nEffectiveness on Different Kinds of Images\", Computer Systems Lab,\r\n2006-2007.\r\n[14] Manfred Kopp and Werner Purgathofer,\" Efficient 3x3 Median Filter\r\nComputations\", Institue of Computer Graphics.\r\n[15] Rajoo Pandey.\" An Improved Switching Median filter for\r\n[16] Uniformly Distributed Impulse Noise Removal\", World Academy of\r\nScience, Engineering and Technology 38 2008.\r\n[17] Gerasimos Louverdis, Ioannis Andreadis and Antonios Gasteratos,\" A\r\nnew content based Median Filter\", Department of Electrical &\r\nComputer Engineering, Democritus University of Thrace.\r\n[18] W. K. Pratt, \"Digital Image Processing\", New York: Wiley, 1991.\r\n[19] Jagadish H. .Pujar, Kiran S.Kunnur,\" A novel approach for Image\r\nRestoration via Nearest Neighbour Method\", Journal of Theoretical and\r\nApplied Information Technology, pp. 76-79, 2010.\r\n[20] Jagadish H. Pujar, Shambhavi D.S., \"A Novel Digital Algorithm for\r\nSobel Edge Detection\", BAIP 2010,CCIS 70, pp. 91-95, 2010.\r\n[21] Jagadish H. Pujar, Pallavi S.Gurjal, \"Binary Data Compression Using\r\nMedial Axis Transform Algorithm\",BAIP 2010,CCIS 70, pp.417-419,\r\n2010.\r\n[22] Frost, V.S., Stiles, J.A., Josephine, A., Shanmugan, K. S., and Holtzman,\r\nJ.C., 1982. A Model for Radar Images and Its Application to Adaptive\r\nDigital Filtering of Multiplicative Noise. IEEE Transactions on Pattern\r\nAnalysis and Machine Intelligence, Vol. PAMI-4, No. 2, March 1982.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 44, 2010"}