Medical Image Segmentation Based On Vigorous Smoothing and Edge Detection Ideology

Authors: Jagadish H. Pujar, Pallavi S. Gurjal, Shambhavi D. S, Kiran S. Kunnur

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.

Keywords: Image Segmentation, Image smoothing, Edge Detection, Impulsive noise, Gaussian noise, Median filter, Canny edge, Eigen values, Eigen vector.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084188

References:

[1] Chen Tie Qi and Lu Yi," Color image segmentation- an innovative approach. Pattern recognition", vol. 35, 2002, pp. 395-405. 134
[2] Reginald L. Lagendijk and Jan Biemond ,"Basic methods for Image Rrestoration and Identification", 15 February, 1999.
[3] X.Z.Sun and Anastasios N. Venetsanopoulos, "Adaptive Schemes for Noise Filtering and Edge Detection by Use of Local Statistics", IEEE transactions on circuits and systems,vol 35, no. 1, January 1988.
[4] Bellon Olga Regina Pereira, Dhirene Alexandre Ibrahim et al. Edge detection to guide image segmentation by clustering techniques, International conference on image processing, Vol. 2, pp. 725-729, 1999.
[5] Cheriet M., Said J.N. and Suen C.Y," A recursive thresholding technique for image segmentation", IEEE transactions on image processing, vol. 7, no.6, June 1998, pp. 918-921
[6] Jianbo Shi and Jitendra Malik,"Normalized Cuts and Image Segmentation", IEEE transactions on pattern analysis and machine intelligence, vol. 22, no. 8, August 2000.
[7] Neeta Nain, Gaurav Jindal, Ashish Garg and Anshul Jain," Dynamic Thresholding Based Edge Detection", Proceedings of the World Congress on Engineering 2008 Vol I,2008.
[8] M. Kass and A.Witkin et al., "Snakes - active contour models,"IJCV, vol. 1, no. 4, pp. 321-331, 1987.
[9] S. Osher and J. Sethian, "Fronts propagation with curvature dependent speed: Algorithms based on hamilton-jacobi formulations," J. of Comp. Phy., vol. 79, pp. 12-49, 1988.
[10] F.R. Hansen and H. Elliott, "Image segmentation using simple markov field models," CGIP, vol. 20, no. 2, pp. 101-132, October 1982.
[11] G.A.Baxes," Digital Image Processing Principles & Applications", Wiley & Sons, 1994.
[12] Behrooz Ghandeharian, Hadi Sadoghi Yazdi,"Modified Adaptive Center Weighted Median Filter for suppressing Impulse noise in images", International Journal of Research and Reviews in Applied Sciences, 2009.
[13] Kevin Liu." An Implementation of the Median Filter and Its Effectiveness on Different Kinds of Images", Computer Systems Lab, 2006-2007.
[14] Manfred Kopp and Werner Purgathofer," Efficient 3x3 Median Filter Computations", Institue of Computer Graphics.
[15] Rajoo Pandey." An Improved Switching Median filter for
[16] Uniformly Distributed Impulse Noise Removal", World Academy of Science, Engineering and Technology 38 2008.
[17] Gerasimos Louverdis, Ioannis Andreadis and Antonios Gasteratos," A new content based Median Filter", Department of Electrical & Computer Engineering, Democritus University of Thrace.
[18] W. K. Pratt, "Digital Image Processing", New York: Wiley, 1991.
[19] Jagadish H. .Pujar, Kiran S.Kunnur," A novel approach for Image Restoration via Nearest Neighbour Method", Journal of Theoretical and Applied Information Technology, pp. 76-79, 2010.
[20] Jagadish H. Pujar, Shambhavi D.S., "A Novel Digital Algorithm for Sobel Edge Detection", BAIP 2010,CCIS 70, pp. 91-95, 2010.
[21] Jagadish H. Pujar, Pallavi S.Gurjal, "Binary Data Compression Using Medial Axis Transform Algorithm",BAIP 2010,CCIS 70, pp.417-419, 2010.
[22] Frost, V.S., Stiles, J.A., Josephine, A., Shanmugan, K. S., and Holtzman, J.C., 1982. A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-4, No. 2, March 1982.