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An Enhanced Level Set Segmentation for Multichannel Images Using Fuzzy Clustering and Lattice Boltzmann Method

Savita Agrawal, Deepak Kumar


In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gray images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi channel images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, by having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the implementation of the proposed method over color images the input color image will be first divided into red, green and blue single channels then by applying the proposed single channel technique on each channels of color image will lead to three different segmentation information for red, green and blue channels. After combining all the three segmentation information true color image segments will be obtained. For the comparative analysis of the proposed segmentation scheme three segmentation parameters have been utilized, they are Probabilistic Rand Index (PRI), Variation of Information (VOI) and Global Consistency Error (GCE). A huge comparative analysis is performed for comparison on the basis of these three parameters. The results obtained clearly indicates that the proposed technique for color image segmentation is efficient as compare to existing technique in all the aspects of segmentation.

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S. Osher and J. Sethian, ―Fronts propagating and curvature dependent speed: Algorithms based on Hamilton–Jacobi formulation,‖ J. Comput.Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988.

M. Kass, A. Witkin, and D. Terzopoulos, ―Snakes: Active contour models,‖ Int. J. Comput. Vis., vol. 1, no. 4, pp. 321–331, Jan. 1988.

V. Casselles, F. Catté, and F. Dibos, ―A geometric model for active contours in image processing,‖ Numer. Math., vol. 66, no. 1, pp. 1–31, 1993.

R. Malladi, J. Sethian, and B. Vemuri, ―A topology independent shape modeling scheme,‖ in Proc. SPIE Conf. Geometric Methods Comput. Vis.II, San Diego, CA, 1993, vol. 2031, pp. 246–258.

V. Caselles, R. Kimmel, and G. Sapiro, ―On geodesic active contours,‖ Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, 1997.

T. Chan and L. Vese, ―Active contours without edges,‖ IEEE Trans. Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.

W. Chen and M. L. Giger, ―A fuzzy c-means (FCM) based algorithm for intensity inhomogeneity correction and segmentation of MR images,‖ in Proc. IEEE Int. Symp. Biomed. Imaging: Nano Macro, 2004, vol. 2, pp. 1307–1310.

W. M. Wells, W. E. Grimson, R. Kikinis, and F. A. Jolesz, ―Adaptive segmentation of MRI data,‖ IEEE Trans. Med.l Imag., vol. 15, no. 4, pp. 429–442, Aug. 1996.

L. C. Evans and R. F. Gariepy, Measure Theory and Fine Properties of Functions. Boca Raton, FL: CRC Press, 1992.

C. Li, C. Xu, C. Gui, andM. Fox, ―Distance regularized level set evolution and its application to image segmentation,‖ IEEE Trans. Image Process. vol. 19, no. 12, pp. 3243–3254, Dec. 2010.

G. Aubert and P. Kornprobst, ―Mathematical problems in image processing:Partial differential equations and the calculus of variations,‖ in Applied Mathematical Sciences, vol. 147. Berlin, Germany: Springer- Verlag, 2001.

Y. Chen, Z. Yan, and Y. Chu, ―Cellular automata based level set method for image segmentation,‖ in Proc. IEEE/ICME, Beijing, China, May 2007, pp. 23–27.

S. Succi, The Lattice Boltzmann Equation for Fluid Dynamics and Beyond Numerical Mathematics and Scientific Computation. New York: Oxford Univ. Press, 2001.

Y. Zhao, ―Lattice Boltzmann based PDE solver on the GPU,‖ Visual Comput., vol. 24, no. 5, pp. 323–333, Mar. 2007.

X. He and L. Luo, ―Lattice Boltzmann model for incompressible Navier–Stokes equation,‖ J. Stat. Phys., vol. 88, no. 3/4, pp. 927–944, 1997.

J. Aujol and G. Aubert, ―Signed distance functions and viscosity solutions of discontinuous Hamilton–Jacobi equations,‖ INRIA, Le Chesnay Cedex, France, 2002, inria-00072081, version 1, ref. RR-4507.

J. G. Rosen, ―The gradient projection method for nonlinear programming, II, nonlinear constraints,‖ J. SIAM, vol. 9, no. 4, pp. 514–532, Dec. 1961.

D. L. Pham and J. L. Prince, ―Adaptive fuzzy segmentation of magnetic resonance images,‖ IEEE Trans. Med. Imag., vol. 18, no. 9, pp. 737–752,Sep. 1999.

P. L. Bhatnagar, E. P. Gross, and M. Krook, ―A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems,‖ Phys. Rev., vol. 94, no. 3, pp. 511–525, 1954.

S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003.

L. D. Cohen, ―On active contour models and balloons,‖ Comput. Vis. Graph., Image Process., vol. 53, no. 2, pp. 211–218, Mar. 1991.

N. Paragios and R. Deriche, ―Geodesic active contours for supervised texture segmentation,‖ in Proc. IEEE Conf. CVPR, 1999, pp. I:1034–I:1040.

R. Ronfard, ―Region based strategies for active contour models,‖ Int. J.Comput. Vis., vol. 13, no. 2, pp. 229–251, Oct. 1994.

D. Mumford and J. Shah, ―Optimal approximations by piecewise smooth functions and associated variational problems,‖ Commun. Pure Appl .Math., vol. 42, no. 5, pp. 577–685, Jul. 1989.

A. Hagan and Y. Zhao, ―Parallel 3-D image segmentation of large data set on a GPU cluster,‖ in Proc. ISVC, 2009, pp. 960–969.

F. Gibou and R. Fedkiw, ―A fast hybrid k-means level set algorithm for segmentation,‖ in Proc. 4th Annu. Hawaii Int. Conf. Stat. Math., 2005,pp. 281–291.

M. Bauchemin, K. Thomson, and G. Edwards, ―On the Hausdorff distance used for the evaluation of segmentation results,‖ Can. J. Remote Sens., vol. 24, no. 1, pp. 3–8, 1998.

S. Chabrier, H. Laurent, C. Rosenberger, and B. Emile, ―Comparative study of contour detection evaluation criteria based on dissimilarity measures,‖EURASIP J. Image Video Process., vol. 2008, pp. 693 053-1–693 053-13, Feb. 2008.

S. Balla-Arabé, B. Wang, and X.-B. Gao, ―Level set region based image segmentation using lattice Boltzmann method,‖ in Proc. 7th Int. Conf-Comput. Intell. Security, Sanya, China, Dec. 2011, pp. 1159–1163.

D. Martin, C. Fowlkes, D. Tal, and J. Malik, ―A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,‖ in Proc. 8th Int. Conf . comput. Vis., Jul. 2001, vol. 2, pp. 416–423.

X.-B. Gao, B.Wang, D. Tao, and X. Li, ―A relay level set method for automatic image segmentation,‖ IEEE Trans. Syst., Man, Cybern. B, Cybern.,vol. 41, no. 2, pp. 518–525, Apr. 2011.

B. Wang, X.-B. Gao, D. Tao, and X. Li, ―A unified tensor level set for image segmentation,‖ IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 3, pp. 857–867, Jun. 2010.

D. R. Martin, ―An empirical approach to grouping and segmentation,‖ Ph.D. dissertation, Univ. California, Berkeley, CA, 2002.

P. Sylvie and G. Laurent, ―Evaluation of image segmentation: State of the art, new criteria and comparison,‖ traitement du signal, vol. 23, no. 2, pp. 109–124, 2006.

M. Polak, H. Zhang, and M. Pi, ―An evaluation metric for image segmentation of multiple objects,‖ Image Vis. Comput., vol. 27, no. 8, pp. 1223–1227, Jul. 2009.

S. Balla-Arabé and X. Gao, ―Image multi-thresholding by combining the lattice Boltzmann model and a localized level set algorithm,‖ Neurocomputing, vol. 93, pp. 106–114, Sep. 2012.

Z. Wang, Z. Yan, and G. Chen, ―Lattice Boltzmann method of active contour for image segmentation,‖ in Proc. 6th ICIG, 2011, pp. 338–343.

J. Ding, R. Ma, J. Yang, and S. Chen, ―A tree-structured framework for purifying ―complex‖ clusters with structural roles of individual data,‖ Pattern Recognit., vol. 43, no. 11, pp. 3753–3767, Nov. 2010.

J. Ding, J. Shen, H. Pang, S. Chen, and J.-Y. Yang, ―Exploiting intensity inhomogeneity to extract textured objects from natural scenes,‖ in Proc.ACCV, 2009, vol. 3, pp. 1–10.



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