Abstract
In this paper, Variable Kernel Fuzzy C-Means (VKFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, VKFCM algorithm computes the fuzzy membership values for each pixel. On the basis of VKFCM the edge indicator function was redefined. Using the edge indicator function the image segmentation of a medical image was performed to extract the regions of interest for further processing. The above process of segmentation showed a considerable improvement in the evolution of the level set function.
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References
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorthims. Plenum Press, New York
Wu KL, Yang MS (2002) Alternative c-means clustering algorithms. Pattern Recognit 35(10):2267–2278
Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorthim’s based on the Hamilton-Jacobi formulation. J Comput Phys 79(1):12–49
Malladi R, Sethain J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175
Staib L, Zeng X, Schultz R, Duncan J (2000) Shape constraints in deformable models. In: Bankman IN (ed) Handbook of medical imaging. Academic Press, New York, pp 147–157
Leventon M, Faugeraus O, Grimson W (2000) Wells W (2000) Level set based segmentation with intensity and curvature priors. Workshop on mathematical methods in biomedical image analysis proceedings, In, pp 4–11
Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans pattern Anal Mach Intell 22:266–280
Vese LA, Chan TF (2002) A multiphase level set frame wor for image segmentation using the mumford and shah model. Int J Comput Vis 50(3):271–293
Shi Y, Karl WC (2005) Real-time tracking using level set. In: Proceedings of IEEE computer society conference on computer vision and, pattern recognition, vol 2, pp 34–42
Li C, Xu C, Gui C et al (2005) Level set evolution without re-initialization: a new varitional formulation. IEEE computer society conference on computer vision and pattern recognition, In, pp 430–436
Sethain I (1999) Level set methods and fast marching methods. Cambridge University Press, Cambridge
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57
Bezedek J (1980) A convergence thheorem for the fuzzy ISODATA clustering algorthims. IEEE Trans Pattern Anal Mach Intell 2:78–82
Zhang L, Zhou WD, Jiao LC (2002) Kernel clustering algorithm (in chinese). Chin J Comput 25(6):587–590
Osher S, Fedkiw R (2002) Level set methods and dynamic implicit surfaces. Springer, New York, pp 112–113
Peng D, Merrimam B, Osher S, Zhao H, Kang M (1996) A PDE-based fast local level set method. J Comp Phys 155:410–438
Gomes J, Faugeras O (2000) Reconciling distance functions and level sets. J Vis Commun Image Represent 11(2):209–223
Mcinerney T, Terzopouls D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):9l–108
Dao-Qiang Z, Song C (2003) Clustering in completed data usig Kernel-based fuzzy c-means algorthim. Neural Process Lett 18(3):155–162
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Saikumar, T., FasiUddin, K., Reddy, B.V., Uddin, M.A. (2013). Image Segmentation Using Variable Kernel Fuzzy C Means (VKFCM) Clustering on Modified Level Set Method. In: Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Computer Networks & Communications (NetCom). Lecture Notes in Electrical Engineering, vol 131. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6154-8_26
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DOI: https://doi.org/10.1007/978-1-4614-6154-8_26
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