Brain Image Segmentation Based on Fuzzy Clustering

Shatha J. Mohammed

Abstract


The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated.
This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

Keywords


Brain image, Fuzzy, clustering, level set, Segmentation.

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References


R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE transactions on pattern analysis and machine intelligence, vol. 17, pp. 158-175, 1995.

V. Caselles, F. Catté, T. Coll, and F. Dibos, "A geometric model for active contours in image processing," Numerische mathematik, vol. 66, pp. 1-31, 1993.

S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations," Journal of computational physics, vol. 79, pp. 12-49, 1988.

T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Transactions on image processing, vol. 10, pp. 266-277, 2001.

M. Y. Kamil, "Brain Tumor Area Calculation in CT-scan image using Morphological Operations," IOSR Journal of Computer Engineering, vol. 17, pp. 125-128, 2015.

S. Madhukumar and N. Santhiyakumari, "Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain," The Egyptian Journal of Radiology and Nuclear Medicine, vol. 46, pp. 475-479, 2015.

A. Liew, S. Leung, and W. Lau, "Fuzzy image clustering incorporating spatial continuity," IEE Proceedings-Vision, Image and Signal Processing, vol. 147, pp. 185-192, 2000.

M. Y. Kamil, "Morphological gradient in brain magnetic resonance imaging based on intuitionistic fuzzy approach," in Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), Al-Sadeq International Conference on, 2016, pp. 1-3.

M. Y. Kamil, "Edge detection for Diabetic Retinopathy using fuzzy logic," IJS, vol. 55, pp. 1395-1401, 2014.

M. Y. Kamil, "Hybrid Algorithm for Edge Detection using Fuzzy Inference System," International Journal of Computer Applications, vol. 102, 2014.

L. Wang and C. Pan, "Robust level set image segmentation via a local correntropy-based K-means clustering," Pattern Recognition, vol. 47, pp. 1917-1925, 2014.

X.-L. Jiang, Q. Wang, B. He, S.-J. Chen, and B.-L. Li, "Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints," Neurocomputing, vol. 207, pp. 22-35, 2016.

P. R. Bai, Q. Y. Liu, L. Li, S. H. Teng, J. Li, and M. Y. Cao, "A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation," Computers in biology and medicine, vol. 43, pp. 1827-1832, 2013.

K. Thapaliya, J.-Y. Pyun, C.-S. Park, and G.-R. Kwon, "Level set method with automatic selective local statistics for brain tumor segmentation in MR images," Computerized Medical Imaging and Graphics, vol. 37, pp. 522-537, 2013.

A. Gharipour and A. W.-C. Liew, "An integration strategy based on fuzzy clustering and level set method for cell image segmentation," in Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on, 2013, pp. 1-5.

S. Kannan, R. Devi, S. Ramathilagam, and K. Takezawa, "Effective FCM noise clustering algorithms in medical images," Computers in biology and medicine, vol. 43, pp. 73-83, 2013.

H. Masoumi, A. Behrad, M. A. Pourmina, and A. Roosta, "Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network," Biomedical Signal Processing and Control, vol. 7, pp. 429-437, 2012.




DOI: http://dx.doi.org/10.23851/mjs.v28i3.553

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