Fast FCM with spatial neighborhood information for brain MR image segmentation

• Autorzy: Biniaz A. , Abbasi A.

Identyfikatory

DOI

10.2478/jaiscr-2014-0002

• Języki publikacji: EN

Abstrakty

EN

Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.

Słowa kluczowe

EN

Fuzzy c-Means clustering; FCM; Fast FCM; FFCM; spatial Fast FCM; sFFCM; MR image; noise interference

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2013
• Tom: Vol. 3, No. 1
• Strony: 15--25
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Biniaz A.

• M.Sc. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

autor

Abbasi A.

• Assistant professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

Bibliografia

• [1] Y. Li, Z. Chi, MR Brain image segmentation based on self-organizing map network, International Journal of Information Technology, 11, 2005, 45-53.
• [2] P. Dastidar, Overview of Neuroradiological MRI, International Journal of Bioelectromagnetism, 1, 1999.
• [3] N. De Stefano, M.L. Bartolozzi, L. Guidi, M.L. Stromillo, A. Federico, Magnetic resonance spectroscopy as a measure of brain damage in multiple sclerosis, Journal of the Neurological Sciences, 233, 2005, 203-208.
• [4] C.-W. Bong, M. Rajeswari, Multi-objective nature-inspired clustering and classification techniques for image segmentation, Applied Soft Computing, 11, 2011, 3271-3282.
• [5] R.B. Dubey, M. Hanmandlu, S.K. Gupta, S.K. Gupta, The Brain MR Image Segmentation Techniques and use of Diagnostic Packages, Academic Radiology, 17, 2010, 658-671.
• [6] X. Wang, H. Wang, Markov random field modeled range image segmentation, Pattern Recognition Letters, 25, 2004, 367-375.
• [7] T. Celik, T. Tjahjadi, Bayesian texture classification and retrieval based on multiscale feature vector, Pattern Recognition Letters, 32, 2011, 159-167.
• [8] M. Siyal, L. Yu, An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI, Pattern Recognition Letters, 26, 2005, 2052-2062.
• [9] M. Mignotte, A de-texturing and spatially constrained K-means approach for image segmentation, Pattern Recognition Letters, 32 ,2011, 359-367.
• [10] Q. Ge, L. Xiao, J. Zhang, Z.H. Wei, A robust patch-statistical active contour model for image segmentation, Pattern Recognition Letters, 2012.
• [11] J. Fan, G. Zeng, M. Body, M.S. Hacid, Seeded region growing: an extensive and comparative study, Pattern Recognition Letters, 26, 2005,1139-1156.
• [12] A. Mekhmoukh, K. Mokrani, M. Cheriet, A modified Kernelized Fuzzy C-Means algorithm for noisy images segmentation: Application to MRI images, 2012.
• [13] M. Yang, H. Tsai, A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction, Pattern Recognition Letters, 29, 2008, 1713-1725.
• [14] S. Ramathilagam, R. Pandiyarajan, A. Sathya, R. Devi, S.R. Kannan, Modified fuzzy c-means algorithm for segmentation of T1–T2-weighted brain MRI, Journal of Computational and Applied Mathematics, 235, 2011, 1578-1586.
• [15] K. Chuang, H. Tzeng, S. Chen, J. Wu, T. Chen, Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics, 30,2006, 9-15.
• [16] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters, 1973. 25
• [17] Y. Li, Y. Shen, Fuzzy c-means clustering based on spatial neighborhood information for image segmentation, Systems Engineering and Electronics, Journal of, 21, 2010, 323-328.
• [18] W. Wang, Y. Zhang, On fuzzy cluster validity indices, Fuzzy Sets and Systems, 158 ,2007, 2095-2117.
• [19] K. Xiao, S.H. Ho, A. Bargiela, Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing, International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 1, 2010, 316-331.