Magnetic Resonsnce image segmentation by modified spatial FCMbased on Gaussian function

Biniaz, Abbas; Abbasi, Ataollah; Shamsi, Mousa

doi:10.22041/ijbme.2013.13091

Document Type : Technical note

Authors

¹ M.Sc Student, Faculty of Biomedical Engineering, Bio Electrical Group,Sahand University

² Assistant Professor, Faculty of Biomedical Engineering, Bio Electrical Group,Sahand University

10.22041/ijbme.2013.13091

Abstract

Segmentation divides an image to some subdivisions where which of ones has similar intensity gray levels. Among clustering methods fuzzy c-means (FCM) clustering has been frequently used for segmentation of medical images. However, this algorithm doesn’t incorporate spatial neighborhood information in segmentation. This approach is very susceptible to nuisance factors. Therefore this paper proposes a Gaussian spatial FCM (gsFCM) to MR image segmentation. Proposed method has less sensitivity to noise specially in tissue boundaries, angles, and borders than spatial FCM (sFCM). Furthermore by the suggested algorithm a pixel which is a separate tissue from structurally point of view for example a tumor in primary stages of its appearance, has more chance to be a unique cluster. Applying quantitative assessments using Jaccard similarity index, Dice coefficient, and other validation functions on FCM,sFCM and gsFCM approaches show efficient performance of the proposed method. In this research the ISBR data bank is used for simmulations.Moreover in medical applications getting patient condition and information with fast methods is very important especially in emergency circumstances. Therefore all effective agents in patient health must be fast even medical algorithms such as clustering ones . Hence in this paper to decrease the time of convergence considerably and decline the number of iterations significantly, cluster centroids are initialized by an algorithm.

Keywords

Main Subjects

Biomedical Image Processing / Medical Image Processing

References

[1] K. Chuang, H. Tzeng, S. Chen, J. Wu, and T. Chen, "Fuzzy c-means clustering with spatial information for image segmentation," Computerized Medical Imaging and Graphics, vol. 30, pp. 9-15, 2006.

[2] P. K. Sahoo, S. Soltani, and A. Wong, "A survey of thresholding techniques," Computer vision, graphics, and image processing, vol. 41, pp. 233- 260, 1988.

[3] R. M. Haralick and L. G. Shapiro, "Image segmentation techniques," Computer vision, graphics, and image processing, vol. 29, pp. 100- 132, 1985.

[4] I .I. T. MODEL, "Unsupervised Texture Segmentation Using Markov Random Field Models".

[5] L. Lin, D. Garcia-Lorenzo, C. Li, T. Jiang, and C. Barillot, "Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields," Pattern Recognition Letters, vol. 32, pp. 1036- 1043, 2011.

[6] W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognition, vol. 40, pp. 825-8 ,2007,38.

[7] W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognition, vol. 40, pp. 825-838, 2007.

[8] H. Van Lung and J.-M. Kim, "A generalized spatial fuzzy c-means algorithm for medical image segmentation," in Fuzzy Systems, 2009. FUZZIEEE 2009. IEEE International Conference on, 2009, pp. 409-414

[9] R. Venkateswaran and S. Muthukumar, "Genetic Approach on Medical Image Segmentation by Generalized Spatial Fuzzy C-Means Algorithmǁ," in 2010 IEEE International Conference on Computational Intelligence and Computing Research, pp. 210-213.

[10] K. Chuang, H. Tzeng, S. Chen, J. Wu, and T. Chen, "Fuzzy c-means clustering with spatial information for image segmentation," Computerized Medical Imaging and Graphics, vol. 30, pp. 9-15, 2006.

[11] P. K. Sahoo, S. Soltani, and A. Wong, "A survey of thresholding techniques," Computer vision, graphics, and image processing, vol. 41, pp. 233- 260, 1988.

[12] R. M. Haralick and L. G. Shapiro, "Image segmentation techniques," Computer vision, graphics, and image processing, vol. 29, pp. 100- 132, 1985.

[13] I .I. T. MODEL, "Unsupervised Texture Segmentation Using Markov Random Field Models".

[14] L. Lin, D. Garcia-Lorenzo, C. Li, T. Jiang, and C. Barillot, "Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields," Pattern Recognition Letters, vol. 32, pp. 1036- 1043, 2011.

[15] W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognition, vol. 40, pp. 825-8 ,2007,38.

[16] W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognition, vol. 40, pp. 825-838, 2007.

[17] H. Van Lung and J.-M. Kim, "A generalized spatial fuzzy c-means algorithm for medical image segmentation," in Fuzzy Systems, 2009. FUZZIEEE 2009. IEEE International Conference on, 2009, pp. 409-414.

[18] R. Venkateswaran and S. Muthukumar, "Genetic Approach on Medical Image Segmentation by Generalized Spatial Fuzzy C-Means Algorithmǁ," in 2010 IEEE International Conference on Computational Intelligence and Computing Research, pp. 210-213.

[19] R. B. Dubey, M. Hanmandlu, S. K. Gupta, and S. K. Gupta, "The Brain MR Image Segmentation Techniquesand use of Diagnostic Packages," Academic Radiology, vol. 17, pp. 658-671, 2010.

[20] Z.-X. Ji, Q.-S. Sun, and D.-S. Xia, "A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image," Computerized Medical Imaging and Graphics, vol. 35, pp. 383-397, 2011.

[21] D. L. Pham, "Spatial models for fuzzy clustering," Computer Vision and Image Understanding, vol. 84, pp. 285-297, 2001.

[22] S. Ramathilagam, R. Pandiyarajan, A. Sathya, R. Devi, and S. R .Kannan, "Modified fuzzy c-means algorithm for segmentation of T1–T2-weighted brain MRI," Journal of Computational and Applied Mathematics, vol. 235, pp. 1578-1586, 2011.

[23] Demirhan and İ. Güler, "Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation," Engineering Applications of Artificial Intelligence, vol. 24, pp. 358-367, 2011.

[24] P. Anbeek, K. L. Vincken, G. S. van Bochove, M. J. P. van Osch, and J. van der Grond, "Probabilistic segmentation of braintissue in MR imaging," NeuroImage, vol. 27, pp. 795-804, 2005.

[25] M. Forouzanfar, N. Forghani, and M. Teshnehlab, "Parameter optimization of improved fuzzy cmeans clustering algorithm for brain MR image segmentation," Engineering Applications of Artificial Intelligence, vol. 23, pp. 160-168, 2010.

Iranian Journal of Biomedical Engineering

Magnetic Resonsnce image segmentation by modified spatial FCMbased on Gaussian function

References

References

Volume 7, Issue 2 - Serial Number 2
June 2013
Pages 175-186

Magnetic Resonsnce image segmentation by modified spatial FCMbased on Gaussian function

References

References

Volume 7, Issue 2 - Serial Number 2June 2013Pages 175-186

Volume 7, Issue 2 - Serial Number 2
June 2013
Pages 175-186