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Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation
https://doi.org/10.22146/ijccs.81521 Wawan Gunawan(1*), Nurul Latifah(2)
(1) UIN Raden Intan Lampung, Lampung
(2) Master of Biology Education, Muhammadiyah University of Metro, Lampung
(*) Corresponding Author
Abstract
based on the Mahalanobis distance; However, this method only needs to consider the color
space situation, not the neighborhood system of the image. It was an effective edge detection
process unwell performed and generated less accuracy in segmentation results. In this article,
we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatial
information (MFCMS). The proposed method combines feature space and images of the
information of the neighborhood (spatial information) to improve the accuracy of the result of
segmentation on the image. The MFCMS consists of two steps, the histogram threshold module
for the first step and the MFCMS module for the second step. The Histogram Threshold module
is used to get the MFCMS initialization conditions for the cluster centroid and the number of
centroids. Test results show that this method provides better segmentation performance than
classification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,
respectively.
Keywords
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[1] D. K.S. and S. G. N., "An Adaptive Color Image Segmentation," ELCVIA Electron. Lett. Comput. Vis. Image Anal., vol. 5, no. 4, 2006, doi: 10.5565/rev/elcvia.115.
[2] A. Z. Arifin and A. Asano, "Image segmentation by histogram thresholding using hierarchical cluster analysis," Pattern Recognit. Lett., vol. 27, no. 13, 2006, doi: 10.1016/j.patrec.2006.02.022.
[3] Z. Ji, Y. Xia, Q. Sun, Q. Chen, D. Xia, and D. D. Feng, "Fuzzy local Gaussian mixture model for brain MR image segmentation," IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, 2012, doi: 10.1109/TITB.2012.2185852.
[4] R. Unnikrishnan, C. Pantofaru, and M. Hebert, "Toward objective evaluation of image segmentation algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, 2007, doi: 10.1109/TPAMI.2007.1046.
[5] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. 1981.
[6] M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, "A modified uzzy C-means algorithm for bias field estimation and segmentation of MRI data," IEEE Trans. Med. Imaging, vol. 21, no. 3, 2002, doi: 10.1109/42.996338.
[7] L. Szilágyi, Z. Benyó, S. M. Szilágyi, and H. S. Adam, "MR Brain Image Segmentation Using an Enhanced Fuzzy C-Means Algorithm," in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2003, vol. 1, doi: 10.1109/iembs.2003.1279866.
[8] X. Zhao, Y. Li, and Q. Zhao, "Mahalanobis distance based on fuzzy clustering algorithm for image segmentation," Digit. Signal Process. A Rev. J., vol. 43, 2015, doi: 10.1016/j.dsp.2015.04.009.
[9] S. Ito, M. Yoshioka, S. Omatu, K. Kita, and K. Kugo, "An image segmentation method using histograms and the human characteristics of HSI color space for a scene image," Artif. Life Robot., vol. 10, no. 1, 2006, doi: 10.1007/s10015-005-0352-x.
[10] C. Rotaru, T. Graf, and J. Zhang, "Color image segmentation in HSI space for automotive applications," J. Real-Time Image Process., vol. 3, no. 4, 2008, doi: 10.1007/s11554-008-0078-9.
[11] C. Zhang and P. Wang, "A new method of color image segmentation based on intensity and hue clustering," Proc. - Int. Conf. Pattern Recognit., vol. 15, no. 3, 2000, doi: 10.1109/icpr.2000.903620.
[12] P. M. Kelly, "An Algorithm for Merging Hyperellipsoidal Clusters," Los Alamos Natl. Lab., Los Alamos, NM, Tech. Rep. LA …, pp. 1–5, 1994, [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.1565&rep=rep1&type=ps.
[13] M. Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation Mehmet," J. Electron. Imaging, vol. 13, no. 1, 2004.
[14] Y. J. Zhang, "A survey on evaluation methods for image segmentation," Pattern Recognit., vol. 29, no. 8, 1996, doi: 10.1016/0031-3203(95)00169-7.
[15] B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Imaging, vol. 13, no. 1, 2004, doi: 10.1117/1.1631315.
DOI: https://doi.org/10.22146/ijccs.81521
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