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Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques

A.H.M. Jaffar Iqbal Barbhuiya1 , K. Hemachandran2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 315-323, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.315323

Online published on Sep 30, 2018

Copyright © A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran, “Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.315-323, 2018.

MLA Style Citation: A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran "Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques." International Journal of Computer Sciences and Engineering 6.9 (2018): 315-323.

APA Style Citation: A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran, (2018). Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques. International Journal of Computer Sciences and Engineering, 6(9), 315-323.

BibTex Style Citation:
@article{Barbhuiya_2018,
author = {A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran},
title = {Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {315-323},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2866},
doi = {https://doi.org/10.26438/ijcse/v6i9.315323}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.315323}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2866
TI - Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - A.H.M. Jaffar Iqbal Barbhuiya, K. Hemachandran
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 315-323
IS - 9
VL - 6
SN - 2347-2693
ER -

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Abstract

An attempt has been made to study the DWT (Discrete Wavelet Transform) based K-Means (KM) clustering and DWT based Fuzzy C-Means (FCM) clustering methods for the segmentation of digital images. The segmentation results of Wavelet KM clustering and Wavelet FCM clustering are compared with the conventional KM clustering and FCM clustering techniques used for the segmentation. The images are split-up into identical areas using KM, FCM, wavelet KM and wavelet FCM algorithms. The algorithms are tested on different image formats available in the literature. The proposed methods are analyzed using discrete wavelet transform (DWT) for enhancing the digital images and various image features like regions, colors and shapes are considered to validate the proposed work. The segmentation results exhibit that the objects in various image clusters of wavelet KM and wavelet FCM performs better as compared to traditional KM and FCM clustering algorithm with respect to CPU execution time, sensitivity analysis, segmentation accuracy and PSNR(Peak Signal to Noise Ratio).

Key-Words / Index Term

Image segmentation, Clustering, K-Means (KM), Fuzzy C-Means (FCM), Wavelet KM, Wavelet FCM, Discrete Wavelet Transform (DWT), CPU execution time, sensitivity analysis and segmentation accuracy

References

[1] S. Nassir. “Image segmentation based on watershed and edge detection techniques”. Int. Arab J. Inf. Technol., vol. 3, no. 2, pp.104-110, April, 2006.
[2] H.S. Kumar, K.B. Raja, K.R. Venugopal, LM. Patnaik. “Automatic image segmentation using wavelets”. International Journal of Computer Science and Network Security, Vol. 9, Issue 2, pp.305-313, Feb , 28, 2009.
[3] F.U Siddiqui, N.A Isa. “Enhanced moving K-means (EMKM) algorithm for image segmentation”. IEEE Transactions on Consumer Electronics, Vol.57, Issue. 2, May, 2011.
[4] S. Dalmiya, A. Dasgupta, S.K. Datta. “Application of wavelet based K-means algorithm in mammogram segmentation”. International Journal of Computer Applications, Vol. 52, No. 15, Jan, 2012.
[5] Y. Shi, Z . Chen, Z. Qi, F. Meng, L. Cui. “A novel clustering-based image segmentation via density peaks algorithm with mid-level feature”. Neural Computing and Applications. Vol.28, Issue.1, pp. 29-39, Dec, 2017.
[6] D. L. Pham, J. L. Prince. “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities”. Pattern recognition letters, Vol. 20, Issue.1, pp.57-68, 1999.
[7] S. L. Jui, , C. Lin, H. Guan, A. Abraham, A. E. Hassanien, K. Xiao. “Fuzzy c-means with wavelet filtration for MR image segmentation”. In NaBIC , pp. 12-16, July, 2014.
[8] R. Sammouda, H. Aboalsamh, F. Saeed. “Comparison between K mean and fuzzy C-mean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer”. 2015 International Conference on In Computer Vision and Image Analysis Applications (ICCVIA) , pp. 1-6, IEEE, January, 2015.
[9] N. Sibini, Raj. Vimal. “Image Segmentation Using Fuzzy Based Clustering Algorithm And Its Application For Denoising The Digital Images”. International Journal of Emerging Trends in Science and Technology (IJETST),Vol. 5, Issue. 3, pp. 681-688, 2016.
[10] D. Liu, L. Ma, H. Chen, K. Meng,. “Medical Image Segmentation Based on Improved Fuzzy C-means Clustering”. 2017 International Conference In Smart Grid and Electrical Automation (ICSGEA), pp. 406-410, IEEE. May, 2017.
[11] S. ShanmugaPriya, A. Valarmathi. “Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images”. Design Automation for Embedded Systems, pp. 1-13, 2018.
[12] R. Shanker, M. Bhattacharya. “Brain Tumor Segmentation of Normal and Pathological Tissues Using K-mean Clustering with Fuzzy C-mean Clustering”. In European Congress on Computational Methods in Applied Sciences and Engineering, pp. 286-296, Springer, Cham. October, 2017.
[13] J. MacQueen. “Some methods for classification and analysis of multivariate observations”. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability Vol. 1, No. 14, pp. 281-297, June ,1967.
[14] A. K. Jain. “Data clustering: 50 years beyond K-means”. Pattern recognition letters, Vol. 31, Issue. 8, pp. 651-666, 2010.
[15] C. Chandhok, S. Chaturvedi, A. A. Khurshid. “An approach to image segmentation using K-means clustering algorithm”. International Journal of Information Technology (IJIT), Vol. 1, Issue. 1, pp.11-17, 2012.
[16] H. Yao, Q. Duan, D. Li, J. Wang. An improved K-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, Vol. 58 Issue 3-4, pp. 790-798. 2013.
[17] V. Jumb, M. Sohani, A. Shrivas. “Color image segmentation using K-means clustering and Otsu’s adaptive thresholding”. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 3 Issue 9, pp. 72-76, 2014.
[18] N. Dhanachandra, K. Manglem, Y. J. Chanu. “Image segmentation using K-means clustering algorithm and subtractive clustering algorithm”. Procedia Computer Science, Issue 54, 764-771.2015.
[19] M. W. Ayech, D. Ziou. “Terahertz image segmentation using k-means clustering based on weighted feature learning and random pixel sampling”. Neurocomputing, Issue. 175, pp. 243-264, 2016.
[20] N. Dhanachandra, Y. J. Chanu. “A New Approach of Image Segmentation Method Using K-Means and Kernel Based Subtractive Clustering Methods”. International Journal of Applied Engineering Research, Vol. 12, Issue. 20, pp. 10458-10464, 2017.
[21] Z. Khan, J. Ni, X. Fan, P. Shi. “AN IMPROVED K-MEANS CLUSTERING ALGORITHM BASED ON AN ADAPTIVE INITIAL PARAMETER ESTIMATION PROCEDURE FOR IMAGE SEGMENTATION”. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, Vol. 13, Issue. 5, pp. 1509-1525, 2017.
[22] A.S.A. Nasir, M.Y. Mashor, Z. Mohamed. “Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation”. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, Vol. 42, Issue 1, pp. 1-15, 2018.

[23] W. Q. Deng, X. M. Li, X. Gao, C. M. Zhang. “A modified fuzzy C-means algorithm for brain MR image segmentation and bias field correction”. Journal of Computer Science and Technology, Vol. 31, Issue 3, pp.501-511, 2016.
[24] J. C. Dunn. “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters”. 1973.
[25] J. C. Bezdek. “Objective function clustering”. In Pattern recognition with fuzzy objective function algorithms, pp. 43-93. Springer, Boston, MA. 1981.
[26] R. Suganya, R. Shanthi. “Fuzzy c-means algorithm-a review”. International Journal of Scientific and Research Publications, Vol. 2, Issue. 11, 2012.
[27] N. R. Pal, K. Pal, J. M. Keller, J. C. Bezdek. “A possibilistic fuzzy c-means clustering algorithm”. IEEE transactions on fuzzy systems, Vol. 13, Issue 4, pp. 517-530, 2005.
[28] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. “Semantic image segmentation with deep convolutional nets and fully connected crfs”. arXiv preprint arXiv:1412.7062, 2014.
[29] S. Naz, H. Majeed, H. Irshad. “Image segmentation using fuzzy clustering: A survey”. In International Conference on ICET , pp. 181-186, October, 2010.

[30] J. H. Song, W. Cong, J. Li. “A Fuzzy C-means Clustering Algorithm for Image Segmentation Using Nonlinear Weighted Local Information”. Journal of Information Hiding and Multimedia Signal Processing, Vol. 8, Issue. 9, pp. 1-11, 2017.
[31] M. G. Mostafa, M. F. Tolba, T. F. Gharib, M. A. Megeed. “Medical image segmentation using a wavelet-based multiresolution EM algorithm”. In IEEE International Conference of Electronics Technology and Application, IETA, December, 2001.
[32] A. Gavlasová, A. Procházka, M. Mudrová,. “Wavelet based image segmentation”. In Proc. of the 14th Annual Conference Technical Computing, Prague, 2006.
[33] Z. Shi, Y. Liu, Q. Li,. “Medical Image Segmentation Based on FCM and Wavelets”. In International Conference on Intelligent Science and Big Data Engineering, pp. 279-286, Springer, Berlin, Heidelberg, July 2013.
[34] Y. Shi, Z. Chen, Z. Qi, F. Meng, L. Cui. “A novel clustering-based image segmentation via density peaks algorithm with mid-level feature”. Neural Computing and Applications, Vol.28, Issue 1, pp. 29-39, 2017.
[35] M. Sharma, G. N. Purohit, S. Mukherjee. “Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN)”. In Networking Communication and Data Knowledge Engineering , pp. 145-157, Springer, Singapore, 2018.
[36] H. Castillejos, V. Ponomaryov, R. Peralta. “Image segmentation in wavelet domain using fuzzy logic”. In Electrical Engineering Computing Science and Automatic Control (CCE), 2011 8th International Conference, pp. 1-6, Oct 26, 2011. IEEE.