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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

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