Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation

Arunkumar Rajendran; Thamarai Muthusamy

doi:doi:10.11648/j.cssp.s.2014030601.11

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Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation

Arunkumar Rajendran, Thamarai Muthusamy

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 6-1)

Received: 17 September 2014 Accepted: 22 September 2014 Published: 20 October 2014

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Abstract

In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 6-1) This article belongs to the Special Issue Computational Intelligence in Digital Image Processing
DOI	10.11648/j.cssp.s.2014030601.11
Page(s)	1-5
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Fuzzy C Means, Image Segmentation, Unsupervised Learning, AUFCM and DCTS

References

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[3]	Chunming Li, Rui Huang, Zhaohua Ding, J. Chris Gatenby, Dimitris N. Metaxas, “A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI”, IEEE Transactions on Image processing, vol. 20, no. 7, july 2011.
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[7]	Said, A.F. ; Sch. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA ; Bennett, B.L. ; Karam, L.J. ; Pettinato, J.S.,” Automated Detection and Classification of Non-Wet Solder Joints”, Automation Science and Engineering, IEEE Transactions on (Volume:8 , Issue: 1), Jan 2011
[8]	SaminaNaz, HammadMajeed, HumayunIrshad,” Image Segmentation using Fuzzy Clustering: A Survey”, IEEE Transaction 2010 6th International Conference on Emerging Technologies.
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[11]	Dr. Ch. G.V.N. Prasad, K. HanumanthaRao, DepaPratima and B.N. Alekhya, ”Unsupervised Learning Algorithms to Identify the Dense Cluster in Large Datasets”, International Journal of Computer Science and Telecommunications [Volume 2, Issue 4, July 2011].
[12]	Jie Cui ; Inst. of Biornaterials& Biomed. Eng., Toronto Univ., Ont. ; Loewy, J. ; Kendall, E.J. “Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-Means analysis”, Biomedical Engineering, IEEE Transactions on (Volume:53 , Issue: 5 May 2006)
[13]	FasahatUllahSiddiqui and NorAshidi Mat Isa, “Enhanced Moving K-Means (EMKM) Algorithm for Image Segmentation”, IEEE Transaction [Volume:57, Issue: 2,May 2011].
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APA Style

Arunkumar Rajendran, Thamarai Muthusamy. (2014). Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Science Journal of Circuits, Systems and Signal Processing, 3(6-1), 1-5. https://doi.org/10.11648/j.cssp.s.2014030601.11

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ACS Style

Arunkumar Rajendran; Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci. J. Circuits Syst. Signal Process. 2014, 3(6-1), 1-5. doi: 10.11648/j.cssp.s.2014030601.11

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AMA Style

Arunkumar Rajendran, Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci J Circuits Syst Signal Process. 2014;3(6-1):1-5. doi: 10.11648/j.cssp.s.2014030601.11

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@article{10.11648/j.cssp.s.2014030601.11,
  author = {Arunkumar Rajendran and Thamarai Muthusamy},
  title = {Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {3},
  number = {6-1},
  pages = {1-5},
  doi = {10.11648/j.cssp.s.2014030601.11},
  url = {https://doi.org/10.11648/j.cssp.s.2014030601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.s.2014030601.11},
  abstract = {In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.},
 year = {2014}
}

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TY  - JOUR
T1  - Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation
AU  - Arunkumar Rajendran
AU  - Thamarai Muthusamy
Y1  - 2014/10/20
PY  - 2014
N1  - https://doi.org/10.11648/j.cssp.s.2014030601.11
DO  - 10.11648/j.cssp.s.2014030601.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
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PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.s.2014030601.11
AB  - In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.
VL  - 3
IS  - 6-1
ER  -

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Author Information

Arunkumar Rajendran

M E Communication Systems, Karpagam College of Engineering, Coimbatore, India
Thamarai Muthusamy

ECE Department, Karpagam College of Engineering, Coimbatore, India

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APA Style

Arunkumar Rajendran, Thamarai Muthusamy. (2014). Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Science Journal of Circuits, Systems and Signal Processing, 3(6-1), 1-5. https://doi.org/10.11648/j.cssp.s.2014030601.11

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ACS Style

Arunkumar Rajendran; Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci. J. Circuits Syst. Signal Process. 2014, 3(6-1), 1-5. doi: 10.11648/j.cssp.s.2014030601.11

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AMA Style

Arunkumar Rajendran, Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci J Circuits Syst Signal Process. 2014;3(6-1):1-5. doi: 10.11648/j.cssp.s.2014030601.11

Copy | Download

@article{10.11648/j.cssp.s.2014030601.11,
  author = {Arunkumar Rajendran and Thamarai Muthusamy},
  title = {Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {3},
  number = {6-1},
  pages = {1-5},
  doi = {10.11648/j.cssp.s.2014030601.11},
  url = {https://doi.org/10.11648/j.cssp.s.2014030601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.s.2014030601.11},
  abstract = {In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation
AU  - Arunkumar Rajendran
AU  - Thamarai Muthusamy
Y1  - 2014/10/20
PY  - 2014
N1  - https://doi.org/10.11648/j.cssp.s.2014030601.11
DO  - 10.11648/j.cssp.s.2014030601.11
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.s.2014030601.11
AB  - In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.
VL  - 3
IS  - 6-1
ER  -

Copy | Download