A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering

E. A. Zanaty; Ashraf Afifi

doi:doi:10.11648/j.ajrs.20130102.14

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A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering

E. A. Zanaty, Ashraf Afifi

Published in American Journal of Remote Sensing (Volume 1, Issue 2)

Received: 14 April 2013 Accepted: Published: 2 April 2013

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Abstract

Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. Thus, the estimation of optimal cluster number during the clustering process is our prime concern. In this paper, we introduce a new validity index method based on multi-degree entropy algorithm for determining the number of clusters automatically. This multi-degree entropy algorithm combines multi-degree immersion and entropy algorithms to partition an image into levels of intensity. The output of the multi-degree immersion processes are regions in which the interior does not contain any sharp grey value transitions, i.e. each level of intensity contains one or more regions of connected points or oversegmentation. These regions are passed to the entropy procedure to perform a suitable merging which produces the final number of clusters based on validity function criteria. Validity functions are used to find a relation between intra-cluster and inter-cluster variability, which is of course a reasonable principle. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The proposed method is experimented on a discrete image example to prove its efficiency and applicability. The existing validation indices like PC, XB, and CE are evaluated and compared with the proposed index when applied on two simulation and one real life data. A direct benefit of this method is being able to determine the number of clusters for given application medical images.

Published in	American Journal of Remote Sensing (Volume 1, Issue 2)
DOI	10.11648/j.ajrs.20130102.14
Page(s)	38-46
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 Clustering, Multi-Degree Immersion, Entropy, Validity Index

References

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APA Style

E. A. Zanaty, Ashraf Afifi. (2013). A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. American Journal of Remote Sensing, 1(2), 38-46. https://doi.org/10.11648/j.ajrs.20130102.14

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ACS Style

E. A. Zanaty; Ashraf Afifi. A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. Am. J. Remote Sens. 2013, 1(2), 38-46. doi: 10.11648/j.ajrs.20130102.14

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AMA Style

E. A. Zanaty, Ashraf Afifi. A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. Am J Remote Sens. 2013;1(2):38-46. doi: 10.11648/j.ajrs.20130102.14

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@article{10.11648/j.ajrs.20130102.14,
  author = {E. A. Zanaty and Ashraf Afifi},
  title = {A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering},
  journal = {American Journal of Remote Sensing},
  volume = {1},
  number = {2},
  pages = {38-46},
  doi = {10.11648/j.ajrs.20130102.14},
  url = {https://doi.org/10.11648/j.ajrs.20130102.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20130102.14},
  abstract = {Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. Thus, the estimation of optimal cluster number during the clustering process is our prime concern. In this paper, we introduce a new validity index method based on multi-degree entropy algorithm for determining the number of clusters automatically. This multi-degree entropy algorithm combines multi-degree immersion and entropy algorithms to partition an image into levels of intensity. The output of the multi-degree immersion processes are regions in which the interior does not contain any sharp grey value transitions, i.e. each level of intensity contains one or more regions of connected points or oversegmentation. These regions are passed to the entropy procedure to perform a suitable merging which produces the final number of clusters based on validity function criteria. Validity functions are used to find a relation between intra-cluster and inter-cluster variability, which is of course a reasonable principle. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The proposed method is experimented on a discrete image example to prove its efficiency and applicability. The existing validation indices like PC, XB, and CE are evaluated and compared with the proposed index when applied on two simulation and one real life data. A direct benefit of this method is being able to determine the number of clusters for given application medical images.},
 year = {2013}
}

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TY - JOUR
T1 - A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering
AU - E. A. Zanaty
AU - Ashraf Afifi
Y1 - 2013/04/02
PY - 2013
N1 - https://doi.org/10.11648/j.ajrs.20130102.14
DO - 10.11648/j.ajrs.20130102.14
T2 - American Journal of Remote Sensing
JF - American Journal of Remote Sensing
JO - American Journal of Remote Sensing
SP - 38
EP - 46
PB - Science Publishing Group
SN - 2328-580X
UR - https://doi.org/10.11648/j.ajrs.20130102.14
AB - Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. Thus, the estimation of optimal cluster number during the clustering process is our prime concern. In this paper, we introduce a new validity index method based on multi-degree entropy algorithm for determining the number of clusters automatically. This multi-degree entropy algorithm combines multi-degree immersion and entropy algorithms to partition an image into levels of intensity. The output of the multi-degree immersion processes are regions in which the interior does not contain any sharp grey value transitions, i.e. each level of intensity contains one or more regions of connected points or oversegmentation. These regions are passed to the entropy procedure to perform a suitable merging which produces the final number of clusters based on validity function criteria. Validity functions are used to find a relation between intra-cluster and inter-cluster variability, which is of course a reasonable principle. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The proposed method is experimented on a discrete image example to prove its efficiency and applicability. The existing validation indices like PC, XB, and CE are evaluated and compared with the proposed index when applied on two simulation and one real life data. A direct benefit of this method is being able to determine the number of clusters for given application medical images.
VL - 1
IS - 2
ER -

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Author Information

E. A. Zanaty

College of Science, Sohag University, Sohag, Egypt.
Ashraf Afifi

Computer Engineering Department, High Technological Institute, 10th of Ramadan City, Egypt.

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APA Style

E. A. Zanaty, Ashraf Afifi. (2013). A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. American Journal of Remote Sensing, 1(2), 38-46. https://doi.org/10.11648/j.ajrs.20130102.14

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ACS Style

E. A. Zanaty; Ashraf Afifi. A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. Am. J. Remote Sens. 2013, 1(2), 38-46. doi: 10.11648/j.ajrs.20130102.14

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AMA Style

E. A. Zanaty, Ashraf Afifi. A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering. Am J Remote Sens. 2013;1(2):38-46. doi: 10.11648/j.ajrs.20130102.14

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@article{10.11648/j.ajrs.20130102.14,
  author = {E. A. Zanaty and Ashraf Afifi},
  title = {A New Approach for Automatic Fuzzy Clustering Applied to Magnetic Resonance Image Clustering},
  journal = {American Journal of Remote Sensing},
  volume = {1},
  number = {2},
  pages = {38-46},
  doi = {10.11648/j.ajrs.20130102.14},
  url = {https://doi.org/10.11648/j.ajrs.20130102.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20130102.14},
  abstract = {Many clustering and segmentation algorithms suffer from the limitation that the number of clusters/segments is specified manually by human operators. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. Thus, the estimation of optimal cluster number during the clustering process is our prime concern. In this paper, we introduce a new validity index method based on multi-degree entropy algorithm for determining the number of clusters automatically. This multi-degree entropy algorithm combines multi-degree immersion and entropy algorithms to partition an image into levels of intensity. The output of the multi-degree immersion processes are regions in which the interior does not contain any sharp grey value transitions, i.e. each level of intensity contains one or more regions of connected points or oversegmentation. These regions are passed to the entropy procedure to perform a suitable merging which produces the final number of clusters based on validity function criteria. Validity functions are used to find a relation between intra-cluster and inter-cluster variability, which is of course a reasonable principle. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The proposed method is experimented on a discrete image example to prove its efficiency and applicability. The existing validation indices like PC, XB, and CE are evaluated and compared with the proposed index when applied on two simulation and one real life data. A direct benefit of this method is being able to determine the number of clusters for given application medical images.},
 year = {2013}
}

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