Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm

Ahmed Mohamed Ali Karrar; Jun Sun

doi:doi:10.11648/j.ajnna.20190501.16

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Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm

Ahmed Mohamed Ali Karrar, Jun Sun

Published in American Journal of Neural Networks and Applications (Volume 5, Issue 1)

Received: 16 April 2019 Accepted: 27 June 2019 Published: 16 July 2019

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Abstract

In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.

Published in	American Journal of Neural Networks and Applications (Volume 5, Issue 1)
DOI	10.11648/j.ajnna.20190501.16
Page(s)	36-44
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), 2019. Published by Science Publishing Group

Keywords

Image Segmentation, K-means Clustering, Partial Contrast Stretching, Gaussian Mixture Models

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APA Style

Ahmed Mohamed Ali Karrar, Jun Sun. (2019). Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. American Journal of Neural Networks and Applications, 5(1), 36-44. https://doi.org/10.11648/j.ajnna.20190501.16

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ACS Style

Ahmed Mohamed Ali Karrar; Jun Sun. Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. Am. J. Neural Netw. Appl. 2019, 5(1), 36-44. doi: 10.11648/j.ajnna.20190501.16

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AMA Style

Ahmed Mohamed Ali Karrar, Jun Sun. Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. Am J Neural Netw Appl. 2019;5(1):36-44. doi: 10.11648/j.ajnna.20190501.16

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@article{10.11648/j.ajnna.20190501.16,
  author = {Ahmed Mohamed Ali Karrar and Jun Sun},
  title = {Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm},
  journal = {American Journal of Neural Networks and Applications},
  volume = {5},
  number = {1},
  pages = {36-44},
  doi = {10.11648/j.ajnna.20190501.16},
  url = {https://doi.org/10.11648/j.ajnna.20190501.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.16},
  abstract = {In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.},
 year = {2019}
}

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TY  - JOUR
T1  - Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm
AU  - Ahmed Mohamed Ali Karrar
AU  - Jun Sun
Y1  - 2019/07/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ajnna.20190501.16
DO  - 10.11648/j.ajnna.20190501.16
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 36
EP  - 44
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20190501.16
AB  - In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.
VL  - 5
IS  - 1
ER  -

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Author Information

Ahmed Mohamed Ali Karrar

School of Internet of Things & Engineering, Jiangnan University, Wuxi, China
Jun Sun

School of Internet of Things & Engineering, Jiangnan University, Wuxi, China

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APA Style

Ahmed Mohamed Ali Karrar, Jun Sun. (2019). Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. American Journal of Neural Networks and Applications, 5(1), 36-44. https://doi.org/10.11648/j.ajnna.20190501.16

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ACS Style

Ahmed Mohamed Ali Karrar; Jun Sun. Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. Am. J. Neural Netw. Appl. 2019, 5(1), 36-44. doi: 10.11648/j.ajnna.20190501.16

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AMA Style

Ahmed Mohamed Ali Karrar, Jun Sun. Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm. Am J Neural Netw Appl. 2019;5(1):36-44. doi: 10.11648/j.ajnna.20190501.16

Copy | Download

@article{10.11648/j.ajnna.20190501.16,
  author = {Ahmed Mohamed Ali Karrar and Jun Sun},
  title = {Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm},
  journal = {American Journal of Neural Networks and Applications},
  volume = {5},
  number = {1},
  pages = {36-44},
  doi = {10.11648/j.ajnna.20190501.16},
  url = {https://doi.org/10.11648/j.ajnna.20190501.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.16},
  abstract = {In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm
AU  - Ahmed Mohamed Ali Karrar
AU  - Jun Sun
Y1  - 2019/07/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ajnna.20190501.16
DO  - 10.11648/j.ajnna.20190501.16
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 36
EP  - 44
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20190501.16
AB  - In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.
VL  - 5
IS  - 1
ER  -

Copy | Download