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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Linda G. Shapiro and George C. Stockman (2001):“Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3.
[2]	Barghout, Lauren, and Lawrence W. Lee. "Perceptual information processing system." Paravue Inc. U.S. Patent Application 10/618, 543, filed July 11, 2003.
[3]	M. R. Khokher, A. Ghafoor and A. M. Siddiqui, “Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system”, IET image processing, 2012.
[4]	V. Dey, Y. Zhang and M. Zhong, “a review on image segmentation techniques with Remote sensing perspective”, ISPRS, Vienna, Austria, Vol. XXXVIII, July 2010.
[5]	F. C. Monteiro and A. Campilho, "Watershed framework to region-based image segmentation," in Proc. International Conference on Pattern Recognition, ICPR 19th, pp. 1-4, 2008.
[6]	M. Hameed, M. Sharif, M. Raza, S. W. Haider, and M. Iqbal, "Framework for the comparison of classifiers for medical image segmentation with transform and moment based features," Research Journal of Recent Sciences, vol. 2277, p. 2502, 2012.
[7]	R. Patil and K. Jondhale, "Edge based technique to estimate number of clusters in k-means color image segmentation," in Proc. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 117-121, 2010.
[8]	W. Cui and Y. Zhang, "Graph based multispectral high resolution image segmentation," in Proc. International Conference on Multimedia Technology (ICMT), pp. 1-5, 2010.
[9]	A. Fabijanska, "Variance filter for edge detection and edge-based image segmentation," in Proc. International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 151-154, 2011.
[10]	M. J. Islam, S. Basalamah, M. Ahmadi, and M. A. S. hmed, "Capsule image segmentation in pharmaceutical applications using edge-based techniques," IEEE International Conference on Electro/Information Technology (EIT), pp. 1-5, 2011.
[11]	L. Yucheng and L. Yubin, "An algorithm of image segmentation based on fuzzy mathematical morphology," in International Forum on Information Technology and Applications, IFITA'09, pp. 517-520, 2009.
[12]	W. Haider, M. Sharif, and M. Raza, "Achieving accuracy in early stage tumor identification systems based on image segmentation and 3D structure analysis," Computer Engineering and Intelligent Systems, vol. 2, pp. 96-102, 2011.
[13]	M. S. A. Shahzad, M. Raza, and K. Hussain, "Enhanced watershed image processing segmentation," Journal of Information & Communication Technology, vol. 2, pp. 1-9, 2008.
[14]	S. Kobashi and J. K. Udupa, "Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images," in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1422-1427, 2012.
[15]	R. Samet, S. E. Amrahov, and A. H. Ziroglu, "Fuzzy rule-based image segmentation technique for rock thin section images," in Proc. 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 402-406, 2012.
[16]	M. R. Khokher, A. Ghafoor, and A. M. Siddiqui,"Image segmentation using fuzzy rule based system and graph cuts," in Proc. 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1148-1153, 2012.
[17]	M. Sharif, S. Mohsin, M. J. Jamal, and M. Raza, "Illumination normalization preprocessing for face recognition," in Proc. International Conference on Environmental Science and Information Application Technology (ESIAT), pp. 44-47, 2010.
[18]	J. Xiao, B. Yi, L. Xu, and H. Xie, "An image segmentation algorithm based on level set using discontinue PDE," in Proc. First International Conference on Intelligent Networks and Intelligent Systems, ICINIS'08., pp. 503-506, 2008.
[19]	F. Zhang, S. Guo, and X. Qian, "Segmentation for finger vein image based on PDEs denoising," in Proc. 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 531-535, 2010.
[20]	C. Yuan and S. Liang, "Segmentation of color image based on partial differential equations," in Proc. Fourth International Symposium on Computational Intelligence and Design (ISCID), pp. 238-240, 2011.
[21]	W. Zhao, J. Zhang, P. Li, and Y. Li, "Study of image segmentation algorithm based on textural features and neural network," in International Conferenceon Intelligent Computing and Cognitive Informatics (ICICCI), pp. 300-303, 2010.
[22]	M. Sharif, M. Y. Javed, and S. Mohsin, "Face recognition based on facial features," Research Journal of Applied Sciences, Engineering and Technology, vol. 4, pp. 2879-2886, 2012.
[23]	M. Yasmin, M. Sharif, and S. Mohsin, "Neural networks in medical imaging applications: A survey," World Applied Sciences Journal, vol. 22, pp. 85-96, 2013.
[24]	L. Zhang and X. Deng, "The research of image segmentation based on improved neural network algorithm," in Proc. Sixth International Conference on Semantics Knowledge and Grid (SKG), pp. 395-397, 2010.
[25]	S. A. Ahmed, S. Dey, and K. K. Sarma, "Image texture classification using Artificial Neural Network (ANN)," in Proc. 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS), pp. 1-4, 2011.
[26]	M. Sharif, M. Raza, S. Mohsin, and J. H. Shah, "Microscopic feature extraction method," Int. J. Advanced Networking and Applications, vol. 4, pp. 1700-1703, 2013.
[27]	I. Irum, M. Raza, and M. Sharif, "Morphological techniques for medical images: A review," Research Journal of Applied Sciences, vol. 4, 2012.
[28]	S. Zhu, X. Xia, Q. Zhang, and K. Belloulata, "An image segmentation Proc. Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, SITIS'0., pp. 673-678, 2007.
[29]	A. Xu, L. Wang, S. Feng, and Y. Qu, "Threshold-based level set method of image segmentation," in Proc. 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 703-706, 2010.
[30]	M. Yasmin, M. Sharif, S. Masood, M. Raza, and S. Mohsin, "Brain image enhancement-A survey," World Applied Sciences Journal, vol. 17, pp. 1192-1204, 2012.
[31]	W. Kaihua and B. Tao, "Optimal threshold image segmentation method based on genetic algorithm in wheel set online measurement," in Proc. Third International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 799-802, 2011.
[32]	F. Jiang, M. R. Frater, and M. Pickering, "Threshold-based image segmentation through an improved particle swarm optimisation," in Proc. International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1-5, 2012.
[33]	D. Barbosa, T. Dietenbeck, J. Schaerer, J. D'hooge, D. Friboulet, and O. Bernard, "B-spline explicit active surfaces: An efficient framework for real-time 3-D region-based segmentation," IEEE Transactions on Image Processing, vol. 21, pp. 241-251, 2012.
[34]	A. N. Aimi Salihah, M. Y. Mashor, N. H. Harun and H. Rosline, Colour Image Enhancement Technique for Acute Leukaemia Blood Cell Morphological Feature, In IEEE International Conference on System, Man and Cybernatic, pp. 3677–3682, (2010).
[35]	Moreno et al. 2013 Towards no-reference of peak signal to noise ratio estimation based on chromatic induction model International Journal of Advanced Computer Science and Applications.
[36]	Wang, Zhou, Bovik and Alan C 2006 Modern image quality assessment (USA: Morgan & Claypool).

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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