Volume 5, Issue 1, June 2019, Page: 36-44
Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm
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
Received: Apr. 16, 2019;       Accepted: Jun. 27, 2019;       Published: Jul. 16, 2019
DOI: 10.11648/j.ajnna.20190501.16      View  759      Downloads  158
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.
Image Segmentation, K-means Clustering, Partial Contrast Stretching, Gaussian Mixture Models
To cite this article
Ahmed Mohamed Ali Karrar, Jun Sun, Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm, American Journal of Neural Networks and Applications. Vol. 5, No. 1, 2019, pp. 36-44. doi: 10.11648/j.ajnna.20190501.16
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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