Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques
Biomedical Statistics and Informatics
Volume 2, Issue 1, March 2017, Pages: 22-26
Received: Dec. 27, 2016; Accepted: Jan. 12, 2017; Published: Feb. 4, 2017
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Authors
Mariam Saii, Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria
Zaid Kraitem, Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria
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Abstract
Kidney Detection and Segmentation in MR images allows extracting meaningful information for nephrologists, also for practical use in clinical routine, thus we should apply an fast, automatic and robust algorithm. We demonstrate the possibility of construct an algorithm that achieve these requirements. Therefore, a novel kidney segmentation algorithm was created depending on multiple stages. The Region of Interest (ROI) is extracted after we convert the input image to binary one via specific thresholding level yields from K Mean Clustering algorithm. The resulted binary image contain both of kidneys as the biggest regions, so we can isolate them after we calculate the objects areas in labeled image. Finally we can use some morphological operation to remove small objects surrounding the kidney region. The effectiveness of this method is demonstrated through experimental results on complex MR slices. Kidneys were accurately detected and segmented in a few seconds.
Keywords
Automatic Kidney Segmentation, K Mean Clustering, Magnetic Resonance Images MRI
To cite this article
Mariam Saii, Zaid Kraitem, Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques, Biomedical Statistics and Informatics. Vol. 2, No. 1, 2017, pp. 22-26. doi: 10.11648/j.bsi.20170201.15
Copyright
Copyright © 2017 Authors retain the copyright of this article.
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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