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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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
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.
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.
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.
D. L. Pham, C. Xu, and J. L. Prince, “Current methods in medical image segmentation” Annu. Rev. Biomed. Eng., vol. 2, pp. 315–338, 2000.
Daw-Tung Lin, "Computer-Aided Kidney Segmentation on Abdominal CT Images", IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 10, NO. 1, 2006.
Moe Myint, Theingi Myint, "Effective Kidney Segmentation Using Gradient Based Approach in Abdominal CT Images", International Conference on Future Computational Technologies, ISBN 978-93-84468-20-0, pp. 130-135, 2015.
A. Reeves and W. Kostis, “Computer-aided diagnosis for lung cancer” Radiologic Clinics of North America, vol. 38, no. 3, pp. 497–509, 2000.
E. L. Chen, P. C. Chung, C. L. Chen, H. M. Tsai, and C. I. Chang, “An automatic diagnostic system for CT liver image classification,” IEEE Trans. Biomed. Eng., vol. 45, no. 6, pp. 783–794, 1998.
Zaid Kraitem and Dr. Mariam Saii, "Automatic Detection and Measurement of Abdominal Circumference in Fetal Ultrasound Images" International Journal of Information Research and Review. Vol. 03, Issue, 03, pp. 1997-2000 March, 2016.
D. Trevathan-Ramirez, “Innovations in breast disease diagnosis,” Radiology Technol., vol. 70, no. 2, pp. 197–203, 1998.
D. T. Lin, C. C. Lei and S. W. Hung, “Computer-aided kidney segmentation on abdominal CT images,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, January 2006.
Nir Ailon, Ragesh Jaiswal, and Claire Monteleoni. "Streaming k-means approximation". In NIPS, pages 10-18, 2009.
Stuart P. Lloyd. "Least squares quantization in pcm". IEEE Transactions on Information Theory, Vol. 28, No. 2, PP: 129-137, 1982.
Chris H. Q. Ding and Xiaofeng He. "K-means clustering via principal component analysis". In ICML, 2004.
Hongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming Gu, and Horst D. Simon. "Spectral relaxation for k-means clustering". In NIPS, pages 1057-1064, 2001.
Dr. Mariam Saii, Dr. Issa Ibraheem, and Zaid Kraitem. "Auto Measurement and Segmentation of Head Region in Fetal Ultrasound Images". Tishreen University Journal, Syria. ISSN: 2079-3081, Vol. 38, No. 3, 2016.
J. S. Hong, T. Kaneko, S. Sekiguchi, and K. H. Park, “Automatic liver tumor detection from CT,” IEICE Trans. Inf. Syst., vol. E84-D, pp. 741–748, 2001.