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.
| Published in | Biomedical Statistics and Informatics (Volume 2, Issue 1) |
| DOI | 10.11648/j.bsi.20170201.15 |
| Page(s) | 22-26 |
| 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), 2017. Published by Science Publishing Group |
Automatic Kidney Segmentation, K Mean Clustering, Magnetic Resonance Images MRI
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APA Style
Mariam Saii, Zaid Kraitem. (2017). Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques. Biomedical Statistics and Informatics, 2(1), 22-26. https://doi.org/10.11648/j.bsi.20170201.15
ACS Style
Mariam Saii; Zaid Kraitem. Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques. Biomed. Stat. Inform. 2017, 2(1), 22-26. doi: 10.11648/j.bsi.20170201.15
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Download@article{10.11648/j.bsi.20170201.15,
author = {Mariam Saii and Zaid Kraitem},
title = {Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques},
journal = {Biomedical Statistics and Informatics},
volume = {2},
number = {1},
pages = {22-26},
doi = {10.11648/j.bsi.20170201.15},
url = {https://doi.org/10.11648/j.bsi.20170201.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170201.15},
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.},
year = {2017}
}
TY - JOUR T1 - Automatic Detection and Segmentation of Kidneys in Magnetic Resonance Images Using Image Processing Techniques AU - Mariam Saii AU - Zaid Kraitem Y1 - 2017/02/04 PY - 2017 N1 - https://doi.org/10.11648/j.bsi.20170201.15 DO - 10.11648/j.bsi.20170201.15 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 22 EP - 26 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20170201.15 AB - 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. VL - 2 IS - 1 ER -
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