Segmentation of magnetic resonance images (MRIs) is challenging due to the poor image contrast and artifacts that result in missing tissue boundaries, i.e. pixels inside the region and on the boundaries have similar intensity. In this paper, we adapt a region growing method to segment MRIs which contain weak boundaries between different tissues. The proposed region growing algorithm is developed to learn its homogeneity criterion automatically from characteristics of the region to be segmented. An automatic homogeneity criterion based on estimating probability of pixel intensities of a given image is described. The homogeneity criterions as well as the probability are calculated for each pixel. The proposed algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.
| Published in | American Journal of Remote Sensing (Volume 1, Issue 2) |
| DOI | 10.11648/j.ajrs.20130102.16 |
| Page(s) | 53-60 |
| 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), 2013. Published by Science Publishing Group |
Mris, Image Segmentation, Region Growing, Probability
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APA Style
E. A. Zanaty. (2013). Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation. American Journal of Remote Sensing, 1(2), 53-60. https://doi.org/10.11648/j.ajrs.20130102.16
ACS Style
E. A. Zanaty. Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation. Am. J. Remote Sens. 2013, 1(2), 53-60. doi: 10.11648/j.ajrs.20130102.16
AMA Style
E. A. Zanaty. Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation. Am J Remote Sens. 2013;1(2):53-60. doi: 10.11648/j.ajrs.20130102.16
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Download@article{10.11648/j.ajrs.20130102.16,
author = {E. A. Zanaty},
title = {Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation},
journal = {American Journal of Remote Sensing},
volume = {1},
number = {2},
pages = {53-60},
doi = {10.11648/j.ajrs.20130102.16},
url = {https://doi.org/10.11648/j.ajrs.20130102.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20130102.16},
abstract = {Segmentation of magnetic resonance images (MRIs) is challenging due to the poor image contrast and artifacts that result in missing tissue boundaries, i.e. pixels inside the region and on the boundaries have similar intensity. In this paper, we adapt a region growing method to segment MRIs which contain weak boundaries between different tissues. The proposed region growing algorithm is developed to learn its homogeneity criterion automatically from characteristics of the region to be segmented. An automatic homogeneity criterion based on estimating probability of pixel intensities of a given image is described. The homogeneity criterions as well as the probability are calculated for each pixel. The proposed algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results.},
year = {2013}
}
TY - JOUR T1 - Improved Region Growing Method for Magnetic Resonance Images (MRIs) Segmentation AU - E. A. Zanaty Y1 - 2013/05/30 PY - 2013 N1 - https://doi.org/10.11648/j.ajrs.20130102.16 DO - 10.11648/j.ajrs.20130102.16 T2 - American Journal of Remote Sensing JF - American Journal of Remote Sensing JO - American Journal of Remote Sensing SP - 53 EP - 60 PB - Science Publishing Group SN - 2328-580X UR - https://doi.org/10.11648/j.ajrs.20130102.16 AB - Segmentation of magnetic resonance images (MRIs) is challenging due to the poor image contrast and artifacts that result in missing tissue boundaries, i.e. pixels inside the region and on the boundaries have similar intensity. In this paper, we adapt a region growing method to segment MRIs which contain weak boundaries between different tissues. The proposed region growing algorithm is developed to learn its homogeneity criterion automatically from characteristics of the region to be segmented. An automatic homogeneity criterion based on estimating probability of pixel intensities of a given image is described. The homogeneity criterions as well as the probability are calculated for each pixel. The proposed algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets. The experimental results show that the proposed technique produces accurate and stable results. VL - 1 IS - 2 ER -
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