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Enhanced Region Growing for Brain Tumor MR Image Segmentation
A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal character...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321280/ https://www.ncbi.nlm.nih.gov/pubmed/34460621 http://dx.doi.org/10.3390/jimaging7020022 |
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author | Biratu, Erena Siyoum Schwenker, Friedhelm Debelee, Taye Girma Kebede, Samuel Rahimeto Negera, Worku Gachena Molla, Hasset Tamirat |
author_facet | Biratu, Erena Siyoum Schwenker, Friedhelm Debelee, Taye Girma Kebede, Samuel Rahimeto Negera, Worku Gachena Molla, Hasset Tamirat |
author_sort | Biratu, Erena Siyoum |
collection | PubMed |
description | A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86. |
format | Online Article Text |
id | pubmed-8321280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83212802021-08-26 Enhanced Region Growing for Brain Tumor MR Image Segmentation Biratu, Erena Siyoum Schwenker, Friedhelm Debelee, Taye Girma Kebede, Samuel Rahimeto Negera, Worku Gachena Molla, Hasset Tamirat J Imaging Article A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86. MDPI 2021-02-01 /pmc/articles/PMC8321280/ /pubmed/34460621 http://dx.doi.org/10.3390/jimaging7020022 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Biratu, Erena Siyoum Schwenker, Friedhelm Debelee, Taye Girma Kebede, Samuel Rahimeto Negera, Worku Gachena Molla, Hasset Tamirat Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title | Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title_full | Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title_fullStr | Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title_full_unstemmed | Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title_short | Enhanced Region Growing for Brain Tumor MR Image Segmentation |
title_sort | enhanced region growing for brain tumor mr image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321280/ https://www.ncbi.nlm.nih.gov/pubmed/34460621 http://dx.doi.org/10.3390/jimaging7020022 |
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