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Enhanced Region Growing for Brain Tumor MR Image Segmentation

Authors :
Erena Siyoum Biratu
Friedhelm Schwenker
Taye Girma Debelee
Samuel Rahimeto Kebede
Worku Gachena Negera
Hasset Tamirat Molla
Source :
Journal of Imaging, Vol 7, Iss 2, p 22 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
2313433X
Volume :
7
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.f9b931869c654ef1b407b1465a12d673
Document Type :
article
Full Text :
https://doi.org/10.3390/jimaging7020022