Enhanced Region Growing for Brain Tumor MR Image Segmentation.
Enhanced Region Growing for Brain Tumor MR Image Segmentation.
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DOI：
10.3390/jimaging7020022
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发表时间：
2021-02-01
影响因子：
3.2
通讯作者：
Molla HT
中科院分区：
文献类型：
--
作者：
Biratu ES;Schwenker F;Debelee TG;Kebede SR;Negera WG;Molla HT
脑瘤是儿童和成人死亡率上升的主要原因之一。脑肿瘤是一团组织，它的繁殖不受大脑内部正常生长调节力量的控制。当一种类型的细胞改变其正常特征并异常生长和繁殖时，就会出现脑瘤。大脑或颅骨内细胞的异常生长，可能是癌变的，也可能是非癌变的，这是发达国家成年人和埃塞俄比亚等欠发展国家儿童死亡的原因。研究表明，区域生长算法对种子点进行人工或半人工初始化，影响分割结果。然而，在本文中，我们提出了一种增强的区域生长算法来自动初始化种子点。使用通用数据集BRATS2015，将所提出方法的性能与最先进的深度学习算法进行了比较。在提出的方法中，我们应用阈值分割技术从每个输入的大脑图像中剥离颅骨。颅骨剥离后，大脑图像被分成8块。然后，对于每个块，我们计算平均强度，并从中从八个块中选择具有最大平均强度的五个块。接下来，将5个最大平均强度分别作为区域生长算法的种子点，得到每个颅骨剥离输入脑图像的5个不同的感兴趣区域（roi）。使用该方法生成的五个ROI使用骰子相似度评分（DSS），交集超过联合（IoU）和准确性（Acc）对地面真相（GT）进行评估，并选择最佳兴趣区域作为最终ROI。最后，将最终ROI与不同最先进的深度学习算法和基于区域的分割算法在DSS方面进行了比较。我们提出的方法在三个不同的实验设置中得到了验证。在第一个实验设置中，随机选择15张脑图像进行测试，DSS值为0.89。在第二和第三个实验设置中，该方法对随机选择的12张和800张脑图像的DSS值分别为0.90和0.80。三个实验装置的平均DSS值为0.86。
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.
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影响因子：
6.9
作者：
Debelee, Taye Girma;Schwenker, Friedhelm;Yohannes, Dereje
通讯作者：
Yohannes, Dereje
TumorGAN：用于脑肿瘤分割的多模态数据增强框架
影响因子：
3.9
作者：
Li, Qingyun;Yu, Zhibin;Zheng, Haiyong
通讯作者：
Zheng, Haiyong
影响因子：
3.2
作者：
Debelee TG;Kebede SR;Schwenker F;Shewarega ZM
通讯作者：
Shewarega ZM
影响因子：
2.8
作者：
Mortazavi, Daryoush;Kouzani, Abbas Z.;Soltanian-Zadeh, Hamid
通讯作者：
Soltanian-Zadeh, Hamid
DOI：
10.4028/www.scientific.net/jera.51.131
发表时间：
2020-01-01
影响因子：
0.7
作者：
Afework, Yidnekachew Kibru;Debelee, Taye Girma
通讯作者：
Debelee, Taye Girma