Eltayeb Engy N, Salem Nancy M, Al-Atabany Walid
Department of Biomedical Engineering, Faculty of Engineering, Helwan University, Cairo, Egypt.
Biomed Mater Eng. 2019;30(4):449-462. doi: 10.3233/BME-191066.
Brain tumors are considered to be a leading cause of cancer death among young people. Early diagnosis is thus essential for treatment. The brain segmentation process is still challenging due to complexity and variation of the tumor structure, intensity similarity between tumor tissues and normal brain tissues. In this paper, a fully automated and reliable brain tumor segmentation system is proposed. This system is able to detect range of slices from a volume that is likely to contain tumor in MRI images. An iterated k-means algorithm is used for the segmentation process in conjunction with a cluster validity index to select the optimal number of clusters. The proposed approach is evaluated using simulated and real MRI of human brain from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge. Our results achieved average for Dice overlap and Jaccard index for complete tumor region of 91.96% and 98.31% respectively when testing a set of 77 volumes. This shows the robustness of the new technique for clinical routine use.
脑肿瘤被认为是年轻人癌症死亡的主要原因之一。因此，早期诊断对于治疗至关重要。由于肿瘤结构的复杂性和变异性、肿瘤组织与正常脑组织之间的强度相似性，脑部分割过程仍然具有挑战性。本文提出了一种全自动且可靠的脑肿瘤分割系统。该系统能够从可能包含MRI图像中肿瘤的体积中检测切片范围。迭代k均值算法与聚类有效性指标结合用于分割过程，以选择最佳聚类数。使用由2012年医学图像计算与计算机辅助干预国际会议（MICCAI）组织的多模态脑肿瘤图像分割基准（BRATS）中的人脑模拟和真实MRI对所提出的方法进行评估。在测试一组77个体积时，我们的结果在完整肿瘤区域的骰子重叠率和杰卡德指数方面分别达到了91.96%和98.31%的平均值。这表明了该新技术在临床常规使用中的稳健性。
