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.

中文翻译：

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从多层FLAIR MRI图像中自动进行脑肿瘤分割。

脑肿瘤被认为是年轻人中癌症死亡的主要原因。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，脑分割过程仍然具有挑战性。本文提出了一种全自动可靠的脑肿瘤分割系统。该系统能够从MRI图像中可能包含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一起用于分割过程，以选择最佳数目的聚类。根据MICCAI 2012挑战组织的多模态脑肿瘤图像分割基准（BRATS），使用模拟的和真实的人脑MRI对提出的方法进行了评估。当测试一组77个体积时，我们的结果获得了完整肿瘤区域的Dice重叠和Jaccard指数的平均值分别为91.96％和98.31％。这显示了新技术在临床常规使用中的鲁棒性。