Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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基于蜻蜓算法的水平集分割修正版3D-MRI脑肿瘤检测模型

从 3D 磁共振成像 (3D-MRI) 中准确分割脑肿瘤是获取诊断和疾病治疗计划所需信息的重要方法。脑肿瘤的大小、结构和形式的变化是肿瘤分割的主要挑战之一，选择初始轮廓对减少水平集方法中的分割误差和迭代次数起着重要作用。为了克服这个问题，本文提出了一种两步蜻蜓算法（DA）聚类技术来准确提取初始轮廓点。在预处理步骤中从头部提取大脑，然后使用两步 DA 提取肿瘤边缘，并将这些提取的边缘用作 MRI 序列的初始轮廓。最后，使用水平集分割方法从所有体积切片中提取肿瘤区域。将所提出的技术应用于来自多模态脑肿瘤分割挑战 (BRATS) 2017 数据集的 3D-MRI 图像的结果表明，所提出的脑肿瘤分割方法与最先进的方法相当。