BACKGROUND:The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE:To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS:In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS:Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS:When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

中文翻译：

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基于张量模糊聚类算法的胼胝体分割

背景：中矢状面的胼胝体在疾病的早期诊断中起着至关重要的作用。在计算正中矢状面扩散张量的各向异性时，胼胝体的各向异性接近于穹窿的各向异性，导致分割区域的边界模糊。目的：应用模糊聚类算法结合新的空间信息实现扩散张量图像中矢状面胼胝体的准确分割。方. 然后，将基于K-means聚类的迭代聚类中心作为张量模糊聚类算法的初始聚类中心。以滤波后的扩散张量为输入数据，不同的度量作为相似度度量，在隶属函数计算中引入Log Euclidean框架的邻域扩散张量像素计算方法，提出张量模糊聚类算法。在这项研究中，对来自人类连接组计划 (HCP) 的 MGH35 数据进行了测试，并讨论了实验结果的方差、准确性和特异性。结果：报告了MGH35数据中三组受试者的分割结果。平均分割准确率为97.34%，平均特异性为98.43%。结论：弥散张量成像分割胼胝体时，