Abstract Nuclear magnetic resonance images have been used for detecting the movement of water molecules in living organisms and moreover exploiting the neural fibers distribution, which is of great significance for the brain disease analysis. However, due to the visual clutter of dense fiber tracts, it is difficult for medical researchers to understand the water molecule diffusion in whole-brain scale and to find the meaningful substructure of neurological pathways. To address the challenges, we provide one fiber visualization workflow that combines fiber tracts selection and fiber clustering approaches with the advanced visualization technique. Local and global fiber selection methods are provided for users to extract fiber tracts with the higher strength of water molecule diffusivity and gain an overall perception of water molecule movement in whole-brain scale. To explore the substructure of brain fibers, we employ an anatomically meaningful similarity matrix combining with density peaks clustering algorithm and compare it with DBSCAN algorithm. The qualitative and quantitative experimental results show that the fiber visualization system helps to confirm the fiber distribution more accurately and efficiently. Graphic abstract .

中文翻译：

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脑纤维束的视觉交互探索与聚类

摘要 核磁共振图像已被用于检测生物体内水分子的运动，并利用神经纤维分布，这对脑疾病分析具有重要意义。然而，由于密集纤维束的视觉混乱，医学研究人员很难理解全脑尺度的水分子扩散并找到有意义的神经通路子结构。为了应对这些挑战，我们提供了一种纤维可视化工作流程，它将纤维束选择和纤维聚类方法与先进的可视化技术相结合。为用户提供局部和全局纤维选择方法，以提取具有较高水分子扩散强度的纤维束，并获得全脑尺度的水分子运动的整体感知。为了探索脑纤维的子结构，我们采用解剖学上有意义的相似矩阵结合密度峰值聚类算法并将其与 DBSCAN 算法进行比较。定性和定量实验结果表明，纤维可视化系统有助于更准确、高效地确认纤维分布。图形摘要。定性和定量实验结果表明，纤维可视化系统有助于更准确、高效地确认纤维分布。图形摘要。定性和定量实验结果表明，纤维可视化系统有助于更准确、更高效地确认纤维分布。图形摘要。