HARDI Model Oriented 3D Visualization System of Brain Fiber

摘要

? 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved. ? 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved. Brain fibers are important channels for information exchange among different brain regions. High angular resolution diffusion imaging (HARDI) is an effective method to explore complex nerve fiber pathways in human brain, which can be used for the diagnosis of diverse neurological diseases. However, the large number of brain fibers is intricately distributed, how to display their local area directly is a challenge issue in the visualization field. In this paper, we design a 3D visualization system for brain fibers, the system can achieve the tracking and processing for HARDI brain fiber data, and visualize brain fibers in 3D space. The system implements the illumination technology to enhance the visualization of spatial layers of brain fibers. We also propose the fiber selection method based on scatter matrix in order to reduce the visual clutter. Two clustering algorithms are exploited to show the local region of brain fibers, which provides an auxiliary analysis tool for clinical diagnosis. Brain fibers are important channels for information exchange among different brain regions. High angular resolution diffusion imaging (HARDI) is an effective method to explore complex nerve fiber pathways in human brain, which can be used for the diagnosis of diverse neurological diseases. However, the large number of brain fibers is intricately distributed, how to display their local area directly is a challenge issue in the visualization field. In this paper, we design a 3D visualization system for brain fibers, the system can achieve the tracking and processing for HARDI brain fiber data, and visualize brain fibers in 3D space. The system implements the illumination technology to enhance the visualization of spatial layers of brain fibers. We also propose the fiber selection method based on scatter matrix in order to reduce the visual clutter. Two clustering algorithms are exploited to show the local region of brain fibers, which provides an auxiliary analysis tool for clinical diagnosis.