English
讲座编号：jz-yjsb-2015-102
讲座问题：Visualization of Human Brain Connectome（人脑毗连组的可视化）
主 讲 人：Shiaofen Fang 美国印第安纳大学-普渡大学印第安纳波利斯团结分校盘算机与信息科学系主任
讲座时间：2015年12月10日（周四）下昼14:00
讲座所在：阜成路东校区耕作楼809聚会室
加入工具：盘算机与信息工程学院全体师生
主理单位：盘算机与信息工程学院
主讲人简介：
Dr. Shiaofen Fang is a Professor of Computer Science and the Chairman of the Department of Computer and Information Science at Indiana University Purdue University Indianapolis (IUPUI). Prof. Fang received his Ph.D in Computer Science from the University of Utah and his BS and MS in Mathematics from Zhejiang University. Prof. Fang’s research interest is in Scientific and Information Visualization, Medical Imaging, Volume Graphics, and Geometric Modeling. He has published extensively in these fields. His research has been funded by the National Science Foundation (NSF), Nation Institutes of Health (NIH), National Institute of Justice (NIJ) and US Department of Defense (DoD). He is a regular panelists and reviewers for NSF and NIH, and has chaired or served in program committees in many international conferences and workshops.
主讲内容：
Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. In this talk I will present some of our recent work on integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of brain anatomic structures. Several techniques will be discussed including (1) a multi-modal neuroimaging visualization tool, (2) a new surface texture technique for attribute mapping and disease features detection, (3) a new spherical volume rendering technique for generating interactive brain maps, and (4) a multi-graph technique for feature detection. This integrated visualization solution can help neuroscientists identify correlated brain regions, their activity patterns, and disease related brain connection features and imaging phenotype biomarkers.
