图 书 馆
扩散张量成像的信息理论算法Information-Theoretic Algorithms for Diffusion Tensor Imaging |
|
| 课程网址: | http://videolectures.net/etvc08_vemuri_itafd/ |
| 主讲教师: | Baba C. Vemuri |
| 开课单位: | 佛罗里达大学 |
| 开课时间: | 2008-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data. This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed. |
| 关 键 词: | 图像处理; 最小描述长度; 加权磁共振 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-16：杨雨(课程编辑志愿者) |
| 阅读次数: | 63 |