Paper Info
Title
Parkinson's disease prediction using diffusion-based atlas approach
Abstract
We study Parkinson's disease (PD) using an automatic specialized diffusion-based atlas. A total of 47 subjects, among who 22 patients diagnosed clinically with PD and 25 control cases, underwent DTI imaging. The EPIs have lower resolution but provide essential anisotropy information for the fiber tracking process. The two volumes of interest (VOI) represented by the Substantia Nigra and the Putamen are detected on the EPI and FA respectively. We use the VOIs for the geometry-based registration. We fuse the anatomical detail detected on FA image for the putamen volume with the EPI. After 3D fibers growing on the two volumes, we compute the fiber density (FD) and the fiber volume (FV). Furthermore, we compare patients based on the extracted fibers and evaluate them according to Hohen&Yahr (H&Y) scale. This paper introduces the method used for automatic volume detection and evaluates the fiber growing method on these volumes. Our approach is important from the clinical standpoint, providing a new tool for the neurologists to evaluate and predict PD evolution. From the technical point of view, the fusion approach deals with the tensor based information (EPI) and the extraction of the anatomical detail (FA and EPI).
Year
DOI
Venue
2010
10.1117/12.844068
Proceedings of SPIE
Keywords
Field
DocType
Automatic ROI/VOI detection,Medical Image Analysis,Medical Image Processing,PD Detection,Prediction
Computer vision,Putamen,Parkinson's disease,Image processing,Atlas (anatomy),Artificial intelligence,Physics
Conference
Volume
ISSN
Citations 
7624
0277-786X
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Roxana Teodorescu1203.01
Daniel Racoceanu219824.30
nicolas smit300.34
Vladimir Cretu4226.32
Eng-King Tan521.05
l l chan601.35