Paper Info
Title
A topology preserving non-rigid registration algorithm with integration shape knowledge to segment brain subcortical structures from MRI images
Abstract
A new non-rigid registration method combining image intensity and a priori shape knowledge of the objects in the image is proposed. This method, based on optical flow theory, uses a topology correction strategy to prevent topological changes of the deformed objects and the a priori shape knowledge to keep the object shapes during the deformation process. Advantages of the method over classical intensity based non-rigid registration are that it can improve the registration precision with the a priori knowledge and allows to segment objects at the same time, especially efficient in the case of segmenting adjacent objects having similar intensities. The proposed algorithm is applied to segment brain subcortical structures from 15 real brain MRI images and evaluated by comparing with ground truths. The obtained results show the efficiency and robustness of our method.
Year
DOI
Venue
2010
10.1016/j.patcog.2010.01.012
Pattern Recognition
Keywords
Field
DocType
segment brain subcortical structure,classical intensity,image intensity,shape knowledge,real brain,non-rigid registration,registration precision,proposed algorithm,non-rigid registration algorithm,new non-rigid registration method,mri image,shape registration,topology preservation,multi-objects segmentation,integration shape knowledge,ground truth,a priori knowledge,optical flow
Signal processing,Edge detection,A priori and a posteriori,Robustness (computer science),Artificial intelligence,Motion estimation,Computer vision,Topology,Pattern recognition,Brain mri,Segmentation,Algorithm,Optical flow,Mathematics
Journal
Volume
Issue
ISSN
43
7
Pattern Recognition
Citations 
PageRank 
References 
7
0.47
32
Authors
4
Name
Order
Citations
PageRank
Xiangbo Lin171.49
Tianshuang Qiu231343.84
Frederic Morain-Nicolier3134.43
Ruan Su455953.00