DocumentCode :
1526753
Title :
Region tracking on level-sets methods
Author :
Bertalmio, Marcelo ; Sapiro, Guillermo ; Randall, Gregory
Author_Institution :
Dept. of Electr. & Comput. Eng., Minnesota Univ., Minneapolis, MN, USA
Volume :
18
Issue :
5
fYear :
1999
fDate :
5/1/1999 12:00:00 AM
Firstpage :
448
Lastpage :
451
Abstract :
Since the work by Osher and Sethian (1988) on level-sets algorithms for numerical shape evolutions, this technique has been used for a large number of applications in numerous fields. In medical imaging, this numerical technique has been successfully used, for example, in segmentation and cortex unfolding algorithms. The migration from a Lagrangian implementation to a Eulerian one via implicit representations or level-sets brought some of the main advantages of the technique, i.e., topology independence and stability. This migration means also that the evolution is parametrization free. Therefore, the authors do not know exactly how each part of the shape is deforming and the point-wise correspondence is lost. In this note they present a technique to numerically track regions on surfaces that are being deformed using the level-sets method. The basic idea is to represent the region of interest as the intersection of two implicit surfaces and then track its deformation from the deformation of these surfaces. This technique then solves one of the main shortcomings of the very useful level-sets approach. Applications include lesion localization in medical images, region tracking in functional MRI (fMRI) visualization, and geometric surface mapping.
Keywords :
biomedical MRI; image segmentation; medical image processing; Eulerian implementation; Lagrangian implementation; cortex unfolding algorithms; functional MRI visualization; geometric surface mapping; implicit surfaces intersection; level-sets method; level-sets methods; numerical shape evolutions; numerical technique; parametrization free evolution; point-wise correspondence; region tracking; segmentation algorithms; stability; surfaces deformation; topology independence; Biomedical imaging; Engineering profession; Image motion analysis; Image segmentation; Lagrangian functions; Level set; Optical computing; Shape; Topology; Visualization; Algorithms; Brain Diseases; Cerebral Cortex; Humans; Magnetic Resonance Imaging;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/42.774172
Filename :
774172
Link To Document :
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