Title
Brain structures segmentation using optimum global and local weights on mixing active contours and neighboring constraints
Abstract
This paper presents a new method for segmenting multiple brain structures by using an optimized mixture of different Active Contour Models (ACMs). Prior constraints and structures' neighboring interaction are modelled for each structure. Prior information is also captured by a training process, in which structure's dependent local and global weights are calculated. The local weights regulate locally the combination of each term during the evolution, acting as an experienced balancer between image and prior information. The ideal proportion of relation between the mixture of different ACMs and the prior model is defined by the optimum global weights. As proof of concept, the method is applied on the very challenging task of segmenting hippocampus and amygdala structures.
Year
DOI
Venue
2011
10.1145/2093698.2093825
ISABEL
Keywords
Field
DocType
multiple brain structure,prior constraint,neighboring constraint,prior model,global weight,active contour,different acms,amygdala structure,different active contour models,local weight,new method,prior information,brain structures segmentation,proof of concept,active contour model,medical imaging
Active contour model,Computer vision,Pattern recognition,Segmentation,Computer science,Medical imaging,Proof of concept,Artificial intelligence
Conference
Citations 
PageRank 
References 
2
0.35
10
Authors
5
Name
Order
Citations
PageRank
Dimitrios Zarpalas130333.96
Anastasios Zafeiropoulos210621.90
Petros Daras31129131.72
Nicos Maglaveras430654.35
Michael Gerasimos Strintzis51171104.83