Brain structures segmentation using optimum global and local weights on mixing active contours and neighboring constraints

Authors:
Dimitrios Zarpalas;Anastasios Zafeiropoulos;Petros Daras;Nicos Maglaveras;Michael G. Strintzis
Affiliations:
Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece, and Aristotle University of Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece;Aristotle University of Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece
Venue:
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Year:
2011

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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.