Title :
Optimization of parameter for extracting brain regions using deformable contour models
Author :
Odagiri, Seiya ; Sato, Kazuhito ; Madokoro, Hirokazu
Author_Institution :
Grad. Sch., Fac. of Syst. Sci. & Technol., Akita Prefectural Univ., Yurihonjo, Japan
Abstract :
This paper presents a method that considers sharing of parameters of Level Set Methods and optimizes method using Genetic Algorithm. We optimized parameters of LSMs that is deformable model using GA of evolutional learning. However, the proposal method needs Ground Truth for each clinical image. Therefore, application with clinical images without GT was challenging task. In this paper, we focus on shaering of parameters and try application of the proposal method for clinical images without GT for an individualization trade-off problem. Moreover, the extraction accuracy performed the optimal value search for iterations of updating to the low case using Active Appearance Models after sharing.
Keywords :
biomedical MRI; computational geometry; feature extraction; genetic algorithms; learning (artificial intelligence); medical image processing; parameter estimation; active appearance models; brain region extraction; clinical images; deformable contour models; deformable model; evolutionary learning; genetic algorithm; ground truth; individualization trade off problem; level set methods; optimized LSM parameters; parameter optimization; parameter sharing; Accuracy; Atrophy; Deformable models; Genetic algorithms; Head; Optimization; Shape; Active Appearance Models; Genetic Algorithm; Level Set Methods;
Conference_Titel :
SICE Annual Conference (SICE), 2012 Proceedings of
Conference_Location :
Akita
Print_ISBN :
978-1-4673-2259-1