Mustafa Ulas Ciftcioglu1, Didem Gokcay1
1Medical Informatics Department,
Informatics Institute, Middle East Technical University, Ankara, Turkey
Automatic
algorithms for subcortical segmentation often suffer due to the complex
anatomic structure of this area and intersubject variability. To overcome
this problem, a method that incorporates age dependent tissue volume
statistics with atlas based intensity normalization is proposed. Age
dependent regression equations for volumetric ratios of the tissues are
constructed and included in a segmentation performed by Maximum Likelihood
(ML) approach. For intensity normalization, the intensity distribution from a
single subject atlas is utilized, after registering the given image with the
atlas image. Improvement on the proposed method is documented by comparison
with a widely accepted segmentation tool.