Enhancing Subcortical Image Segmentation Based on Age Dependent Intensity Normalization

Mustafa Ulas Ciftcioglu¹, Didem Gokcay¹

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.