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Abstract #5104

Enhancing Subcortical Image Segmentation Based on Age Dependent Intensity Normalization

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.