Cerebellum segmentation employing texture properties and knowledge based image processing: applied to normal adult controls and patients.

Abstract

A semi-automated method is described for segmenting the cerebellum from T(1)-weighted 3-dimensional magnetic resonance imaging scans of adult controls and patients. The method relies on prior knowledge involving a user-defined template as a guide to aid the segmentation of the cerebellum. As the gray and white matter intensity distribution in the cerebellum has a complex pattern, texture information that identified the "graininess" was employed to capture the intensity distribution of voxels. The textural information was used to group voxels in a small circular structuring element as belonging to the cerebellum region. The cerebella from scans of 15 of the 20 subjects were segmented both manually and using the semi-automated procedure; the results were strongly correlated (r = 0.985, n = 15, p < 0.0001), and the volumes obtained from the two methods differed by 2.3%. The cerebellar volumes in 10 normal subjects and 10 age- and sex-matched patients with a neuropsychiatric disorder (schizophrenia) did not differ significantly (p = 0.18). The whole cerebellum was segmented in approximately 30 min using the semi-automated procedure. The method described is robust, easy-to-use, fairly fast and gives objective measurements.