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Brain segmentation in magnetic resonance (MR) images is an important step in quantitative analysis applications, including the characterization of multiple sclerosis (MS) lesions over time. Our approach is based on a priori knowledge of the intensity and three-dimensional (3D) spatial relationships of structures in MR images of the head. Optimal thresholding and connected-components analysis are used to generate a starting point for segmentation. A 3D radial search is then performed to locate probable locations of the intra-cranial cavity (ICC). Missing portions of the ICC surface are interpolated in order to exclude connected structures. Partial volume effects and inter-slice intensity variations in the image are accounted for automatically. Several studies were conducted to validate the segmentation. Accuracy was tested by calculating the segmented volume and comparing to known volumes of a standard MR phantom. Reliability was tested by comparing calculated volumes of individual segmentation results from multiple images of the same subject. The segmentation results were also compared to manual tracings. The average error in volume measurements for the phantom was 1.5% and the average coefficient of variation of brain volume measurements of the same subject was 1.2%. Since the new algorithm requires minimal user interaction, variability introduced by manual tracing and interactive threshold or region selection was eliminated. Overall, the new algorithm was shown to produce a more accurate and reliable brain segmentation than existing manual and semi-automated techniques.
Fisher et al. (Fri,) studied this question.