Authors:
Kai Li
1
;
Allen D. Malony
1
and
Don M. Tucker
2
Affiliations:
1
University of Oregon, United States
;
2
Electrical Geodesics, Inc., United States
Keyword(s):
Segmentation, brain, MR, intensity inhomogeneity, relative thresholding, mathematical morphology, skeleton-based opening, geodesic opening, a priori knowledge, first-order logic.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Medical Image Analysis
Abstract:
We present an automatic method for segmentation of white matter, gray matter and cerebrospinal fluid in T1-weighted brain MR images. We model images in terms of spatial relationships between near voxels. Brain tissue segmentation is first performed with relative thresholding, a new segmentation mechanism which compares two voxel intensities against a relative threshold. Relative thresholding considers structural, geometrical and radiological a priori knowledge expressed in first-order logic. It makes intensity inhomogeneity transparent, avoids using any form of regularization, and enables global searching for optimal solutions. We augment relative thresholding mainly with a series of morphological operations that exploit a priori knowledge about the shape and geometry of brain structures. Combination of relative thresholding and morphological operations dispenses with the prior skull stripping step. Parameters involved in the segmentation are selected based on a priori knowledge and
robust to inter-data variations.
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