KEYWORDS: Image segmentation, Fuzzy logic, Brain, 3D modeling, Neuroimaging, Magnetic resonance imaging, Thalamus, Data modeling, Image visualization, Binary data
This paper presents a method for segmenting internal brain structures in MR
images. It introduces prior information in an original way through descriptions
of the spatial arrangement of structures
by means of spatial relations, which are represented in the fuzzy set framework.
The method is hierarchical as the segmentation of a given structure
is based on
the previously segmented ones. The processing of each structure is
decomposed into two stages: an initialization stage which makes extensive
use
of prior knowledge and a refinement stage using a 3D deformable model.
The deformable model is guided by an external force representing the combination
of a classical data term derived from an edge map and a force corresponding
to a given spatial relation. We propose different ways to compute a force from
a fuzzy set representing a relation or a combination of relations.
Results obtained for
the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising.
The proposed combination of spatial relations and deformable models has proved to be very useful to segment
parts of the structures were no visible edges are present, improving the segmentation accuracy.
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