Authors:
Sami Bourouis
1
;
Kamel Hamrouni
1
and
Mounir Dhibi
2
Affiliations:
1
Ecole Nationale d’Ing´enieurs de Tunis, Tunisia
;
2
Ensieta E312, France
Keyword(s):
Brain segmentation, MRI, Statistical classification, Progressive meshes, Mesh segmentation, discrete curvatures.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Medical Image Analysis
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
;
Surface Geometry and Shape
Abstract:
This paper presents a method for brain tissue segmentation and characterization of magnetic resonance imaging (MRI) scans. It is based on statistical classification, differential geometry, and multiresolution representation. The Expectation Maximization algorithm and k-means clustering are applied to generate an initial mask of tissue classes of data volume. Then, a hierarchical multiresolution representation is applied to simplify processing. The idea is that the low-resolution description is used to determine constraints for the segmentation at the higher resolutions. Our contribution is the design of a pipeline procedure for brain characterization/labeling by using discrete curvature and multiresolution representation. We have tested our method on several MRI data.