Multiresolution mesh segmentation of MRI brain using classification and discrete curvature

被引：0

作者：

Bourouis, Sami ^{[1
]}

Hamrouni, Kamel ^{[1
]}

Dhibi, Mounir

机构：

[1] Ecole Natl Ingenieurs Tunis, LSTS, Tunis 1002, Tunisia

来源：

VISAPP 2008: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 1 | 2008年

关键词：

brain segmentation; MRI; statistical classification; progressive meshes; mesh segmentation; discrete curvatures;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：421 / 426

页数：6

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