Brain tissue classification in MR images based on a 3D MRF model

被引：0

作者：

Ruan, S ^{[1
]}

Jaggi, C ^{[1
]}

Bloyet, D ^{[1
]}

Mazoyer, B ^{[1
]}

机构：

[1] ISMRA Univ Caen, GREYC, UPRESA 6072, Caen, France

来源：

PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 20, PTS 1-6: BIOMEDICAL ENGINEERING TOWARDS THE YEAR 2000 AND BEYOND | 1998年 / 20卷

关键词：

classification; segmentation; MRF; volume partial effect; multifractal dimension and MRI;

D O I：

暂无

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. The partial volume effect and the inhomogeneity are usually sources of difficulties. In this paper, we propose a new classification method using 3D MRF models and the multifractal dimension measure for segmenting CSF, gray matter and white matter in MR T1-weighted images Mixclasses (mixture of two pure tissue classes) result from the partial volume effect, are taken into account in our tissue class model. Results are described with two acquisition sequences: IR-FGRE and SPGR. The accuracy of the classification is found by the way of a phantom validation study.

引用

页码：625 / 628

页数：2

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