Detection and Classification of MS Lesions in Multispectral MR Images

被引：0

作者：

Chen, Hsian-Min ^{[1
]}

Chai, Jyh-Wen ^{[1
,2
]}

Chen, Clayton Chi-Chang ^{[1
,2
]}

Ouyang, Yen-Chieh ^{[3
]}

Yang, Ching-Wen ^{[4
]}

Lee, San-Kan ^{[1
,2
]}

Chang, Chein-I ^{[5
]}

机构：

[1] Taichung Vet Gen Hosp, Dept Med Res, Ctr Quantitat Imaging Med CQUIM, Taichung, Taiwan

[2] Taichung Vet Gen Hosp, Dept Radiol, Taichung, Taiwan

[3] Natl Chung Hsing Univ, Dept Elect Engn, Taichung, Taiwan

[4] Taichung Vet Gen Hosp, Comp Ctr, Taichung, Taiwan

[5] Univ Maryland, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21201 USA

来源：

INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014) | 2015年 / 274卷

关键词：

multispectral MRI; MS Lesions; Detection; Classification;

D O I：

10.3233/978-1-61499-484-8-2044

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Quantitative analysis of patients with multiple sclerosis (MS) is an important issue in both diagnosis and therapy monitoring. We propose a new spectral signature detection approach for quantitative volumetric analysis of multispectral MRI. It is called constrained energy minimization (CEM) method, which is derived from the hyperspectral imaging processing. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. The results show that the CEM method is a promising and effective spectral technique for lesions detection in multispectral MRI.

引用

页码：2044 / 2049

页数：6

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