Ischemic segment detection using the support vector domain description

被引：2

作者：

Hansen, Michael S. ^{[1
]}

Olafsdottir, Hildur ^{[1
]}

Sjostrand, Karl ^{[1
]}

Erbou, Soren G. ^{[1
]}

Stegmann, Mikkel B. ^{[2
]}

Larsson, Henrik B. W. ^{[3
]}

Larsen, Rasmus ^{[1
]}

机构：

[1] Tech Univ Denmark, Informat & Math Modeling, Bldg 321, DK-2800 Lyngby, Denmark

[2] 3Shape A S, Copenhagen, Denmark

[3] Glostrup Cty Hosp, Glostrup, Denmark

来源：

MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3 | 2007年 / 6512卷

关键词：

myocardial perfusion; support vector domain description (SVDD); regularization; image registration;

D O I：

10.1117/12.709492

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Myocardial perfusion Magnetic Resonance (MR) imaging has proven to be a powerful method to assess coronary artery diseases. The current work presents a novel approach to the analysis of registered sequences of myocardial perfusion MR images. A previously reported active appearance model (AAM) based segmentation and registration of the myocardium provided pixel-wise signal intensity curves that were analyzed using the Support Vector Domain Description (SVDD). In contrast to normal SVDD, the entire regularization path was calculated and used to calculate a generalized distance, which is used to discriminate between ischemic and healthy tissue. The results corresponded well to the ischemic segments found by assessment of the three common perfusion parameters, maximum upslope, peak and time-to-peak obtained pixel-wise.

引用

页数：8

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