Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images

Michael Sass Hansen; Fei Zhao; Honghai Zhang; Bjarne Kjær Ersbøll; Andreas Wahle; Thomas Scholz; Milan Sonka

Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images

Michael Sass Hansen, Fei Zhao, Honghai Zhang, Bjarne Kjær Ersbøll, Andreas Wahle, Thomas Scholz, Milan Sonka

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Independent component analysis (ICA) is employed for com\$\backslash\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first implementation reported in the field of medical imaging. Prior knowledge of the source distribution is utilized using an appropriate ordering of the components. The CAD method distinguished between normal and diseased subjects with a classification accuracy of 96.8\verb+~+\$\backslash\$%.

Original language	English
Title of host publication	The 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. CVII 2006
Publisher	IEEE
Publication date	2006
Publication status	Published - 2006

Access to Document

http://www2.imm.dtu.dk/pubdb/p.php?4819

OpenUrl availability

Full text

Cite this

Hansen, M. S., Zhao, F., Zhang, H., Ersbøll, B. K., Wahle, A., Scholz, T., & Sonka, M. (2006). Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images. In The 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. CVII 2006 IEEE. http://www2.imm.dtu.dk/pubdb/p.php?4819

@inproceedings{f15036bda70e4d65b7bc84f2e87fb1d8,

title = "Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images",

abstract = "Independent component analysis (ICA) is employed for com\textbackslash{}\$\textbackslash{}backslash\textbackslash{}\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first implementation reported in the field of medical imaging. Prior knowledge of the source distribution is utilized using an appropriate ordering of the components. The CAD method distinguished between normal and diseased subjects with a classification accuracy of 96.8\textbackslash{}verb+\textasciitilde{}+\textbackslash{}\$\textbackslash{}backslash\textbackslash{}\$\%.",

author = "Hansen, \{Michael Sass\} and Fei Zhao and Honghai Zhang and Ersb{\o}ll, \{Bjarne Kj{\ae}r\} and Andreas Wahle and Thomas Scholz and Milan Sonka",

year = "2006",

language = "English",

booktitle = "The 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. CVII 2006",

publisher = "IEEE",

address = "United States",

}

Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images. / Hansen, Michael Sass; Zhao, Fei; Zhang, Honghai et al.
The 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. CVII 2006. IEEE, 2006.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Diagnosis of Connective Tissue Disorders based on Independent Component Analysis of Aortic Shape and Motion from 4D MR Images

AU - Hansen, Michael Sass

AU - Zhao, Fei

AU - Zhang, Honghai

AU - Ersbøll, Bjarne Kjær

AU - Wahle, Andreas

AU - Scholz, Thomas

AU - Sonka, Milan

PY - 2006

Y1 - 2006

N2 - Independent component analysis (ICA) is employed for com\$\backslash\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first implementation reported in the field of medical imaging. Prior knowledge of the source distribution is utilized using an appropriate ordering of the components. The CAD method distinguished between normal and diseased subjects with a classification accuracy of 96.8\verb+~+\$\backslash\$%.

AB - Independent component analysis (ICA) is employed for com\$\backslash\$-puter-aided diagnosis (CAD) allowing objective identification of subjects with connective tissue disorder from 4D aortic MR images. Stationary independent components assist in the disease detection, which is the first implementation reported in the field of medical imaging. Prior knowledge of the source distribution is utilized using an appropriate ordering of the components. The CAD method distinguished between normal and diseased subjects with a classification accuracy of 96.8\verb+~+\$\backslash\$%.

M3 - Article in proceedings

BT - The 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. CVII 2006

PB - IEEE

ER -