Conference proceeding
Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis
Computer Vision Approaches to Medical Image Analysis, pp.13-24
Lecture Notes in Computer Science
2006
DOI: 10.1007/11889762_2
Abstract
A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from 3D aortic MR images using segmentation and independent component analysis (ICA). The first step to extend the model to 4D (3D + time) has also been taken. ICA is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually.
3D+time MR image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta. The CAD method distinguished between normal and connective tissue disorder subjects with a classification accuracy of 93.5 %.
Details
- Title: Subtitle
- Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis
- Creators
- Michael Sass Hansen - Department of Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, DenmarkFei Zhao - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USAHonghai Zhang - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USANicholas E Walker - Department of Internal Medicine, University of Iowa, Iowa City, USAAndreas Wahle - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USAThomas Scholz - Department of Pediatrics, University of Iowa, Iowa City, USAMilan Sonka - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA
- Resource Type
- Conference proceeding
- Publication Details
- Computer Vision Approaches to Medical Image Analysis, pp.13-24
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/11889762_2
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Language
- English
- Date published
- 2006
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Cardiology; Stead Family Department of Pediatrics; Cardiovascular Medicine; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Child and Community Health; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047640302771
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