Early detection of aortic aneurysm risk from 4-D MR image data

M Sonka; F Zhao; H Zhang; A Wahle; A Stolpen; T Scholz

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Conference proceeding

Early detection of aortic aneurysm risk from 4-D MR image data

M Sonka, F Zhao, H Zhang, A Wahle, A Stolpen and T Scholz

2006 Computers in Cardiology, Vol.33, pp.69-72

09/2006

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Abstract

A computer-aided diagnosis method is reported that allows to objectively identify subjects with connective tissue disorders from sixteen-phase 4D (3D+time) aortic MR images. Our automated segmentation method combines level-set and optimal surface segmentation algorithms so that the final aortic surfaces in all 16 cardiac phases are determined in a single optimization process. The resulting aortic lumen surface is registered with an aortic model followed by calculation of modal indices of aortic shape and motion. The modal indices reflect the differences of any individual aortic shape and motion from an average aortic behavior. Support Vector Machine (SVM) classifier is used for classification of normal and connective disease disorder subjects. 4D MR image data sets acquired from 30 normal and connective tissue disorder subjects were used to evaluate the performance of our method. The automated 4D segmentation result produced accurate aortic surfaces in all 16 cardiac phases, covering the aorta from the left- ventricular outflow tract to the diaphragm, yielding sub- voxel accuracy. The computer aided diagnosis method distinguished between normal and connective tissue disorder subjects with a classification correctness of 96.7%.

Connective Tissue

Aneurysm

Cardiac disease

Cardiovascular diseases

Computer aided diagnosis

Image segmentation

Optimization methods

Shape

Support vector machine classification

Support vector machines

Details

Title: Subtitle: Early detection of aortic aneurysm risk from 4-D MR image data
Creators: M Sonka - University of Iowa
F Zhao - Univ. of Iowa, Iowa City, IA
H Zhang - University of Iowa
A Wahle - University of Iowa
A Stolpen - University of Iowa
T Scholz - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2006 Computers in Cardiology, Vol.33, pp.69-72
ISSN: 0276-6574
eISSN: 2325-8853
Publisher: IEEE
Language: English
Date published: 09/2006
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Cardiology; Stead Family Department of Pediatrics; 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: 9984318808202771

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