Journal article
Anatomical Modeling with Fuzzy Implicit Surface Templates: Application to Automated Localization of the Heart and Lungs in Thoracic MR Volumes
Computer vision and image understanding, Vol.80(1), pp.1-20
10/2000
DOI: 10.1006/cviu.2000.0864
Abstract
In this paper, a novel model-driven segmentation approach for thoracic MR-images is presented. The goal of this work is to coarsely, but fully automatically localize the boundary surfaces of the heart and lungs in thoracic MR sets. The major organs in the thorax are described in a three-dimensional analytical model template by combining a set of fuzzy implicit surfaces by means of constructive solid geometry and formulating model registration as an energy minimization. The method has been validated on 20 thoracic MR volumes from two centers (patients and normal subjects). On average 90% of the contour length of the heart and lung contours was localized with sufficient accuracy (average positional error 6 mm) to automatically provide the initial conditions for a subsequently applied locally accurate segmentation method.
Details
- Title: Subtitle
- Anatomical Modeling with Fuzzy Implicit Surface Templates: Application to Automated Localization of the Heart and Lungs in Thoracic MR Volumes
- Creators
- Boudewijn P.F Lelieveldt - Department of Radiology, Leiden University Medical Center, Leiden, The NetherlandsMilan Sonka - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, 52242Lizann Bolinger - Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, 52242Thomas D Scholz - Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, Iowa, 52242Hein Kayser - Department of Radiology, Leiden University Medical Center, Leiden, The NetherlandsRob van der Geest - Department of Radiology, Leiden University Medical Center, Leiden, The NetherlandsJohan H.C Reiber - Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Resource Type
- Journal article
- Publication Details
- Computer vision and image understanding, Vol.80(1), pp.1-20
- DOI
- 10.1006/cviu.2000.0864
- ISSN
- 1077-3142
- eISSN
- 1090-235X
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 10/2000
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Cardiology; Stead Family Department of Pediatrics; Radiation Oncology; Injury Prevention Research Center; Child and Community Health; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047894602771
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