Journal article
3D airway segmentation via hyperpolarized 3He gas MRI by using scale-based fuzzy connectedness
Computerized medical imaging and graphics, Vol.28(1), pp.77-86
2004
DOI: 10.1016/j.compmedimag.2003.06.001
PMID: 15127752
Abstract
Computerized segmentation of 3D tracheobronchial tree is a necessary first step for subsequent registration and analysis of pulmonary airway and vascular magnetic resonance (MR) images obtained by using hyperpolarized
3Helium gas and Gadolinium. The scientific and clinical implications of acquiring these data on the tracheobronchial tree (for studying ventilation, V) and on the coinciding pulmonary arterioles (for studying perfusion, Q), is the next frontier for static and dynamic pulmonary MRI. In this paper, we report an airway segmentation method from
3He MR images based on the scale-based fuzzy connectedness approach. Incorporated in this method are the pre-processing steps of inhomogeneity correction and intensity standardization. The basic sequential steps in the proposed airway segmentation method are: (1) image acquisition, (2) radio frequency field inhomogeneity correction, (3) standardization of MR image intensity scale, (4) seed specification, (5) scale-based fuzzy connected segmentation of airways, and (6) thresholding and binarization. The majority of these steps are automatically executed; others allow interaction through a graphical interface provided in the 3DVIEWNIX software system, in which the algorithms are implemented. The method achieves an overall precision of about 98% in terms of the extent of overlap in repeated segmentations. Its level of accuracy can be described by a true positive volume fraction of about 98% (considering manual delineation as the surrogate of true delineation), and a false negative and positive volume fraction of about 1%. The total operator and computational time required per study are on the average 2 and 20 min.
Details
- Title: Subtitle
- 3D airway segmentation via hyperpolarized 3He gas MRI by using scale-based fuzzy connectedness
- Creators
- Binquan Wang - B1, Stellar-Chance Laboratories, Metabolic Magnetic Resonance Research and Computing Center, Department of Radiology, Hospital of the University of Pennsylvania, 422 Curie Boulevard, Philadelphia, PA 19104, USAPunam K Saha - Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USAJayaram K Udupa - Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USAMargaret A Ferrante - Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USAJames Baumgardner - Department of Anesthesiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USADavid A Roberts - Department of Radiology, University of Pennsylvania, 422 Curie Boulevard, Philadelphia, PA 19104, USARahim R Rizi - B1, Stellar-Chance Laboratories, Metabolic Magnetic Resonance Research and Computing Center, Department of Radiology, Hospital of the University of Pennsylvania, 422 Curie Boulevard, Philadelphia, PA 19104, USA
- Resource Type
- Journal article
- Publication Details
- Computerized medical imaging and graphics, Vol.28(1), pp.77-86
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.compmedimag.2003.06.001
- PMID
- 15127752
- ISSN
- 0895-6111
- eISSN
- 1879-0771
- Language
- English
- Date published
- 2004
- Academic Unit
- Radiology; Electrical and Computer Engineering
- Record Identifier
- 9984051713602771
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