Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm

Panfang Hua; Qi Song; M Sonka; E A Hoffman; J M Reinhardt

doi:10.1109/ISBI.2011.5872820

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Conference proceeding

Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm

Panfang Hua, Qi Song, M Sonka, E A Hoffman and J M Reinhardt

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.2072-2075

03/2011

DOI: 10.1109/ISBI.2011.5872820

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Abstract

Lung segmentation is an important first step for quantitative lung CT image analysis and computer aided diagnosis. However, accurate and automated lung CT image segmentation may be made difficult by the presence of the abnormalities. Since many lung diseases change tissue density, resulting in intensity changes in the CT image data, intensity-only segmentation algorithms will not work for most pathological lung cases. This paper presents an automatic algorithm for pathological lung CT image segmentation that uses a graph search driven by a cost function combining the intensity, gradient, boundary smoothness, and the rib information. This method was trained by four pathological lung CT images and tested on fifteen 3-D thorax CT data sets with lung diseases. We validate our method by comparing our automatic segmentation result with manually traced segmentation result. Sensitivity, specificity, and Hausdorff distance were calculated to evaluate the method.

Bars

Computed tomography

Graph Search

Image segmentation

Lung

Lungs

Pathology

Pulmonary

Segmentation

Sensitivity

Training

Details

Title: Subtitle: Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm
Creators: Panfang Hua - University of Iowa
Qi Song - University of Iowa
M Sonka - University of Iowa
E A Hoffman - University of Iowa
J M Reinhardt - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.2072-2075
DOI: 10.1109/ISBI.2011.5872820
ISSN: 1945-7928
eISSN: 1945-8452
Publisher: IEEE
Language: English
Date published: 03/2011
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984186594702771

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