MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies

Ye Xu; Milan Sonka; Geoffrey McLennan; Junfeng Guo; Eric A Hoffman

doi:10.1109/TMI.2006.870889

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MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies

Journal article

Peer reviewed

MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies

Ye Xu, Milan Sonka, Geoffrey McLennan, Junfeng Guo and Eric A Hoffman

IEEE transactions on medical imaging, Vol.25(4), pp.464-475

04/2006

DOI: 10.1109/TMI.2006.870889

PMID: 16608061

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Abstract

Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs.

Algorithms

Smoking - adverse effects

Information Storage and Retrieval - methods

Pulmonary Emphysema - etiology

Humans

Middle Aged

Imaging, Three-Dimensional - methods

Tomography, X-Ray Computed - methods

Male

Pulmonary Emphysema - classification

Radiographic Image Enhancement - instrumentation

Radiographic Image Interpretation, Computer-Assisted - methods

Stochastic Processes

Sensitivity and Specificity

Female

Tomography, X-Ray Computed - instrumentation

Severity of Illness Index

Reproducibility of Results

Transducers

Artificial Intelligence

Radiographic Image Enhancement - methods

Pulmonary Emphysema - diagnostic imaging

Radiation Dosage

Artifacts

Pattern Recognition, Automated - methods

Details

Title: Subtitle: MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies
Creators: Ye Xu - Iowa Comprehension Lung Imaging Center, University of Iowa, Iowa City, IA 52240, USA
Milan Sonka
Geoffrey McLennan
Junfeng Guo
Eric A Hoffman
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.25(4), pp.464-475
DOI: 10.1109/TMI.2006.870889
PMID: 16608061
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers; United States
Grant note: HL-064368 / NHLBI NIH HHS HL-060158 / NHLBI NIH HHS CA-74325 / NCI NIH HHS
Language: English
Date published: 04/2006
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984046901902771

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