Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema with a Hidden Markov Measure Field Model

Yrjö Häme; Elsa D Angelini; Eric A Hoffman; R. Graham Barr; Andrew F Laine

doi:10.1109/TMI.2014.2317520

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Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema with a Hidden Markov Measure Field Model

Journal article

Peer reviewed

Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema with a Hidden Markov Measure Field Model

Yrjö Häme, Elsa D Angelini, Eric A Hoffman, R. Graham Barr and Andrew F Laine

IEEE transactions on medical imaging, Vol.33(7), pp.1527-1540

07/2014

DOI: 10.1109/TMI.2014.2317520

PMCID: PMC4104988

PMID: 24759984

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Abstract

The extent of pulmonary emphysema is commonly estimated from CT images by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions in the lung and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the present model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was used to quantify emphysema on a cohort of 87 subjects, with repeated CT scans acquired over a time period of 8 years using different imaging protocols. The scans were acquired approximately annually, and the data set included a total of 365 scans. The results show that the emphysema estimates produced by the proposed method have very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. In addition, the generated emphysema delineations promise great advantages for regional analysis of emphysema extent and progression, possibly advancing disease subtyping.

lung

Computed tomography

image segmentation

emphysema index

Markov random fields

Details

Title: Subtitle: Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema with a Hidden Markov Measure Field Model
Creators: Yrjö Häme - Columbia University, Department of Biomedical Engineering, New York, NY, USA
Elsa D Angelini - Telecom ParisTech, Institut Mines-Telecom, LTCI CNRS, Paris, France and with Columbia University, Department of Biomedical Engineering, New York, NY, USA
Eric A Hoffman - University of Iowa, Department of Radiology, Iowa City, IA, USA
R. Graham Barr - Columbia University, College of Physicians and Surgeons, Department of Medicine, New York, NY, USA
Andrew F Laine - Columbia University, Department of Biomedical Engineering, New York, NY, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.33(7), pp.1527-1540
DOI: 10.1109/TMI.2014.2317520
PMID: 24759984
PMCID: PMC4104988
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Language: English
Date published: 07/2014
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984051996602771

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