Ansatte – Københavns Universitet

Pattern Recognition-Based Analysis of COPD in CT

Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandlingForskning

Standard

Pattern Recognition-Based Analysis of COPD in CT. / Sørensen, Lauge Emil Borch Laurs.

København : Faculty of Science, University of Copenhagen, 2010.

Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandlingForskning

Harvard

Sørensen, LEBL 2010, Pattern Recognition-Based Analysis of COPD in CT. Faculty of Science, University of Copenhagen, København.

APA

Sørensen, L. E. B. L. (2010). Pattern Recognition-Based Analysis of COPD in CT. København: Faculty of Science, University of Copenhagen.

Vancouver

Sørensen LEBL. Pattern Recognition-Based Analysis of COPD in CT. København: Faculty of Science, University of Copenhagen, 2010.

Author

Sørensen, Lauge Emil Borch Laurs. / Pattern Recognition-Based Analysis of COPD in CT. København : Faculty of Science, University of Copenhagen, 2010.

Bibtex

@phdthesis{bf5d38ff9cce45989d8587ffa6e4639d,
title = "Pattern Recognition-Based Analysis of COPD in CT",
abstract = "Computed tomography (CT), a medical imaging technique, offers a detailed view of the human body that can be used for direct inspection of the lung tissue. This allows for in vivo measurement of subtle disease patterns such as the patterns associated with chronic obstructive pulmonary disease (COPD). COPD, also commonly referred to as “smokers’ lungs”, is a lung disease characterized by limitation of the airflow to and from the lungs causing shortness of breath. The disease is expected to rank as the fifth most burdening disease worldwide by 2020 according the the World Health Organization. COPD comprises two main components, chronic bronchitis, characterized by inflammation in the airways, and emphysema, characterized by loss of lung tissue. Emphysema basically looks like black blobs of varying sizes within the normal, gray lung tissue in CT, and can therefore be seen as a family of texture patterns. Commonly employed CT-based quantitative measures in the clinical literature are rather simplistic and do not take the texture appearance of the lung tissue into account. This includes measures such as the relative area (RA), also called emphysema index, that applies a fixed threshold to each individual lung voxel in the CT image and counts the number of voxels below the threshold relative to the total amount of lung voxels. This thesis presents several methods for texture-based quantification of emphysema and/or COPD in CT images of the lungs. The methods rely on image processing and pattern recognition. The image processing part deals with characterizing the lung tissue texture using a suitable texture descriptor. Two types of descriptors are considered, the local binary pattern histogram and histograms of filter responses from a multi-scale Gaussian derivative filter bank. The pattern recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema and/or COPD in CT. Compared to RA and other common clinical CT-based measures, the texture-based measures are better at discriminating between CT images from healthy and COPD subjects, they correlate better with the lung function of the subjects, they are more reproducible, and they are less influence by the inspiration level of the subject during CT scanning – a major source of variability in CT.",
author = "S{\o}rensen, {Lauge Emil Borch Laurs}",
year = "2010",
language = "English",
publisher = "Faculty of Science, University of Copenhagen",

}

RIS

TY - BOOK

T1 - Pattern Recognition-Based Analysis of COPD in CT

AU - Sørensen, Lauge Emil Borch Laurs

PY - 2010

Y1 - 2010

N2 - Computed tomography (CT), a medical imaging technique, offers a detailed view of the human body that can be used for direct inspection of the lung tissue. This allows for in vivo measurement of subtle disease patterns such as the patterns associated with chronic obstructive pulmonary disease (COPD). COPD, also commonly referred to as “smokers’ lungs”, is a lung disease characterized by limitation of the airflow to and from the lungs causing shortness of breath. The disease is expected to rank as the fifth most burdening disease worldwide by 2020 according the the World Health Organization. COPD comprises two main components, chronic bronchitis, characterized by inflammation in the airways, and emphysema, characterized by loss of lung tissue. Emphysema basically looks like black blobs of varying sizes within the normal, gray lung tissue in CT, and can therefore be seen as a family of texture patterns. Commonly employed CT-based quantitative measures in the clinical literature are rather simplistic and do not take the texture appearance of the lung tissue into account. This includes measures such as the relative area (RA), also called emphysema index, that applies a fixed threshold to each individual lung voxel in the CT image and counts the number of voxels below the threshold relative to the total amount of lung voxels. This thesis presents several methods for texture-based quantification of emphysema and/or COPD in CT images of the lungs. The methods rely on image processing and pattern recognition. The image processing part deals with characterizing the lung tissue texture using a suitable texture descriptor. Two types of descriptors are considered, the local binary pattern histogram and histograms of filter responses from a multi-scale Gaussian derivative filter bank. The pattern recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema and/or COPD in CT. Compared to RA and other common clinical CT-based measures, the texture-based measures are better at discriminating between CT images from healthy and COPD subjects, they correlate better with the lung function of the subjects, they are more reproducible, and they are less influence by the inspiration level of the subject during CT scanning – a major source of variability in CT.

AB - Computed tomography (CT), a medical imaging technique, offers a detailed view of the human body that can be used for direct inspection of the lung tissue. This allows for in vivo measurement of subtle disease patterns such as the patterns associated with chronic obstructive pulmonary disease (COPD). COPD, also commonly referred to as “smokers’ lungs”, is a lung disease characterized by limitation of the airflow to and from the lungs causing shortness of breath. The disease is expected to rank as the fifth most burdening disease worldwide by 2020 according the the World Health Organization. COPD comprises two main components, chronic bronchitis, characterized by inflammation in the airways, and emphysema, characterized by loss of lung tissue. Emphysema basically looks like black blobs of varying sizes within the normal, gray lung tissue in CT, and can therefore be seen as a family of texture patterns. Commonly employed CT-based quantitative measures in the clinical literature are rather simplistic and do not take the texture appearance of the lung tissue into account. This includes measures such as the relative area (RA), also called emphysema index, that applies a fixed threshold to each individual lung voxel in the CT image and counts the number of voxels below the threshold relative to the total amount of lung voxels. This thesis presents several methods for texture-based quantification of emphysema and/or COPD in CT images of the lungs. The methods rely on image processing and pattern recognition. The image processing part deals with characterizing the lung tissue texture using a suitable texture descriptor. Two types of descriptors are considered, the local binary pattern histogram and histograms of filter responses from a multi-scale Gaussian derivative filter bank. The pattern recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema and/or COPD in CT. Compared to RA and other common clinical CT-based measures, the texture-based measures are better at discriminating between CT images from healthy and COPD subjects, they correlate better with the lung function of the subjects, they are more reproducible, and they are less influence by the inspiration level of the subject during CT scanning – a major source of variability in CT.

M3 - Ph.D. thesis

BT - Pattern Recognition-Based Analysis of COPD in CT

PB - Faculty of Science, University of Copenhagen

CY - København

ER -

ID: 33171770