Image dissimilarity-based quantification of lung disease from CT

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Image dissimilarity-based quantification of lung disease from CT. / Sørensen, Lauge; Loog, Marco; Lo, Pechin Chien Pau; Ashraf, Haseem; Dirksen, Asger; P. W. Duin, Robert; de Bruijne, Marleen.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I. red. / Tianzi Jiang; Nassir Navab; Josien P. W. Pluim; Max A. Viergever. Bind Part I Springer, 2010. s. 37-44 (Lecture notes in computer science; Nr. 6361).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Sørensen, L, Loog, M, Lo, PCP, Ashraf, H, Dirksen, A, P. W. Duin, R & de Bruijne, M 2010, Image dissimilarity-based quantification of lung disease from CT. i T Jiang, N Navab, JPW Pluim & MA Viergever (red), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I. bind Part I, Springer, Lecture notes in computer science, nr. 6361, s. 37-44, 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, Kina, 20/09/2010. https://doi.org/10.1007/978-3-642-15705-9_5

APA

Sørensen, L., Loog, M., Lo, P. C. P., Ashraf, H., Dirksen, A., P. W. Duin, R., & de Bruijne, M. (2010). Image dissimilarity-based quantification of lung disease from CT. I T. Jiang, N. Navab, J. P. W. Pluim, & M. A. Viergever (red.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I (Bind Part I, s. 37-44). Springer. Lecture notes in computer science, Nr. 6361 https://doi.org/10.1007/978-3-642-15705-9_5

Vancouver

Sørensen L, Loog M, Lo PCP, Ashraf H, Dirksen A, P. W. Duin R o.a. Image dissimilarity-based quantification of lung disease from CT. I Jiang T, Navab N, Pluim JPW, Viergever MA, red., Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I. Bind Part I. Springer. 2010. s. 37-44. (Lecture notes in computer science; Nr. 6361). https://doi.org/10.1007/978-3-642-15705-9_5

Author

Sørensen, Lauge ; Loog, Marco ; Lo, Pechin Chien Pau ; Ashraf, Haseem ; Dirksen, Asger ; P. W. Duin, Robert ; de Bruijne, Marleen. / Image dissimilarity-based quantification of lung disease from CT. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part I. red. / Tianzi Jiang ; Nassir Navab ; Josien P. W. Pluim ; Max A. Viergever. Bind Part I Springer, 2010. s. 37-44 (Lecture notes in computer science; Nr. 6361).

Bibtex

@inproceedings{37bf2280635411df928f000ea68e967b,
title = "Image dissimilarity-based quantification of lung disease from CT",
abstract = "In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.",
author = "Lauge S{\o}rensen and Marco Loog and Lo, {Pechin Chien Pau} and Haseem Ashraf and Asger Dirksen and {P. W. Duin}, Robert and {de Bruijne}, Marleen",
year = "2010",
doi = "10.1007/978-3-642-15705-9_5",
language = "English",
isbn = "978-3-642-15704-2",
volume = "Part I",
series = "Lecture notes in computer science",
publisher = "Springer",
number = "6361",
pages = "37--44",
editor = "Tianzi Jiang and Nassir Navab and Pluim, {Josien P. W.} and Viergever, {Max A.}",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010",

}

RIS

TY - GEN

T1 - Image dissimilarity-based quantification of lung disease from CT

AU - Sørensen, Lauge

AU - Loog, Marco

AU - Lo, Pechin Chien Pau

AU - Ashraf, Haseem

AU - Dirksen, Asger

AU - P. W. Duin, Robert

AU - de Bruijne, Marleen

PY - 2010

Y1 - 2010

N2 - In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.

AB - In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.

U2 - 10.1007/978-3-642-15705-9_5

DO - 10.1007/978-3-642-15705-9_5

M3 - Article in proceedings

SN - 978-3-642-15704-2

VL - Part I

T3 - Lecture notes in computer science

SP - 37

EP - 44

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010

A2 - Jiang, Tianzi

A2 - Navab, Nassir

A2 - Pluim, Josien P. W.

A2 - Viergever, Max A.

PB - Springer

ER -

ID: 19823761