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/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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",
address = "Switzerland",
note = "null ; Conference date: 20-09-2010 Through 24-09-2010",
}
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
N1 - Conference code: 13
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
Y2 - 20 September 2010 through 24 September 2010
ER -