Image dissimilarity-based quantification of lung disease from CT

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

T2 - 13th International Conference on Medical Image Computing and Computer Assisted Intervention

Y2 - 20 September 2010 through 24 September 2010

ER -