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.

doi.org/10.1007/978-3-642-15705-9_5, hdl.handle.net/1765/27962
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Erasmus MC: University Medical Center Rotterdam

Sorensen, L, Loog, M, Lo, P, Ashraf, H, Dirksen, A, Duin, R.P.W, & de Bruijne, M. (2010). Image dissimilarity-based quantification of lung disease from CT. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6361, pp. 37–44). doi:10.1007/978-3-642-15705-9_5