Towards exaggerated emphysema stereotypes

C. Chen*, L. Sørensen, F. Lauze, C. Igel, M. Loog, A. Feragen, M. De Bruijne, M. Nielsen

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

1 Citation (Scopus)


Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Original languageEnglish
Title of host publicationMedical Imaging 2012
Subtitle of host publicationComputer-Aided Diagnosis
Publication statusPublished - 2012
EventMedical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: 7 Feb 20129 Feb 2012

Publication series

SeriesProgress in Biomedical Optics and Imaging - Proceedings of SPIE


ConferenceMedical Imaging 2012: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA


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