Visual and Quantitative Comparison of Real and Simulated Biomedical Image Data

Tereza Necasova, David Svoboda; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

The simulations in biomedical image analysis provide a solution when the real image data are difficult to be annotated or if they are available only in small quantities. The progress in simulations rapidly grows in the recent years. Nevertheless, the comparative techniques for the assessment of the plausibility of generated data are still unsatisfactory or none. This paper aims to point out the problem of insufficient comparison of real and synthetic data, which is done in many cases only by visual inspection or based on subjective measurements. The selected texture features are first compared in a univariate manner by quantilequantile plots and Kolmogorov-Smirnov test. The evaluation is then extended into multivariate assessment using the PCA for a visualization and furthermore for a quantitative measure of similarity by Jaccard index. Two different image datasets were used to show the results and the importance of the validation of simulated data in many aspects.