Evaluation of multiparameter micrograph analysis with synthetical benchmark images

To analyze multiparametric images of biological systems in vivo, advanced image processing, visualization and data mining tools are under development. To evaluate the algorithms, fit the parameters and assess the algorithm's accuracy, benchmark data sets supplied with a ground truth label are required. Because manual evaluation results, done by expensive experts are hard to achieve, new innovative strategies for evaluation are needed to allow the computer scientists to evaluate their algorithms. We propose an algorithm for the generation of synthetical multiparameter images of cell bodies. The algorithm generates a stack of intensity images, with different grey value characteristics from a reasonable set of parameters. Each image is composition of single synthesized grey value images. Tuning the parameters allow an individual incorporation of expert knowledge and supplies the developer with ground truth labels. We describe and motivate the algorithm and present three applications of evaluation tools to synthetical micrographs. The results show the approach's high relevance to application oriented image analysis in biomedicine.