Institute of Computer Vision and Applied Computer science IBaI

Leipzig, Germany

Institute of Computer Vision and Applied Computer science IBaI

Leipzig, Germany

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Perner P.,Institute of Computer Vision and Applied Computer science IBaI
Quality and Reliability Engineering International | Year: 2014

Data mining methods are widely used across many disciplines to identify patterns, rules, or associations among huge volumes of data. Data mining methods with explanation capability such as decision tree induction are preferred in many domains. The aim of this paper is to discuss how to deal with the result of decision tree induction methods. This paper has been prompted by the fact that domain experts are able to use the tools for decision tree induction but have great difficulties in interpreting the results. When the domain expert has learnt two decision trees that are from the same domain but based on different data sets as a result of further data collection, he is faced with the problem of how to interpret the different trees. The comparison of two decision trees is therefore an important issue as the user needs such a comparison in order to understand what has changed. We have proposed to provide him with a measure of correspondence between the two trees that allows him to judge if he can accept the changes or not. In this paper, we propose a proper similarity measure. In case of a low similarity value, the expert has evidence to start exploring the reason for this change. Often, he can find things in the data acquisition that might have resulted in some noise and might be fixed. Copyright © 2014 John Wiley & Sons, Ltd.


Perner P.,Institute of Computer Vision and Applied Computer science IBaI
Procedia Computer Science | Year: 2015

Live-cell assays are used to study the dynamic functional cellular processes in High-Content Screening (HCA) of drug discovery processes or in computational biology experiments. The large amount of image data created during the screening requires automatic image-analysis procedures that can describe these dynamic processes. One class of tasks in this application is the tracking of cells. We describe in this paper a fast and robust cell tracking algorithm applied to High-Content Screening in drug discovery or computational biology experiments. We developed a similarity-based tracking algorithm that can track the cells without an initialization phase of the parameters of the tracker. The similarity-based detection algorithm is robust enough to find similar cells although small changes in the cell morphology have been occurred. The cell tracking algorithm can track normal cells as well as mitotic cells by classifying the cells based on our previously developed texture classifier. Results for the cell path are given on a test series from a real drug discovery process. We present the path of the cell and the low-level features that describe the path of the cell. This information can be used for further image mining of high-level descriptions of the kinetics of the cells. © 2015 The Authors. Published by Elsevier B.V.

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