Incremental Learning of the Different Dynamic Signatures of Mitochondrial Movement in Drug Discovery and System Biology

Abstract

In this paper, we describe how prototype-based classification can be used for knowledge acquisition and automatic image classification. We developed the prototypical methods and techniques of the system in order to serve the special development issues of an expert when starting a new image-based application. Often an expert can present a catalogue of prototypical images instead of a large enough image data base for setting up the system. Starting with the set of prototypical images we can learn the important image features and the concept description of an image class. Win this paper, we describe the necessary functions a prototype-based classifier should have. Besides the similarity calculated based on the numerical image features we introduce the experts estimated similarity as new knowledge piece and a new function that optimizes between this similarity and the automatically calculated similarity by the system in order to improve the system accuracy. This function reduces the influence of the uncertainty in the calculated features and the similarity measure and brings the similarity value closer to the true similarity value. The test of the system is done on the study of the internal mitochondrial movement of cells. The basis for the development are fluorescent cell images. The aim was to discover the different dynamic signatures of mitochondrial movement. For this application the expert knows from the literature how the different signatures should look like and based on this knowledge he picks prototypical images from his experiment. We present our results and give an outlook for future work.