Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay
Christian Bodenstein, Markus Goetz, Annika Jansen, Henrike Scholz, Morris Riedel
In conference proceedings of the 15th IEEE International Conference on Machine Learning and Applications, IEEE ICMLA’16, Anaheim, USA, 18 Dec 2016 – 20 Dec 2016, ISBN 978-1-5090-6167-9, pp. 746 – 751, 2017
[ DOI ] [ Juelich ]
In this paper, we propose an instrumentation and computer vision pipeline that allows automatic object detection on images taken from multiple experimental set ups. We demonstrate the approach by autonomously counting intoxicated flies in the FLORIDA assay. The assay measures the effect of ethanol exposure onto the ability of a vinegar fly Drosophila melanogaster to right itself. The analysis consists of a three-step approach.
First, obtaining an image of a large set of individual experiments, second, identify areas containing a single experiment, and third, discover the searched objects within the experiment. For the analysis we facilitate well-known computer vision and machine learning algorithms—namely color segmentation, threshold imaging and DBSCAN. The automation of the experiment enables an unprecedented reproducibility and consistency, while significantly decreasing the manual labor.