Laura Leal-Taixé, Matthias Heydt, Sebastian Weiße, Axel Rosenhahn, and Bodo Rosenhahn
Digital in-line holography is a 3D microscopy technique which has gotten an increasing amount of attention over the last few years in the fields of microbiology, medicine and physics. In this paper we present an approach for automatically classifying complex microorganism motions observed with this microscopy technique. Our main contribution is the use of Hidden Markov Models (HMMs) to classify four different motion patterns of a microorganism and to separate multiple patterns occurring within a trajectory. We perform leave-one-out experiments with the training data to prove the accuracy of our method and to analyze the importance of each trajectory feature for classification. We further present results obtained on four full sequences, a total of 2500 frames. The obtained classification rates range between 83.5% and 100%.
