Classification of Swimming Microorganisms Motion Patterns in 4D Digital In-Line Holography Data

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-oneout 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%.

• Type: Journal
• Year: 2010
• Where: DAGM
• Authors: Laura Leal-Taixé, Matthias Heydt, Sebastian Weiße, Axel Rosenhahn, Bodo Rosenhahn
• Keywords: Complex Microorganism Motions, DAGM 2010, Digital In-line Holography, Image Processing, Microscopy Technique