Multi-worm tracking using superposition of merit functions

Linda Ivonne Rivera; Benjamin Potsaid; John Ting-Yung Wen

doi:10.1117/12.816468

17 November 2008 Multi-worm tracking using superposition of merit functions

Linda Ivonne Rivera, Benjamin Potsaid, John Ting-Yung Wen

Proceedings Volume 7266, Optomechatronic Technologies 2008; 72661C (2008) https://doi.org/10.1117/12.816468
Event: International Symposium on Optomechatronic Technologies, 2008, San Diego, California, United States

Abstract

Traditional solutions for long term imaging of living small biological specimens and microorganisms lack efficiency due to computationally expensive algorithms, and field of view limitations in optical microscopes. This paper describes a new algorithm that allows for real time tracking of multiple 1mm nematodes called Caenorhabditis elegans with a novel optical microscope design called the Adaptive Scanning Optical Microscope (ASOM), developed at the Center for Automation Technologies and Systems (CATS). Based on the real time experimentation, an improved algorithm to track multiple worms in the presence of entanglements is generated. The stages of this development start with an enhanced digital motion controller for the ASOM high speed scanning mirror to suppress undesired vibrations that limit the system capacity to track multiple organisms. The second phase is the integration of the ASOM apparatus, the high speed motion control, and a base tracking algorithm, all which allows for rapid image acquisition to track multiple C. elegans in real time. The base algorithm was developed at CATS and has been proven to track a single C. elegans in real time. Results demonstrating the efficacy of the complete system are presented. Lastly, an enhanced tracking algorithm is described that shows improved accuracy and robustness in tracking worms even when they become entangled. Taking into account the unique ASOM design, individual segments of the worm are tracked throughout an image sequence, and a mosaic pattern covering the entire worm is subsequently created. The algorithm takes advantage of geometric and dynamic knowledge of the C. elegans such as size, and movement patterns. The enhanced algorithm is tested on previously recorded footage. Simulated tracking experiments also illustrate the effectiveness of the enhanced algorithm and are presented.

Citation Download Citation

Linda Ivonne Rivera, Benjamin Potsaid, and John Ting-Yung Wen "Multi-worm tracking using superposition of merit functions", Proc. SPIE 7266, Optomechatronic Technologies 2008, 72661C (17 November 2008); https://doi.org/10.1117/12.816468

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