Authors: Mark R. Winter; Cheng Fang; Gary Banker; Badrinath Roysam; Andrew R. Cohen
Addresses: Department of Electrical Engineering and Computer Science, University of Wisconsin Milwaukee, Milwaukee, WI 53211, USA. ' Jungers Center for Neurosciences Research, Oregon Health and Science University, Portland, OR 97239, USA. ' Jungers Center for Neurosciences Research, Oregon Health and Science University, Portland, OR 97239, USA. ' Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA. ' Department of Electrical Engineering and Computer Science, University of Wisconsin Milwaukee, Milwaukee, WI 53211, USA
Abstract: Multitemporal Association Tracking (MAT) is a new graph-based method for multitarget tracking in biological applications that reduces the error rate and implementation complexity compared to approaches based on bipartite matching. The data association problem is solved over a window of future detection data using a graph-based cost function that approximates the Bayesian a posteriori association probability. MAT has been applied to hundreds of image sequences, tracking organelle and vesicles to quantify the deficiencies in axonal transport that can accompany neurodegenerative disorders such as Huntington's Disease and Multiple Sclerosis and to quantify changes in transport in response to therapeutic interventions.
Keywords: organelle tracking; axonal organelle transport; multi-target tracking; bipartite matching; bioimage informatics; multitemporal association tracking; vesicle tracking; image sequences; neurodegenerative disorders; Huntington's disease; multiple sclerosis; therapeutic intervention; graph based cost function.
International Journal of Computational Biology and Drug Design, 2012 Vol.5 No.1, pp.35 - 48
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 16 Mar 2012 *