Alternate Title

Suppresion of the Eyelash Artifact in Ultra Wide Field Retinal Images


We present a physically motivated approach to modeling the structure as well as dynamic movements of the nematode C. elegans as observed in time-lapse microscopy image sequences. The model provides a flexible description of a broad range of worm shapes and movements, and provides strong constraints on feasible deformation patterns to image-based segmentation, morphometry, and tracking algorithms. Specifically, we model the predictable pattern of deformations of the spinal axis of the nematodes. Model based algorithms are presented for segmentation and simultaneous tracking of an entire imaging field containing multiple worms, some of which may be interacting in complex ways. Central to our method is the observation that the spinal axis undergoes deformations obeying a pattern that can be modeled thus reducing the complexity of the measurement process from one frame to the next. Tracking is performed using a recursive Bayesian filter that performs well in the presence of clutter. Interaction between worms lead to unpredictable behaviors that are resolved using a variant of multiple-hypothesis tracking. The net result is an integrated method to understand and quantify worm interactions. Experimental results indicate that the proposed algorithms are demonstrably robust to the presence of imaging artifacts and clutter, such as old worm tracks, in the field. An edits-based validation strategy was used to quantify the algorithm performance. Overall, the method provides the basis for high-throughput automated observation, morphometry, and locomotory analysis of multiple worms in a wide field, and a new range of quantification metrics for nematode social behaviors.


Poster presented at the 2007 Thrust R2B Localized Probing and Mosaicing Methods Conference


C. elegans Locomotory, Tracking

Subject Categories

Microscopy, Diagnostic imaging


Biomedical Engineering and Bioengineering


Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

Publication Date


Rights Holder

Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

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