Features Events

Worm Features and Events

In our system, after tracking the results are fed to the Features and Events module for quantitative analysis. Our automated system quantitates features describing the worm shape(morphology,posture), motion(movement, bending or waving), and events(reversal,omega bend or foraging), etc. Most features are collected from the literature(references below) or requested by the Biologists, and some are based on our old features : Old Worm Features

Features and Events List

All the features and events measured by the automated system are listed below. Details about the computation of some features can be found in next section.

Algorithms and Examples

Track and SktAgl Feature Sets

Both feature sets are based on the skeleton curve after rotation(so the resulting skewer line (line passing through centroid and parallel to the line liking head and tail) becomes the x-axis) and offset(so the resulting centroid becomes the origin).

• Track:
• SkeAgl:

Figure 1: Rotated and offset skeleton curve for Track and SktAgl features computation

Figure 2: Examples for features computed based on the rotated and offset skeleton curve

Symmetry and Curvature Feature Sets

Figure 3: Information used for Symmetry and Curvature features computation

Figure 4: Examples for Symmetry and Curvature features

Source and Waving Feature Sets

Figure 5: Information used for Source features calculation

Figure 6: Examples for Source and Waving features

Other Features

• Bending Frequency:

Figure 7: Curvature as a function of position along skeleton curve and time, left panel shows the curvature heatmap of Worm 1(Blue one in the figure below), right panel shows the heatmap of Worm 2(Red one in the figure below)

Figure 8: An example for some derived features and events

References

• [1] Nicolas Roussel, A Computational Model for C.elegans locomotory behavior: Application to Multi-Worm tracking. Phd Thesis 2007.
• [2] Christopher J Cronin, An automated system for measuring parameters of nematode sinusoidal movement. BMC Genet 2005.
• [3] Huang KM, Cosman P, Schafer WR, Machine vision based detection of omega bends and reversals in C. elegans. Journal of Neuroscience Methods, Vol. 158, Issue 2, pp. 323-336, December 2006.
• [4] Wei Geng, Pamela Cosman, Automatic Tracking, Feature Extraction and Classification of C. elegans Phenotypes. IEEE Transactions on Biomedical Engineering, Vol. 51, No. 10, pp. 1811--1820, October 2004.
• [5] Huang KM, Cosman P, Schafer WR, Automated detection and analysis of foraging behavior in Caenorhabditis elegans. Journal of Neuroscience Methods,2008.
• [6]Ebraheem Fontaine, Automated visual tracking for studying the ontogeny of zebrafish swimming. Journal of Experimental Biology, 2008.
• [7] W.H. Wang, Y. Sun, A Micropositioning System with Real-Time Feature Extraction Capability for Quantifying C. elegans *Locomotive Behavior. IEEE International Conference on Automation Science and Engineering, 2007. CASE 2007.
• [8] Zhaoyang Feng, Quantitative analysis of C. elegans: Algorithms to calculate behavioral and morphological features. BMC Bioinformatics 2004.
• [9] Zhaoyang Feng, An imaging system for standardized quantitative analysis of C.elegans behavior. BMC Bioinformatics 2004.
• [10] Jesse M. Gray, Joseph J. Hill, A circuit for navigation in Caenorhabditis elegans. PNAS 2005.