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Inexpensive, Easy-To-Use, and Feature-Rich Worm Tracking

WORM TRACKER 2.0. Inexpensive, Easy-To-Use, and Feature-Rich Worm Tracking. Part I. A Short Overview. The Benefits. Inexpensive Easy-to-Use Feature Rich A simple pipeline to Analysis Well-Documented (easily extended, updated, and maintained). Inexpensive.

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Inexpensive, Easy-To-Use, and Feature-Rich Worm Tracking

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  1. WORM TRACKER 2.0 Inexpensive, Easy-To-Use, and Feature-Rich Worm Tracking

  2. Part I A Short Overview

  3. The Benefits • Inexpensive • Easy-to-Use • Feature Rich • A simple pipeline to Analysis • Well-Documented (easily extended, updated, and maintained)

  4. Inexpensive • Works FAST and beautifully with CHEAP (<$30) USB cameras. • Supports most stages: • Ludl & Prior now • National Instruments/Parker later • Works well with any computer: • Windows, Mac, & Unix variants • Does NOT require any software purchases.

  5. Easy-To-Use • Plug-n-Play • Plug the camera and stage into the PC. • Download and run the software. • Automatic Calibration • Stage-to-Pixels • Pixels-to-Microns • Tracking • Simple functionality is on the main screen. • Advanced Options and tweaks are also easily accessible.

  6. Attaching Your USB Camera What Do You Need?

  7. Attaching Your USB Camera ( Don’t worry it’ll be on the web page  ) 1. Open it up. 2. Remove the lens. 3. Add glue. 4. Glue on the C-mount.

  8. Voila!

  9. Show Me The Video!

  10. Feature Rich LIMITS • NO Recording Limits • Unlimited Length (as long as you have space) • ANY Frame Rate & Resolution • Track EVEN in Difficult Conditions, e.g.: • Young Worms • Thick Food • Contamination • Plate Edges • Time-Lapse recording. • An easy pipeline to our Analysis software • Over 100 Features extracted.

  11. More Features (x,y) = • Continually Log the worm’s Real-World Location. • Snap images in many formats: • JPG, GIF, BMP, PNG • Advanced control over recording: • Resolution • Video Format • Frame Rate • Time/Frame/Intermittent Lengths • Advanced control over tracking: • Boundaries • Thresholds • Coordinate stage movements with recording to Minimize Blur

  12. Standardization • Identical Video & Tracking Data REGARDLESS of the Hardware & OS Configuration • Allows Searchable Databases of videos & analyses • Dry Lab Experimentation • Phenotypic Identification • Cross-Experimental Comparisons • Meta Studies • Open Source for reuse (e.g., A Neuronal Imaging Tracker)

  13. Extensive Documentation • Design overview • FAQ • Tutorials • Troubleshooting guide • Javadoc Web Pages • ANY Java programmer can Understand, Edit, & Extend the code • Easy Updates & Maintenance

  14. Example:Supporting a New Motorized Stage (A Tutorial Document will be Included with the Software) • Fill in a motorized stage wrapper. • ~5 Lines of Java • Translate “Move” into the stage’s language • A text command for the serial port. • ~1 Line of Java • Or, call the API to move the stage. • A Few Lines of code

  15. Part II The Details

  16. Tracking Overview

  17. Worm Tracker 2.0 Java Media Framework USB Camera USB Camera USB Camera The Java Media Framework • Supports most USB Cameras • Synchronizes multiple data sources • Cameras, microphones, etc. • Can Multiplex and Combine data sources Example: 3 synchronized videos • Cyan & yellow filtered fluorescent neurons. • A low magnification worm behavior video. • Monitor while recording for Real-Time Video Analysis

  18. The Tracking Algorithm • Convert the image to Grayscale. • Find the Worm: • Threshold to find the foreground (worm). • Find an appropriate “8-connected” component. • FindMotion: • Ignore tracked in/out image boundaries. • Subtract successive frames (motion). • Threshold to find movement (worm). • Find large 8-connected components. • Re-Center the worm upon boundary violation.

  19. Adaptive Thresholding • The Otsu Method • Assume a bimodal distribution of pixels (worm & background). • Find a threshold to split the modalities: • Maximize the variance between modalities. • Minimize the variance within modalities. • O(# of pixels) – Optimal!

  20. Adaptive Thresholding

  21. 8-Connected Components • 8 Neighbors (vs. 4 – no diagonals) • “Two Strategies to Speed Up Connected Component Labeling”, 2005, Wu et al. • 1 forward scan • Union find • Decision trees • Sequential memory access • Area and Boundary discovery during the scan. • O(# of Pixels) – Optimal!

  22. Movement Detection • Calibrate to Ignore Video Noise Automatically establish thresholds: • Minimum area • ∆ pixel value • Subtract successive Frames • Motion = large 8-connected components where: | ∆ pixel value | > 0 • Move stage to Keep Motion In Bounds

  23. Movement Detection

  24. Acknowledgements • Chris Cronin • Ryan Lustig • Kathleen, Katie, Callie, & Andrew • Bill Schafer & Paul Sternberg • Yechiam Yemini (My Dad) • Zhaoyang (John) Feng

  25. Contact Us • Email • Ev • eyemini@ucsd.edu • Chris • cjc@caltech.edu • WWW • http://sourceforge.net/projects/worm-tracking

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