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Project Elegance: Computer Vision with C. Elegans

The aim of this project is to build some tools that would assist in analysing the behaviour of the worm (Caenorhabditis Elegans) in real-time. The particular focus is developing an algorithm that can track the worm as it moves in a three-dimensional environment.

The animation below is split into three parts: The top part is the raw video, the bottom left is the difference between successive video frames, and the bottom right is the tracking algorithm applied on the video (notice the white squares).

Project Elegance was one of my summer internship projects and was halted due to a shift in business needs so the algorithm remains incomplete.

Key Features

  • Implementation of several image filtering algorithms to track the worm.
  • Capability of pre-rendering the 2D images for each tracking algorithm.
  • Playing the 2D images as an animation using pyqtgraph.
  • Ability to generate test images to study the tracking algorithms.

Requirements (see requirements.txt)

  • Python 3+
    • OpenCV 3.1.0
    • pyqtgraph 0.10.0

Authors

  • Author: Othman Alikhan, oz.alikhan@gmail.com
  • Supervisors: Dr. Netta Cohen, Dr. Thomas Ranner, Dr. Robert Holbrook

Credits

  1. Input data (C. elegans movie): Movie S1 from Kwon N, Pyo J, Lee S, Je J (2013). "3-D Worm Tracker for Freely Moving C. elegans". PLOS ONE. DOI:10.1371/journal.pone.0057484. PMID 23437394.