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QuantWorm: A Comprehensive Software Package for Caenorhabditis elegans Phenotypic Assays

Figure 2

QuantWorm image processing algorithms.

(A) WormLifespan. Two time-lapse images are obtained, and a differential image is created by subtracting the second time-lapse image from the first time-lapse image. Independently, individual worms are detected in the second image and are defined as region of interest (ROI). Worm movement is determined by counting the number of white pixels. (B) WormLocomotion. Image frames from videos are binarized and region-extracted to detect objects. Worms are indentified by analyzing the morphology of detected objects. An individual worm track is constructed by connecting all centroid points of a moving worm. (C) WormLength. Source image is binarized, and worms are detected by region extraction and shape analysis. Once a worm object is identified, a skeleton curve is created through the middle of the worm. The length of the worm is calculated by measuring the length of the skeleton curve (D) WormEgg. Single eggs are detected by applying edge detection, gap filling, flood filling, and morphology analysis. Egg detection parameters are determined by analyzing the detected single eggs. Multi-thresholding binarization is applied to create multiple binary images from which eggs are detected. Results are compiled to conduct clustering to identify highly probable eggs and remove duplicate findings.

Figure 2

doi: https://doi.org/10.1371/journal.pone.0084830.g002