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Automatic tracking, feature extraction and classification of C elegans phenotypes.

Wei Geng·Pamela Cosman·Charles C Berry·Zhaoyang Feng·William R Schafer
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Abstract

This paper presents a method for automatic tracking of the head, tail, and entire body movement of the nematode Caenorhabditis elegans (C. elegans) using computer vision and digital image analysis techniques. The characteristics of the worm's movement, posture and texture information were extracted from a 5-min image sequence. A Random Forests classifier was then used to identify the worm type, and the features that best describe the data. A total of 1597 individual worm video sequences, representing wild type and 15 different mutant types, were analyzed. The average correct classification ratio, measured by out-of-bag (OOB) error rate, was 90.9%. The features that have most discrimination ability were also studied. The algorithm developed will be an essential part of a completely automated C. elegans tracking and identification system.

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AlgorithmsAnimalsArtificial IntelligenceCaenorhabditis elegansHeadImage Interpretation, Computer-AssistedLocomotionMicroscopy, VideoMovementPattern Recognition, AutomatedPhenotypeReproducibility of ResultsSensitivity and SpecificitySpecies SpecificityTailComparative StudyEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, P.H.S.Validation Study

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Wei Geng, Pamela Cosman, Charles C Berry, Zhaoyang Feng, William R Schafer. 2004. Automatic tracking, feature extraction and classification of C elegans phenotypes.. https://doi.org/10.1109/tbme.2004.831532

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