A semi-automated system based on level sets and invariant spatial interrelation shape features for Caenorhabditis elegans phenotypes

Caenorhabditis elegans shares several molecular and physiological homologies with humans and thus plays a key role in studying biological processes. As a consequence, much progress has been made in automating the analysis of C. elegans. However, there is still a strong need to achieve more progress in automating the analysis of static images of adult worms. In this paper, a three-phase semi-automated system has been proposed. As a first phase, a novel segmentation framework, based on variational level sets and local pressure force function, has been introduced to handle effectively images corrupted with intensity inhomogeneity. Then, a set of robust invariant symbolic features for high-throughput screening of image-based C. elegans phenotypes are extracted. Finally, a classification model is applied to discriminate between the different subsets. The proposed system demonstrates its effectiveness in measuring morphological phenotypes in individual worms of C. elegans.