Experiments on model organisms are used to extend the understanding of complex biological processes. In Caenorhabditis elegans studies, populations of specimens are sampled to measure certain morphological properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. The images are affected by clutter, overlap and image degradations. In this paper, we exploit shape and appearance differences between C. elegans and non-C. elegans segmentations. Shape information is captured by optimizing a parametric open contour model on training data. Features derived from the contour energies are proposed as shape descriptors and integrated in a probabilistic framework. These descriptors are evaluated for C. elegans detection in culture images. Our experiments show that measurements extracted from these samples correlate well with ground truth data. These positive results indicate that the proposed approach can be used for quantitative analysis of complex nematode images.