Contour energy features for recognition of biological specimens in population images

Abstract

In this paper we present an approach to perform automated analysis of nematodes in population images. Occlusion, shape variability and structural noise make reliable recognition of individuals a task difficult. Our approach relies on shape and geometrical statistical data obtained from samples of segmented lines. We study how shape similarity in the objects of interest, is encoded in active contour energy component values and exploit them to define shape features. Without having to build a specific model or making explicit assumptions on the interaction of overlapping objects, our results show that a considerable number of individual can be extracted even in highly cluttered regions when shape information is consistent with the patterns found in a given sample set.