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.
