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Axonal tree classification using an Elastic Shape Analysis based distance

The analysis of the morphological differences between normal and pathological neuronal structures is of paramount importance. Some methods for the comparison of axonal trees only take into account topological information (such as TED), while others also include geometrical information (such as Path2Path). In a previous work, we have presented a new method for comparing tree-like shapes based on the Elastic Shape Analysis Framework (ESA). In this paper, we extend this method by computing the mean shape of a population. Moreover, we propose to evaluate and compare these 3 approaches (TED, Path2Path and ESA) with a classification scheme based on feature computation and K-means. We evaluate these approaches on a database of 44 real 3D confocal microscopy images of two populations of neurons. Results show that the proposed method distinguishes better between the two populations.