  This work presents a procedure to extract morphological information from
neuronal cells based on the variation of shape functionals as the cell geometry
undergoes a dilation through a wide interval of spatial scales. The targeted
shapes are alpha and beta cat retinal ganglion cells, which are characterized
by different ranges of dendritic field diameter. Image functionals are expected
to act as descriptors of the shape, gathering relevant geometric and
topological features of the complex cell form. We present a comparative study
of classification performance of additive shape descriptors, namely, Minkowski
functionals, and the nonadditive multiscale fractal. We found that the proposed
measures perform efficiently the task of identifying the two main classes alpha
and beta based solely on scale invariant information, while also providing
intraclass morphological assessment.
