Neuromorphometric characterization with shape functionals

TL;DR

This paper introduces a method using shape functionals to extract morphological features from neuronal cells, enabling effective classification of alpha and beta retinal ganglion cells based on their geometry across multiple scales.

Contribution

It presents a novel application of scale-invariant shape functionals, including Minkowski functionals and multiscale fractal analysis, for neuronal cell classification and morphological assessment.

Findings

Shape functionals effectively classify alpha and beta cells.

Scale-invariant measures capture relevant morphological features.

Method provides intraclass morphological insights.

Abstract

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.