Automatic Classification of Traced Neurons Using Morphological Features

Authors

  • José Daniel López-Cabrera Centro de Estudios de Informática, Universidad Central “Marta Abreu” de Las Villas
  • Juan Valentin Lorenzo-Ginori Centro de Estudios de Informática, Universidad Central “Marta Abreu” de Las Villas

DOI:

https://doi.org/10.13053/cys-21-3-2495

Keywords:

Neuron tracing, morphological features, feature selection, automatic classification, non- parametric tests

Abstract

The great advances in the field of neuron tracing have made possible a high availability of free-access data in the Internet, which encourages the realization of automatic classifications. The increase of neuronal reconstruction databases makes the manual classification of neurons a time-consuming and tedious task for human experts. Classification by human experts is also prone to inter- and intra-analyst variability due to the process’ inherent subjectivity. In this context, the need arises to find new descriptors having discriminative properties which allow separating the various neuron classes, and this constitutes currently an open problem.  Such descriptors would contribute to improve the results of automatic classification. In this study the attention is focused on the use of new morphological features in supervised classification of traced neurons. Furthermore, we present a comparative analysis of different supervised learning algorithms oriented to the classification of reconstructed neurons. The results were validated using a non-parametric statistical test and show the usefulness of the proposed solution.

Author Biographies

José Daniel López-Cabrera, Centro de Estudios de Informática, Universidad Central “Marta Abreu” de Las Villas

Centro de Estudios de Informática, Instructor

Juan Valentin Lorenzo-Ginori, Centro de Estudios de Informática, Universidad Central “Marta Abreu” de Las Villas

Centro de Estudios de Informática, Profesor Titular Consultante.

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Published

2017-09-28

Issue

Vol. 21 No. 3 (2017): Thematic issue: Advances in Logic-based Methods for Intelligent Systems (J. C. Nieves, E. Hermann Haeusler, B. Lopes)

Section

Articles

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