Neuron detection in stack images: a persistent homology interpretation

TL;DR

This paper introduces a novel neuron detection method in stack images using persistent homology, implemented as an ImageJ plugin, enhancing automation and reliability in neural morphology analysis.

Contribution

The paper presents a new neuron recognition algorithm based on persistent homology, validated through experimental testing in biomedical image analysis.

Findings

Successful implementation of NeuronPersistentJ plugin

Validated approach with experimental results

Enhanced reliability in neuron detection

Abstract

Automation and reliability are the two main requirements when computers are applied in Life Sciences. In this paper we report on an application to neuron recognition, an important step in our long-term project of providing software systems to the study of neural morphology and functionality from biomedical images. Our algorithms have been implemented in an ImageJ plugin called NeuronPersistentJ, which has been validated experimentally. The soundness and reliability of our approach are based on the interpretation of our processing methods with respect to persistent homology, a well-known tool in computational mathematics.