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Automation and reliability are the two main requirements when computers areapplied in Life Sciences. In this paper we report on an application to neuronrecognition, an important step in our long-term project of providing softwaresystems to the study of neural morphology and functionality from biomedicalimages. Our algorithms have been implemented in an ImageJ plugin calledNeuronPersistentJ, which has been validated experimentally. The soundness andreliability of our approach are based on the interpretation of our processingmethods with respect to persistent homology, a well-known tool in computationalmathematics.

Neuron detection in stack images: a persistent homology interpretation