Image Processing with Spiking Neuron Networks

International audienceArtificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation.Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach

Segmentation of Histopathological Sections using Snakes

This paper presents a semi-automatic method for segmentation of digital images. The segmentation method is based on snakes and a novel implementation of the snake evolution algorithm is presented. Analytical expressions describing the snake evolution are derived using the Fourier transform. These expressions can be sampled and used in a fast algorithm for snake propagation. Experiments are carried out on images of histopathological tissue sections and the results are very promising. In particular the method is able to cope with overlapping nuclei