An AutoAssociative Neural Network for Image Segmentation

In this paper, it is proposed a neural network based on by AutoAssociative Pyramidal Neural Network and their architecture, which uses concepts of receptive fields and autoassociative memory. These concepts are widely used in models of artificial neural networks and were incorporated into model proposed in this work. Furthermore, the proposed neural network also uses the concept of sharing weights aiming the applications on problems invariant translations. The neural network is able of perform implicit feature extraction and learns how to reconstruct a pattern of such features. The evaluation of the neural network is performed by two experiments. The first experiment is conducted with image processing problems. The Neural Network Autoassociative learns about the transformation applied to the images, mapping a domain of images to another. In the second experiment the AutoAssociative Neural Network gets satisfactory results in image segmentation. The second task uses the dataset of skin lesion images for segmentation. This work indicates that proposed model of neural network is valid due to the obtained results achieved in the performed experiments.