Automatic registration of retina images based on genetic techniques.

The aim of this paper is to develop an automatic method for the registration of multitemporal digital images of the fundus of the human retina. The images are acquired from the same patient at different times by a color fundus camera. The proposed approach is based on the application of global optimization techniques to previously extracted maps of curvilinear structures in the images to be registered (such structures being represented by the vessels in the human retina): in particular, a genetic algorithm is used, in order to estimate the optimum transformation between the input and the base image. The algorithm is tested on two different types of data, gray scale and color images, and for both types, images with small changes and with large changes are used. The comparison between the registered images using the implemented method and a manual one points out that the proposed algorithm provides an accurate registration. The convergence to a solution is not possible only when dealing with images taken from very different view-points.

Parallel consensual neural networks.

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

Parallel principal component neural networks for classification of event-related potential waveforms.

Artificial neural networks (ANNs) are discussed in terms of classification of brain auditory event-related potentials (ERPs). A new ANN architecture for the classification of ERPs is proposed. The new architecture is called the parallel principal component neural network (PPCNN). The use of the PPCNN for classification of ERP data obtained from both normal control subjects and chronic schizophrenic patients is discussed. Experimental results are given.