Automatic Design of Aperture Filters Using Neural Networks Applied to Ocular Image Segmentation

Abstract

Aperture filters are image operators which combine mathematical morphology and pattern recognition theory to design windowed classifiers. Previous works propose designing and representing such operators using large decision tables and classic linear pattern classifiers. These approaches demand an enormous computational cost in order to solve real image problems. The current work presents a new method to automatically design Aperture filters for color and grayscale image processing. This approach consists of designing a family of Aperture filters using artificial feed-forward neural networks. The resulting Aperture filters are combined into a single one using an ensemble method. The performance of the proposed approach was evaluated by segmenting blood vessels in ocular images of the DRIVE database. The results show the suitability of this approach: It outperforms window operators designed using neural networks and logistic regression as well as Aperture filters designed using logistic regression and support vector machines.