General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images

Abstract

The local binary patterns (LBP) operator is a powerful multi-resolution micro-texture descriptor, which can be applied to many image-processing applications. However, existing LBP operators cannot use the information of non-uniform patterns efficiently. This paper presents a general extension of LBP operator to extract all uniform and non-uniform pattern types by using suitable rotation-invariant labeling scheme. Since the proposed LBP operator can extract all micro-texture structures, we combined it with artificial neural networks (ANN) to present a new supervised technique for automatic blood vessel enhancement and detection. The thin and thick blood vessels are detected by applying proper top-hat transform and length filtering on the enhanced blood vessels. The performance of the proposed method is evaluated on manually labeled images of the publicly available DRIVE and STARE databases and compared with several state-of-the-art approaches. The obtained results show the high accuracy of the proposed method on detecting thin and thick blood vessels.