Haar Pattern Based Binary Feature Descriptor for Retinal Image Registration

Abstract

Image registration is an important step in several retinal image analysis tasks. Robust detection, description and accurate matching of landmark points (also called keypoints) between images are crucial for successful registration of image pairs. This paper introduces a novel binary descriptor named Local Haar Patter of Bifurcation point (LHPB), so that retinal keypoints can be described more precisely and matched more accurately. LHPB uses 32 patterns that are reminiscent of Haar basis function and relies on pixel intensity test to form 256 bit binary vector. LHPB descriptors are matched using Hamming distance. Experiments are conducted on publicly available retinal image registration dataset named FIRE. The proposed descriptor has been compared with the state-of-the art Chen et al.'s method and ALOHA descriptor. Experiments show that the proposed LHPB descriptor is about 2% more accurate than ALOHA and 17% more accurate than Chen et al.'s method.