This paper presents a review of automated image registration methodologies that have been
used in the medical field. The aim of this paper is to be an introduction to the field, provide …

This paper presents a new supervised method for blood vessel detection in digital retinal
images. This method uses a neural network (NN) scheme for pixel classification and …

Diabetic retinopathy (DR) is one of the leading cause of visual impairments in the working
age population in the developed world. It is a complication of both types of diabetes mellitus …

Recently, many methods based on hand-designed convolutional neural networks (CNNs)
have achieved promising results in automatic retinal vessel segmentation. However, these …

Motivated by the goals of improving detection of low-contrast and narrow vessels and
eliminating false detections at nonvascular structures, a new technique is presented for …

In this paper, motivated by the problem of multimodal retinal image registration, we introduce
and improve the robust registration framework based on partial intensity invariant feature …

This paper presents a novel feature-based method that is built upon a convolutional neural
network (CNN) to learn the deep representation for multimodal retinal image registration. We …

Deep learning has become an important tool for image classification and natural language
processing. However, the effectiveness of deep learning is highly dependent on the quality …

Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners
such as edge-driven dual-bootstrap iterative closest point and Harris-partial intensity …

Motivated by the need for multimodal image registration in ophthalmology, this paper
introduces an algorithm which is tailored to jointly align in a common reference space all the …