Retinal image registration feature descriptors-a survey

Image registration is the process of matching two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and align these images into one image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Feature descriptors are mainly used for the feature based registration. Feature descriptors are used to detect local features in each of the input images, to find corresponding feature pairs in different images and it uses the feature correspondences to align the images. This paper analyses about the retinal image registration and the comparison of some of the feature descriptors used for retinal image registration. The descriptors discussed here are Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)-SIFT, speeded up robust features (SURF) and salient feature region descriptor (SFR). The performance of the feature descriptors are compared and average time required for feature descriptors are tabulated.