An Efficient Feature Point Registration Method for Retinal Images
Image registration is a fundamental problem to several image processing and computer vision applications. In the case of medical imaging, disease diagnosis and treatment planning are often supported by multiple images acquired from the same patient. Image registration techniques, hence, are needed in order to integrate the information gained from several images to obtain a comprehensive understanding. A broad range of image registration methods has been proposed for different medical imaging applications including retinal image registration. In this paper, we have developed a robust registration algorithm for estimating a rigid transformation using genetic algorithms. The aim is to develop a highly accurate and automated feature point image registration method of retinal images. Feature detection is first performed based on vessel blood segmentation using the Kirsch edge operator, followed by a matching process. Then for registration, we have adopted a genetic algorithm as an optimization method to estimate the optimal parameters of the rigid transformation. The simulation results have shown that this method is really efficient for registration of retinal images.
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