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An Automated Point Set Registration Framework for Multimodal Retinal Image

Multimodal retinal image registration plays an important role in medical image analysis. In this field, retinal images from different modalities are aligned together to achieve a more evaluable fusion image for diagnoses. One of the challenging problem solved in this paper is the low success rate in multimodal retinal image registration. An automated point set registration framework is proposed to solve the problem. The framework includes three parts: feature point extraction and robust initial point matching, matching postprocessing, adaptive mismatches removing and transformation estimation. The experimental results show that our proposed framework is robust to outliers and repeated pattern and it obtains a more stable and accurate result than state-of-the-art methods.