Neonatal Fundus Image Registration and Mosaic Using Improved Speeded Up Robust Features Based on Shannon Entropy

Fundus examination of the newborn is quite important, which needs to be done timely so as to avoid irreversible blindness. Ophthalmologists have to review at least five images of each eye during one examination, which is a time-consuming task. To improve the diagnosis efficiency, this paper proposed a stable and robust fundus image mosaic method based on improved Speeded Up Robust Features (SURF) with Shannon entropy and make real assessment when ophthalmologists used it clinically. Our method is characterized by avoiding the useless detection and extraction of the feature points in the non-overlapping region of the paired images during registration process. The experiments showed that the proposed method successfully registered 90.91% of 110 different field of view (FOV) image pairs from 22 eyes of 13 screening newborns and acquired 93.51% normalized correlation coefficient and 1.2557 normalized mutual information. Also, the total fusion success rate reached 86.36% and a subjective visual assessment approach was adopted to measure the fusion performance by three experts, which obtained 84.85% acceptance rate. The performance of our proposed method demonstrated its accuracy and effectiveness in the clinical application, which can help ophthalmologists a lot during their diagnosis.