Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3004-3007. doi: 10.1109/EMBC46164.2021.9630593.
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.
新生儿眼底检查非常重要，需要及时进行，以避免不可逆转的失明。眼科医生在一次检查中至少要检查每只眼睛的 5 张图像，这是一项耗时的任务。为了提高诊断效率，本文提出了一种基于改进的 Speeded Up Robust Features（SURF）的稳定而鲁棒的眼底图像拼接方法，并在临床应用中对其进行了真实评估。我们的方法的特点是在配准过程中避免在配对图像的非重叠区域中无用的特征点检测和提取。实验表明，该方法成功地对 13 名筛查新生儿的 22 只眼中的 110 个不同视场（FOV）图像对进行了配准，获得了 93.51%的归一化相关系数和 1.2557 的归一化互信息。此外，总融合成功率达到 86.36%，并采用主观视觉评估方法由三位专家对融合性能进行评估，获得了 84.85%的接受率。我们提出的方法在临床应用中的性能证明了其准确性和有效性，这将对眼科医生的诊断有很大帮助。
