Accurate Joint-Alignment of Indocyanine Green and Fluorescein Angiograph Sequences for Treatment of Subretinal Lesions

作者：Tsai Chia Ling; Hsu Hung Chuan; Wu Xin Chang; Chen Shih Jen; Lin Wei Yang^*

来源：IEEE Journal of Biomedical and Health Informatics, 2017, 21(3): 785-793.

DOI：10.1109/JBHI.2016.2538265

• Joint registration
• multimodality
• retinal angiography

In ophthalmology, aligning images in indocyanine green and fluorescein angiograph sequences is important for the treatment of subretinal lesions. This paper introduces an algorithm that is tailored to align jointly in a common reference space all the images in an angiogram sequence containing both modalities. To overcome the issues of low image contrast and low signal-to-noise ratio for late-phase images, the structural similarity between two images is enhanced using Gabor wavelet transform. Image pairs are pairwise registered and the transformations are simultaneously and globally adjusted for a mutually consistent joint alignment. The joint registration process is incremental and the success depends on the correctness of matches from the pairwise registration. To safeguard the joint process, our system performs the consistency test to exclude incorrect pairwise results automatically to ensure correct matches as more images are jointly aligned. Our dataset consists of 60 sequences of polypoidal choroidal vasculopathy collected by the EVEREST Study Group. On average, each sequence contains 20 images. Our algorithm successfully pairwise registered 95.04% of all image pairs, and joint registered 98.7% of all images, with an average alignment error of 1.58 pixels.

• 出版日期2017-5