Liu Ruixue, Wang Xiaolin, Hoshi Sujin, Zhang Yuhua
Doheny Eye Institute, Pasadena, CA 91103, USA.
Department of Ophthalmology, University of California - Los Angeles, Los Angeles, CA 90024, USA.
Biomed Opt Express. 2024 Jan 31;15(2):1311-1330. doi: 10.1364/BOE.514447. eCollection 2024 Feb 1.
Precise registration and montage are critical for high-resolution adaptive optics retinal image analysis but are challenged by rapid eye movement. We present a substrip-based method to improve image registration and facilitate the automatic montaging of adaptive optics scanning laser ophthalmoscopy (AOSLO). The program first batches the consecutive images into groups based on a translation threshold and selects an image with minimal distortion within each group as the reference. Within each group, the software divides each image into multiple strips and calculates the Normalized Cross-Correlation with the reference frame using two substrips at both ends of the whole strip to estimate the strip translation, producing a registered image. Then, the software aligns the registered images of all groups also using a substrip based registration, thereby generating a montage with cell-for-cell precision in the overlapping areas of adjacent frames. The algorithm was evaluated with AOSLO images acquired in human subjects with normal macular health and patients with age-related macular degeneration (AMD). Images with a motion amplitude of up to 448 pixels in the fast scanner direction over a frame of 512 × 512 pixels can be precisely registered. Automatic montage spanning up to 22.6 degrees on the retina was achieved on a cell-to-cell precision with a low misplacement rate of 0.07% (11/16,501 frames) in normal eyes and 0.51% (149/29,051 frames) in eyes with AMD. Substrip based registration significantly improved AOSLO registration accuracy.
精确配准和拼接对于高分辨率自适应光学视网膜图像分析至关重要,但快速的眼球运动对其构成了挑战。我们提出了一种基于子条带的方法来改进图像配准,并促进自适应光学扫描激光眼科显微镜(AOSLO)的自动拼接。该程序首先根据平移阈值将连续图像分批分组,并在每组中选择失真最小的图像作为参考。在每组内,软件将每个图像划分为多个条带,并使用整个条带两端的两个子条带计算与参考帧的归一化互相关,以估计条带平移,从而生成配准图像。然后,软件也使用基于子条带的配准方法对齐所有组的配准图像,从而在相邻帧的重叠区域生成细胞对细胞精度的拼接图。该算法使用在黄斑健康正常的人类受试者和年龄相关性黄斑变性(AMD)患者中获取的AOSLO图像进行评估。在512×512像素的一帧中,快速扫描方向上运动幅度高达448像素的图像可以精确配准。在正常眼睛中,以细胞对细胞精度实现了在视网膜上跨度达22.6度的自动拼接,错配率低至0.07%(11/16,501帧);在AMD患者的眼睛中,错配率为0.51%(149/29,051帧)。基于子条带的配准显著提高了AOSLO的配准精度。