CAPTURE: A Clustered Adaptive Patchwork Technique for Unified Registration Enhancement in Biological Imaging.

Image registration is important in biological image analysis; however, it is often challenged by distortions and non-linear transformations. In this paper, we present a novel patch-wise image registration method to address the mentioned issues. Our method begins with global registration to correct linear transformations, followed by a detailed examination of geometrical distortions. After that, each image is adaptively divided into patches to isolate and correct non-linear distortions, followed by reconstruction and combining patches using Otsu thresholding. We evaluated our method against state-of-the-art techniques using mutual information (MI), phase congruency-based (PCB), and gradient-based metrics (GBM) across four real biology datasets. Our results demonstrate superior feature alignment and image coherence, especially in serial-stack registrations. While the proposed method has longer processing times compared to linear registration methods, its enhanced accuracy and reliability to handle non-uniform distortion makes it beneficial for precision-demanding applications. We have created a public GitHub repository containing the code used in our research, available at https://github.com/NabaviLab/CAPTURE.