# Anchor point based image registration for absolute scale topographic structure detection in microscopy

## TL;DR

This paper introduces an image registration method using anchor points to correct microscopy images, enabling accurate and automated analysis of structures on an absolute scale.

## Contribution

A novel image registration technique using anchor points and homography transform for absolute scale topographic structure detection in microscopy.

## Key findings

- The method successfully restores microscopy images to their undistorted form using homography transform.
- Anchor point alignment enables precise localization of objects within the structure.
- The technique outperforms a previous keypoint detection AI approach in precision.

## Abstract

Microscopy images obtained through remote sensing often suffer from misalignment and deformation, complicating accurate data analysis. As experimental instruments improve and scientific discoveries deepen, the volume of data requiring processing continues to grow. Image registration can contribute to microscopy automation, which enables more efficient data analysis and experimental workflows. For this implementation, image processing techniques that can handle both image registration and localized object analysis are required. This research introduces a computer interface designed to calibrate and analyze specific structures with prior knowledge of the observed target. Our method achieves image registration by aligning anchor points, which correspond to the coordinates of a structural model within the image. It employs homography transform to correct images, restoring them to their original, undistorted form, thus enabling consistent quantitative comparisons across different images on an absolute scale. Additionally, the method provides valuable information from the registered anchor points, allowing for the precise localization of local objects in the structure. We demonstrate this technique across various microscopy scenarios at different scales and evaluate its precision against a keypoint detection AI approach from our previous research, which promises its enhancement in microscopy data analysis and automation.