Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscopes need to be optimally stitched before accurate physical information can be extracted from post-analysis. Existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images. To address these issues, a bi-channel aided feature-based image stitching method is proposed, demonstrated on AFM generated biofilm images. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. The amplitude channel of AFM data is utilized to maximize the matching features and to estimate the position of the original topographical images, showing that the proposed bi-channel aided stitching method outperforms the traditional stitching approach. Furthermore, it is found that the differentiation of the topographical images along the x-axis provides similar feature information to the amplitude channel image, which generalizes the approach when the amplitude images are not available. Although demonstrated on AFM, similar approaches could be employed for optical microscopy with brightfield and fluorescence channels. This proposed workflow is believed to benefit experimentalists by avoiding erroneous analysis and discovery due to incorrect stitching.
Feature-based image stitching
Bi-channel feature extraction
AFM generated biofilm images
Stitching accuracy, Feature matching rate
On-premises
No
Yes
Bi-channel feature extraction, Improved stitching accuracy
No
Standard computing hardware with image processing capabilities
Windows, Linux, macOS
Compatible with various microscopy data formats
Data integrity checks
None specified
None
Yes
Available through GitHub issues and discussions
Research team from the study
Not applicable
Real-time processing
Standard computational efficiency
Visual representation of stitching process
Ensuring accurate representation of microscopy data
Limited to specific microscopy types
Scientific Research, Biotechnology
Biofilm analysis, Material science
Research institutions, Universities
Can be integrated with existing microscopy software
Scalable with computational resources
Community support
None
Command-line interface
No
Not applicable
Open-source
Yes
None
None
None
1.0
Software
No
Not applicable
Open-source
0.00
USD
MIT License
01/03/2025
01/03/2025
123-456-7890
Supports various microscopy data formats
Yes