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An autofocus algorithm considering wavelength changes for large scale microscopic hyperspectral pathological imaging

Qing Zhang1, Yan Wang1,2, Qingli Li1,3,2

  • 1Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.

Journal of Biophotonics
|January 12, 2022
PubMed
Summary
This summary is machine-generated.

A new autofocus algorithm for microscopic hyperspectral imaging precisely focuses each wavelength, improving pathological slide image quality and capture speed. This method enhances large-scale image acquisition for detailed tissue analysis.

Keywords:
autofocuslarge scale imagingmicroscopic hyperspectral imagingpathology

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Area of Science:

  • Microscopy and Imaging Technologies
  • Pathology and Medical Diagnostics
  • Optical Engineering

Background:

  • Microscopic hyperspectral imaging is crucial for acquiring detailed pathological information from tissue sections.
  • High-quality, high-speed autofocus is essential for capturing large-scale or whole slide images in pathological analysis.
  • Existing autofocus algorithms are limited for microscopic hyperspectral imaging systems.

Purpose of the Study:

  • To develop and validate a novel autofocus algorithm for microscopic hyperspectral imaging systems.
  • To address the challenge of wavelength-dependent focal variations in hyperspectral image acquisition.
  • To enhance both the precision and speed of capturing large-scale pathological hyperspectral images.

Main Methods:

  • A Laplace operator-based autofocus algorithm was developed, considering wavelength-specific focal adjustments.
  • An adaptive image sharpness evaluation method was employed for precise focusing of individual band images.
  • The relationship between wavelength and focal length was derived to calculate focal offsets for pre-focusing, optimizing capture speed.

Main Results:

  • The proposed algorithm successfully achieved precise autofocus for each wavelength in microscopic hyperspectral imaging.
  • Adaptive sharpness evaluation ensured accurate focusing across different spectral bands.
  • Calculated focal offsets enabled efficient pre-focusing, significantly increasing image capture speed.
  • Experimental results demonstrated the capability to capture large-scale microscopic hyperspectral pathology images with high precision.

Conclusions:

  • The developed Laplace operator-based autofocus algorithm effectively addresses the challenges in microscopic hyperspectral imaging.
  • The method ensures precise focusing across all wavelengths, leading to high-quality pathological image data.
  • The incorporation of pre-focusing strategies enhances system speed, making it suitable for large-scale image acquisition.