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Robust autofocus method based on patterned active illumination and image cross-correlation analysis.

Caiwei LiKehan LiuXiaoguang GuoYinghao XiaoYingjun ZhangZhen-Li Huang
Published in: Biomedical optics express (2024)
For the effectiveness of a computer-aided diagnosis system, the quality of whole-slide image (WSI) is the foundation, and a useful autofocus method is an important part of ensuring the quality of WSI. The existing autofocus methods need to balance focusing speed and focusing accuracy, and need to be optimized separately for different samples or scenes. In this paper, a robust autofocus method based on fiber bundle illumination and image normalization analysis is proposed. For various application scenes, it meets the requirements of autofocusing through active illumination, such as bright field imaging and fluorescence imaging. For different structures on samples, it ensures the autofocusing accuracy through image analysis. The experimental results imply that the autofocusing method in this paper can effectively track the change of the distance from the sample to the focal plane and significantly improve the WSI quality.
Keyphrases
  • fluorescence imaging
  • deep learning
  • high resolution
  • randomized controlled trial
  • photodynamic therapy
  • quality improvement