Li Caiwei, Liu Kehan, Guo Xiaoguang, Xiao Yinghao, Zhang Yingjun, Huang Zhen-Li
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 570228, China.
Biomed Opt Express. 2024 Mar 29;15(4):2697-2707. doi: 10.1364/BOE.520514. eCollection 2024 Apr 1.
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.
对于计算机辅助诊断系统的有效性而言，全切片图像（WSI）的质量是基础，而一种有效的自动对焦方法是确保WSI质量的重要组成部分。现有的自动对焦方法需要在对焦速度和对焦精度之间取得平衡，并且需要针对不同的样本或场景分别进行优化。本文提出了一种基于光纤束照明和图像归一化分析的鲁棒自动对焦方法。对于各种应用场景，它通过主动照明满足自动对焦的要求，如明场成像和荧光成像。对于样本上的不同结构，它通过图像分析确保自动对焦精度。实验结果表明，本文中的自动对焦方法能够有效地跟踪样本到焦平面距离的变化，并显著提高WSI质量。
