Peng Guojin, Yu Zhenming, Zhou Xinjian, Pang Guangyao, Wang Kuikui
Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China.
Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China.
Sensors (Basel). 2024 Dec 27;25(1):102. doi: 10.3390/s25010102.
A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for low-power objective (specifically 4×) automatic OBMs. The proposed method encompasses reference objective (RO) identification and alignment processes. For identification, a model depicting spot movement with objective rotation near the optical axis is developed, elucidating the influence of optical path parameters on spot characteristics. This insight leads to the proposal of an RO identification method utilizing an edge gradient and edge position probability. In the alignment phase, a symmetry-based weight distribution scheme for concentric arcs is introduced. A significant observation is that the received energy stabilizes with improved alignment precision, prompting the design of an advanced alignment evaluation method that surpasses conventional energy-based assessments. The experimental results confirm that the proposed RO identification method can effectively differentiate between 4× and 10× objectives across diverse light intensities and exposure levels, with a significant numerical difference of up to 100. The error-radius ratio of the weighted circular fitting method is maintained below 1.16%, and the fine alignment stage's evaluation curve is notably sharper. Moreover, tests under various imaging conditions in artificially saturated environments indicate that the alignment estimation method, predicated on critical saturation positions, achieves an average error of 0.875 pixels.
高质量的光路对准对于在光学生物显微镜（OBM）系统中实现卓越的图像质量至关重要。传统的OBM自动对准方法严重依赖复杂的掩膜检测技术。本文介绍了一种创新的、基于图像传感器的光路对准方法，专为低倍物镜（特别是4倍）自动OBM设计。所提出的方法包括参考物镜（RO）识别和对准过程。对于识别，建立了一个描述光轴附近物镜旋转时光斑移动的模型，阐明了光路参数对光斑特性的影响。这一见解促使提出了一种利用边缘梯度和边缘位置概率的RO识别方法。在对准阶段，引入了一种基于对称的同心圆弧权重分布方案。一个重要的发现是，随着对准精度的提高，接收能量趋于稳定，从而促使设计一种超越传统基于能量评估的先进对准评估方法。实验结果证实，所提出的RO识别方法能够在不同光强和曝光水平下有效区分4倍和10倍物镜，数值差异高达100。加权圆拟合方法的误差半径比保持在1.16%以下，精细对准阶段的评估曲线明显更陡峭。此外，在人工饱和环境下的各种成像条件下进行的测试表明，基于临界饱和位置的对准估计方法平均误差为0.875像素。
