Automatic Optical Path Alignment Method for Optical Biological Microscope

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.

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Publication Details

Journal
Sensors
Published
2024-12-27
DOI
https://doi.org/10.3390/s25010102
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Automatic Optical Path Alignment Method for Optical Biological Microscope

Guangyao Pang, Zhenming Yu, Guojin Peng, Xinjian Zhou et al.
Sensors
Image Processing Techniques and Applications
article

Automatic Optical Path Alignment Method for Optical Biological Microscope

Guangyao Pang, Zhenming Yu, Guojin Peng, Xinjian Zhou, Kuikui Wang
article en

Abstract

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.

SensorsVol. 25(1)
Wuzhou University (CN), Guilin University of Electronic Technology (CN)
National Natural Science Foundation of China, Fundo para o Desenvolvimento das Ciências e da Tecnologia, Wuzhou University, Division of Graduate Education
Affordable and clean energy
Openalex Percentile: Top 64%
Image Processing Techniques and Applications
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