Brenner Score Ratio for Microscope Focus Assessment
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Solution Overview
Problem
Computer-controlled focusing systems in microscopic imaging face challenges in assessing the focus quality of single images and determining the optimal focal height, especially in medical imaging applications where efficiency and accuracy are critical, as existing methods are computationally intensive and perform poorly with low signal-to-noise ratios.
Innovation Solution
The method involves calculating high-resolution and low-resolution Brenner scores by squaring the differences of neighboring pixel means and summing these differences, then using their ratio to estimate displacement from the ideal focal height based on experimentally derived data, with a best-fit equation to map scores to predicted displacements, allowing for efficient focus quality assessment and adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fourier transform methods are used to evaluate focus quality, then focus assessment can be performed, but the computational complexity increases and performance degrades with low signal-to-noise ratios
Solution Approach 1:
The patent extracts only the essential frequency information needed for focus assessment by calculating the ratio of high-frequency to low-frequency pixel intensity variations, rather than performing a complete Fourier transform. This selective extraction maintains focus quality measurement capability while dramatically reducing computational complexity and improving robustness to noise.
Solution Approach 2:
The patent replaces the computationally expensive and noise-sensitive Fourier transform with a simple, lightweight calculation based on pixel intensity differences. This simpler approach is faster to compute and more resilient to low signal-to-noise conditions, effectively substituting a complex method with a simpler alternative that achieves the same functional goal.
2Measurement precision
If multiple images at different focal heights are acquired to determine optimal focus, then focus quality can be assessed, but the time required increases
Solution Approach 1:
The patent segments the focus assessment task into two parts: (1) a fast single-image evaluation using the pixel intensity ratio method to get immediate feedback, and (2) optional refined multi-image optimization if needed. This segmentation allows the system to achieve useful focus assessment quickly without always requiring time-consuming multiple image acquisitions.
Solution Approach 2:
The patent performs a preliminary focus assessment using the computationally simple pixel intensity ratio method before deciding whether additional images are needed. This preliminary action provides a quick indication of focus quality, allowing the system to avoid unnecessary multiple image acquisitions and reduce overall focusing time while maintaining accuracy.
3Ease of operation
If autofocus functions based on image differentiation are used, then relative focus quality can be judged, but absolute quality assessment is not possible
Solution Approach 1:
The patent changes the parameter being measured from relative pixel differences (as in traditional autofocus functions) to a ratio of high-frequency to low-frequency pixel intensity variations. This parameter change enables the system to provide both relative focus quality judgment and absolute quality assessment, as the ratio has a meaningful relationship to actual focus displacement from the ideal focal plane.
Data Source
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AI summary
Method for determining quality of focus of a digital image of a biological specimen includes obtaining a digital image of a specimen using a specimen imaging apparatus. Measure of image texture is calculated at two different scales, and the measurements are compared to determine how much high-resolution data the image contains compared to low-resolution data. The texture measurement may, for example, be a Brenner auto-focus score calculated from the means of adjacent pairs of pixels for the high-resolution measurement and from the means of adjacent triples of pixels for the low-resolution measurement. A score indicative of the quality of focus is then established based on a function of the low-resolution and high-resolution measurements, and may be used by an automated imaging device to verify that image quality is acceptable, or to adjust the focus and acquire new images to replace any deemed unacceptable.