Cell Image Evaluation Device Adapting to Autofocus Errors
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Solution Overview
Problem
Existing cell image evaluation methods face challenges in obtaining accurate and reliable results due to blurred or dark images caused by autofocus errors and light fluctuations, leading to low evaluation accuracy when deteriorated images are evaluated using the same methods as undeteriorated ones.
Innovation Solution
A cell image evaluation device and method that includes a deterioration determination unit to assess whether captured images are blurred or affected by light fluctuations, switching between evaluation methods resistant to deterioration for clear images and those using image feature values for deteriorated images, utilizing machine learning for blur and light fluctuation discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autofocus control is performed in each part to be observed during scanning, then image focus should be improved, but autofocus errors still occur causing blurred images
Solution Approach 1:
The patent introduces an intermediary evaluation method that acts as a mediator between the autofocus control system and the final image quality assessment. Instead of relying solely on autofocus control to ensure image quality, the system uses a separate image quality evaluation unit to assess whether images are blurred or dark, and applies appropriate evaluation methods based on the assessment results. This intermediary evaluation layer compensates for autofocus failures without requiring perfect autofocus control.
2Device complexity
If the same evaluation method is used for both clear and deteriorated images, then evaluation process is simplified, but evaluation accuracy decreases for deteriorated images
Solution Approach 1:
The patent applies local quality by selecting different evaluation methods based on the local condition of each image. The image quality evaluation unit assesses each captured image individually to determine if it is deteriorated (blurred or dark), and then applies an appropriate evaluation method specifically suited for that image's condition. This allows the system to optimize evaluation accuracy for each local case rather than using a uniform approach.
Solution Approach 2:
The patent implements dynamics by making the evaluation method adaptable and changeable based on image conditions. Instead of a static evaluation process, the system dynamically selects between different evaluation methods (first evaluation method for clear images, second evaluation method for deteriorated images) based on real-time image quality assessment. This dynamic adaptation ensures optimal evaluation accuracy across varying image conditions.
3Reliability
If divided images with inappropriate brightness or contrast are excluded from evaluation, then evaluation reliability for those regions is improved, but information loss reduces overall evaluation accuracy
Solution Approach 1:
The patent converts the harm of image deterioration (blurred or dark images) into a benefit by developing a specialized second evaluation method that is specifically designed to handle deteriorated images. Instead of excluding these problematic images from evaluation, the system recognizes their deteriorated state and applies an evaluation method optimized for such conditions, thereby extracting useful information even from poor-quality images.
Data Source
AI summary
Provided are a cell image evaluation device, method, and program which are capable of more accurate and high reliable evaluation even though a captured image of each part to be observed within a container deteriorates. The cell image evaluation device includes an image evaluation unit that evaluates a state of a cell included in a captured image obtained by capturing an inside of the container that contains the cell based on the captured image, and a deterioration determination unit that determines whether or not the captured image deteriorates. The image evaluation unit changes an evaluation method of the captured image according to a determination result of the deterioration determination unit.