Optical Microscope Region Setting for Texture Noise
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Solution Overview
Problem
In optical microscopes, the presence of texture on sample surfaces leads to pseudo irregular regions during analysis, causing the aperture to be set too small, resulting in reduced signal intensity and deteriorated S/N ratio, especially when analyzing multiple samples with varying optimal threshold values.
Innovation Solution
An analysis target region setting apparatus that divides the observation region into sub-regions based on pixel information, consolidates adjacent sub-regions using statistical values like average, median, and standard deviation, and adjusts consolidation conditions to eliminate pseudo irregular regions, allowing for accurate aperture positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic region extraction is performed using threshold values, then processing time is reduced, but pseudo irregular regions are generated due to sample surface texture
Solution Approach 1:
The observation region is divided into multiple sub-regions based on pixel information, and each sub-region is evaluated independently using statistical parameters. This segmentation allows the system to process regions automatically while maintaining accuracy by identifying and excluding pseudo irregular regions through comparative statistical analysis.
Solution Approach 2:
The invention uses statistical parameters (average, median, standard deviation) of pixel values to characterize sub-regions and identifies pseudo irregular regions by comparing these parameters across adjacent sub-regions. This parameter-based approach enables automatic distinction between real irregularities and texture-induced pseudo irregularities.
2Measurement precision
If aperture size is reduced to exclude pseudo irregular regions, then measurement precision is improved, but signal intensity is reduced
Solution Approach 1:
The invention extracts and identifies pseudo irregular regions using statistical parameter comparison, then excludes only these specific regions from analysis while preserving the rest of the observation region. This selective extraction approach maintains aperture size and signal intensity while improving measurement precision by removing only the problematic pseudo irregular regions.
3Measurement precision
If manual aperture setting is performed, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs self-service by automatically calculating statistical parameters for each sub-region, comparing these parameters to identify pseudo irregular regions, and determining the optimal aperture position and size without user intervention. This automated process achieves both high precision and efficiency.
Solution Approach 2:
The system uses feedback from statistical parameter comparisons to automatically adjust aperture positioning. By continuously evaluating sub-region characteristics and comparing adjacent regions, the system refines its aperture setting to exclude pseudo irregular regions while maintaining signal intensity, achieving both precision and speed.
Data Source
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AI summary
Provided is an analysis target region setting apparatus that can accurately set an analysis target region, based on an observation image of a sample obtained with an optical microscope and the like irrespective of texture on the sample surface when the analysis target region is set therein. The analysis target region setting apparatus according to the present invention divides the observation image into a plurality of sub-regions based on pixel information on each pixel constituting the observation image. Subsequently, consolidation information on each sub-region is calculated, and two adjacent sub-regions themselves are consolidated based on the consolidation information. According to this, it is possible to divide the observation image into sub-regions having similar pixel information with a disregard of noise attributed to the shape of a surface and the like. A user designates one sub-region from among the sub-regions finally obtained, as the analysis target region.