Image Processing Apparatus Noise Weighting for Spectral Transmittance
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Solution Overview
Problem
Current image processing methods for estimating spectral transmittance in pathological specimens, particularly after H&E staining, face challenges in accurately correcting stain dispersion and estimating stain amounts due to noise interference, which affects the precision of image analysis.
Innovation Solution
An image processing apparatus employing an estimation operator to analyze the relative effect of noise on spectroscopic properties in the wavelength axis, calculating a weighting factor to enhance stain amount estimation by minimizing noise impact, and using the Wiener estimation method to estimate spectral transmittance from multiband images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an estimation operator is used to estimate spectral transmittance from multiband images, then the spectroscopic property can be obtained, but noise interference degrades the measurement precision
Solution Approach 1:
The patent converts the harmful noise into a useful indicator by analyzing its effect extent through the estimation operator. The noise variance at each wavelength is calculated and used to determine weighting factors, transforming the previously harmful noise into a basis for optimizing the estimation process and improving spectral transmittance accuracy.
Solution Approach 2:
The patent dynamically adjusts the weighting factors based on the calculated noise effect extent at different wavelengths. By changing the weight parameters according to noise characteristics, the estimation process optimizes the contribution of each wavelength band, thereby improving measurement precision in the presence of noise.
2Reliability
If weighting factors are applied to minimize noise impact, then the reliability of stain amount estimation is improved, but the device complexity increases
Solution Approach 1:
The system performs self-optimization by automatically calculating noise variances and determining weighting factors without external intervention. The estimation operator itself provides the information needed to assess noise impact, and the system uses this information to adjust its own parameters, reducing the need for external calibration or complex control mechanisms.
Solution Approach 2:
The patent implements a feedback mechanism where the estimated noise effect extent from the estimation operator is used to adjust the weighting factors. This closed-loop approach continuously optimizes the estimation process based on the actual noise conditions, improving reliability while keeping the complexity manageable through systematic feedback utilization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution improves the accuracy of stain amount estimation and image correction by predicting noise sensitivity and adjusting weighting factors based on noise impact, thereby enhancing the reliability and precision of image processing for pathological specimens.
Implementation Method 1
An image processing apparatus employing an estimation operator to analyze the relative effect of noise on spectroscopic properties in the wavelength axis, calculating a weighting factor to enhance stain amount estimation by minimizing noise impact, and using the Wiener estimation method to estimate spectral transmittance from multiband images.
Data Source
AI summary
An image processing apparatus uses an estimation operator to estimate a spectroscopic property. The image processing apparatus includes an effect extent analyzing unit which analyzes, based on the estimation operator, a relative extent of an effect on the spectroscopic property against a noise in a wavelength axis.