Frequency-Based Artifact Removal in Microscopic Tissue Images
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Solution Overview
Problem
Existing digital pathology techniques struggle to reliably detect and remove artifacts from whole slide histology images due to variability in tissues and artifacts, and require costly deep learning models and substantial labeled data.
Innovation Solution
The system converts microscopic images into frequency values and filters out regions corresponding to artifacts based on these values, using pixel clustering and filtering to remove artifacts without requiring extensive training or computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for artifact detection, then detection accuracy is improved, but computational cost and training requirements increase significantly
Solution Approach 1:
The patent replaces complex deep learning models with a simpler frequency-based image processing approach. By transforming the artifact detection problem from a learning-based task to a signal processing task using frequency domain analysis, the system achieves comparable detection accuracy without requiring complex neural network architectures, extensive training data, or significant computational resources.
Solution Approach 2:
The patent changes the parameter space for artifact detection by moving from pixel intensity values to frequency domain representations. This parameter transformation allows the system to detect artifacts based on their characteristic frequency signatures, providing a more efficient and interpretable approach that avoids the complexity of deep learning while maintaining detection effectiveness.
2Reliability
If deep learning models are trained on labeled data, then detection reliability is improved, but training time and data requirements increase
Solution Approach 1:
The patent eliminates the training phase by substituting deep learning with frequency-based signal processing. The method relies on the inherent frequency characteristics of artifacts in the image domain, which can be detected directly without requiring labeled training data or iterative model optimization, thereby achieving reliable detection instantaneously.
Solution Approach 2:
The artifact detection system serves itself by automatically identifying artifacts through their intrinsic frequency properties. The method does not require external training data or manual annotation; instead, it uses the natural frequency patterns present in the image data to detect and segment artifacts, making the system self-sufficient and immediately deployable.
3Productivity
If traditional image processing techniques are used, then computational efficiency is improved, but detection accuracy deteriorates due to tissue and artifact variability
Solution Approach 1:
The patent bridges the gap between speed and accuracy by changing from spatial domain processing to frequency domain processing. This parameter transformation enables the system to capture variability in tissue and artifact appearances through their frequency signatures, achieving both high detection accuracy and computational efficiency without requiring complex deep learning models.
Data Source
AI summary
A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.