Tissue Recognition via Selective Image Filtering
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Solution Overview
Problem
Current digital pathology methods for analyzing microscope images of tissue samples are subjective and lack reproducibility, as they rely on human judgment, leading to variations in diagnoses and prognostic assessments due to differences in image interpretation by pathologists.
Innovation Solution
An image analysis and tissue recognition system that uses mathematical operators and comparator data to identify tissue types and structures in Haematoxylin and Eosin stained images, enabling objective comparisons and improving computational efficiency for automated diagnoses and prognostic assessments by selecting appropriate image operators and quantitative metrics based on descriptor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated image analysis is implemented, then objectivity and reproducibility improve, but device complexity increases
Solution Approach 1:
The analysis system divides the complex task of tissue diagnosis into multiple discrete image operators that can be independently selected and applied. Each operator handles specific image processing functions (e.g., segmentation, feature extraction, classification), allowing the system to manage complexity through modular components rather than a monolithic algorithm.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific tissue type and diagnostic requirements. By changing parameters such as operator selection, image processing thresholds, and analysis depth, the system adapts to different diagnostic scenarios without requiring complete system redesign, thus managing complexity while maintaining reliability.
2Measurement precision
If comprehensive image analysis is performed on all tissue samples, then diagnostic accuracy improves, but processing time increases
Solution Approach 1:
The system applies image analysis operators selectively rather than uniformly to all image regions. By identifying and analyzing only the most diagnostically relevant areas or applying different levels of analysis intensity based on preliminary assessment, the system achieves high diagnostic accuracy without the time cost of exhaustive analysis of every pixel and region.
Solution Approach 2:
The system performs preliminary image processing and pre-screening operations before applying more computationally intensive analysis methods. This staged approach allows quick filtering and identification of regions of interest, so that comprehensive analysis is applied only where necessary, reducing overall processing time while maintaining accuracy.
3Measurement precision
If multiple image operators are applied to all image data, then tissue identification accuracy improves, but computational efficiency decreases
Solution Approach 1:
The system applies different image operators and processing intensities to different regions of the image based on local characteristics. Rather than uniformly applying all operators to the entire image, the system tailors the analysis to local tissue features, applying comprehensive operators only where needed and simpler operators elsewhere, thus maintaining accuracy while improving computational efficiency.
Data Source
AI summary
A computer implemented image processing method is disclosed. The method comprises applying a selected filter to image data to identify a subset of the image data that defines a number of discrete spatial regions of the image wherein the discrete spatial regions comprise less than all of the area of the image; selecting, from a data store, a set of quantitative image metrics wherein the quantitative image metrics are selected based on descriptor data indicating tissue type, determining, for each discrete spatial region, a sample region data value for each of the set of quantitative image metrics based on the subset of image data associated with the or each discrete spatial region, using the descriptor data to select, from the data store, at least one comparator set of tissue model data values, wherein each comparator set is associated with a different corresponding comparator tissue structure and each comparator set comprises data values of the set of quantitative image metrics for the corresponding comparator tissue structure; comparing the sample region data value for each discrete region with the at least one comparator set; and in the event that the sample region data value for the or each discrete region matches the comparator set, determining based on an identity of the corresponding comparator tissue structure, whether to further analyse the or each discrete region.