Artifact Exclusion in Digital Pathology Image Normalization
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Solution Overview
Problem
Digital pathology images often contain artifacts such as out-of-focus, staining, pressure, and fixation artifacts, which can lead to sub-optimal image processing and analysis, especially when using automatic image analysis programs.
Innovation Solution
A system that detects and excludes artifact regions from the image processing parameter determination, allowing for improved image processing by using a parameter based on relevant image areas, and provides a user interface for visualization and control of the detected regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed on the complete tissue including artifacts, then processing coverage is maximized, but measurement precision deteriorates due to distortion from undesirable regions
Solution Approach 1:
The image processing method segments the tissue image into multiple regions based on staining characteristics. By dividing the image into different stained and unstained regions, the system can selectively process only the relevant stained areas for parameter determination, excluding artifact-containing unstained regions from influencing the normalization parameters. This segmentation enables precise parameter measurement while maintaining focus on diagnostically relevant areas.
Solution Approach 2:
The invention applies different processing qualities to different regions of the image. Stained regions undergo full normalization and parameter analysis, while unstained regions are identified and excluded from parameter determination. This local quality approach ensures that high-precision processing is applied only where necessary (in stained regions containing diagnostic information) while avoiding the distortion caused by artifact-prone unstained regions.
2Productivity
If automatic image analysis is performed on images containing artifacts, then productivity is improved, but reliability deteriorates due to difficulty in artifact recognition
Solution Approach 1:
The system performs preliminary identification of stained and unstained regions before conducting the main parameter determination and analysis. By pre-segmenting the image and marking unstained regions that likely contain artifacts, the system prepares the data structure in advance to exclude these regions from subsequent processing. This preliminary action enables automatic analysis to proceed efficiently while maintaining reliability by preventing artifact contamination of diagnostic parameters.
3Manufacturing precision
If normalization is performed on the complete field of view, then processing completeness is maximized, but manufacturing precision deteriorates due to inconsistent staining in artifact regions
Solution Approach 1:
The normalization process is segmented to operate only on stained regions identified through color deconvolution and region classification. By separating stained and unstained areas, the system applies normalization consistently across all stained regions while excluding unstained regions that would introduce inconsistency. This segmented normalization maintains manufacturing precision by ensuring uniform processing of diagnostically relevant areas without contamination from artifact-prone regions.
Data Source
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AI summary
A system for processing an image comprises a region detector (1). The region detector comprises an artifact detector (7) for detecting a region comprising an artifact in the image. The system comprises a parameter determining unit (2) for determining a parameter, based on a portion of the image excluding the detected region. The system comprises an image processing module (3) for processing the image, using the derived parameter. The system comprises a display unit (5) for displaying the processed image with an indication of the detected region. The parameter determined by the parameter determining unit (2) can comprise a normalization parameter, and the image processing module (3) can be arranged for performing a normalization of the image according to the normalization parameter.