Histological Image Color Normalization Through Iterative Segmentation
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Solution Overview
Problem
Variability in histological tissue staining due to operator experience, dye aging, and scanner type affects diagnostic accuracy and efficiency, particularly impacting less experienced pathologists and automatic recognition systems.
Innovation Solution
An iterative image processing method normalizes histological tissue images by calculating comparison colors, segmenting regions, and applying corrective factors to align with a predetermined reference image, ensuring convergence and reliability regardless of initial conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual initialization technique is used for color normalization, then processing can be performed, but variability in results and time consumption increase
Solution Approach 1:
The system performs self-service by automatically determining color normalization parameters through iterative calculation of average colors and comparison with reference images, eliminating the need for manual initialization while achieving reliable and consistent results across different histological preparations
Solution Approach 2:
The system dynamically adjusts color parameters through iterative optimization, calculating average colors from segmented regions and computing correction factors to transform image colors toward reference values, thereby achieving consistent normalization without manual intervention
2Productivity
If staining variability is present, then histological preparation is completed, but diagnostic accuracy and inspection time are adversely affected
Solution Approach 1:
The system corrects staining variability by calculating correction factors that transform the actual color parameters of the histological image toward the reference color parameters, thereby normalizing the appearance regardless of staining variations and improving both diagnostic accuracy and consistency
Solution Approach 2:
The color normalization system serves multiple functions: it corrects staining variability, compensates for scanner differences, and provides consistent color representation across different preparations, making the diagnostic process more reliable and efficient for both human pathologists and automated systems
Data Source
AI summary
Method of processing a digital image relating to a histological tissue, to vary a color by forcing it towards a "target average color" of a digital reference image. The method includes a segmentation of the image regions that express a hue in a neighborhood of the hue of a "comparison color" and the calculation of an "average coloration" of the segmented area and if this "average coloration" differs under a predetermined threshold from the "comparison color", then calculation and application of a corrective factor for each point of the image that expresses a hue around the hue of the "average color", if instead the "average color" deviates beyond the predetermined one threshold from the "comparison staining", then (Step 5) the "average staining" is set as the "comparison staining" and the segmentation is resumed from (Step 2).