Biological Image Pixel Classification via Color Space Transformation
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Solution Overview
Problem
Automated classification of pixels in digital images of biological samples is often unsatisfactory, frequently misclassifying pixels as stained when they are actually unstained due to inadequate color space analysis.
Innovation Solution
A process involving matrix multiplication to transform pixel values from a first digital image into a second color space, where a classification condition is determined based on multiple dimensions, using distribution fitting and empirical calibration parameters to accurately classify pixels as stained or unstained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated classification of pixels is performed using conventional color space analysis, then the processing speed and automation level are improved, but the classification accuracy deteriorates with frequent misclassification of unstained pixels as stained
Solution Approach 1:
The patent transforms pixel values from the conventional RGB color space to a new color space defined by matrix multiplication with transformation matrix A. This changes the parameter representation from (R, G, B) to (I1, I2, I3) where I1 represents intensity and I2, I3 represent color information. This parameter transformation enables better separation of stained and unstained pixels by redistributing the information content across different dimensions, thereby improving classification accuracy while maintaining automation.
Solution Approach 2:
The patent reorganizes the color information by separating intensity (I1) from color (I2, I3) dimensions through linear transformation. This dimensional reorganization allows the classification algorithm to independently analyze intensity and color characteristics, preventing misclassification of unstained pixels that may have high intensity but incorrect color signature. The dimensional separation addresses the contradiction by providing more discriminative features for accurate automated classification.
2Measurement precision
If multiple color values and dimensions are combined for pixel classification, then the classification accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs color space transformation and distribution fitting in advance before actual pixel classification. The transformation matrix A and distribution parameters are predetermined through calibration using reference samples. This preliminary preparation stores the computational complexity in an offline phase, allowing the online classification to use simple threshold-based decisions on the transformed coordinates, thus reducing real-time computational complexity while maintaining high accuracy.
Solution Approach 2:
The patent transforms the classification problem from operating on raw RGB values to operating on transformed coordinates (I1, I2, I3) with pre-determined distribution parameters. This parameter change simplifies the classification logic by converting a complex multi-dimensional classification problem into a series of simpler comparisons against calibrated thresholds, reducing computational complexity while preserving the benefits of multi-dimensional analysis.
Data Source
AI summary
Computer-implemented process on an image of a biological sample The present invention relates to a computer-implemented process to automatically analyze a digital image (103) of abiological sample (101). The process involves a change (203) from a first color space to a second color space. Then, fits are performed taking into account several dimensions of the second color space to classify pixels.