Digital Pathology Image Compression via Stain Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital pathology images require large amounts of data for representation, leading to significant storage and computational costs, and existing compression techniques often result in diminished image quality.
Innovation Solution
The method involves stain separation, resampling based on importance, and applying linear transforms to reduce bit precision and spatial/spectral resolution, combined with image coding using discrete cosine or wavelet transform coefficients to generate a compressed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital pathology images are represented with full color precision and spatial resolution, then image quality is maintained, but storage requirements and computational costs increase significantly
Solution Approach 1:
The patent segments the color information in digital pathology images by separating stains into distinct color components (e.g., hematoxylin, eosin, mucin). This allows selective compression of less important color channels while preserving critical diagnostic information, thereby reducing storage requirements without sacrificing essential image quality
Solution Approach 2:
The patent applies different levels of compression and bit precision to different color components based on their diagnostic importance. Critical color channels maintain higher precision while less important channels use lower precision, achieving a balance between storage efficiency and image quality preservation
2Loss of information
If digital pathology images are transmitted with full data representation, then complete information is delivered, but transmission time and computing resources increase
Solution Approach 1:
The patent extracts and identifies the most diagnostically important color components and features from the full-color image. By separating and prioritizing essential information (such as nuclear staining patterns or cytoplasmic features), the system transmits only the critical data needed for accurate diagnosis, reducing transmission time while maintaining information completeness
Solution Approach 2:
The patent performs preliminary stain separation and importance assessment before transmission. By pre-processing the image to identify and separate critical color components, the system prepares a compressed representation that maintains diagnostic accuracy while minimizing data size and transmission time
3Quantity of substance
If standard compression techniques are applied to digital pathology images, then data size is reduced, but image quality and diagnostic accuracy diminish
Solution Approach 1:
The patent changes the parameter representation by transforming images from standard RGB color space to stain-specific color spaces (e.g., separating hematoxylin, eosin, and mucin components). This parameter transformation allows for selective compression of less important channels while preserving critical diagnostic features, achieving data size reduction without quality loss
Solution Approach 2:
The patent implements dynamic, adaptive compression where the level of compression applied to each color channel is adjusted based on its diagnostic importance. Critical channels receive minimal or no compression while less important channels undergo higher compression, creating a dynamic quality-preserving compression strategy
Data Source
AI summary
Efficient representation of color digital pathology images (DPI) is described herein, which is accomplished by exploiting properties unique to such images. The method decomposes the data into constituent parts whose relative importance is able to be specified, allowing the data to be accurately represented with less bit precision, less spatial resolution or less spectral resolution. Two specific areas where the method is able to be utilized include: (1) more-efficient image compression; and (2) more efficient processing of the data. Efficient image compression is accomplished by assigning fewer bits to less-important colors. Efficient data processing is accomplished by processing only those colors, or combinations of colors, that are deemed important.