Histology Image Color Mapping for Color Vision Deficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Color blindness impairs the ability of individuals to interpret stained pathology slides for medical diagnosis, affecting professionals in veterinary and human medical environments, and limits the ability of biologists to interpret microscopy imagery, hindering the acquisition of pathology skills.
Innovation Solution
Systems and methods for processing digital medical images using AI and machine learning to identify color vision deficiencies and apply pixel transformations to enhance color discrimination, allowing users with impaired color vision to interpret histology images more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard color displays are used for pathology images, then color information is preserved accurately for individuals with normal vision, but individuals with color vision deficiency cannot adequately discriminate between different tissue structures
Solution Approach 1:
The patent applies different color mapping strategies to different regions of the color spectrum based on the specific color vision deficiency detected. For example, users with red-green color blindness receive enhanced differentiation in the red-green range while maintaining normal blue-yellow perception, thereby providing localized quality enhancement where needed most
Solution Approach 2:
The system dynamically changes color space parameters and transformation matrices based on the user's diagnosed color vision deficiency type. By adjusting the mapping parameters in real-time according to the user's specific condition, the system optimizes color discrimination for each individual user while preserving diagnostic accuracy
2Adaptability or versatility
If color transformations are applied to enhance discrimination for color blind users, then accessibility is improved, but the original color information may be altered or lost
Solution Approach 1:
The system creates transformed copies of the original pathology images with enhanced color discrimination for color blind users, while preserving the original unaltered images for reference. This allows users to view both the original color information and the enhanced version simultaneously or alternately, preventing information loss
Solution Approach 2:
The patent introduces an intermediary color transformation layer that maps original colors to enhanced colors through defined transformation matrices. This intermediary layer preserves the relationship between original and transformed colors, allowing reversible or referenceable mappings that maintain fidelity to the original diagnostic information
3Adaptability or versatility
If multiple color mapping options are provided for different types of color blindness, then adaptability to individual user needs is improved, but system complexity increases
Solution Approach 1:
The system incorporates automated detection of the user's color vision deficiency type through interactive testing or profile selection, then automatically applies the appropriate color transformation matrix. This self-service approach eliminates the need for manual configuration by users or technicians, reducing operational complexity while maintaining high adaptability
Solution Approach 2:
The patent implements a universal color transformation framework that handles multiple types of color vision deficiencies (protanopia, deuteranopia, tritanopia, etc.) through a single integrated system. The same software platform adapts to different user needs by selecting from predefined transformation matrices, avoiding the need for separate systems for each deficiency type
Data Source
AI summary
A computer-implemented method for processing medical images, the method including receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a patient, wherein the medical image is a stained histology image. The method may further include receiving a stain type associated with the one or more medical images and identifying a color vision deficiency for one or more users. Next the method may include identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users. Next the method may include applying a pixel transformation to each pixel within the one or more medical images. Lastly the method may include displaying the transformed one or more medical images to the one or more users.