Artificial Hyperspectral Image Generation via Co-Registered Tissue Tile Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing co-registered tissue slices are inefficient and complex, particularly in extracting valuable information from high-resolution images, as they require extensive computing resources and often lose important features by focusing on pixel-by-pixel analysis rather than object-based analysis.
Innovation Solution
A method that generates artificial hyperspectral images by co-registering and combining high-resolution tiles from multiple tissue slices, using image-object statistics to create a down-scaled hyperspectral image, which reduces computational complexity and enhances information extraction by representing tissue properties in a compressed, multispectral format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel analysis is performed on co-registered tissue slices, then detailed information can be extracted, but computing resources are excessively consumed and important features are lost
Solution Approach 1:
The patent segments the continuous image data into discrete super-pixels or regions, transitioning from pixel-by-pixel analysis to region-based analysis. This segmentation reduces the number of analysis units while preserving important tissue structures and features, thereby decreasing computational complexity without sacrificing measurement precision.
Solution Approach 2:
The patent introduces a new dimension by creating hyperspectral images that combine information from multiple tissue slices along the spectral dimension. This dimensional transformation allows the system to capture heterogeneity across slices while analyzing fewer spatial units, resolving the contradiction between detailed information extraction and computational efficiency.
2Reliability
If high-resolution images of multiple tissue slices are analyzed in detail, then comprehensive medical evaluation is achieved, but data handling and processing become inefficient
Solution Approach 1:
The patent merges information from multiple high-resolution tissue slice images into a single hyperspectral image. By combining data along the spectral dimension while reducing spatial resolution through super-pixel aggregation, the system maintains comprehensive medical evaluation capability while dramatically improving data handling efficiency and processing speed.
3Measurement precision
If co-registration of multiple tissue slices is performed with high precision, then accurate correlation of features across slices is achieved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent applies segmentation to divide the co-registration task into manageable regions or super-pixels. This allows accurate feature correlation to be performed on reduced data units, maintaining measurement precision while significantly reducing the computational time and resources required for processing multiple tissue slices.
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
High-resolution digital images of adjacent slices of a tissue sample are acquired, and tiles are defined in the images. Values associated with image objects detected in each tile are calculated. The tiles in adjacent images are co-registered. A first hyperspectral image is generated using a first image, and a second hyperspectral image is generated using a second image. A first pixel of the first hyperspectral image has a first pixel value corresponding to a local value obtained using image analysis on a tile in the first image. A second pixel of the second hyperspectral image has a second pixel value corresponding to a local value calculated from a tile in the second image. A third hyperspectral image is generated by combining the first and second hyperspectral images. The third hyperspectral image is then displayed on a computer monitor using a false-color encoding generated using the first and second pixel values.