Adaptive Particle Image Compression for Large 3D Tissue Data
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Solution Overview
Problem
Existing biological and medical imaging technologies face challenges in efficiently handling, processing, and managing large volume multi-dimensional image data, leading to issues with computer memory storage, retrieval speed, and display performance, particularly in systems like CT scans and MRI machines.
Innovation Solution
A computer-implemented method utilizing Adaptive Particle Representation (APR) for image compression and analysis, which processes each tile of a biological mass independently, compressing low-information areas while maintaining high-resolution in high-information areas, and storing tiles in a datastore for efficient retrieval and reconstruction of the complete image without requiring additional memory or processor resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional image compression algorithms are used to store large volume multi-dimensional image data, then storage space is reduced, but retrieval and processing speed deteriorates due to required decompression
Solution Approach 1:
The patent segments large volume multi-dimensional image data into multiple three-dimensional tiles, where each tile is independently compressed and stored. This segmentation allows selective retrieval of only the needed tiles rather than decompressing entire large datasets, thereby maintaining storage efficiency while dramatically improving retrieval and processing speed.
2Manufacturing precision
If full resolution is maintained in all areas of the image, then image quality is preserved, but processing and storage requirements increase significantly
Solution Approach 1:
The patent applies local quality by differentiating between high-information content areas and low-information content areas within each tile. High-information areas (containing diagnostically relevant features) are maintained at full resolution, while low-information areas are compressed more aggressively. This selective approach preserves essential image quality while reducing overall processing and storage requirements.
Data Source
AI summary
An efficient image analysis, processing, and handling based on adaptive particle representation (APR) for processing large scale color image datasets is disclosed. Analyzing large biological mass or tissue samples is achieved at 100+ times faster computation and at improved memory and storage compression ratios. Embodiments are well suited for large 3D cleared tissue samples, large-scale imaging projects such as whole-brain mapping initiatives and human neurohistopathology. A datastore holds adaptive sampling representations of tiles, individual tile positions, and corresponding other image data of a biological mass of interest. An image output using single loading of tiles from the datastore supports a complete image at full resolution of the biological mass.