Medical Imaging Codebooks for Lossless Compression of Unseen Data
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Solution Overview
Problem
Current data storage and transmission methods are inadequate due to the rapid growth of data exceeding storage capacity and bandwidth limitations, especially with multimedia data, and existing entropy encoding methods inefficiently handle previously unseen data.
Innovation Solution
A system and method using mismatch probability estimation to improve entropy encoding by incorporating mismatch codewords for previously unseen data, employing codebooks for medical imaging data compression through frequency analysis and sequential registration techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency is improved, but data transmission bandwidth requirements increase and data security is compromised
Solution Approach 1:
The patent segments the compressed data into multiple shares using secret sharing techniques, distributing the data across multiple storage locations. No single location contains the complete data, thereby improving security while maintaining compression efficiency. The original data can be reconstructed only by combining a threshold number of shares.
Solution Approach 2:
The patent introduces cryptographic intermediaries (encryption algorithms and secret sharing protocols) between the compression process and storage. These intermediaries protect the compressed data during transmission and storage, addressing the security concern while preserving the bandwidth benefits of compression.
2Reliability
If lossless compression is used to retain all original data, then data integrity is improved, but compression ratio decreases substantially
Solution Approach 1:
The patent applies different compression strategies to different portions of the medical imaging data based on their importance. Critical diagnostic regions are compressed with lossless methods to preserve integrity, while less critical areas use more aggressive compression, achieving a balance between data integrity and storage efficiency.
3Productivity
If existing entropy encoding methods are used, then compression efficiency is improved for common data, but previously unseen data is handled inefficiently
Solution Approach 1:
The patent implements dynamic codebook adaptation where the entropy encoding system continuously learns from incoming medical imaging data and updates its codebook structures. This allows the system to maintain high compression efficiency for common data patterns while adapting to handle previously unseen data types effectively.
Solution Approach 2:
The patent incorporates feedback mechanisms where compression performance on unseen data is monitored and used to refine the entropy encoding parameters and codebook structures. This feedback loop enables the system to improve its handling of novel data types while maintaining efficiency on established data patterns.
Data Source
AI summary
Compression of medical imaging data using codebooks and entropy encoding. Medical imaging data such as tomosynthesis imagery data may be compressed using codewords based on frequency analysis. In an implementation sequential registration technique may be applied to the medical imaging data to create a plurality transformation matrices. The plurality of transformation matrices may be compressed using a matrix codebook. The compressed medical imaging data may be represented as an image codebook and the matrix codebook, providing secure storage and lossless compression of sensitive medical information.