Medical Image Archiving via Class-Based Migration and Compression
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Solution Overview
Problem
Current medical image archiving systems face challenges in managing large volumes of data, with rapid access requirements and long storage times, leading to disk space saturation and inefficient storage practices that do not consider the unique properties of medical images, such as capture mode and pathology, which impact their aging process.
Innovation Solution
An image archiving method and system that partitions images into classes based on scheduling rules, migrating them through containers with varying storage properties, applying transformation functions to reduce memory usage and optimize access, allowing automated aging-based migration without human intervention, and ultimately storing images on ROM for long-term archiving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If medical images are stored in traditional PACS systems with file management systems, then image access and management is straightforward, but disk space saturation occurs quickly and storage capacity is limited
Solution Approach 1:
The patent segments the storage system into multiple containers (C1, C2, C3, etc.) with different storage capacities and access characteristics. Images are partitioned and distributed across these containers based on their class, allowing the system to scale storage capacity while maintaining manageable complexity through modular organization.
Solution Approach 2:
The patent introduces a new dimension to storage management by adding the container class dimension. Instead of a single flat storage space, images are organized in a multi-dimensional space considering both image properties (modality, pathology, access frequency) and container characteristics (capacity, access speed), enabling efficient use of heterogeneous storage resources.
2Speed
If all medical images are kept in fast storage for rapid access, then access speed is maximized, but storage space is quickly exhausted and costs increase
Solution Approach 1:
The patent applies local quality by assigning different storage characteristics to different image classes. Frequently accessed images (class 1) are stored in fast storage containers (C1), while less frequently accessed images (classes 2, 3, etc.) are stored in slower, higher-capacity containers (C2, C3). This ensures rapid access for critical images while maximizing overall storage capacity.
Solution Approach 2:
The patent implements dynamic image migration between containers based on image age, access patterns, and class reclassification. Images automatically migrate from fast storage containers to slower containers as they age and their access frequency decreases, allowing the system to adapt storage allocation dynamically rather than statically.
3Productivity
If images are migrated between storage containers based on aging, then storage efficiency improves and space is optimized, but system complexity increases and migration overhead occurs
Solution Approach 1:
The patent implements self-service through automated image migration driven by image aging and class reclassification rules. The system automatically determines when and where to migrate images based on pre-defined criteria (image age, access frequency, container capacity), eliminating the need for manual migration management and reducing operational complexity.
Solution Approach 2:
The patent uses feedback mechanisms where image access patterns and aging continuously inform migration decisions. The system monitors image usage and automatically adjusts migration timing and destination based on actual access behavior, creating a closed-loop system that optimizes storage efficiency while adapting to changing access patterns.
4Loss of information
If lossless compression is applied to medical images, then image quality is preserved, but storage space reduction is limited and processing time increases
Solution Approach 1:
The patent changes the compression parameter based on image class and container destination. Lossless compression is applied to class 1 images in fast storage containers where quality is critical, while lossy compression with adjustable quality levels is applied to images in slower containers (C2, C3) where storage efficiency is prioritized. This adaptive approach optimizes both quality preservation and space reduction.
Data Source
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AI summary
The method involves partitioning a set of medical images out of N-classes according to scheduling rules that are provided in a scheduler (1). An image is distributed between a set of storage containers (3), where each container is dedicated for storage of image of a given class. A migration of one of the set of images of one storage container dedicated to a class is controlled towards another container that is dedicated to another class. A transformation function is applied to the set of images during the migration of the set of images. An independent claim is also included for a system for archiving of an image.