Distributed Cell Image Search Indexing via Feature Extraction
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Solution Overview
Problem
Cell culture imagers generate vast amounts of image data, necessitating an efficient method for searching and analyzing cell images across distributed systems while minimizing central storage and bandwidth requirements.
Innovation Solution
A distributed imaging system that stores image descriptors locally and centrally, using machine learning and data mining to generate Index Data, which is much smaller than original image data, allowing for global searching while maintaining privacy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If all image data is stored centrally for global searching, then search capability is improved, but storage requirements and bandwidth consumption increase significantly
Solution Approach 1:
The system divides the centralized image storage into distributed local storage across multiple user sites. Each site maintains its own image data locally, eliminating the need for a single centralized repository. This segmentation reduces the storage burden on any single server while enabling global search through distributed query coordination.
Solution Approach 2:
The system extracts only the essential search-relevant features (morphological descriptors, metadata) from the full image data and stores these extracted features centrally or in distributed indexes. This allows the system to perform searches using compact feature representations rather than transmitting or accessing complete high-resolution images, dramatically reducing storage and bandwidth requirements.
2Quantity of substance
If image data is stored locally at each user site, then storage requirements at central location are reduced, but global search capability deteriorates
Solution Approach 1:
The system introduces distributed index servers as intermediaries between local user sites and the global search network. These index servers store compressed morphological descriptors and metadata locally, enabling fast local searches. For global searches, the intermediary coordinates queries across multiple sites by exchanging only the compact index data rather than full images, maintaining search capability while reducing central storage needs.
3Measurement precision
If complete image data is transmitted for analysis, then analysis accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the essential morphological descriptors, features, and metadata required for analysis rather than complete image data. Machine learning models and data mining algorithms operate on these extracted features locally at each site, achieving accurate analysis without the bandwidth overhead of transmitting full-resolution images across the network.
Data Source
AI summary
A method and apparatus for searching cell images comprising storing cell images in an image database, generating index data for the stored images in the image database, wherein the index data includes image metadata and data extracted from the stored image data by analysis including morphological descriptors, applications of algorithms, machine learning and/or data mining.