Medical Image Indexing via Patch Clustering and Feature Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image management systems face challenges in efficiently and consistently indexing and retrieving medical images due to limitations in universal adaptability, computational resources, and storage capacity, leading to inconsistent descriptors across different systems, which hinders timely and accurate diagnosis and research.
Innovation Solution
The method involves dividing images into patches, detecting features using techniques like SIFT, LBP, and deep features from artificial neural networks, assigning patches to clusters, selecting representative patches, generating encoded representations, and creating index identifiers for efficient image indexing and retrieval, allowing for similarity comparison across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual observation and human judgement are used to generate descriptors for digital content, then the descriptors can be created with human insight, but the process requires significant time and the descriptors are not universal or adaptable between different systems
Solution Approach 1:
The patent replaces manual human observation and judgement with automated computer-based image processing systems. The system uses digital image processing algorithms to automatically generate descriptors from medical images, eliminating the need for manual human analysis while producing universal, machine-readable descriptors that can be consistently applied across different systems and used for automated retrieval and comparison operations
Solution Approach 2:
The system enables self-service by allowing the image processing system to automatically generate its own descriptors without human intervention. The automated descriptor generation process extracts features directly from the images using computational algorithms, making the system self-sufficient in creating searchable metadata and eliminating dependency on manual human labor for descriptor creation
2Productivity
If existing descriptor methods are used, then the process is simpler, but the extent of digital content processing is limited
Solution Approach 1:
The patent segments the image processing task into multiple distinct stages: image acquisition, pre-processing, feature extraction, descriptor generation, and retrieval operations. By dividing the overall process into modular segments, the system can handle complex medical images systematically while maintaining manageable complexity at each stage, thereby increasing overall processing capacity without overwhelming system complexity
Solution Approach 2:
The system transitions from traditional two-dimensional visual inspection to multi-dimensional digital processing by extracting features across multiple domains (spatial, frequency, texture). This dimensional expansion allows the system to process images more comprehensively and increase productivity by analyzing images in multiple feature spaces simultaneously rather than relying on single-dimensional visual assessment
3Measurement precision
If comprehensive image indexing is implemented to improve retrieval accuracy, then more detailed information can be stored, but computational resources and storage capacity requirements increase
Solution Approach 1:
The patent extracts only the most relevant and discriminative features from medical images to create compact descriptors. Instead of storing or processing entire images or all possible image characteristics, the system selectively extracts key features (such as edge patterns, texture descriptors, and shape characteristics) that are sufficient for accurate retrieval. This extraction approach maintains high retrieval accuracy while significantly reducing the quantity of data that must be stored and processed
Data Source
AI summary
Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.