Rotationally Invariant Descriptors for Medical Device Localization
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Solution Overview
Problem
Current medical imaging technologies face challenges in accurately identifying and locating medical devices within blood vessels during procedures like catheterizations, particularly in distinguishing between vessel edges and device edges, which affects precise diagnostic and therapeutic interventions.
Innovation Solution
A computer processor analyzes images by sampling concentric circles around pixels to generate rotationally invariant descriptors through time-frequency domain transforms, enabling the identification of medical devices such as guide catheters, wires, and stents within blood vessels, and determines transformation functions for aligning and mapping between different image types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis methods are used to identify medical devices in blood vessels, then the analysis process is simple, but the accuracy of distinguishing device edges from vessel edges deteriorates
Solution Approach 1:
The patent segments the image analysis process into multiple distinct stages: (1) generating rotationally invariant descriptors from image pixels, (2) clustering descriptors to identify device regions versus vessel regions, and (3) localizing devices based on cluster assignments. This segmentation allows each stage to be optimized independently, improving overall accuracy while managing complexity through modular processing.
Solution Approach 2:
The patent transforms the image analysis problem from spatial domain to descriptor space by extracting rotationally invariant features and organizing them into clusters. This dimensional transformation creates a new feature space where device and vessel regions can be separated more effectively, improving discrimination accuracy without requiring complex spatial analysis.
2Measurement precision
If complex image analysis methods with multiple processing stages are applied, then the accuracy of device identification improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing rotationally invariant descriptors from image pixels before clustering. These descriptors capture essential device characteristics in advance, allowing the subsequent clustering stage to focus only on pattern recognition rather than feature extraction, thereby reducing overall processing time while maintaining high identification accuracy.
Solution Approach 2:
The patent creates simplified representations (copies) of the original image data through rotationally invariant descriptors that capture the essential characteristics of medical devices without preserving all original image details. This copying process reduces data complexity while retaining the critical information needed for accurate device identification, balancing processing speed and accuracy.
3Reliability
If rotationally invariant descriptors are generated through multiple transform functions, then the robustness of device identification improves, but the computational complexity increases
Solution Approach 1:
The patent segments the descriptor generation process into three distinct transformation functions: (1) generating initial rotationally invariant descriptors from pixel data, (2) creating difference descriptors by comparing pairs of initial descriptors, and (3) generating final rotationally invariant descriptors from the difference descriptors. This segmentation allows each transformation to be optimized for its specific purpose, improving robustness while managing computational complexity through functional decomposition.
Solution Approach 2:
The patent introduces intermediate descriptors (difference descriptors) as mediators between the initial pixel-based descriptors and the final rotationally invariant descriptors. These intermediate representations capture relational information between pixel groups and facilitate the generation of more robust final descriptors, improving reliability while organizing computational complexity into manageable intermediate steps.
Data Source
AI summary
Apparatus and methods are described including, using a computer processor, automatically identifying whether a given pixel within an image corresponds to a portion of an object. A set of concentric circles that are disposed around the pixel are sampled, and a first function is applied to each of the circles such that the circles are defined by a first set of rotationally invariant descriptors. A second function is applied to the set of circles to generate a second set of descriptors, each of which represents a difference between respective pairs of the circles. A third function is applied such that the second set of descriptors becomes rotationally invariant. The processor identifies whether the given pixel corresponds to the portion of the object, based upon the first and second sets of rotationally invariant descriptors. Other applications are also described.