Choroidal Vascular Image Generation for Precise Watershed Detection
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Solution Overview
Problem
There is a demand for technology to analyze choroidal blood vessels from a fundus image, which has not been adequately addressed by existing methods.
Innovation Solution
An image processing method and device that acquire a fundus image, generate a choroidal vascular image, and detect the watershed of the choroidal vascular network using various detection methods, including identifying the macula, optic nerve head, and vortex veins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing image processing methods are used to analyze fundus images, then general fundus image analysis is available, but choroidal blood vessel analysis capability is insufficient
Solution Approach 1:
The patent introduces an intermediate processing step that generates a choroidal vascular image from the original fundus image. This intermediate representation isolates choroidal blood vessels from other fundus structures, enabling specialized analysis while maintaining accuracy. The intermediate image serves as a mediator between the raw fundus image and the final analysis results.
Solution Approach 2:
The patent segments the fundus image into distinct components, specifically isolating the choroidal blood vessel network. By dividing the complex fundus image into separable elements (choroidal vessels, retina, other structures), the system can apply specialized processing to each component, improving both adaptability and reliability of choroidal analysis.
2Measurement precision
If simple image processing is applied to fundus images, then processing speed is fast, but detection precision of choroidal vascular networks is insufficient
Solution Approach 1:
The patent performs preliminary processing to generate a choroidal vascular image before conducting watershed detection. This pre-processing step prepares the data in an optimal format for subsequent analysis, separating choroidal vessels from other structures and reducing noise. This preliminary action simplifies the complexity of the main detection task while improving precision.
Solution Approach 2:
The patent replaces direct watershed detection on raw fundus images with a two-stage process: first generating a choroidal vascular image, then performing watershed detection on this processed image. This substitution of the detection mechanism with an intermediate representation reduces the computational complexity of the watershed algorithm while enhancing detection precision.
3Reliability
If comprehensive choroidal vascular analysis is performed, then diagnostic capability is improved, but processing time increases
Solution Approach 1:
The patent segments the analysis process into distinct phases: choroidal vascular image generation followed by watershed detection. This segmentation allows each phase to be optimized independently, performing comprehensive analysis only where needed rather than applying complex processing to the entire fundus image, thus reducing overall processing time while maintaining diagnostic capability.
Solution Approach 2:
The patent extracts the choroidal blood vessel information from the complete fundus image into a separate choroidal vascular image. This extraction isolates the diagnostically relevant information from unrelated fundus structures, enabling focused analysis that maintains high diagnostic capability while reducing the computational burden and processing time.
Data Source
AI summary
A processor acquires a fundus image, generates a choroidal vascular image from the fundus image, and detects a watershed of a choroidal vascular network in the choroidal vascular image.