Retinal Damage Image Processing for Predicted Patient Vision
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Solution Overview
Problem
Existing image processing methods fail to accurately depict the visual impact of retinal cell degradation and death on a patient's vision, as they do not account for the brain's compensatory mechanisms in reconstructing damaged visual fields.
Innovation Solution
A computer-implemented method that generates a predicted view for a patient by identifying damaged retinal areas, distorting corresponding image areas using pixels from undamaged regions, and applying blurring functions to simulate the brain's compensation for retinal damage, using techniques like inpainting and blurring based on patient-specific retinal scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to adjust photographs of visual scenes, then the processing is simple and fast, but the accuracy in portraying the visual impact of retinal cell degradation is insufficient
Solution Approach 1:
The method segments the retinal image to identify specific damaged areas (lesions) and divides the visual scene into corresponding regions that need distortion. This segmentation allows precise application of distortion only to affected areas rather than the entire image, improving accuracy while managing complexity through localized processing.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image based on retinal damage locations. Undamaged retinal areas contribute high-quality pixels to their corresponding visual regions, while damaged areas receive distortion and infilling. This local quality approach ensures accurate portrayal of visual impact without unnecessarily processing the entire image at high complexity.
2Measurement precision
If generic image processing is applied without patient-specific data, then the processing is faster and simpler, but the personalization and accuracy for individual patients is lost
Solution Approach 1:
The method performs preliminary analysis of the patient's retinal image to identify damaged areas and map them to the visual scene before applying distortion. This preliminary action of segmenting and mapping retinal lesions to image regions enables patient-specific personalization while organizing the processing steps efficiently, reducing overall processing time through structured preliminary analysis.
Solution Approach 2:
The patent creates a copy of the visual scene and applies distortion only to specific regions corresponding to retinal damage, rather than modifying the entire original image. This copying approach allows patient-specific personalization through selective region manipulation, improving accuracy while managing processing time by working with targeted copies rather than the complete image dataset.
3Measurement precision
If distortion is applied to simulate retinal damage effects, then the visual impact portrayal is more accurate, but the computational complexity and processing requirements increase
Solution Approach 1:
The method extracts only the damaged regions from the retinal image and applies distortion specifically to the corresponding regions in the visual scene, rather than processing the entire image. This extraction approach improves accuracy by focusing computational resources on affected areas while reducing overall device complexity through selective processing.
Solution Approach 2:
The patent uses infilling techniques that generate synthetic pixel data to replace distorted regions, effectively creating temporary placeholder content that simulates vision defects without requiring complex long-term processing. This approach achieves accurate visual impact portrayal through computationally efficient infilling operations rather than sustained complex processing.
Data Source
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AI summary
A computer-implemented method and an apparatus for generating a predicted view for a patient is provided. The computer-implemented method comprises receiving a first image indicative of a first view, receiving a retinal image for the patient, the retinal image comprising an image of a scan of the patient's retina including a damaged area, and generating a second image indicative of a predicted view for the patient in dependence on the first image and the retinal image.