Ocular Fundus Image Analysis System for Diagnostic Difference Detection
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Solution Overview
Problem
Automated detection of differences in ocular fundus images is hindered by the difficulty in determining normal conditions, leading to less than satisfactory results, and manual screening is time-consuming and operator-dependent, often resulting in misdiagnoses.
Innovation Solution
An image processing system that imports and normalizes ocular fundus images, co-registers them for analysis, and synthesizes differences over time, highlighting significant changes relative to structural features of the eye, using additional information for fine-tuned corrections and comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection of differences between ocular fundus images is performed, then productivity is improved, but measurement precision deteriorates due to difficulty in determining normal conditions
Solution Approach 1:
The system performs preliminary normalization and co-registration of images before difference detection. The normalization module corrects images for illumination variations, geometric distortions, and refractive index differences beforehand, enabling automated detection to achieve both high productivity and acceptable precision by preparing images in advance for comparison
Solution Approach 2:
The system changes multiple parameters simultaneously including illumination correction factors, geometric transformation parameters, and refractive index compensation values. By adjusting these parameters through normalization and co-registration, the system enables automated detection to work effectively despite variations in imaging conditions
2Measurement precision
If manual screening of ocular fundus images is performed, then measurement precision is improved through expert observation, but productivity deteriorates due to time-consuming process
Solution Approach 1:
The system introduces an intermediary automated processing layer that performs preliminary normalization, co-registration, and difference detection. This intermediary process filters and prepares images before presenting them for review, allowing expert observers to focus on significant findings rather than performing complete manual screening, thus improving both productivity and maintaining precision
3Adaptability or versatility
If manual comparison of images captured at different times and conditions is performed, then adaptability is improved through operator judgment, but reliability deteriorates due to operator dependency and misdiagnoses
Solution Approach 1:
The system provides automated feedback through standardized normalization and co-registration processes that objectively correct for variations in imaging conditions. This feedback mechanism reduces operator dependency by providing consistent, reproducible results that can be verified independently, improving diagnostic reliability while maintaining the ability to interpret findings
Data Source
AI summary
An image analysis system provides for importing, normalizing and co-registering a plurality of ocular fundus images, or images obtained by their decomposition, analysis, processing, or synthesis. Differences between the image are determined and of those differences those that are meaningful (e.g., from a diagnostic point of view) are highlighted. Sequences of these differences, which reflect changes in the eye represented in the images, are synthesized so as to present a view of how the differences manifest over time. Structures of importance in the eye are overlapped so that the information may be presented in a meaningful fashion for diagnostic purposes.