Automated Eye Type Detection in Retinal Fundus Images
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Solution Overview
Problem
Current methods for automatically detecting the type of eye (left or right) in retinal fundus images are time-consuming, expensive, and unreliable, especially for mass screening and diagnosis, as they require clear identification of specific optic features which is not always feasible.
Innovation Solution
A computer-implemented method that extracts features from retinal fundus images using anatomical domain knowledge and pre-trained deep convolutional neural networks, combined with support vector machines to classify the images as left or right eyes, minimizing manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to detect eye type by identifying specific optic features, then detection accuracy may be maintained in ideal cases, but the process becomes time-consuming and unreliable for mass screening
Solution Approach 1:
The patent replaces manual identification of optic features with an automated computer-implemented method that uses image processing algorithms to detect eye type. The system automatically analyzes retinal fundus images to determine whether they depict left or right eyes, eliminating the need for manual inspection and thereby increasing productivity while maintaining reliability through consistent automated application of detection criteria
Solution Approach 2:
The detection system is designed to autonomously process retinal fundus images and determine eye type without requiring manual intervention or clear identification of specific optic features by operators. The method self-sufficiently extracts relevant information from the images and produces reliable classification results, enabling mass screening operations
2Measurement precision
If manual identification of optic features is required, then detection accuracy can be maintained, but the cost and time requirements increase significantly
Solution Approach 1:
The system performs preliminary automated analysis of retinal fundus images to determine eye type before any manual review or interpretation. By pre-processing the images and extracting eye type information automatically using computer-implemented algorithms, the system eliminates the need for time-consuming manual identification of optic features while maintaining detection accuracy
3Reliability
If specific optic features must be clearly identified, then reliable detection is possible, but the method becomes infeasible when features are not clearly visible
Solution Approach 1:
The patent develops a universal detection method that can reliably determine eye type from retinal fundus images regardless of whether specific optic features are clearly visible. The computer-implemented method uses multiple analysis approaches and does not depend on the clear identification of particular optic features, making it applicable to a wide variety of image qualities and conditions
Data Source
AI summary
A computer-implemented method includes obtaining an image of a retinal fundus. A plurality of features is extracted from the image of the retinal fundus. The plurality of features includes at least one feature based on anatomical domain knowledge of the retinal fundus and at least one response of a pre-trained deep convolutional neural network to at least a portion of the image of the retinal fundus. The retinal fundus is determined to belong to a left eye or a right eye, based on an analysis of the plurality of features.