Fundus Observation Device Vascular Cross-Section Interpolation
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Solution Overview
Problem
Conventional fundus observation devices struggle to clearly identify the layer boundaries of the fundus oculi, particularly under vascular cross-sectional regions, due to image distortion caused by fundus vessels, making it difficult to measure layer thickness accurately.
Innovation Solution
A fundus observation device comprising image forming parts for two-dimensional and tomographic imaging, an accumulated image generation unit, and processing units to extract vascular territories and specify vascular cross-sectional regions, allowing for accurate interpolation of layer regions and boundary measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fundus observation devices are used to image the fundus oculi, then the imaging process is simple, but the layer boundaries cannot be clearly identified particularly under vascular cross-sectional regions due to image distortion caused by fundus vessels
Solution Approach 1:
The patent segments the fundus image analysis into multiple processing stages: obtaining original fundus images, generating en face images through optical coherence tomography data processing, detecting vascular structures, identifying vascular cross-sectional regions, and performing layer boundary detection. This segmentation allows each processing stage to be optimized independently, improving layer boundary identification accuracy while managing overall system complexity.
Solution Approach 2:
The patent introduces an en face image as an intermediary between the original fundus image and the final layer boundary measurement. The en face image serves as a processed intermediate representation that enhances vascular structure visibility and facilitates more accurate detection of vascular cross-sectional regions, thereby improving subsequent layer boundary identification.
2Measurement precision
If conventional imaging methods are used, then the device operation is straightforward, but it is difficult to measure layer thickness accurately under vascular regions
Solution Approach 1:
The patent performs preliminary processing to generate en face images and detect vascular structures before attempting layer boundary detection. By pre-identifying vascular cross-sectional regions and preparing enhanced images in advance, the system improves layer thickness measurement accuracy while automating the complex processing steps, thereby maintaining ease of operation.
Solution Approach 2:
The patent replaces manual measurement methods with automated image processing algorithms. The system automatically detects vascular structures, identifies vascular cross-sectional regions, and measures layer boundaries through computational analysis of optical coherence tomography data, eliminating the need for manual intervention in complex measurement tasks.
3Productivity
If manual analysis of fundus images is performed, then the processing is simple, but it requires considerable time and labor
Solution Approach 1:
The patent implements self-service through automated algorithms that perform en face image generation, vascular structure detection, vascular cross-sectional region identification, and layer boundary measurement without human intervention. The system serves itself by automatically processing fundus images and generating measurements, significantly improving analysis efficiency while the modular architecture manages processing system complexity.
Solution Approach 2:
The patent substitutes manual mechanical analysis with automated computational processing. Image processing algorithms automatically analyze fundus images, detect features, and generate measurements, replacing the manual mechanical process of visual inspection and manual measurement with efficient computational methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables clear identification of vascular cross-sectional regions and layer boundaries, facilitating precise measurement of layer thicknesses that were previously immeasurable, reducing the time and labor required for analysis.
Implementation Method 1
a signal light LS to illuminate the fundus oculi Ef and detect the reflected light
Data Source
AI summary
First image forming part forms a two-dimensional surface image of a fundus oculi of an eye based on optically obtained data. Second image forming part forms tomographic images of fundus oculi based on data obtained by optically scanning a region of the surface of fundus oculi corresponding to at least part of two-dimensional image. Accumulated image generating part generates an accumulated image by accumulating the formed tomographic images in a depth-wise direction. Extracting part extracts first vascular territory corresponding to a fundus oculi vessel from two-dimensional image formed by first image forming part, and also extracts second vascular territory corresponding to a fundus oculi vessel from accumulated image generated by accumulated image generating part. Specification part specifies a position of a vascular cross sectional region corresponding to a cross section of a fundus oculi vessel in the tomographic image based on extracted first vascular territory and extracted second vascular territory.