Slit Lamp Cataract Imaging for 3D Opacity Distribution
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Solution Overview
Problem
Diaphanoscopy in slit lamp microscopes faces challenges in managing brightness and image quality, leading to subjective diagnosis and lack of three-dimensional opacity distribution, hindering quantitative analysis and application of automatic image analysis.
Innovation Solution
A slit lamp microscope equipped with a scanner to collect cross-sectional images and a data processor to generate three-dimensional opacity distribution information, enabling controlled brightness and providing depth-wise opacity distribution through reconstruction and segmentation of crystalline lens images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If diaphanoscopy is used to obtain transillumination images, then anterior eye segment observation is enabled, but brightness control and image quality management become difficult
Solution Approach 1:
The patent replaces the subjective visual observation mechanism of diaphanoscopy with an automated image processing system that captures transillumination images and processes them through algorithms to objectively evaluate cataract opacity. This substitution enables consistent brightness control and image quality management through technical systems rather than human perception.
Solution Approach 2:
The patent introduces an image processing apparatus as an intermediary between the transillumination imaging and diagnostic evaluation. This intermediary captures images with controlled illumination, processes them through standardized algorithms, and provides objective measurements, thereby mediating between the variable brightness of transillumination and the need for consistent diagnostic quality.
2Extent of automation
If diaphanoscopy is used for cataract observation, then transillumination images are obtained, but quantitative diagnosis becomes impossible due to subjective interpretation
Solution Approach 1:
The patent replaces subjective human interpretation with automated image processing algorithms that objectively measure cataract opacity. The system captures transillumination images and uses computational methods to quantify opacity distribution, replacing the mechanical process of human visual assessment with an automated analytical system that provides precise measurements.
Solution Approach 2:
The patent implements a feedback mechanism where the image processing system continuously refines opacity measurements based on captured images. The system provides objective quantitative data that feeds back into the diagnostic process, enabling automated evaluation that improves measurement precision through systematic analysis rather than subjective judgment.
3Loss of information
If transillumination imaging is used, then planar opacity distribution is obtained, but three-dimensional opacity distribution information is lost
Solution Approach 1:
The patent applies dimensionality change by reconstructing three-dimensional opacity distribution from two-dimensional transillumination images. The system uses computational algorithms to infer depth information and generate volumetric representations of cataract opacity, adding the third dimension (depth) to the otherwise planar image data without requiring complex three-dimensional imaging hardware.
Solution Approach 2:
The patent creates a virtual three-dimensional copy of the cataract opacity distribution based on processed transillumination images. Instead of directly capturing three-dimensional data, the system generates a computational model that replicates the spatial distribution of opacity in three dimensions, preserving depth information through digital reconstruction rather than physical measurement.
Data Source
AI summary
A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.


