Endoscopic Depth Estimation via LED Reflection Analysis
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Solution Overview
Problem
Current endoscopic techniques face challenges in obtaining accurate three-dimensional measurements of biological features within a patient's internal passages, as they require additional costly and space-consuming hardware for depth estimation.
Innovation Solution
The system utilizes existing endoscope hardware to calculate image depth by analyzing the level of reflected light off biological tissue, correcting for estimated tissue color, and employing the characteristics of the LED illumination source to estimate depth, thereby providing three-dimensional measurements without the need for additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional hardware is used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system uses the endoscope's existing LED illumination source and camera to perform depth estimation, making the hardware serve dual purposes (illumination and imaging) without requiring separate depth sensing devices. The LED characteristics and reflected light intensity are utilized to calculate depth, eliminating the need for additional specialized hardware.
Solution Approach 2:
The LED illumination source serves multiple functions: it provides illumination for the camera and simultaneously acts as a depth estimation reference. By analyzing the reflected light intensity from the LED and comparing it with known LED characteristics, the system extracts depth information from the same optical path used for imaging.
2Measurement precision
If additional hardware is used for depth estimation, then measurement precision is improved, but operational cost increases
Solution Approach 1:
The system leverages the existing LED illumination source and camera components to perform depth estimation, eliminating the need to purchase and operate additional specialized depth sensing hardware. This self-service approach reduces operational costs while maintaining measurement precision.
Solution Approach 2:
The patent uses inexpensive, readily available components (LED characteristics and reflected light intensity) to achieve depth estimation, replacing expensive specialized hardware with a cost-effective computational approach based on optical physics principles.
3Measurement precision
If additional hardware is used for depth estimation, then measurement precision is improved, but space consumption increases
Solution Approach 1:
The endoscope's existing LED illumination source and camera are utilized for depth estimation, eliminating the need for separate depth sensing devices. This self-service approach maintains measurement precision while avoiding the space consumption of additional hardware components.
Solution Approach 2:
The system merges the illumination function and depth estimation function into a single optical path. The LED illumination source serves both as the lighting source for imaging and as the reference for depth calculation, combining multiple functions into one integrated system that consumes less space.
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
This approach allows for accurate three-dimensional measurements of biological features using standard endoscope hardware, eliminating the need for costly and space-consuming additional equipment, thus enhancing clinical efficiency and reducing operational costs.
Implementation Method 1
analyzing the level of reflected light off biological tissue
Data Source
AI summary
An endoscopic camera captures an image of a scene that is illuminated by the light source. A processor performs image linearization for the image based on a stored gamma curve, estimates tissue colors in the image based on stored tissue color estimation data, and corrects for incident light intensity based on the estimated tissue color. The processor also corrects for light beam pattern intensity, based on a calibration image, to obtain corrected light intensity for the image. The processor generates a depth map for the image based on the corrected light intensity and provides a measurement of an object in the image based on the depth map.