Robust Monocular Surface Depth Estimation With Illumination Data
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Solution Overview
Problem
Existing medical imaging technologies face challenges in accurately reconstructing three-dimensional images of internal body structures during medical procedures, particularly in monocular optical imaging systems where depth perception is limited.
Innovation Solution
The method involves illuminating a body structure with a light source on a medical device, capturing images with a digital camera, and using algorithms to calculate relative distances and surface albedo factors, while integrating illumination data to enhance depth estimation and image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple image frames are accumulated and processed to reconstruct three-dimensional representation, then depth estimation accuracy is improved, but computational processing requirements increase
Solution Approach 1:
The patent segments the image processing task by identifying and extracting only the most informative pixels (local intensity maxima) from each frame, rather than processing all pixels. This segmentation approach maintains depth estimation accuracy while significantly reducing computational load by focusing processing resources on key depth-indicative features.
Solution Approach 2:
The patent extracts and utilizes illumination data as a separate, independent feature from the standard image data. By taking out the illumination information and using it specifically for depth estimation, the system achieves improved depth accuracy without requiring full processing of all image data, thus reducing overall computational requirements.
2Measurement precision
If image processing algorithms register video frames and reconstruct imaged environment, then visual field clarity and accuracy are improved, but computational processing requirements increase
Solution Approach 1:
The patent performs preliminary registration of video frames by identifying corresponding features and establishing transformation relationships before full reconstruction. This preliminary action prepares the data in advance, enabling more efficient subsequent processing and reducing the computational burden during real-time reconstruction while maintaining visual field accuracy.
Solution Approach 2:
The patent incorporates illumination data as an additional dimension of information beyond standard intensity values. By using illumination intensity as a separate dimension for depth estimation, the system achieves improved three-dimensional reconstruction accuracy without requiring increased processing of traditional image dimensions.
3Measurement precision
If algorithms calculate relative distances and surface albedo factors using illumination data, then depth estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameters used for depth estimation by incorporating illumination intensity as an additional parameter alongside standard image intensity. This parameter change enables more accurate depth estimation through the relationship between illumination and surface distance, while the mathematical model remains relatively simple and does not significantly increase algorithmic complexity.
Data Source
AI summary
Systems and methods related to estimating the distance of a body structure from a medical device are disclosed. An example method includes illuminating the body structure with a light source of a medical device, capturing a first input image of the body structure with a digital camera positioned on the medical device, representing the first image with a first plurality of pixels, wherein the first plurality of pixels includes one or more pixels displaying a local intensity maxima, defining a first pixel group from the one or more pixels displaying a local intensity maxima, wherein the first pixel group corresponds to a plurality of surface points of the body structure and wherein the first pixel group further includes a first image intensity. The method further includes calculating a relative distance from the digital camera to a first surface point of the plurality of surface points.