Camera Probe Navigation in Bladder Cavity
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Solution Overview
Problem
In flexible cystoscopy procedures, it is challenging to accurately determine the location and orientation of the camera within the bladder without tactile feedback, making it difficult to correlate captured images with specific locations, especially in remote or automated procedures.
Innovation Solution
A probe system that captures images using a camera inserted into the bladder, employs a scale-invariant feature transform algorithm to identify feature locations, generates a three-dimensional model using a structure from motion algorithm, and compresses images to create feature vectors, allowing for accurate camera positioning and orientation determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a camera is inserted into the bladder for flexible cystoscopy, then images of the bladder interior can be captured, but the location and orientation of the camera cannot be accurately determined without tactile feedback
Solution Approach 1:
The patent introduces an intermediary system consisting of image processing algorithms (SIFT, SfM) and computational models that mediate between the captured images and the camera position determination. These intermediaries process the visual information to infer camera location and orientation without direct tactile feedback, resolving the contradiction by adding an information processing layer.
Solution Approach 2:
The patent replaces the mechanical tactile feedback system with a computational vision system. Instead of relying on physical contact and tactile sensors to determine camera position, the system uses image processing and structure-from-motion algorithms to substitute mechanical feedback with optical information processing, enabling remote and automated procedures.
2Measurement precision
If image processing algorithms are used to determine camera position, then navigation precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing feature descriptors and structure-from-motion models during an initial phase. These pre-processed data structures are then reused during navigation, reducing real-time computational complexity while maintaining high positioning accuracy. The system prepares reference frameworks in advance to simplify subsequent processing.
Solution Approach 2:
The patent uses copying by creating simplified representations (feature vectors, descriptors) of the complex image data. Instead of processing full-resolution images repeatedly, the system works with compressed feature representations that capture essential information while reducing computational burden, thus lowering device complexity while preserving measurement precision.
3Quantity of substance
If feature vectors are generated through image compression, then data storage and transmission requirements decrease, but information loss may occur
Solution Approach 1:
The patent applies extraction by selectively removing and retaining only the essential features from the full image data. The SIFT algorithm extracts key feature points and descriptors that are critical for position determination, while discarding redundant pixel information. This selective extraction reduces data volume while preserving the information necessary for accurate camera positioning.
Solution Approach 2:
The patent applies local quality by concentrating computational and storage resources on the most informative parts of the image data. Instead of uniformly processing or compressing all image regions, the system identifies and prioritizes feature-rich areas (edges, corners, distinctive patterns) while using more aggressive compression in less critical regions, optimizing the balance between data reduction and information retention.
Data Source
AI summary
A method includes capturing, via a camera that is inserted into a cavity, a first image of a surface defining the cavity and compressing the first image, using a compression algorithm, to generate a first feature vector. The method also includes identifying a second feature vector of a plurality of second feature vectors that best matches the first feature vector. The plurality of second feature vectors was generated by compressing second images of the surface using the compression algorithm. The second images were captured prior to insertion of the camera into the cavity and prior to capturing the first image. The method also includes generating, using the second feature vector, output indicating a position and/or an orientation of the camera as the camera captured the first image.