Real-Time Medical Image Processing with Incremental Analysis
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Solution Overview
Problem
Current image and video processing technologies in medical devices, such as endoscopy, struggle to perform computationally intensive processing in real-time due to hardware constraints, leading to incomplete information extraction from images, especially when freezing images for analysis, which can result in loss of detail or blurriness.
Innovation Solution
A system and method that allows computationally intensive algorithms to run on a frozen set of images during a 'freeze period', using incremental processing that provides intermediate results, which can be evaluated and displayed or stored, enabling enhanced image processing tasks like video mosaicing, super-resolution, and blood velocity measurement, even if they cannot be completed in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computationally intensive algorithms are run in real-time on acquired images, then processing speed is improved, but hardware constraints prevent complete information extraction
Solution Approach 1:
The system performs preliminary real-time processing to identify and freeze images of interest before executing computationally intensive algorithms. This preliminary action captures the essential information in a stable state, allowing subsequent detailed processing without losing critical data that would be present in the original real-time stream.
Solution Approach 2:
The processing is divided into two segments: real-time preprocessing that runs on hardware constraints to maintain speed, and offline intensive processing that extracts complete information without time pressure. This segmentation allows each part to operate within its optimal performance envelope.
2Loss of information
If images are frozen for detailed analysis, then information extraction is improved, but the frozen image may be bad or blurred
Solution Approach 1:
The system uses feedback from real-time processing metrics to determine when to freeze images. By monitoring image quality and processing progress, the system selects freeze moments when images are most likely to be clear and informative, avoiding freezes during motion or blur while still enabling subsequent detailed analysis.
3Duration of action of moving object
If freeze function is used to allow more time for analysis, then diagnostic time is improved, but the location of investigated area may be lost
Solution Approach 1:
The system merges the frozen image with contextual information from the video sequence, including spatial location data and temporal positioning. This combination preserves the diagnostic context while allowing extended analysis time, so physicians can examine detailed images without losing track of where they originate in the overall procedure.
4Loss of information
If two parallel pipelines are used to keep information during freeze period, then information retention is improved, but device complexity increases
Solution Approach 1:
Instead of maintaining parallel pipelines, the system performs preliminary buffering of the video sequence before the freeze. This preliminary action stores the necessary contextual information in advance, eliminating the need for complex parallel processing while still retaining all information during the freeze period.
Data Source
AI summary
A method for processing images acquired in real time through a medical device, said images being loaded into a buffer, comprising the steps of : stopping the loading of the images into the buffer, processing loaded images using an incremental algorithm, displaying successively intermediate results of the processing, resuming the loading and stopping the processing based on an evaluation of said intermediate results.