Image Processing System for Minimally Invasive Surgery Video
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Solution Overview
Problem
In minimally invasive surgeries, accurately visualizing target treatment sites, such as tumors or lesions in the gastrointestinal tract, is challenging due to the limitations of existing image enhancement techniques, which can cause image processing delays and reduce effectiveness for video images.
Innovation Solution
The proposed solution involves an image processing system that generates sub-blocks from image frames, calculates histogram data, and applies histogram enhancement logic to improve image contrast, thereby enhancing video images in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histogram enhancement methods are used to improve image quality, then image contrast and quality are improved, but image processing delays increase and processor performance is overloaded
Solution Approach 1:
The image is divided into multiple sub-blocks for parallel histogram enhancement processing. Each sub-block is processed independently, allowing the system to handle multiple regions simultaneously, thereby reducing overall processing time while maintaining enhanced image quality across the entire image
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing histogram data for sub-blocks during idle periods or between frames. This pre-computation allows the main enhancement process to proceed faster by retrieving pre-processed data rather than calculating histograms in real-time during critical processing windows
2Measurement precision
If highly iterative histogram enhancement processes are applied, then image quality is improved, but processor workload increases and effectiveness for video images is limited
Solution Approach 1:
The video stream is processed by dividing each frame into sub-blocks that can be enhanced in parallel. This segmentation enables the system to process multiple regions of video frames simultaneously, maintaining high frame rates while applying quality-enhancing histogram adjustments to each segment
Solution Approach 2:
The system applies histogram enhancement selectively to critical sub-blocks or regions of interest within video frames rather than uniformly processing every pixel. This partial action approach maintains video processing speed while still improving quality in areas that matter most for diagnostic or viewing purposes
Data Source
AI summary
A method is provided for enhancing video images in a medical device. The method includes receiving a first image frame and a second image frame from an image sensor. First image sub-blocks are generated by dividing the first image frame. Second image sub-blocks are generated by dividing the second image frame based on the first image sub-blocks. Histogram data of the first image sub-blocks is generated. Histogram data of the second image sub-blocks is generated based on the histogram data of the first image sub-blocks. A histogram enhanced image frame is generated based on the histogram data of the second image sub-blocks. A video image stream is generated based on the histogram enhanced image frame.