Imaging System Skin Area Background Overlay for Consistent Medical Images
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Solution Overview
Problem
Current imaging technologies face challenges in capturing consistent and repeatable images, particularly in medical applications, due to varying lighting environments and image backgrounds, requiring high training and skill, and are expensive and time-consuming to use.
Innovation Solution
An imaging system that acquires original images, extracts protocols, and records them to an image history, determines a skin area, creates a background inverse, and captures subsequent images while providing feedback to ensure imaging parameters meet thresholds, displaying the background overlaid on the image and storing it only if within the thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional imaging technology is used to capture images in varying lighting environments, then image capture is possible, but image consistency and repeatability deteriorate
Solution Approach 1:
The system changes imaging parameters such as exposure time, gain, and white balance automatically to compensate for varying lighting conditions. The processor analyzes lighting parameters from captured images and adjusts camera settings to maintain consistent image quality across different environmental conditions.
Solution Approach 2:
The system implements feedback mechanisms where captured images are analyzed by the processor to determine lighting parameters, and this information is used to adjust subsequent imaging parameters. The system continuously monitors and refines image capture settings based on previous image quality assessment.
2Manufacturing precision
If professional imaging systems with high training requirements are used, then image quality improves, but ease of operation deteriorates
Solution Approach 1:
The imaging system performs self-adjustment of imaging parameters without requiring user expertise. The processor automatically analyzes captured images, determines lighting conditions, and modifies camera settings autonomously, eliminating the need for professional training while maintaining high image quality.
Solution Approach 2:
The system performs preliminary analysis of lighting parameters and pre-adjusts imaging settings before actual image capture. By preparing optimal camera parameters in advance based on environmental assessment, the system ensures high image quality from the first capture without requiring user intervention or training.
3Measurement precision
If manual image processing and analysis methods are used, then image analysis accuracy improves, but productivity deteriorates
Solution Approach 1:
The system replaces manual mechanical image processing with automated digital image processing algorithms. The processor automatically performs image enhancement, feature extraction, and analysis tasks that previously required manual intervention, maintaining high accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system implements periodic automated image processing cycles where captured images are systematically analyzed, enhanced, and stored through programmed algorithms. This automated periodic processing maintains consistent analysis accuracy across large volumes of images without the time burden of manual processing.
Data Source
AI summary
A method and instructions for operating an image integrity and repeatability system can comprise: acquiring an original image; extracting a protocol from the original image; recording the original image to an image history; determining an area within an image frame of the original image as a skin area; creating a background as the inverse of the skin area within the image frame of the original image; and acquiring a subsequent image including: extracting an image parameter from the subsequent image, providing feedback based on the image parameter being outside a threshold, prohibiting capture of the subsequent image based on the image parameter being outside the threshold, displaying the background overlaid on the subsequent image, and storing the subsequent image to the image history based on the image parameter of the subsequent image being within the threshold.