Medical Image Diagnosis Frame-Rate Switching for Motion Blur
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Solution Overview
Problem
Existing image recognition techniques in medical imaging, such as those using endoscopes, struggle with accurately estimating diagnostic candidate areas due to relative movement between the imaging device and the subject, leading to potential image blur and inefficiencies in processing and diagnosis.
Innovation Solution
An image diagnosis assistance apparatus and method that adjusts the input frame rate and processing mode based on the magnitude of relative movement, using trained models to enhance image definition and estimate diagnostic candidate areas, switching between normal and high-frame rates and illumination modes to maintain clarity and accuracy during rapid movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the input frame rate for estimation processing is increased to maintain accuracy during movement, then measurement precision is improved, but productivity deteriorates due to increased processing load
Solution Approach 1:
The system dynamically adjusts the input frame rate based on the detected movement magnitude. When movement exceeds a threshold, the frame rate is increased to maintain estimation accuracy; when movement is minimal, the frame rate is reduced to improve processing efficiency. This dynamic adaptation resolves the contradiction between maintaining precision and ensuring productivity.
Solution Approach 2:
The system changes the processing parameter (input frame rate) based on movement conditions. By monitoring movement magnitude and adjusting the frame rate parameter accordingly, the system optimizes the balance between estimation accuracy and processing efficiency, avoiding unnecessary high frame rates during stable conditions while ensuring sufficient frame rates during movement.
2Manufacturing precision
If high-definition processing is applied to all processed images, then manufacturing precision is improved, but use of energy deteriorates due to increased computational requirements
Solution Approach 1:
The system applies high-definition processing selectively rather than uniformly to all images. By identifying images that require enhanced definition and applying processing only to those cases, the system maintains manufacturing precision where needed while reducing overall energy consumption compared to processing every image at high definition.
Solution Approach 2:
The system changes the processing quality parameter based on image characteristics and movement conditions. High-definition processing is applied only when necessary, adjusting the definition parameter dynamically to balance image quality with energy consumption requirements.
Data Source
AI summary
An image diagnosis assistance apparatus includes a processor including hardware, the processor being configured to: acquire a captured image captured by an imaging device configured to image a subject; determine magnitude of relative movement between the imaging device and the subject; execute image processing on the captured image to output a processed image; generate, based on the processed image, an image for diagnosis; estimate a diagnostic candidate area serving as a diagnostic candidate in the processed image, by executing an estimation process for the processed image using a trained model; and when the magnitude of the movement is at a predetermined threshold or more, set the input frame rate for the processed image on which the estimation process is sequentially executed to be larger than the input frame rate for the processed image with which a process of generating the image for diagnosis is sequentially executed.