Medical Image Processing for ROI-Targeted Super-Resolution
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Solution Overview
Problem
Existing medical image processing systems face inefficiencies in super-resolution processes, particularly in specifying target areas for image enhancement, leading to prolonged processing times and reduced efficiency.
Innovation Solution
A medical image processing apparatus that includes processing circuitry to acquire medical image data, specify target areas for super-resolution, and output information on these areas, thereby reducing unnecessary processing on non-target areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If super-resolution process is applied to entire medical image, then image quality improvement is achieved, but processing time increases
Solution Approach 1:
The patent divides the medical image into multiple regions of interest (ROIs) and non-ROI areas. The super-resolution process is selectively applied only to the ROI areas where enhancement is most needed, such as regions containing lesions or abnormalities. This segmentation approach maintains image quality improvement in critical areas while significantly reducing the overall processing time compared to applying the full super-resolution process to the entire image.
Solution Approach 2:
The patent implements local quality enhancement by applying different processing strategies to different regions of the medical image. High-quality super-resolution processing is applied locally to regions of interest where diagnostic accuracy is most critical, while standard processing or no processing is applied to background areas. This local quality approach optimizes the balance between image quality improvement and processing time.
2Productivity
If target area specification is performed, then processing efficiency is improved, but additional processing steps are required
Solution Approach 1:
The patent performs preliminary actions to identify and specify regions of interest before applying the super-resolution process. This includes pre-processing steps such as image segmentation, feature detection, and ROI delineation. By performing these actions in advance, the system can efficiently target only the necessary areas for super-resolution processing, improving overall processing efficiency while keeping the additional steps manageable through automated algorithms.
Data Source
AI summary
A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires medical image data to be processed, specifies a target area to be subjected to a super-resolution process for improving image quality from the medical image data on the basis of the medical image data, and outputs information on the specified target area.