Image Processing Apparatus for Pressure Ulcer Boundary Detection
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Solution Overview
Problem
Current image processing techniques for automatically identifying and measuring affected regions, such as pressure ulcers, face challenges in accurately defining the boundaries and extending from the center to the periphery, leading to incomplete or inaccurate image capture, which burdens medical personnel and patients with repeated measurements.
Innovation Solution
An apparatus with a processor that generates outer edge candidates of a specific region from an image and allows user selection, using deep learning for semantic segmentation to identify the affected region and calculate its area, reducing the need for manual adjustments and re-photography.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image processing is used to automatically identify the affected region, then the burden on medical personnel and patients is reduced, but the accuracy of boundary identification deteriorates
Solution Approach 1:
The patent introduces an outer edge candidate generation unit that creates multiple candidate boundaries between the affected region and surrounding skin. These candidates serve as intermediaries that allow medical personnel to select the most accurate boundary without manually tracing the entire region, thus maintaining high measurement precision while reducing operational burden.
Solution Approach 2:
The system performs automatic identification of the affected region using image processing, allowing the system to serve itself in the initial detection phase. This automated self-identification reduces the burden on medical personnel while maintaining acceptable accuracy, which can then be refined through candidate selection.
2Productivity
If the affected region is automatically identified by image processing, then manual measurement work is reduced, but the completeness of region capture deteriorates
Solution Approach 1:
The system generates multiple outer edge candidates that extend beyond the strictly necessary boundary, providing a margin of excess coverage. This ensures that the complete affected region is captured while allowing medical personnel to select the appropriate boundary, thus maintaining reliability without sacrificing productivity.
3Measurement precision
If manual measurement is performed to ensure accurate boundary identification, then measurement precision is improved, but the burden on medical personnel and patients increases
Solution Approach 1:
The patent segments the boundary identification process into two parts: automatic identification of the affected region by the system, and selection of the final boundary from generated candidates by medical personnel. This segmentation maintains measurement precision while significantly reducing the operational burden compared to complete manual measurement.
Solution Approach 2:
The system creates multiple copies of the affected region boundary as candidate outer edges. Instead of requiring medical personnel to create the boundary from scratch, they select from pre-generated copies, maintaining accuracy while reducing effort.
Data Source
AI summary
At least one processor of an apparatus functions as a generation unit that identifies at least an outer edge of a specific region in a surface layer of an object and that generates outer edge candidates, and a control unit that selects an outer edge candidate based on an instruction from a user among the generated outer edge candidates.