Tubing Detection in Radiographic Images Using Segment Merging
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Solution Overview
Problem
The accuracy and usefulness of diagnostic images in ICU settings are limited by variations in image quality, making it difficult for clinicians to detect the proper positioning of internal tubes, such as endotracheal tubes, due to differences in exposure settings, patient and apparatus positioning, and scattering, which complicates the detection of tube tips.
Innovation Solution
A method for detecting tubing in radiographic images using a control logic processor that involves obtaining image data, detecting possible tube segments, and forming tubing candidates by extending or merging detected segments, with techniques such as enhanced tube-pattern feature template processing and gradient feature template processing to improve detection accuracy and adapt to different imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If portable radiography is used for ready accessibility in ICU, then ease of operation is improved, but image quality consistency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing images to correct exposure variations, positioning artifacts, and scattering effects before analysis. Image normalization and enhancement techniques are applied in advance to establish consistent quality standards across portable radiographs, enabling reliable tube detection despite varying acquisition conditions.
Solution Approach 2:
The invention changes image parameters through digital processing techniques including exposure correction, contrast enhancement, and noise filtering. These parameter transformations convert variable-quality portable images into a standardized format suitable for automated tube positioning analysis, resolving the inconsistency caused by different exposure settings and positioning conditions.
2Adaptability or versatility
If image quality varies due to exposure settings and positioning, then adaptability to different conditions is improved, but detection precision of tube tips deteriorates
Solution Approach 1:
The detection system segments the tube structure into identifiable components (tube body, connector, tip) and analyzes each segment separately. This segmentation allows the system to adapt to varying image qualities by focusing detection algorithms on specific tube portions that remain recognizable despite exposure or positioning variations, thereby maintaining tip detection precision across different imaging conditions.
Solution Approach 2:
The invention introduces intermediary processing steps including image enhancement, feature extraction, and pattern recognition algorithms that act as mediators between the variable-quality input images and the detection output. These intermediaries normalize the input data before final tube tip localization, enabling precise detection even when adaptability to different conditions introduces variability.
3Device complexity
If visual detection of tube tip position is attempted in poor image quality, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The invention replaces the mechanical/visual inspection system with an automated computer-based image analysis system. Digital processing algorithms, edge detection, and pattern recognition replace the clinician's visual detection, maintaining simplicity in operation while dramatically improving measurement precision for tube tip positioning in poor-quality portable radiographs.
Data Source
AI summary
A method for detecting tubing in a radiographic image of a patient, executed at least in part by a control logic processor, obtains a radiographic image data for a patient and detects one or more possible tube segments in the image. At least one tubing candidate is formed by growing at least one detected tube segment or merging two or more detected tube segments.