Camera System for Medical Imaging Range Specification
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Solution Overview
Problem
In medical imaging setups, especially in CT and MRI apparatuses, the insufficient lightness of the examination room leads to increased noise in camera images, making it difficult to accurately detect feature points necessary for specifying the imaging range.
Innovation Solution
An information processing apparatus that acquires images from both a standard camera and a high-sensitivity camera, selects appropriate detection models based on lightness conditions, and uses these models to detect feature points and specify the imaging range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a standard camera is used for capturing subject images, then the device complexity is reduced and cost is lowered, but the image quality deteriorates in low-light conditions leading to increased noise
Solution Approach 1:
The camera system is segmented into multiple specialized cameras (first camera for visible light, second camera for infrared or low-light conditions). Each camera is optimized for specific lighting conditions, allowing the system to maintain high detection accuracy across varying environments without requiring a single complex camera system.
Solution Approach 2:
The system changes the operational parameters by selecting different cameras based on lightness conditions. When lightness is insufficient, the system switches from the standard camera to the high-sensitivity camera, effectively changing the detection parameters to maintain feature point detection accuracy without increasing overall device complexity.
2Ease of operation
If the lightness of the examination room is insufficient, then the imaging environment is more practical for patient comfort, but the image quality deteriorates with increased noise
Solution Approach 1:
The high-sensitivity camera acts as an intermediary solution that bridges the gap between patient comfort (low-light environment) and detection precision. This intermediary device enables accurate feature point detection in low-light conditions without requiring bright examination rooms, thus maintaining both patient comfort and measurement precision.
Solution Approach 2:
The system adapts to different lightness conditions by changing detection parameters - switching to a high-sensitivity camera when lightness is insufficient. This parameter change allows the system to maintain feature point detection precision regardless of the examination room's lightness level, enabling practical low-light environments while preserving measurement accuracy.
3Adaptability or versatility
If multiple detection models are prepared for different lighting conditions, then the adaptability to different environments is improved, but the device complexity increases
Solution Approach 1:
The detection system is made dynamic by implementing automatic selection logic that switches between different detection models based on real-time lightness conditions. This dynamic adaptation allows the system to handle various lighting environments effectively while keeping the complexity management straightforward through automated decision-making rather than manual configuration.
Solution Approach 2:
The detection system achieves multi-functionality by incorporating multiple detection models that can handle different lighting conditions. The first detection model processes images from the standard camera in sufficient light conditions, while the second detection model processes images from the high-sensitivity camera in low-light conditions. This universal approach allows a single system to adapt to various environments without requiring separate specialized systems for each condition.
Data Source
AI summary
A processor acquires at least one of a first camera image generated by capturing a moving image of a subject on an examination table via a first camera or a second camera image generated by capturing a moving image of the subject via a second camera having higher imaging sensitivity than the first camera, selects at least one detection model from among a plurality of detection models including a first detection model constructed to detect a plurality of feature points on the subject included in the first camera image, and a second detection model constructed to detect the plurality of feature points on the subject included in the second camera image, detects the plurality of feature points on the subject included in the first camera image or in the second camera image by using the selected detection model, and specifies an imaging range of the subject based on the plurality of feature points.