Dynamic Binning Control for Autofluorescence Endoscope Imaging
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Solution Overview
Problem
Current electronic endoscope systems fail to obtain optimal images during special imaging functions like autofluorescence imaging (AFI) and narrow band imaging (NBI), particularly in varying object distances and for distinguishing lesions in bright or minute portions.
Innovation Solution
The system incorporates a light source device, object distance detection, binning processing, and binning processing control to adjust the binning number based on object distance, ensuring optimal intensity and resolution in endoscopic images, and includes modes for AFI, NBI, and blood vessel information capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If the binning number is increased to improve image intensity in far view, then the image intensity is improved, but the resolution deteriorates
Solution Approach 1:
The binning number is made dynamically adjustable based on object distance. The system automatically changes the binning number according to the detected object distance, allowing optimal balance between intensity and resolution for different imaging scenarios. This dynamic adjustment resolves the contradiction by adapting the binning number to specific viewing conditions rather than using a fixed value.
Solution Approach 2:
The system changes the binning number parameter based on object distance measurements. By detecting the object distance and selecting appropriate binning numbers from a predefined set, the system optimizes the balance between image intensity and resolution. Different binning numbers are applied for near view (lower binning) versus far view (higher binning) to resolve the intensity-resolution tradeoff.
2Measurement precision
If the binning number is decreased to improve resolution in near view, then the resolution is improved, but the image intensity deteriorates
Solution Approach 1:
The binning number is dynamically adjusted based on object distance detection. For near view imaging, the system automatically selects lower binning numbers to preserve resolution while maintaining sufficient intensity. This dynamic adaptation resolves the contradiction by optimizing parameters for specific imaging distances rather than using a fixed setting.
Solution Approach 2:
The system changes the binning number parameter according to detected object distance. When objects are detected at near distances, the system applies lower binning numbers to maximize resolution. This parameter change strategy resolves the intensity-resolution contradiction by matching the binning number to the imaging distance requirements.
3Device complexity
If a fixed binning number is used, then the device complexity is reduced, but the adaptability to different imaging purposes deteriorates
Solution Approach 1:
The system performs self-adjustment of the binning number based on automatic object distance detection. The processor device automatically selects the appropriate binning number without requiring manual intervention, enabling the system to adapt to different imaging purposes while maintaining relatively simple operation. This self-service approach resolves the contradiction between complexity and adaptability.
Solution Approach 2:
The system automatically changes the binning number parameter based on detected object distance and imaging requirements. By implementing automatic parameter selection based on distance thresholds, the system achieves high adaptability for different imaging purposes without significantly increasing operational complexity. The processor device handles the complexity of parameter selection automatically.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the production of high-quality images suitable for different diagnostic purposes by adjusting image intensity and resolution according to object distance, effectively distinguishing lesions and providing detailed blood vessel information.
Implementation Method 1
autofluorescence imaging (AFI)... special light having a specific wavelength is applied as excitation light to the internal body part, and the image sensor captures an image of autofluorescence that is emitted from an endogenous fluorescent substance of living body tissue in response to the excitation light
Implementation Method 2
Narrow band imaging (NBI)... by taking advantage of a light absorbing property of the blood vessel that occurs upon application of the special light
Implementation Method 3
Narrow band imaging (NBI)... and a light scattering property of the living body tissue around the blood vessel
Data Source
Figure 1
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Figure 3~4
AI summary
White light and excitation light are applied to an internal body part. An electronic endoscope (11) captures a normal image of the internal body part irradiated with the white light, and a special image of autofluorescence emitted from living body tissue of the internal body part irradiated with the excitation light. An object distance detector (130) detects an object distance between a CCD (100) and an inspection area (T) of the internal body part based on the normal image. A binning processing section (131) applies a binning process to the special image. There are two types of binning processes, i.e. an intensity adjustment process and a resolution adjustment process. In the intensity adjustment process, the binning number is increased with increase in the object distance. In the resolution adjustment process, the binning number is decreased with increase in the object distance. Which process to perform is determined by operation on a processing type selector (133).