Multiband Endoscopic Imaging for Clearer Lesion Boundaries
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Solution Overview
Problem
Current clinical endoscopes primarily use visible light images, which struggle to provide optimal image quality for different tissues and treatment methods, necessitating improved image processing techniques to enhance detection and treatment accuracy.
Innovation Solution
An endoscopic image processing method that captures and integrates images with varying wavelengths or polarized angles, including visible light, near-infrared light, short-wave infrared light, and polarized light, through binary and pseudo-color processing to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visible light images are used for endoscopic imaging, then the imaging system is simple and easy to operate, but the image quality for different tissues and treatment methods is insufficient
Solution Approach 1:
The imaging spectrum is segmented into multiple bands (visible light, near-infrared, short-wave infrared) with different characteristics. Each band captures specific tissue information, and the system segments the processing into distinct modules: image acquisition, binary processing, multiplication, pseudo-color processing, and integration. This segmentation allows the system to leverage the advantages of different spectral bands while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a composite imaging system that combines multiple imaging modalities (visible light, near-infrared, short-wave infrared) into a unified endoscopic system. By integrating images from different spectral bands using the described processing methods, the system achieves composite imaging capabilities that provide both the simplicity of visible light imaging and the enhanced tissue differentiation of infrared imaging.
2Measurement precision
If multiple bands or polarized angles images are acquired and integrated, then endoscopic image quality of diseased parts is improved, but the processing complexity and time consumption increase
Solution Approach 1:
The patent applies preliminary binary processing to the infrared images before integration with visible light images. This preliminary action segments the infrared image into binary form, identifying key features and structures. By performing this preprocessing step, the system reduces the complexity of the subsequent integration process, as the binary image provides a simplified template for multiplication and pseudo-color processing, thereby managing overall processing complexity while maintaining enhanced image quality.
3Measurement precision
If multiple bands or polarized angles images are acquired and integrated, then endoscopic image quality of diseased parts is improved, but the processing time increases
Solution Approach 1:
The patent performs binary processing on infrared images as a preliminary step before integration. This preprocessing converts the infrared image into a simplified binary format that highlights key features. By doing this beforehand, the system reduces the computational burden during the integration phase, as the multiplication operation with the visible light image becomes more efficient when one operand is binary rather than full-grayscale, thereby reducing overall processing time while maintaining image quality enhancement.
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
The method effectively improves endoscopic image quality by highlighting diseased parts, providing clearer boundaries and deeper tissue penetration, aiding medical professionals in better judgment and treatment decisions.
Implementation Method 1
provides a first image and a second image of the diseased parts, where wavelengths or polarized angles of the first image and the second image are different; the first image is a visible light image, and the second image is a near-infrared light image, a short-wave infrared light image, or a polarized light image
Data Source
AI summary
An endoscopic image processing method suitable for using an endoscope to capture images of diseased parts, which comprises following steps: providing a first image and a second image of the diseased parts, wherein wavelengths or polarized angles of the first image and the second image are different; performing binary image processing on the second image; performing multiply processing on the second image to produce a first multiply image according to a result of the binary image processing; performing pseudo-color processing on the first multiply image to produce a first pseudo-color processed image; and integrating the first pseudo-color processed image with the first image to produce a first integrated image. The endoscopic image processing method acquires images with a plurality of bands or polarized angles of a same area, and improves endoscopic image quality of the diseased parts by processing and integrating these images.