Capsule Endoscope Image Processing for Lesion Analysis
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Solution Overview
Problem
Capsule endoscopes produce low-brightness, blurry, and poor-quality images of the small intestinal mucosa due to size, power consumption, and photography environment restrictions, leading to inaccurate lesion analysis and diagnosis.
Innovation Solution
An image processing method involving denoising, contrast enhancement, saturation adjustment, sharpening, and dynamic range enhancement using specific algorithms such as Gaussian filtering, gamma transformation, Laplacian pyramid, and local histogram enhancement to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If capsule endoscope is used for digestive tract examination, then the examination can be performed, but the image quality is poor due to size and power consumption restrictions
Solution Approach 1:
The patent applies preliminary action by performing multiple image processing operations (denoising, contrast enhancement, saturation adjustment, sharpening, and dynamic range enhancement) on the captured images before they are displayed or stored. This preprocessing ensures that the images are optimized for diagnostic accuracy before reaching the physician, thereby resolving the contradiction between the limited imaging capabilities of the capsule and the high diagnostic requirements.
2Illumination intensity
If capsule endoscope is used for digestive tract examination, then the examination can be performed, but the images have low brightness and are blurry
Solution Approach 1:
The patent applies parameter changes by systematically adjusting multiple image parameters including brightness, contrast, saturation, and sharpness through various processing algorithms. The contrast enhancement module adjusts brightness and contrast parameters, while the sharpening module modifies edge sharpness parameters. These parameter transformations convert the low-quality captured images into high-quality display images with improved brightness and clarity, resolving the contradiction between illumination limitations and image quality requirements.
3Manufacturing precision
If multiple image processing operations are performed, then image quality is improved, but processing time and complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into five distinct functional modules: denoising module, contrast enhancement module, saturation adjustment module, sharpening module, and dynamic range enhancement module. Each module performs a specific processing operation independently, allowing for targeted optimization of each processing step. This modular segmentation manages the overall processing complexity while achieving comprehensive image quality improvement through coordinated operation of specialized sub-modules.
Data Source
AI summary
An image processing method, an electronic device, and a readable storage medium are provided. The method includes: obtaining an original image; denoising the original image using an image filtering algorithm to form a preprocessed image S; performing contrast enhancement in the preprocessed image S to form a contrast-enhanced image J; adjusting saturation of the contrast-enhanced image J to form an enhanced display image M; sharpening the enhanced display image M to form a sharpened image N; and performing dynamic range enhancement on the sharpened image N using a dynamic range image enhancement algorithm to form an output image. The image processing method, electronic device, and readable storage medium enable multi-level processing on the original image and improve the display accuracy of the output image.