Intraluminal Image 3D Shape Estimation Excluding Reflections
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Solution Overview
Problem
Existing image processing methods for intraluminal images struggle to accurately estimate three-dimensional shapes due to areas in the images where luminance values do not correspond to the three-dimensional shape, such as blood vessels and mirror reflections, leading to incomplete or inaccurate pixel value acquisition.
Innovation Solution
An image processing apparatus and method that classify and complement pixel values for areas like blood vessels, mirror reflections, and color-changed lesions based on peripheral pixel values, using morphology processing and function approximation to calculate accurate three-dimensional pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If luminance information from intraluminal images is used directly as pixel values for three-dimensional shape estimation, then the processing is simple and fast, but the accuracy of three-dimensional shape estimation deteriorates due to areas like blood vessels and mirror reflections that do not correspond to the actual three-dimensional shape
Solution Approach 1:
The image processing apparatus segments the intraluminal image into multiple regions: areas corresponding to three-dimensional shape (mucous membrane surface), areas with light absorption (blood vessels), and areas with mirror reflection. By identifying and separating these regions, the system can selectively process only the relevant areas for three-dimensional shape estimation, improving accuracy without requiring complete reprocessing of the entire image.
Solution Approach 2:
The invention extracts and removes problematic areas (blood vessels and mirror reflection regions) from the image data before performing three-dimensional shape estimation. By taking out these interfering elements, the system prevents them from contaminating the shape estimation results, thereby improving measurement precision without adding significant complexity to the overall process.
2Measurement precision
If areas with light absorption and mirror reflection are included in the image data, then the image processing is straightforward, but the pixel values do not accurately represent the three-dimensional shape of the imaging target
Solution Approach 1:
The image processing apparatus performs preliminary identification and classification of problematic areas (blood vessels and mirror reflection regions) before the main three-dimensional shape estimation process. By detecting these areas in advance and marking them for exclusion, the system ensures that only valid pixel values are used in the subsequent shape estimation, improving accuracy without significantly impacting processing efficiency.
Solution Approach 2:
The invention converts the harmful effect of light absorption and mirror reflection into a beneficial process by using the characteristics of these areas (such as extreme luminance values or specific texture patterns) as indicators to identify and exclude them. The very features that cause measurement errors are utilized as detection signals, turning a problem into a solution mechanism.
3Measurement precision
If geometric conversion is performed on all pixel values including those from blood vessels and mirror reflections, then the processing is uniform and simple, but the resulting three-dimensional shape estimation becomes inaccurate
Solution Approach 1:
The image processing apparatus applies different processing qualities to different regions of the image. Areas identified as corresponding to three-dimensional shape (mucous membrane surface) undergo geometric conversion for shape estimation, while areas identified as blood vessels or mirror reflections are excluded from this processing. This localized approach ensures high accuracy for the relevant areas without unnecessarily complicating the processing of the entire image.
Solution Approach 2:
The system segments the image processing into distinct pathways: one for valid shape-representing areas that undergo geometric conversion, and another for problematic areas that are excluded. This segmentation allows the system to maintain simple, uniform processing for the majority of valid pixels while applying selective exclusion only where necessary, balancing accuracy with processing simplicity.
Data Source
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AI summary
An image processing apparatus (10) includes: an area extracting unit (21) that extracts a candidate area of a classification target area in which a pixel value does not correspond to a three-dimensional shape of an imaging target based on pixel values of an intraluminal image acquired by imaging the inside of a lumen or information of a change in pixel values of peripheral pixels; and an area classifying unit (22) that classifies the classification target area out of the candidate area based on the pixel values of the inside of the candidate area, a boundary portion of the candidate area, or a periphery portion of the candidate area.