Biomedical Image Processing for Hollow Organ Thickness Measurement
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Solution Overview
Problem
Users face difficulties in smoothly ascertaining necessary information from tomographic images due to variations in invasion depth, which affects the clarity and accuracy of hollow organ assessments.
Innovation Solution
An information processing method that classifies biomedical image data into luminal, extraluminal, and living tissue regions, generates region contour data by removing portions exceeding a predetermined threshold from the living tissue region, and creates three-dimensional images for enhanced visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tomographic images are acquired by inserting an image acquisition catheter into a hollow organ, then internal structure information can be obtained, but the invasion depth varies depending on the state of the hollow organ which prevents smooth ascertainment of necessary information
Solution Approach 1:
The living tissue region is segmented into multiple portions based on thickness, with the first portion representing the inner wall thickness and the second portion representing additional tissue. This segmentation allows the system to accurately measure and display only the clinically relevant inner wall thickness while filtering out extraneous tissue information, thereby improving measurement precision and ease of information interpretation.
Solution Approach 2:
The system performs preliminary processing by generating classification data that identifies and separates different tissue regions before final image display. By pre-processing the tomographic image data to classify and segment regions of interest, the system prepares the information in advance for optimal presentation, enabling users to smoothly ascertain necessary information without being overwhelmed by raw, unprocessed data.
2Loss of information
If the entire living tissue region is displayed in the tomographic image, then complete tissue information is provided, but portions exceeding a predetermined threshold create artifacts that reduce image clarity
Solution Approach 1:
The system extracts and removes portions of the living tissue region that exceed a predetermined thickness threshold from the displayed image. By taking out these excess portions, the system eliminates artifacts that reduce image clarity while preserving the complete and accurate representation of the inner wall thickness, thus maintaining both information completeness and image reliability.
Solution Approach 2:
The system applies different quality standards to different portions of the tissue region. The first portion (inner wall) is displayed with high fidelity and detail, while portions exceeding the threshold are removed or treated differently. This local differentiation ensures that the most clinically relevant information is presented with optimal clarity while eliminating distracting artifacts.
Data Source
AI summary
An information processing method for assisting a user to be capable of smoothly ascertaining necessary information. An information processing method causes a computer to execute processing for acquiring classification data in which pixels constituting biomedical image data indicating an internal structure of a living body are classified into a plurality of regions including a living tissue region in which a luminal region exists, the luminal region, and an extraluminal region outside the living tissue region, and generating region contour data, in which a portion where a thickness from an inner surface of the living tissue region facing the luminal region exceeds a predetermined threshold is removed from the living tissue region, based on the classification data.