Breathing Distance Image Segmentation for Chest-Abdomen Separation
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Solution Overview
Problem
Existing techniques fail to accurately identify the chest and abdominal regions in line with anatomical knowledge, leading to inaccurate assessment of breathing movements.
Innovation Solution
An information processing apparatus that calculates a standard deviation image from time-series distance data and divides it into chest and abdominal regions using differential values along the sagittal and frontal planes, superimposing the results on the image for precise identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques are used to detect chest and abdominal movements, then the measurement process is simple, but the region identification accuracy is poor and cannot align with anatomical knowledge
Solution Approach 1:
The standard deviation image is segmented into chest and abdominal regions by detecting the intersection line between sagittal and frontal planes. This segmentation approach divides the measurement area into anatomically correct regions, improving identification accuracy while maintaining processing feasibility through systematic division.
Solution Approach 2:
The patent introduces a new dimension of analysis by calculating standard deviation across time-series distance images. This temporal dimension transforms static distance measurements into dynamic movement patterns, enabling accurate region identification through multi-dimensional data analysis.
2Measurement precision
If time-series distance image data is processed to calculate standard deviation, then measurement accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent extracts only the essential movement information by calculating standard deviation from time-series distance data. This extraction focuses computational resources on the most relevant metrics for breathing analysis, improving detection accuracy while reducing unnecessary processing of redundant data.
Solution Approach 2:
The standard deviation calculation is performed as a preliminary processing step before region division and analysis. This preliminary action prepares the data in advance, organizing temporal variations into a standardized format that facilitates subsequent processing and reduces overall computational time.
Data Source
AI summary
To provide an information processing apparatus and the like that are capable of dividing an image indicating movement of a subject into a chest region and an abdominal region along anatomical knowledge. The information processing apparatus inputs time-series distance image data acquired by measuring a distance from a subject during a breathing exercise. The apparatus calculates a standard deviation image indicating a standard deviation of values for each pixel in a distance image indicated by the time-series distance image data. The apparatus executes division processing of dividing the standard deviation image into the chest region and the abdominal region of the subject by using a differential value in a direction of an intersection line between a sagittal plane and a frontal plane of the subject with respect to the standard deviation image. The apparatus outputs a result of the division processing by superimposing the result on the standard deviation image.