Betti Number Analysis for Pulmonary Emphysema Severity
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Solution Overview
Problem
Current image analysis techniques, such as using LAA % for diagnosing pulmonary emphysema, struggle to accurately assess the severity of pulmonary emphysema and its associated risk of developing into lung cancer, leading to challenges in determining the risk of canceration in various organs and structures.
Innovation Solution
An image analyzing method and device that generate multiple binarized images with different binarization reference values, calculate zero-dimensional and one-dimensional Betti numbers, and compare these values to a predetermined reference pattern to determine changes in the structure, enabling accurate assessment of pulmonary emphysema severity and canceration risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LAA % is used to assess pulmonary emphysema severity, then the assessment process is simple and quick, but the accuracy of severity assessment and canceration risk determination is insufficient
Solution Approach 1:
The patent segments the lung image analysis into multiple components: generating multiple binarized images with different threshold values, calculating Betti numbers for each binarized image, and analyzing the relationship between threshold values and Betti number changes. This segmentation allows comprehensive assessment of pulmonary emphysema severity and canceration risk while maintaining systematic efficiency.
Solution Approach 2:
The patent introduces a new dimension of analysis by using Betti numbers (topological features) instead of traditional area-based metrics like LAA %. This dimensional shift from simple area proportion to topological structure analysis enables more accurate detection of pulmonary emphysema severity and lung cancer risk while providing richer diagnostic information.
2Measurement precision
If multiple binarized images with different reference values are generated and Betti numbers are calculated, then the assessment accuracy of pulmonary emphysema severity and canceration risk is improved, but the calculation complexity and processing time increase
Solution Approach 1:
The patent performs preliminary binarization of the lung image at multiple different threshold values before the actual assessment. By pre-generating multiple binarized images and calculating their Betti numbers in advance, the system prepares comprehensive topological data that facilitates accurate canceration risk assessment without requiring complex real-time calculations during diagnosis.
Solution Approach 2:
The patent replaces traditional mechanical image analysis methods with topological data analysis using Betti numbers. This substitution transforms complex image processing into mathematical topology calculations, which, while computationally different, provide more robust and accurate assessment of pulmonary emphysema severity and lung cancer risk by focusing on structural invariants rather than pixel-level variations.
3Reliability
If Betti numbers are used to analyze topological features, then the ability to detect structural changes and assess canceration risk is enhanced, but the difficulty of detecting and measuring these features increases
Solution Approach 1:
The patent uses binarized images as intermediaries between the original lung CT image and the topological analysis. By first converting the grayscale lung image into binary images at multiple thresholds, then calculating Betti numbers on these binary representations, the system bridges the gap between medical imaging and topological data analysis, making the complex Betti number calculations more manageable and interpretable in a clinical context.
Data Source
AI summary
In order to assess, with high accuracy, a degree of a change having occurred in a structure, the present invention includes: a Betti number calculating section (42) configured to (I) generate, with respect to a single captured image obtained by capturing an image of a structure, a plurality of binarized images having respective binarization reference values different from each other and (II) calculate, for each of the plurality of binarized images, a first characteristic numerical value indicative of the number of connected regions each of which is obtained by connecting pixels each having one of pixel values obtained by binarization; and a prediction score determining section (44) configured to determine, in accordance with a result of a comparison, information on a change having occurred in the structure, the comparison having been made between (i) a pattern indicative of a relationship between (a) the respective binarization reference values and (b) the first characteristic numerical value and (ii) a predetermined reference pattern.