Circular Intensity Distribution for Surface Shape Characterization
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Solution Overview
Problem
Current methods for characterizing the shape of anatomical structures in medical images, such as MRI and CT scans, face challenges in accurately differentiating between convex, concave, and flat surfaces due to surface noise and complexity, especially when compared to synthetic structures.
Innovation Solution
A method involving circular intensity distribution analysis, where image data is analyzed at predetermined distances from a point of interest to determine foreground and background points, calculating a ratio of background to foreground points to characterize the surface as convex, concave, or flat, without direct curvature calculation or fitting to geometric primitives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional curvature calculation or geometric primitive fitting is used to characterize surface shape, then parametric descriptions of surfaces can be achieved, but the method becomes substantially more difficult and surface noise becomes a large factor in segmentation when analyzing anatomical structures
Solution Approach 1:
The patent extracts only the essential information needed for shape characterization by analyzing intensity distribution patterns in circular neighborhoods around surface points. Instead of performing full geometric primitive fitting or curvature calculations, the method extracts simple foreground/background point ratios from circular intensity distributions, significantly reducing computational complexity while maintaining accuracy in distinguishing convex, concave, and flat surfaces
Solution Approach 2:
The patent replaces mechanical/geometric approaches (curvature calculation, geometric primitive fitting) with an intensity-based analytical approach. By substituting the mechanical system of geometric modeling with an optical/intensity-based system that analyzes pixel intensity distributions in circular neighborhoods, the method achieves simpler computation that is more robust to surface noise in anatomical structures
2Loss of information
If geometric primitive fitting is used to characterize anatomical surfaces, then parametric descriptions may be achieved, but surface noise becomes a large factor in segmentation
Solution Approach 1:
The patent converts the harmful effect of surface noise into a beneficial feature by using intensity distribution patterns. Instead of trying to eliminate or avoid noise that interferes with geometric fitting, the method uses the intensity variations caused by noise as part of the analysis - the circular intensity distribution approach naturally handles noisy anatomical surfaces by analyzing the statistical distribution of intensities in circular neighborhoods, making the noise characteristic rather than problematic
Solution Approach 2:
The patent changes the parameters used for surface characterization from geometric parameters (curvature, primitive fitting residuals) to intensity distribution parameters (foreground/background point ratios in circular neighborhoods). This parameter transformation makes the analysis more robust to surface noise because intensity-based statistical measures are less sensitive to local surface irregularities than geometric fitting approaches
Data Source
AI summary
A method for characterizing a shape of an object surface includes acquiring image data including the object. The image data is analyzed at a locus of points that are at a predetermined distance from a point of interest proximate to the object surface to determine which of the locus of points represents a foreground and which of the locus of points represents a background. The shape of the object surface is characterized based on the characterization of the locus of points.