Image Segmentation Using Curvature and Intensity Probability Maps
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Solution Overview
Problem
Existing digital image processing techniques struggle to accurately segment images containing arbitrary mixtures of blob-like and ridge-like structures, particularly in medical and life sciences imaging applications, where structures like nuclei and membrane features are often intertwined, leading to inefficiencies in biomarker quantification and analysis.
Innovation Solution
A likelihood function estimator is developed to generate probability maps by iteratively estimating empirical likelihood functions of curvature and intensity-based features, using geometric constraints and non-parametric methods to categorize pixels into background, foreground, and indeterminate subsets, allowing for the detection of complex structures like epithelial tissue images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation techniques are used, then the process is simpler, but the accuracy of segmenting mixed blob-like and ridge-like structures deteriorates
Solution Approach 1:
The patent segments the image processing task into multiple distinct stages: curvature calculation, shape index computation, intensity analysis, and probability map generation. Each stage handles specific features (blob-like or ridge-like structures) independently before integration, improving segmentation accuracy for mixed structures without overwhelming complexity
Solution Approach 2:
The patent transforms the segmentation problem by changing parameters from direct intensity thresholding to multi-feature probability estimation. It uses curvature (λ1, λ2 eigenvalues), shape index (φ), and intensity (I) as transformed parameters to differentiate blob-like and ridge-like structures, achieving higher accuracy through parameter transformation
2Reliability
If intensity thresholds are used for segmentation, then the process is faster, but the differentiation of epithelial and stromal tissues deteriorates
Solution Approach 1:
The patent introduces probability maps as intermediary representations between raw image data and final segmentation. These probability maps integrate multiple features (curvature, shape index, intensity) and provide confidence levels for each pixel's tissue type classification, improving reliability without requiring simple thresholding
Solution Approach 2:
The patent combines multiple feature types (geometric features from curvature and shape index, plus intensity features) into a composite probability estimation framework. This composite approach uses weighted integration of different feature probabilities to achieve reliable tissue differentiation that simple intensity thresholds cannot provide
3Adaptability or versatility
If parametric approaches are used, then the model is more constrained, but the ability to analyze arbitrary mixtures of structures deteriorates
Solution Approach 1:
The patent employs dynamic, data-driven probability distribution estimation rather than fixed parametric models. The curvature and shape index features are processed through adaptive thresholds and probability calculations that adjust to the specific image content, enabling versatile analysis of arbitrary structure mixtures while managing complexity through modular processing
Solution Approach 2:
The patent adds dimensional complexity by transforming 2D image space into multi-dimensional feature space. It computes curvature (λ1, λ2), shape index (φ), and intensity (I) as separate dimensions, then integrates them through probability maps. This dimensional expansion enables differentiation of arbitrary structure mixtures that cannot be separated in original image space
Data Source
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AI summary
Methods and systems for segmenting images, wherein the image pixels are categorized into a plurality of subsets using one or more indexes, then a log-likelihood function of one or more of the indexes is determined, and one or more maps are generated based on the determination of the log-likelihood function of one or more of the indexes.