Ground Glass Opacity Lesion Extraction via Pixel Range Adjustment
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Solution Overview
Problem
Current image processing techniques face challenges in accurately extracting lesion regions, particularly those with ground glass opacity, due to vague boundaries and limitations in noise resistance and imaging condition variations, leading to inaccurate segmentation and information overlap between regions.
Innovation Solution
An image processing apparatus and method that changes pixel values of candidate regions to a predetermined range, calculates texture feature amounts, and uses graph cuts for accurate extraction of lesion regions, distinguishing between ground glass opacity and other lesions based on feature amounts and pixel value changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph cuts are directly applied to CT images for solid nodule segmentation, then accurate nodule region extraction is achieved, but the method fails for GGO lesions due to vague boundaries
Solution Approach 1:
The patent segments the GGO nodule extraction process into multiple stages: first identifying candidate regions using threshold processing, then applying graph cuts specifically to these candidates, and finally performing boundary refinement. This multi-stage segmentation approach adapts the graph cuts method to work effectively with GGO's vague boundaries by preprocessing the image to highlight potential lesion areas before applying the segmentation algorithm.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. Threshold processing with optimized parameters is applied to identify candidate GGO regions, while graph cuts is applied specifically to refine boundaries in these candidate areas. This local differentiation allows the system to handle the unique characteristics of GGO lesions (faint, vague boundaries) differently from solid nodules, improving overall extraction accuracy.
2Ease of manufacture
If threshold processing is used to segment GGO regions based on density ranges, then separation of regions is attempted, but overlapping densities limit separation accuracy and increase noise sensitivity
Solution Approach 1:
The patent introduces graph cuts as an intermediary step between threshold processing and final segmentation. Threshold processing first identifies candidate regions, then graph cuts acts as a mediator to refine these regions by optimizing boundary detection based on intensity gradients and region coherence. This intermediary step resolves the limitation of threshold processing by adding a layer that specifically addresses boundary definition and noise resistance.
Solution Approach 2:
The patent combines multiple processing techniques (threshold processing, graph cuts, and boundary refinement algorithms) into a composite segmentation approach. Each technique contributes its strengths: threshold processing provides initial region identification, graph cuts provides robust boundary detection, and refinement algorithms provide final precision. This composite approach overcomes the weaknesses of individual methods, particularly the noise sensitivity and poor boundary definition of simple thresholding.
3Reliability
If anisotropic Gaussian fitting is used to approximate GGO regions, then robust temporal change rate derivation is achieved, but detailed shape information is lost
Solution Approach 1:
The patent employs a dynamic, multi-resolution approach to GGO region extraction. Instead of using a fixed elliptical model, the system adaptively refines the region boundaries through graph cuts and boundary refinement algorithms that preserve detailed shape characteristics. This dynamic refinement process maintains both the robustness needed for temporal comparison and the detailed shape information required for accurate lesion characterization.
Solution Approach 2:
The patent adds temporal dimensionality to the GGO extraction process by incorporating multi-timepoint CT data. By analyzing GGO regions across multiple timepoints with consistent extraction methodology, the system derives temporal change rates that are robust to noise while preserving spatial detail information. This multi-dimensional approach allows simultaneous achievement of measurement reliability and shape information retention.
Data Source
AI summary
An image processing apparatus which extracts a lesion having a ground glass opacity from an image includes a change unit which changes a pixel value corresponding to a candidate region for the ground glass opacity to a predetermined pixel value range, a first feature amount extraction unit which obtains a first feature amount from the image, the pixel value of which is changed, and an extraction unit which extracts the lesion from the image based on the first feature amount.