Trachea Branch Point Detection via Region Growing
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Solution Overview
Problem
Existing image processing methods for determining the position of a branch point between the trachea and bronchi, and detecting lung regions are inefficient due to manual seed point selection and high noise susceptibility, leading to inaccurate detection and erroneous identification of lung edges.
Innovation Solution
An image processing device and method that automatically determines a reference trachea region using a region growing method, reducing manual intervention and improving accuracy by sequentially analyzing slice images to identify the branch point between the trachea and bronchi, and subsequently determining lung regions using different thresholds to avoid misidentification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual seed point selection is used for region growth, then the initial method can be implemented, but processing efficiency is low and manual intervention is required
Solution Approach 1:
The system performs automatic seed point selection through algorithmic analysis of image characteristics, eliminating the need for manual intervention. The processor automatically identifies candidate regions and selects seed points based on predefined criteria, making the system self-sufficient in the seed point selection task.
Solution Approach 2:
The system changes the approach from manual parameter selection to automatic parameter determination by analyzing image data characteristics. Seed points are selected based on quantitative image analysis rather than manual input, transforming the parameter selection process into an automated computational task.
2Reliability
If thinning algorithm is used for skeleton analysis, then branch point detection can be performed, but noise and erroneous detection occur reducing robustness
Solution Approach 1:
The system extracts and analyzes specific image characteristics such as gray value distributions and regional patterns to identify branch points, rather than relying on the thinning algorithm's skeleton structure. This extraction of key features avoids the noise and errors inherent in skeleton-based methods.
Solution Approach 2:
The system replaces the mechanical thinning algorithm with a different detection mechanism based on region growth and characteristic analysis. Instead of reducing the image to a skeleton and analyzing connectivity, the system uses gray value-based region analysis to directly identify branch points, substituting one detection mechanism for a more robust alternative.
3Measurement precision
If only gray value information is used for lung region extraction, then simple extraction can be performed, but misidentification with other tissues occurs
Solution Approach 1:
The system applies different analysis criteria to different regions of the image. By analyzing local image characteristics and patterns specific to lung tissue versus other tissues, the system achieves more accurate identification. Different regions are evaluated with region-specific criteria rather than a uniform gray value threshold.
Solution Approach 2:
The system combines multiple types of information including gray values, regional patterns, and spatial characteristics to identify lung regions. This composite approach integrates multiple data dimensions, similar to how composite materials combine different properties, to achieve more accurate and reliable tissue identification than gray value analysis alone.
Data Source
AI summary
An image processing device and an image processing method are provided. The image processing device includes: an acquisitor configured to acquire multiple slice images arranged in an order; a selector configured to detect the multiple slice images sequentially, to determine a reference slice image and a reference trachea region in the reference slice image; and a branch point determiner configured to determine, with a region growing method, trachea regions of slice images following the reference slice image sequentially by using the reference trachea region as a seed region, and determine connectivity of the trachea regions, until a branch point slice image is determined, where a trachea region of the branch point slice image includes two disconnected regions. With the image processing device and the image processing method, manual intervention can be reduced and a position of the branch point can be determined more accurately.