Lung Region Extraction via Trachea Luminance Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing methods for chest radiography struggle to accurately extract and analyze the lung region from radiographic images, often including the trachea due to similar pixel values, making it difficult for medical experts to diagnose diseases effectively.
Innovation Solution
A method and apparatus that adjust pixel values of the trachea region to enhance its luminance, allowing for correct extraction of the lung region using geometric active contour models and convex hull smoothing to isolate and refine the lung boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to extract the lung region, then the extraction process is simple, but the trachea region is incorrectly included in the lung region due to similar pixel values
Solution Approach 1:
The image processing method segments the chest radiographic image into distinct regions: trachea region, lung region, and other regions. By dividing the image into these separate segments and processing each independently with appropriate pixel value adjustments, the method achieves accurate extraction of the lung region while excluding the trachea, resolving the contradiction between extraction accuracy and processing complexity.
Solution Approach 2:
The method applies different pixel value adjustment strategies to different regions of the image. Specifically, it adjusts pixel values in the trachea region differently from the lung region, using local quality modifications to enhance the distinction between these regions. This localized approach enables accurate lung region extraction without requiring complex global processing.
2Measurement precision
If pixel values of the trachea region are adjusted to enhance luminance distinction, then the lung region can be correctly extracted, but the image processing complexity increases
Solution Approach 1:
The method changes the pixel value parameters in the trachea region to enhance luminance distinction from the lung region. By adjusting these parameters within a controlled range and applying simple transformation operations, the method achieves accurate region distinction without requiring complex processing algorithms, thus resolving the contradiction between precision and complexity.
3Reliability
If the lung region is extracted without trachea exclusion, then the processing is faster and simpler, but the diagnostic accuracy is reduced due to inclusion of non-lung structures
Solution Approach 1:
The method performs preliminary action by first identifying and adjusting pixel values in the trachea region before extracting the lung region. This preliminary processing step ensures that the trachea is properly distinguished from the lung region, enabling accurate extraction without requiring complex post-processing or reprocessing, thus maintaining both diagnostic accuracy and processing efficiency.
Data Source
AI summary
A method of processing an image so as to correctly and better extract an image of a lung region, the method including operations of extracting a trachea region_image by using pixel values in a predetermined region of a radiographic image, adjusting pixel values corresponding to the trachea region in the radiographic image, extracting the lung region image from the radiographic image in which the pixel values of the trachea region image have been adjusted, and smoothing outer boundaries of the lung region image.