Fiducial Detection in Digital X-ray Images Using Gradient Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting fiducials in digital projection images, particularly X-ray images, are slow, inaccurate, and prone to errors due to manual interaction, and are sensitive to image noise and contrast variations, making it difficult to locate fiducials that are partly covered or in low-contrast images.
Innovation Solution
A method that calculates the direction of intensity gradients in digital projection images, defines regions where the gradient direction changes, and uses a matching rate based on a predetermined model to accurately detect fiducials, even if they are partly covered, by employing filters like Prewitt or Sobel filters and correlation kernels to enhance detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used, then user interaction is required, but detection speed is extremely slow and accuracy is poor
Solution Approach 1:
The patent replaces manual mechanical interaction (mouse clicking) with an automated computer vision system that uses image processing algorithms to automatically detect fiducials. The system calculates intensity gradients, applies filtering operations, and performs correlation analysis to identify fiducial locations without human intervention, thereby simultaneously improving both accuracy and detection speed.
Solution Approach 2:
The system enables the image processing software to automatically detect and locate fiducials without requiring user interaction. The automated algorithm performs gradient calculation, noise filtering, and pattern recognition independently, making the detection process self-sufficient and eliminating the bottlenecks of manual operation.
2Extent of automation
If correlation-based search methods are used, then detection can be automated, but the method is sensitive to image noise and contrast variations
Solution Approach 1:
The patent applies noise filtering operations (such as Gaussian filtering or median filtering) and contrast enhancement techniques before performing correlation-based fiducial detection. By preprocessing the image to reduce noise and enhance contrast, the subsequent correlation search becomes more robust and reliable, maintaining automated detection capability while improving resistance to image quality variations.
3Ease of manufacture
If manual visual estimation is used, then no additional equipment is needed, but location accuracy is extremely poor with high variance
Solution Approach 1:
The patent replaces manual visual estimation with an automated image processing system that calculates intensity gradients and performs correlation analysis. This substitution maintains simplicity by using standard computational methods while dramatically improving measurement precision through objective, algorithm-based detection rather than subjective human judgment.
4Productivity
If standard correlation search is used, then detection can be performed, but processor power requirements are high
Solution Approach 1:
The patent segments the image processing task into distinct stages: gradient calculation, noise filtering, and correlation search. By dividing the processing workload and applying appropriate optimizations at each stage (such as selective filtering and intelligent search regions), the system maintains effective fiducial detection capability while reducing overall processor power requirements compared to applying heavy correlation search across the entire image without preprocessing.
Data Source
AI summary
The invention relates to a method, system and computer software product for detecting a fiducial in digital projection images, particularly for detecting a fiducial in digital X-ray images automatically, without any operations required of the user. The invention for detecting the image of a fiducial positioned in a digital projection image is characterized in that for at least part of the digital projection image pixels, there is calculated a direction of the intensity gradient; there is defined a region of the projection image, on the basis of the directions of the intensity gradients, where the direction of the intensity gradient is changed according to predetermined limits; and there is defined a matching rate for how well the model describing the image of the predetermined fiducial matches the defined region.