Radiographic Image Enhancement Using Steerable Pyramid Filtering
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Solution Overview
Problem
Existing image enhancement algorithms for digital medical images, particularly CT images, suffer from distortions near sharp grey level transitions, leading to unnatural appearances and potential artefacts due to excessive non-linear modifications, and lack effective orientation analysis.
Innovation Solution
A method utilizing a steerable pyramid decomposition that includes non-linear modification of detail images based on amplitude, phase, and orientation, using steerable filters to enhance contrast while preserving natural image appearance and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If non-linear amplification or conversion functions are applied to enhance contrast in multi-scale image processing, then image contrast is improved, but grey value transitions are distorted and unnatural appearance occurs
Solution Approach 1:
The patent applies different amplification factors to different spatial locations and scale levels. The amplification function is locally adapted based on the standard deviation of pixel values in local neighborhoods, allowing stronger contrast enhancement in regions with low variance and milder enhancement where edges are present, thus preserving natural appearance while improving contrast.
Solution Approach 2:
The patent uses a dynamic amplification function that adapts to local image characteristics at each scale level. The amplification factor is computed based on the standard deviation of pixel values in local neighborhoods, making the contrast enhancement dynamic and context-dependent rather than applying a fixed non-linear transformation throughout the image.
2Illumination intensity
If excessive non-linear modification is applied to translation difference images, then contrast enhancement is improved, but calculation time increases and artefacts are created
Solution Approach 1:
The patent applies non-linear amplification selectively only to translation difference images at specific scale levels where it is most beneficial, rather than uniformly to all scale levels. The amplification is applied with controlled intensity based on local standard deviation, avoiding excessive computation in regions where it would not provide meaningful enhancement and would only increase processing time.
3Illumination intensity
If non-linear modification is applied to all translation difference images at all scales, then contrast enhancement is improved, but computational complexity increases
Solution Approach 1:
The patent limits the application of non-linear amplification to specific scale levels and specific translation difference images rather than uniformly applying it to all scales. The amplification function is selectively applied based on the standard deviation threshold, reducing the overall computational burden while maintaining effective contrast enhancement where needed.
Solution Approach 2:
The patent changes the parameter of amplification intensity based on the standard deviation of pixel values in local neighborhoods. By using the standard deviation as a threshold parameter, the system dynamically adjusts which images receive amplification and at what intensity, optimizing the balance between contrast enhancement and computational complexity.
Data Source
AI summary
A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail images, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.