Digital Image Intensity Correction via Sparse Grid Interpolation
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Solution Overview
Problem
Digital imaging techniques, particularly in medical and MRI applications, face challenges with image intensity inhomogeneities due to factors like receiver coil variations, leading to compromised image quality and diagnosis delays, as existing methods require knowledge of coil geometry, patient positioning, and are impractical or require manual intervention.
Innovation Solution
A method that enhances image contrast by minimizing the error between the histogram of the original image and a specified histogram through two-dimensional interpolation of a sparse grid of control points, correcting intensity inhomogeneities without assuming their source, and increasing contrast in localized regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI imaging techniques are used, then image acquisition is achieved, but image intensity inhomogeneities occur due to receiver coil variations and magnetic field variations
Solution Approach 1:
The patent applies preliminary action by acquiring a low-resolution preliminary image before the main high-resolution image, using this preliminary image to calculate correction factors for intensity inhomogeneity, and then applying these correction factors to the final image. This preliminary correction step ensures that the main diagnostic image has improved intensity uniformity without requiring changes to the actual MRI scanning parameters.
2Measurement precision
If manual intervention methods are used to correct intensity inhomogeneities, then image quality can be improved, but productivity decreases due to time-consuming manual processes
Solution Approach 1:
The patent implements self-service by designing an automated system that performs intensity inhomogeneity correction without requiring manual intervention. The system automatically acquires preliminary images, calculates correction factors through algorithmic processing, and applies these corrections to the final diagnostic images. This automated workflow eliminates the need for manual adjustment while maintaining correction effectiveness, thereby preserving productivity.
3Measurement precision
If detailed knowledge of coil geometry and patient positioning is required for correction, then correction accuracy can be improved, but device complexity increases
Solution Approach 1:
The patent uses the preliminary low-resolution image as an intermediary element that bridges the gap between the complex physical factors (coil geometry, patient positioning) and the correction process. Instead of directly measuring and compensating for each physical factor, the system uses the preliminary image to capture the overall intensity distribution pattern, which then serves as the basis for calculating correction factors. This intermediary approach simplifies the system while maintaining correction accuracy.
4Measurement precision
If existing correction methods are applied, then some intensity inhomogeneities can be corrected, but the methods are impractical for routine clinical use due to manual intervention requirements
Solution Approach 1:
The patent replaces manual mechanical adjustment operations with automated computational processing. Instead of requiring operators to manually adjust imaging parameters or perform manual correction procedures, the system uses algorithmic processing of preliminary images to automatically generate and apply correction factors. This substitution of manual operations with automated computational methods maintains correction effectiveness while dramatically improving ease of operation for routine clinical use.
Data Source
AI summary
The present invention provides a method, system and image to enhance the image contrast of a digital image device while simultaneously compensating for image intensity inhomogeneity, regardless of the source. The present invention corrects intensity inhomogeneities producing a more uniform image appearance. Also, the image is enhanced through increased contrast, e.g., tissue contrast in a medical image. The method makes no assumptions as to the source of the inhomogeneities, e.g., physical device characteristics or positioning of the object being imaged. In the method, the error between the histogram of the spatially-weighted original image and a specified histogram is minimized. The specified histogram may be selected to increase contrast generally or particularly for accentuation, e.g., on localized regions of interest. The weighting is preferably achieved by two-dimensional interpolation of a sparse grid of control points overlaying the image. A sparse grid is used rather than a dense one to compensate for slowly-varying image non-uniformity. Also, sparseness reduces the computational complexity, as the final weight set involves the solution of simultaneous linear equations whose number is the size of the chosen grid.