System and method for digital image intensity correction
The present invention provides a method and apparatus 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.
1. A method of correcting intensity inhomogeneity in an image, said method comprising the steps of:
receiving pixel information for an image portion of an object;
generating, using a processor, a histogram from said pixel information; and
transforming, using said processor, at least one pixel in said pixel information by minimizing error between said histogram and a specified histogram,
wherein a plurality of grid points overlay said image portion of said object, each said grid point having associated therewith at least one interpolating function,
thereby minimizing said error.
2. The method according to claim 1 , wherein said step of transforming comprises:
modifying said pixel information;
computing said weighting vector by minimizing the error between said histogram and said specified histogram; and
correcting said pixel information using a weighting vector.
3. The method according to claim 2 , wherein said weighting vector (W) is described by the formula:
W=B −1 Z.
4. The method according to claim 3 , wherein matrix B and vector Z are described by the formulas:
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where s i is the histogram specified transformed gray level of the i th pixel, N is the number of pixels in the image, P i is the original gray level of the i th pixel, b ji is the interpolation coefficient of the j th grid point acting on the i th pixel, W j is the weight value of the j th interpolating function, and M is the number of points in the grid.
5. The method according to claim 1 , wherein said at least one interpolating function is selected from the group consisting of:
Gaussian functions and multiquadratic basis functions.
6. The method according to claim 5 , wherein said Gaussian function is selected from the group consisting of:
two-dimensional Gaussian functions and three-dimensional Gaussian functions.
7. The method according to claim 1 , wherein said specified histogram is selected from the group consisting of:
uniform histograms, linearly-rising histograms, tissue contrast enhancement histograms, tissue class accentuation histograms and combinations thereof.
8. The method according to claim 1 , wherein said histogram is generated from said digital image.
9. An imaging apparatus, said apparatus comprising:
a receiver, said receiver receiving image data of an object; and
a processor for processing the image data acquired of said object, a histogram being generated from a portion of said image data,
wherein said processor transforms pixel information in said image data by minimizing error between said histogram of said image data and a specified histogram, and
wherein a plurality of grid points overlay said image portion of said object, each said grid point having associated therewith at least one interpolating function,
thereby minimizing said error.
10. The imaging apparatus according to claim 9 , wherein said processor transforms image pixel information by weighting each pixel according to a weighting function.
11. The imaging apparatus according to claim 9 , wherein said processor in transforming:
modifies said pixel information;
computes a weighting vector by minimizing the error between said histogram and said specified histogram; and
corrects said pixel information using said weighting vector.
12. The imaging apparatus according to claim 11 , wherein said weighting vector (W) is described by the formula:
W=B −1 Z.
13. The imaging apparatus according to claim 12 , wherein matrix B and vector Z are described by the formulas:
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where s i is the histogram-specified transformed gray level of the i th pixel, N is the number of pixels in the image, P i is the original gray level of the i th pixel, b ji is the interpolation coefficient of the j th grid point acting on the i th pixel, W j is the weight value of the j th interpolating function, and M is the number of points in the grid.
14. The imaging apparatus according to claim 9 , wherein said at least one interpolating function is selected from the group consisting of:
Gaussian functions and multiquadratic basis functions.
15. The imaging apparatus according to claim 14 , wherein said Gaussian function is selected from the group consisting of:
two-dimensional Gaussian functions and three-dimensional Gaussian functions.
16. The imaging apparatus according to claim 9 , wherein said specified histogram is selected from the group consisting of:
uniform histograms, linearly-rising histograms, tissue contrast enhancement histograms, tissue accentuation histograms and combinations thereof.
17. The imaging apparatus of claim 9 , wherein said histogram is generated from said digital image.
18. An imaging apparatus, said apparatus comprising:
receiver means, said receiver means receiving image data of an object; and
processor means for processing the image data acquired of said object, a histogram being generated from a portion of said image data,
wherein said processor means transforms pixel information in said image data by minimizing error between said histogram of said image data and a specified histogram, and
wherein a plurality of grid points overlay said image portion of said object, each said grid point having associated therewith at least one interpolating function,
thereby minimizing said error.
19. The imaging apparatus according to claim 18 , wherein said processor means transforms image pixel information by weighting each pixel according to a weighting function.
20. The imaging apparatus according to claim 18 , wherein said at least one interpolating function is selected from the group consisting of:
two-dimensional Gaussian functions, three-dimensional Gaussian functions and multiquadratic basis functions, and
wherein said specified histogram is selected from the group consisting of:
uniform histograms, linearly-rising histograms, tissue contrast enhancement histograms, tissue accentuation histograms and combinations thereof.