Image processor for enhancing contrast between subregions of a region of interest

• US 4,991,092 A
• Filed: 08/12/1988
• Issued: 02/05/1991
• Est. Priority Date: 08/12/1988
• Status: Expired due to Fees

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Greensite, Fred
• Primary Examiner(s): Jablon, Clark A.
• Application Number:
  US07/231,830
• Time in Patent Office: 907 Days
• Field of Search: 364/413.22, 364/413.13, 382/54, 382/6, 358/111, 379/99
• US Class Current: 382/131
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20056 Discrete and fast Fourier transform, [DFT, FFT]
• CPC Class Codes: G06T 5/10 using non-spatial domain filtering
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 5/92 based on global image properties

Abstract

An imaging technique is disclosed for enhancing the contrast of an image, in particular for enhancing the contrast between subregions of a region of interest which may have similar signal characteristics and significantly distinct physical properties. A distribution density function describing the density of the signal in a signal space assigned to a voxel of the region to be imaged is first computed. This distribution function is then convolved with a resolution function, preferably a Gaussian function. Advantageously, the variance of the Gaussian is greater and a multiple of the variance of the noise statistics of the input image. The result of the convolution of the distribution function with the resolution function defines a scale, preferably a grey scale which assigns a particular tone to a pixel of the image corresponding to the voxel of the region to be imaged. The standard deviation is preferably chosen by the user and defines the resolution of the final image in the signal space. The noise in the output image can be decreased by increasing the standard deviation of the convolving Gaussian. For large values of the variance of the Gaussian, the contrast-to-noise ratio is comparable to standard images. The resulting gray scale creates a greater contrast between areas of different volumes having similar signal characteristics. Other resolution functions can be used.

Claims

1. A method for processing an image, said image comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, said method comprising the steps of:

providing a plurality of multidimensional signals, each of said signals being associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M;

computing a density function representative of the frequency of occurrence of each of said multidimensional signals in said signal space; and

assigning a tone value to each of said plurality of voxels in accordance with said density function

2. A method for processing an image, said image comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, comprising the steps of:

providing a plurality of multidimensional signals, each of said multidimensional signals comprising a plurality of parameters containing information about said region of interest, each of said signals being further associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M, said mapping M being a multidimensional random variable having a density function;

computing said density function representative of the frequency of occurrence of each of said multidimensional signals in said signal space;

convolving said density function with a resolution function so as to provide a real valued probability density function defined for each of said plurality of voxels;

assigning a tone value to each of said plurality of voxels corresponding to the value of said probability density function for each of said voxels; and

displaying a tone level image wherein each voxel is represented by a pixel with said assigned tone value.

3. A method for processing an image as defined in claim 2, wherein said convolving step comprises the step of convolving a Gaussian function of mean zero and variance σ

with said density function

4. A method for processing an image as defined in claim 3, wherein said Gaussian has a variance σ

equal to or greater than the variance ν

of said density function of said signal mapping M.

5. A method for processing an image as defined in claim 4, wherein said variance σ

of said Gaussian is a multiple of said variance ν

of said density function of said signal mapping M.

6. A method for processing an image as defined in claim 3, wherein said variance σ

defines the resolution of said image in said signal space, whereby high values of the variance σ

with respect to the variance ν

produce a low resolution image in said signal space and whereby low values of the variance σ

with respect to the variance σ

produce a high resolution image in said signal space.

7. A method for processing an image as defined in claim 3, wherein said image has a contrast defined by said tone value and said signal space is defined with a metric, said contrast between two voxels of said plurality of voxels being independent on the metric in said signal space.

8. A method for processing an image as defined in claim 2, wherein said convolving step includes the step of convolving a cosine function with said density function of said mapping M.

9. A method for processing an image as defined in claim 2, wherein said convolving step includes the step of convolving a pulse function having a width σ

with said density function of said mapping M.

10. A method for processing an image as defined in claim 2, wherein said convolving step includes the step of convolving a sinc function with said density function of said mapping M.

11. A method for processing an image as defined in claim 2, wherein said convolving step includes the step of convolving an exponential function with said density function of said mapping M.

12. A method for processing an image as defined in claim 2, wherein said convolving step includes the step of convolving a filter function with said density function of said mapping M.

13. A method for processing an image as defined in claim 2, wherein said density function is given by the following formula:

##EQU14## wherein δ

is the delta function, where w is the voxel in the set of voxels comprising said region of interest, and s is any possible signal value that a voxel could have as its measured value.

14. A method for processing an image as defined in claim 2, wherein said convolution product is performed using Fourier transforms.

15. A method for processing an image as defined in claim 2, wherein said convolution product is performed using Fast Fourier transforms.

16. A method for processing an image as defined in claim 2, wherein said probability density function is given by the following equation:

space="preserve" listing-type="equation">Hσ

(s)=Gσ

(s)*H.sub.o (s),

wherein * denoted the convolution product, Gσ

(s) represents said distribution function and H_o (s) represents the density function of same mapping M, and where σ

is any real number, σ

being the standard deviation of said distribution function Gσ

(s).

17. A method for processing an image as defined in claim 2, wherein said image is a medical image

18. A method for processing an image as defined in claim 2, wherein said signals are NMR signals.

19. A method for processing an image as defined in claim 2, wherein said signals are obtained by computer tomography.

20. A method for processing an image as defined in claim 2, wherein said signals are obtained from X-ray projections of said region of interest.

21. A method for processing an image as defined in claim 2, wherein said tone value is a grey value.

22. A method for processing an image as defined in claim 2, wherein said tone value is a color.

23. A method for processing an image as defined in claim 2, wherein said step of assigning a tone value is performed by segmentation.

24. A method for processing an image as defined in claim 2, wherein said convolving step is performed by using a multidimensional resolution function so as to obtain a single image incorporating said information from all of said plurality of parameters.

25. A method for processing an image as defined in claim 2, wherein said convolving step is performed by using a plurality of unidimensional resolution functions so as to obtain a plurality of images corresponding to each of said parameters of said multidimensional signals in said signal space.

26. A method for processing an image, said method comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, comprising the steps of:

providing a plurality of multidimensional signals, each of said signals being associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M, said mapping M being a multidimensional random variable and defining a probability measure in said signal space;

computing the Radon-Nikodym derivative of said probability measure of said mapping M, said Radon-Nikodym derivative defining a probability density function;

assigning a tone value to each of said plurality of voxels corresponding to the value of said probability density function for each of said voxels; and

displaying a tone level image wherein each voxel is represented by a pixel with said assigned tone value.

27. An apparatus for processing an image comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, said apparatus comprising:

an input imaging device for providing a plurality of multidimensional signals, each of said signals being associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M, said mapping M being a multidimensional random variable and defining a probability measure in said signal space;

means for computing a density function representative of the frequency of occurrence of each of said multidimensional signals in said signal space;

means for convolving said density function with a resolution function so as to provide a real valued probability density function defined for each of said plurality of voxels;

means for assigning a tone value to each of said plurality of voxels corresponding to the value of said probability density function for each of said voxels; and

means for displaying a tone level image wherein each voxel is represented by a pixel with said assigned tone value.

28. An apparatus for processing an image comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, said apparatus comprising:

an input imaging device for providing a plurality of multidimensional signals, each of said signals being associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M, said mapping M being a multidimensional random variable and defining a probability measure in said signal space;

means for computing the Radon-Nikodym derivative of said probability measure of said mapping M, said Radon-Nikodym derivative defining a probability density function;

means for assigning a tone value to each of said plurality of voxels corresponding to the value of said probability density function for each of said voxels; and

means for displaying a tone level image wherein each voxel is represented by a pixel with said assigned tone value.

29. An apparatus for processing an image comprising a plurality of pixels representing a region of interest partitioned into a plurality of voxels, said apparatus comprising:

an input imaging device for providing a plurality of multidimensional signals, each of said signals being associated with each of said voxels, said plurality of voxels being mapped into said plurality of signals in a signal space by a mapping M, said mapping M being a multidimensional random variable and defining a probability measure in said signal space;

means for computing a density function representative of the frequency of occurrence of each of said multidimensional signals in said signal space;

means for assigning a tone value to each of said plurality of voxels corresponding to the value of said probability density function for each of said voxels; and

means for displaying a tone level image wherein each voxel is represented by a pixel with said assigned tone value.

30. An apparatus for processing an image according to claim 29, wherein said input imaging device is a NMR device.

31. An apparatus for processing an image according to claim 29, wherein said input imaging device is an X-ray camera.

32. An apparatus for processing an image according to claim 29, wherein said input imaging device is a CT device.