Image processing.

This invention relates to image processing. It finds particular application in conjunction with image enhancement, image smoothing, and other image improvement techniques for magnetic resonance images and will be described with particular reference thereto. It is to be apreciated, however, that the present invention is also applicable to improving digital x-ray images, computed tomographic images, nuclear camera images, positron emission scanners, and the like.

Techniques capable of producing images of the internal structure of a body, such as are required for example, for medical diagnostic purposes, have commonly been subject to image degradation from noise, system imperfections, and the like. Various image processing techniques have been utilized to remove the effects of the noise. See for example, "Digital Image Enhancement: A survey" Wang, et al., Computer Vision, Graphics, and Image Processing, Vol. 24, pages 363-381 (1983). In one technique, each pixel was adjusted in accordance with the mean of surrounding pixels and the variance or difference between the surrounding pixels. Each filter enhanced pixel value g'(i,j) was a weighted average of the local mean and variance values: g,(i,j) = S (i.J) + k[g(i.j)- <o>g</o> (i.j)] (1), where (i,j) was the local mean, g(i,j)- g (i,j) was the variance, and k was a constant that weighted the relative contributions therebetween. It is to be appreciated that when k was set larger than 1, the variance or difference between the local mean value, hence the contribution of the measured gray scale level of the pixel (i,j) was magnified. As k was set smaller, the image was smoothed or blurred as if acted upon by a low-pass filter. At the extreme at which k was set equal to zero, each pixel value was replaced by the local mean of the neighboring pixel values.

One of the drawbacks in this technique resided in selecting an appropriate value for the weighting factor k. The smaller k was set, the more the image was blurred and the more difficult it became to withdraw accurate diagnostic information. As k was set larger, edges and fine details, including noise, became enhanced. Frequently, in a medical image, the selected weighting factor k was too large for some regions and too small for other regions.

"Digital Image Processing by Use of Local Statistics" by J.S. Lee, Naval Research Laboratory, Washington, D.C. (1980), recognized that a different weighting factor k could be selected for each pixel to be enhanced. Specifically, Lee suggested setting the k for each pixel equal to the square root of the ratio of a preselected desirable local variance to the actual local variance of the selected pixel. Although the Lee pixel variable weighting factor achieved better resultant images than the constant weighting factor, there was still room for improvement.

It is an object of the present invention to provide a method of and apparatus for imaging utilising a novel technique for image improvement.

In accordance with one aspect of the present invention there is provided a method of imaging comprising: collecting image data; converting the collected data into an electronic image representation which includes an array of pixel values; for each pixel value determining (i) a variance between the pixel value and neighboring pixel values and (ii) an average of the neighboring pixel values; and replacing each pixel value of the array by a weighted combination of the replaced pixel value and the average of pixel values neighboring the replaced pixel value; and characterised in that the method further includes determining noise statistics of the collected data; and in that the weighting is in accordance with the determined noise statistics and the variance between the replaced pixel value and its neighboring pixel values.

In accordance with another aspect of the present invention there is provided an imaging apparatus comprising: data generating apparatus A for generating image data indicative of at least a selected region of a body; a data memory means 30 for storing image data from the data generating apparatus A; a transform means 32. 34, 36 for converting the data from the data memory means 30 into an electronic image representation which includes an array of pixel values; an average pixel value determining means 38, 70, 72 for determining an average pixel value of pixel values neighboring each pixel value; and combining means 82, 84, 86, 88 for adjusting each pixel value, in accordance with the corresponding said average pixel value; characterised in that the apparatus further includes a data noise statistic determining means 40, 76 for determining a noise statistic from the data stored in said data memory means 30; said combining means 82, 84, 86, 88 combines each pixel value with the corresponding average pixel value and the data noise statistic.

A first advantage of the present invention is that it provides a highest appropriate filtering level for each individual image. Because the filter function is image noise dependent, more noise free images are not overfiltered or blurred unnecesarily.

Another advantage of the present invention is that it is regionally adaptive. That is, noisier regions of the image are filtered more than regions of each image with less noise.

Other advantages of the present invention reside in an improved technique for determining image wide noise, in achieving an improved signal-to-noise ration without a loss of spatial resolution, and in an improved technique for determining image noise before image reconstruction.

Still further advantages of the present invention will become apparent to those of ordinary skill in the art upon reading and understanding the following detailed description of the preferred embodiment.

One method and apparatus in accordance with the invention will now be described, by way of example, with reference to the accompanying drawing which is a diagrammatic illustration of a medical diagnostic imaging apparatus in accordance with the present invention.

Referring to the drawing, the apparatus A generates medical diagnostic data which is reconstructed by an imager into an electronic image representation. An adaptive filter circuit operates on the electronic image representation to improve the image quality and viewability thereof.

Although a magnetic resonance imager is illustrated, the medical diagnostic apparatus A may be a computerized tomographic scanner, a digital x-ray apparatus, a positron emission scanner, a nuclear camera, or other diagnostic apparatus which generates data that is able to be reconstructed into an image representative of a region of an examined patient or subject. The illustrated magnetic resonance imager includes a field control means 10 which controls a main, homogeneous polarizing magnetic field through an image region generated by electromagnets 12. The field control means 10 also controls gradient magnetic fields created across the image region by gradient field coils 14 to provide spatial encoding, phase encoding, and slice select gradients. The field control means 10 further generates radio frequency electromagnetic excitation signals which are applied to radio frequency coils 16 to excite resonance of dipoles in the image region. A central computer 18 controls the relative timing and strengths of the gradient and radio frequency electromagnetic fields.

Magnetic resonance signals generated by resonating dipoles in the image region are received by the radio frequency coils and conveyed to a radio frequency receiver 20. The radio frequency receiver 20 demodulates the received signals which are converted from analog to digital format by an AiD converter 22. The digital format magnetic resonance medical diagnostic data is supplied to the imager.

The imager under control of the central computer 18 reconstructs the medical diagnostic data into an electronic image representation. More specifically, the imager reconstructs the diagnostic data which has minor noise components into an array of digital pixel values which are degraded by the noise. Each pixel value corresponds to a preselected subregion of the image region, conventionally a corresponding voxel or cubic subregion of an imaged slice. The imager includes a diagnostic data memory 30 which strores the diagnostic data from the A_/D converter 22. The diagnostic data from magnetic resonance echoes are reconstructed into a single image representation. More specific to the preferred embodiment, each echo signal or view is converted into a line of ditigal values for a data matrix d(x,y). With Gaussian noise as is found in a magnetic resonance imager, each data matrix value is the sum of a data component and a noise component.

The diagnostic data memory 30 stores the diagnostic data in the data matrix format until a sufficient amount of data is received for a complex two dimensional Fourier transform means or routine 32 to transform the received data into a real image representation for storage in a real image memory 34 and an imaginary image representation for storage in an imaginary image memory 36. The sum of a data matrix value and Gaussian noise, d(x,y) + ng , Fourier transforms linearly into the sum of an image pixel value and Gaussian noise, I(i,j)+n'₉. An image select means 38 transfers either real, imaginary, or magnitude pixel values as may be selected by the operator to the filtering means. If the real image is selected, the contents of the real image memory 34 are transferred. If the imaginary image is selected, the contents of the imaginary memory 36 are transferred. The real and imaginary images again have a Gaussian noise distribution. If a magnitude image is selected, an array of magnitude pixel values is transferred. Each magnitude pixel value is set equal to the square root of the sum of the squares of the corresponding real and imaginary image pixel values, i.e. (I (real)² + l(imaginary)2)12 The magnitude image has a Rayleigh noise distribution.

The algorithm implemented by the reconstruction means is, of course, selected in accordance with the medical diagnostic apparatus selected. For other diagnostic imagers, other known transform and reconstruction techniques are selected.

The filtering circuit includes an image noise level determining means 40 which calculates a value V-(noise) which is indicative of the noise level of the diagnostic data or the overall image. In magnetic resonance and many other applications, the diagnostic data stored in memory 30 has substantially signal-independent, uncorrelated additive noise. This enables image noise statistics to be determined from the data noise statistics. In magnetic resonance imaging in which the diagnostic data is a Fourier transform of the image, the noise is primarily signal-independent, uncorrelated, and additive.

The noise statistics of the data are readily determined from an area of the data which contains no diagnostic data or signal, merely noise. In some applications, such a region may be created artificially by appropriate placement of the patient, inclusion of phantom or phantom regions, adjustment of the imaging sequence, or the like. However, in conventional Fourier transform magnetic resonance imaging, the majority of the signal is concentrated in the center of the data matrix d(x,y) stored in memory 30. Accordingly, the probability of finding a signal-free region is greatest around the periphery of the data matrix. In the preferred embodiment, the data around the periphery of the data matrix is examined for uniformity. The most uniform region is assumed to be a region with substantially no signal, just noise. The degree of non-uniformity in this region is utilized as the indicator of overall image noise.

A matrix addressing means 42 serially addresses each of a plurality of preselected data matrix points (x,y) around the periphery of the data matrix. At each matrix point, a variance means 44 determines the variance V(x,y) between the data values corresponding to the addressed matrix point and its neighboring matrix points. In the preferred embodiment, the noise at each point V(x,y) is determined from the equation:<maths id="math0001" num=""><img file="EP0280412A2_D0001.tif" /></maths>where d(t,k) represents the data values surrounding the matrix point (x,y) and d (x,y) is the average of data values surrounding the data point d(x,y). Each local data variance V(x,y) is compared by a comparing means 46 with a previously measured variance stored in a memory 48. The smaller of the two variances is returned to the memory 48 and stored for the next comparison. In this manner, the neighborhood of the data matrix with the least signal is determined. For Gaussian noise, the noise variance is equal to the sqaure of standard deviation for Gaussian noise <chemistry id="chem0001" num="0001"><img file="EP0280412A2_D0002.tif" /></chemistry>

In the preferred embodiment, the data matrix variance determining means 44 includes a means 50 for serially receiving the data value from each point surrounding matrix point (x,y), and an averaging means 52 for determining the average of the neighboring data values. The neighboring points are again addressed serially. A subtraction means 54 determines the difference between each neighboring point data value and the average. A squaring means 56 sqares the difference and a summation means 58 sums the sqaure of the difference with the accumulated sum in a cumulative memory 60. The weighting means 62 adjusts the sum by a constant to compensate for the size of the neighborhood or the like.

The filtering means further includes a first pixel memory 70 which receives pixel values P(i,j) from the reconstruction means. As discussed above, the pixel values may be the real image pixel values, the imaginary image pixel values or the magnitude image pixel values.

A pixel value average or mean determining means 72 determines the mean or average of pixel values surrounding each pixel (i,j) of the pixel memory 70. In the preferred embodiment, the pixel mean value P-(i,j) is determined in accordance with:<maths id="math0002" num=""><img file="EP0280412A2_D0003.tif" /></maths>where the values of the constants n and m indicate the size of the neighborhood.

A pixel value variance determining means 74 determines the variance V(i,j) corresponding to the neighborhood around each pixel (i,j). In the preferred embodiment, the variance is determined in accordance with:<maths id="math0003" num=""><img file="EP0280412A2_D0004.tif" /></maths>

A data noise to image noise converting means 76 converts the Gaussian data noise variance to an image noise variance V(noise). For Gaussian image noise, the image noise variance is equal to the data noise variance. For Rayleigh image noise, the image noise variance is equal to the Gaussian data noise variance times the constant (2-₇ri2). For other image noise distributions, the data noise may be operated on by other constants or functions.

A weighting factor determining means 78 determines a weighting factor G(i,j) corresponding to each pixel location in accordance with the corresponding pixel variance V(i,j) and the image noise level V(noise). In the preferred embodiment, the weighting factor is determined in accordance with:<maths id="math0004" num=""><img file="EP0280412A2_D0005.tif" /></maths>

In most instances, the noise variance V(noise) will be less than or equal to the local variance V(i,j). When the noise variance of the image is very low, the weighting factor approaches one. As the noise varianceincreases toward the local variance, the weighting factor becomes smaller approaching one half. If the local variance is less than the noise variance, the weighting factor will become less than one-half and approach zero.

It must be noted that the noise variance is individually selected for each image and is normally different from image to image. Thus, the weighting factor at pixels in two images with the same local variance is commonly different due to the different noise variance of the two images. Optionally, other weighting factors which weight in accordance with both the local variance and the image noise variance may be selected.

A mean noise determining means 80 determines the mean magnitude of the image noise correction value F . A Gaussian noise distribution has a zero mean magnitude, whereas a Rayleigh distribution does not. When a real, imaginary or other image with a Gaussian noise distribution is being processed, the mean noise is zero, i.e. ii =0. When a magnitude or other image with a Rayleigh noise distribution is being processed, the mean noise n is:<maths id="math0005" num=""><img file="EP0280412A2_D0006.tif" /></maths>Other mean noise values can be calculated for other noise distributions as is known in the art.

A first subtraction means 82 subtractively combines each pixel value, P(i,j) with the corresponding average neighborhood pixel value, P (i,j). A multiplying means 84 multiplies the difference of the pixel and neighborhood values by the weighting factor corresponding to the same pixel G(i,j). An adding means 86 adds the corresponding neighborhood average to the weighted difference. A second subtracting means 88 subtracts the mean noise correction value from the sum. This sum is stored in a filtered image memory 90 and displayed on a video monitor or other display means 92. That is, each filtered pixel value P^*(i,j) is equal to:<maths id="math0006" num=""><img file="EP0280412A2_D0007.tif" /></maths>

With this relationship, with less data noise variance, the corresponding pixel value P(i,j) is weighted most heavily. Similarly, in a local region with alot of variance, the actual pixel value is again weighted heavily so as not to blur the local resolution. However, in images that are very noisy or in regions that are very uniform, the neighborhood average is weighted more heavily.

The invention has been described with reference to the preferred embodiment. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such alterations and modifications insofar as they come within the scope of the appended claims or the equivalents thereof.