CN104809713A - CBCT panorama nonlinear sharpening enhancing method based on neighborhood information and Gaussian filter - Google Patents
CBCT panorama nonlinear sharpening enhancing method based on neighborhood information and Gaussian filter Download PDFInfo
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Abstract
The invention provides a CBCT nonlinear sharpening enhancing method based on neighborhood information and Gaussian filter. The method includes the following steps: reading a CBCT original image f (x, y), conducting convolution processing on the image to obtain a smooth image, conducting subtraction on the original image and the smoothened image to obtain an unsharp mask image, then looking through the original image f (x, y), calculating a weighting coefficient KW (x, y), finally adding the weighting portion of the unsharp mask image composed of the unsharp mask image and the weighting coefficient KW (x, y) on the original image to form a reinforced image. Compared with the CBCT panorama sharpening enhancing method in the prior art, the weighting coefficient KW (x, y) is arranged, the effect on the CBCT image integral contrast caused by soft tissue images in the original CBCT image and unapparent image boundary tissue enhancing effect caused by noise point amplification in the original image f (x, y) can be effectively restrained, and the method is simple in algorithm and high in computation speed and has good robustness.
Description
Technical field
The invention belongs to field of medical image processing, be specifically related to a kind of non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering.
Background technology
CBCT (Cone Beam Computer Tomography), namely pencil-beam throws illuminated computerized tomography image scanner, is to start to be applied to a kind of novel imaging technique of clinical oral in the beginning of this century.Its principle is that x ray generator is annular DR (digital throwing shine) around throwing according to body with lower quantity of X-rays X (usual tube current is at about 10 milliamperes), then will around throwing according to body repeatedly (180 times-360 times, different and different according to product) numeral throw according to the data obtained in rear " commons factor " that " reconstruction " afterwards and then acquisition 3-D view in a computer.The maximum difference of it and spiral CT is its data for projection is two-dimentional, and be three-dimensional after reconstruction, and the data for projection of spiral CT is one dimension, data for projection is two-dimentional, and obtain three-dimensional data needs the multiple two dimension slicing of continuous sweep.Relative to traditional CT, pencil-beam x-ray utilization factor is high, and roentgen dose X is low, and spatial resolution is high, and cost is low, and floor area is little, scans more flexible.
The major defect of CBCT imaging technique is that density resolution is low, poor to soft tissue structure's imaging effect, and image boundary is organized greatly affected by noise.By can give prominence to the detailed information of image to the sharpening enhancement of image, obtain image boundary more clearly, facilitate doctor's delineating target area.
The enhancing algorithm of CBCT mainly comprises two kinds: the method based on spatial domain and the method based on frequency domain.It is all based on local pixel information that spatial domain method calculates at every turn, can not better embody integral image characteristic, have impact on the raising of CBCT integral image contrast to a certain extent, and the impact for the CBCT image of low contrast is comparatively serious.Although frequency domain method is to the effect of the sharpening enhancement of CBCT panorama sketch in overall contrast, comparatively spatial domain method is good, and calculate comparatively complicated, computing velocity is slow, has the ring effect of trace, cannot meet the CBCT image request of high resolving power, low contrast.
Summary of the invention
The present invention is for solving the problem and carrying out, spatial resolution for CBCT image is high, density resolution is low, the features such as low-density imaging of tissue is unintelligible, provide a kind of non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering, utilize the spatial domain method of the neighborhood information of pixel to classics to improve.
Present invention employs following technical scheme:
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, has such feature, comprises the following steps:
Step one, reads width CBCT original image f (x, y);
Step 2, employing radius is R, and standard deviation is that the gauss low frequency filter of σ carries out process of convolution to CBCT original image, obtains the image f smoothly
c(x, y);
Step 3, the image fc (x, y) after to be deducted by the original image f (x, y) in step one in step 2 level and smooth, obtains unsharp masking image f
mask(x, y)=f (x, y)-ffc
c((x, y);
Step 4, traversal original image f (x, y), with each pixel X
kcentered by, calculate average value mu (x, y) and the meansquaredeviationσ (x, y) of pixel in its m*n neighborhood, and by formula
Calculate weights COEFFICIENT K W (x, y),
Wherein, a (x, y) is noise intensity coefficient.
Step 5, CBCT original image adds described unsharp masking image f
maskweight portion g (x, y)=f (x, the y)+K*f of (x, y)
mask(x, y) * KW (x, y), the image after being enhanced.
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, it is characterized in that: in step 2, the value of radius R is preferably 3, and the value of standard deviation is preferably 20.
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, it is characterized in that: in step 2, gauss low frequency filter formula is
wherein, H
lP(u, v) is gauss low frequency filter function; D (u, v) is for each point (u, v) in picture frequency territory is to the distance of filter center, and σ is standard deviation.
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, it is characterized in that: in step 2, Convolution Formula is:
Wherein, w (x, y) is gauss low frequency filter function, and f (x, y) is original image.
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, is characterized in that: the preferred 3*3 neighborhood of m*n neighborhood.
CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering provided by the invention, is characterized in that: the value of k described in step 5 scope is 1 ~ 4.
Invention effect and effect
The invention provides a kind of non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering, comprise the following steps: read a width CBCT original image f (x, y), the image is smoothly obtained after process of convolution is carried out to it, original image and level and smooth after image do difference and obtain unsharp mask image, travel through original image f (x afterwards, y), calculate weights COEFFICIENT K W (x, y), finally on original image, add unsharp mask image and weights COEFFICIENT K W (x, the weight portion of the unsharp mask image y) formed, image after being enhanced.Compare with CBCT panorama sketch sharpening enhancement method of the prior art, weights COEFFICIENT K W (x is provided with in CBCT panorama sketch sharpening enhancement method in the present invention, y), effectively can suppress original image f (x, y) in, noise spot is exaggerated and soft-tissue image in the CBCT original image that causes and image boundary tissue enhancing DeGrain, and the phenomenon affecting CBCT integral image contrast occurs, simultaneously, the algorithm of the method is simple, fast operation, and there is good robustness.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the non-linear sharpening enhancement method of CBCT panorama sketch of the present invention;
Fig. 2 is the primitive oral cavity CBCT panorama sketch before enhancing of the present invention;
Fig. 3 is the oral cavity CBCT panorama sketch after directly utilizing unsharp masking operator to strengthen in the present invention;
Fig. 4 is the CBCT panorama sketch after utilizing the CBCT panorama sketch sharpening enhancement method in the present invention to strengthen.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described.
Fig. 1 is the process flow diagram of the non-linear sharpening enhancement method of CBCT panorama sketch in the present embodiment.
As shown in Figure 1, the non-linear sharpening enhancement method of the CBCT panorama sketch in the present embodiment comprises the steps:
Step one (S
1), read width CBCT panorama original image f (x, y), read CBCT panorama original image f (x, y) of input, and be converted into double-length floating image;
Step 2 (S
2), employing radius is R, and standard deviation is that the gauss low frequency filter of σ carries out process of convolution to image, obtains the image f smoothly
c(x, y).
The value of radius R is preferably 3, and the value of standard deviation is preferably 20.
Gauss low frequency filter formula is:
wherein, H
lP(u, v) is gauss low frequency filter function; D (u, v) is for each point (u, v) in picture frequency territory is to the distance of filter center, and σ is standard deviation.
Convolution Formula is:
Wherein, w (x, y) is gauss low frequency filter function, and f (x, y) is CBCT panorama original image;
Step 3 (S
3), traversal original image f (x, y), with each pixel X
kcentered by, calculate average value mu (x, y) and the meansquaredeviationσ (x, y) of pixel in its 3*3 neighborhood, by formula
Calculate weights COEFFICIENT K W (x, y).
The pixel of CBCT panoramic picture is mainly divided three classes: marginal point, image internal point and noise spot.If formula
middle finger is several | X
k-u (x, y) |-σ (x, y) > 0, then in image, this point is noise spot, otherwise is then edge and the internal point of image.Formula
the scope of value is (0,2), and therefore, α (x, y) > 1 represents that this point is noise spot, and α (x, y)≤1 item represents that this point is non-noise point.
The object arranging weight coefficient is to prevent noise spot in original image f (x, y) to be exaggerated, therefore directly can not using α
k(x, y) is as weight coefficient item.α
kin (x, y) value larger key diagram picture, the noise intensity of this point is larger, in order to the weight coefficient value of restraint speckle point, utilizes formula
In
the weights coefficient of noise spot is carried out inverse proportion function, made it much smaller than weights coefficient corresponding to non-noise point.
Step 4 (S
4): original image f (x, y) is deducted the image f smoothly
c(x, y), produces unsharp masking image f
mask(x, y)=f (x, y)-fcf
c((xx, yy)).
Step 5 (S
5): on original image, add unsharp masking image f
maskweight portion g (x, y)=f (x, the y)+K*f of (x, y)
mask(x, y) * KW (x, y), the image after being enhanced.
Fig. 2 is the primitive oral cavity CBCT panorama sketch before the enhancing of the present embodiment.
Fig. 3 is the oral cavity CBCT panorama sketch after directly utilizing unsharp masking operator to strengthen in the present embodiment.
Fig. 4 is the oral cavity CBCT panorama sketch after utilizing the CBCT panorama sketch sharpening enhancement method in the present embodiment to strengthen.
As shown in Figures 2 to 4, the contrast of the oral cavity CBCT panorama sketch after directly utilizing unsharp masking operator to strengthen is better than the primitive oral cavity CBCT panorama sketch before strengthening.The image utilizing the method for the present embodiment to strengthen all is better than directly utilizing unsharp masking to strengthen the effect of image method in the enhancing effect of soft tissue and the contrast of whole image.
Embodiment effect and effect
The invention provides a kind of non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering, comprise the following steps: read a width CBCT original image f (x, y), the image is smoothly obtained after process of convolution is carried out to it, original image and level and smooth after image do difference and obtain unsharp mask image, travel through original image f (x afterwards, y), calculate weights COEFFICIENT K W (x, y), finally on original image, add unsharp mask image and weights COEFFICIENT K W (x, the weight portion of the unsharp mask image y) formed, image after being enhanced.Compare with CBCT panorama sketch sharpening enhancement method of the prior art, weights COEFFICIENT K W (x is provided with in CBCT panorama sketch sharpening enhancement method in the present invention, y), effectively can suppress original image f (x, y) in, noise spot is exaggerated and soft-tissue image in the CBCT original image that causes and image boundary tissue enhancing DeGrain, and the phenomenon affecting CBCT integral image contrast occurs, simultaneously, the algorithm of the method is simple, fast operation, and there is good robustness.
Claims (6)
1., based on a CBCT panorama sketch sharpening enhancement method for neighborhood information and gaussian filtering, for improving the overall contrast of described CBCT image, it is characterized in that, comprise the following steps:
Step one, reads width CBCT original image f (x, y);
Step 2, employing radius is R, and standard deviation is that the gauss low frequency filter of σ carries out process of convolution to described CBCT original image, obtains the image f smoothly
c(x, y);
Step 3, travels through described original image f (x, y), with each pixel X
kcentered by, calculate average value mu (x, y) and the meansquaredeviationσ (x, y) of pixel in its m*n neighborhood, and by formula
Calculate weights COEFFICIENT K W (x, y),
Wherein, described a (x, y) is noise intensity coefficient.
Step 4, the image f after to be deducted by the original image f (x, y) in described step one in described step 2 level and smooth
c(x, y), obtains unsharp masking image f
mask(x, y)=f (x, y)-ffc
c((xx, yy));
Step 5, described CBCT original image adds described unsharp masking image f
maskweight portion g (x, y)=f (x, the y)+K*f of (x, y)
mask(x, y) * KW (x, y), the image after being enhanced.
2. the CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering according to claim 1, is characterized in that:
Wherein, in step 2, the value of described radius R is preferably 3, and the value of described standard deviation is preferably 20.
3. the CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering according to claim 1, is characterized in that:
Wherein, in step 2, described gauss low frequency filter formula is
wherein, H
lP(u, v) is gauss low frequency filter function; D (u, v) is for each point (u, v) in picture frequency territory is to the distance of filter center, and σ is standard deviation.
4. the CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering according to claim 1, is characterized in that:
Wherein, in step 2, described Convolution Formula is:
Wherein, w (x, y) is Gaussian low pass wave function, and f (x, y) is original image.
5. the CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering according to claim 1, is characterized in that:
Wherein, the preferred 3*3 neighborhood of described m*n neighborhood.
6. the CBCT panorama sketch sharpening enhancement method based on neighborhood information and gaussian filtering according to claim 1, is characterized in that:
Wherein, the value of k described in step 5 scope is 1 ~ 4.
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CN105894444A (en) * | 2016-03-31 | 2016-08-24 | 深圳市菲森科技有限公司 | Method and device for generating dental panoramic image on the basis of CBCT image |
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CN108024103A (en) * | 2017-12-01 | 2018-05-11 | 重庆贝奥新视野医疗设备有限公司 | Image sharpening method and device |
CN115409833A (en) * | 2022-10-28 | 2022-11-29 | 一道新能源科技(衢州)有限公司 | Hot spot defect detection method of photovoltaic panel based on unsharp mask algorithm |
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CN115409833A (en) * | 2022-10-28 | 2022-11-29 | 一道新能源科技(衢州)有限公司 | Hot spot defect detection method of photovoltaic panel based on unsharp mask algorithm |
CN115409833B (en) * | 2022-10-28 | 2023-01-31 | 一道新能源科技(衢州)有限公司 | Hot spot defect detection method of photovoltaic panel based on unsharp mask algorithm |
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