CN104809713B - The non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering - Google Patents
The non-linear sharpening enhancement method of CBCT panorama sketch based on neighborhood information and gaussian filtering Download PDFInfo
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- CN104809713B CN104809713B CN201510201648.XA CN201510201648A CN104809713B CN 104809713 B CN104809713 B CN 104809713B CN 201510201648 A CN201510201648 A CN 201510201648A CN 104809713 B CN104809713 B CN 104809713B
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Abstract
The invention provides a kind of non-linear sharpening enhancement method of the CBCT panorama sketch based on neighborhood information and gaussian filtering, comprise the following steps：Read a width CBCT original image f (x, y), it is carried out obtain after process of convolution it is smooth after image, original image and it is smooth after image make the difference and obtain unsharp mask image, original image f (x are traveled through afterwards, y), weight coefficient KW (x, y) is calculated, unsharp mask image and weight coefficient KW (x are finally added on original image, y) weight portion of the unsharp mask image of composition, obtains enhanced image.Compared with CBCT panorama sketch sharpening enhancement method of the prior art, weight coefficient KW (x are provided with CBCT panorama sketch sharpening enhancement methods in the present invention, y), original image f (x can effectively be suppressed, y) soft-tissue image and image boundary tissue enhancing effect caused by noise spot is exaggerated in CBCT original images be not obvious, and influence the phenomenon of CBCT image overall contrasts to occur, simultaneously, the algorithm of this method is simple, arithmetic speed is fast, and with good robustness.
Description
Technical field
The invention belongs to field of medical image processing, and in particular to a kind of CBCT based on neighborhood information and gaussian filtering is complete
The non-linear sharpening enhancement method of scape figure.
Background technology
CBCT (Cone Beam ComputerTomography), i.e. pencil-beam throw illuminated computerized tomography image scan and set
It is standby, it is to start a kind of new imaging technique applied to clinical oral in the beginning of this century.Its principle be X-ray emitter with compared with
Low quantity of X-rays X (usual tube current is at 10 milliamperes or so) is annular DR (digital throwing photograph) around throwing according to body, then will be around
Throw and shine body multiple (180 times -360 times, different according to product difference) digital data thrown after photograph obtained in " common factor " in computer
In " reconstruction " afterwards so that obtain 3-D view.Its difference maximum with spiral CT is that its data for projection is two-dimentional, after reconstruction
It is three-dimensional, and the data for projection of spiral CT is one-dimensional, data for projection is two-dimentional, and to obtain three-dimensional data needs continuously
Scan multiple two dimension slicings.Relative to traditional CT, pencil-beam x-ray utilization rate is high, and roentgen dose X is low, and spatial resolution is high, cost
Low, floor space is small, scans more flexible.
The major defect of CBCT imaging techniques is that density resolution is low, poor to soft tissue structure's imaging effect, and image
Border tissue is affected by noise big.The detailed information of image can be protruded by the sharpening enhancement to image, more visible figure is obtained
As border, doctor is facilitated to delineate target area.
CBCT enhancing algorithm mainly includes two kinds：Method based on spatial domain and the method based on frequency domain.Spatial domain method
Calculate every time and be all based on local pixel information, it is impossible to preferably embody image overall permanence, have impact on to a certain extent
The raising of CBCT image overall contrasts, the influence for the CBCT images of low contrast is more serious.Frequency domain method is although right
Effect of the sharpening enhancement of CBCT panorama sketch in terms of overall contrast is good compared with spatial domain method, but calculates complex, calculates speed
Degree is slow, has micro ring effect, it is impossible to meet high-resolution, the CBCT image requests of low contrast.
The content of the invention
The present invention is carried out to solve the above problems, and the spatial resolution for CBCT images is high, density resolution
Low, there is provided a kind of CBCT panorama sketch based on neighborhood information and gaussian filtering is non-for the features such as low-density imaging of tissue is unintelligible
Linear sharpening enhancement method, is improved using the neighborhood information of pixel to classical spatial domain method.
Present invention employs following technical scheme：
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, with such
Feature, comprises the following steps：
Step one, a width CBCT original image f (x, y) are read；
Step 2, uses radius for R, and standard deviation is carried out at convolution for σ gauss low frequency filter to CBCT original images
Reason, the image f after obtaining smoothlyc(x,y)；
Step 3, the original image f (x, y) in step one is subtracted the image fc (x, y) after smooth in step 2, obtained
Unsharp masking image fmask(x, y)=f (x, y)-fc (x, y)；
Step 4, traversal original image f (x, y), with each pixel XkCentered on, calculate pixel in its m*n neighborhood
Average value mu (x, y) and meansquaredeviationσ (x, y), and by formula Weight coefficient KW (x, y) is calculated,
Wherein, α (x, y) is noise intensity coefficient；
Step 5, adds the unsharp masking image f on CBCT original imagesmaskThe weight portion g (x, y) of (x, y)
=f (x, y)+K*fmask(x, y) * KW (x, y), obtain enhanced image.
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, its feature exists
In：In step 2, radius R value is preferably 3, and the value of standard deviation is preferably 20.
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, its feature exists
In：In step 2, gauss low frequency filter formula isWherein, HLP(u, v) is Gaussian low pass
Ripple device function；D (u, v) arrives the distance of filter center for each point (u, v) in picture frequency domain, and σ is standard deviation.
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, its feature exists
In：In step 2, Convolution Formula is：
Wherein, w (x, y) is gauss low frequency filter function, and f (x, y) is original image.
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, its feature exists
In：The preferred 3*3 neighborhoods of m*n neighborhoods.
The CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering that the present invention is provided, its feature exists
In：The scopes of K values described in step 5 are 1~4.
Invention effect and effect
The invention provides a kind of non-linear sharpening enhancement method of the CBCT panorama sketch based on neighborhood information and gaussian filtering,
Comprise the following steps：A width CBCT original image f (x, y) are read, the image after being obtained after process of convolution smoothly, artwork are carried out to it
Picture and it is smooth after image make the difference and obtain unsharp mask image, original image f (x, y) is traveled through afterwards, weight coefficient KW is calculated
(x, y), the unsharp mask image finally constituted on original image plus unsharp mask image and weight coefficient KW (x, y)
Weight portion, obtain enhanced image.Compared with CBCT panorama sketch sharpening enhancement method of the prior art, in the present invention
CBCT panorama sketch sharpening enhancement methods in be provided with weight coefficient KW (x, y), can effectively suppress noise in original image f (x, y)
Soft-tissue image and image boundary tissue enhancing effect caused by point is exaggerated in CBCT original images be not obvious, and influences
The phenomenon of CBCT image overall contrasts occurs, meanwhile, the algorithm of this method is simple, and arithmetic speed is fast, and has well
Robustness.
Brief description of the drawings
Fig. 1 is the flow chart 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 the enhancing of the present invention；
Fig. 3 is the direct utilization enhanced oral cavity CBCT panorama sketch of unsharp masking operator in the present invention；
Fig. 4 is to utilize the enhanced CBCT panorama sketch of CBCT panorama sketch sharpening enhancement methods in the present invention.
Embodiment
Illustrate the embodiment of the present invention below in conjunction with accompanying drawing.
Fig. 1 be the present embodiment in the non-linear sharpening enhancement method of CBCT panorama sketch flow chart.
As shown in figure 1, the non-linear sharpening enhancement method of CBCT panorama sketch in the present embodiment comprises the following steps：
Step one (S1), read a width CBCT panorama original image f (x, y), read input CBCT panorama original images f (x,
Y), and it is converted into double-length floating image；
Step 2 (S2), radius is used for R, and standard deviation carries out process of convolution for σ gauss low frequency filter to image,
Image f after obtaining smoothlyc(x,y)。
Radius R value is preferably 3, and the value of standard deviation is preferably 20.
Gauss low frequency filter formula is：Wherein, HLP (u, v) is gauss low frequency filter
Function；D (u, v) arrives the distance of filter center for each point (u, v) in picture frequency domain, and σ is standard deviation.
Convolution Formula is：Wherein, w (x, y) is
Gauss low frequency filter function, f (x, y) is CBCT panorama original images；
Step 3 (S3), traversal original image f (x, y), with each pixel XkCentered on, calculate its 3*3 neighborhood
The average value mu (x, y) and meansquaredeviationσ (x, y) of interior pixel, by formula Calculate weight coefficient KW (x, y).
The pixel of CBCT panoramic pictures is broadly divided into three classes：Marginal point, image internal point and noise spot.If formulaMiddle exponential term | Xk- u (x, y) |-σ (x, y) ＞ 0, then the point of this in image be noise spot, it is on the contrary then
For the edge and internal point of image.FormulaThe scope of value is (0,2), and therefore, α (x, y) ＞ 1 is then
It is noise spot to represent the point, and α (x, y)≤1 item represents that the point is non-noise point.
The purpose for setting weight coefficient is, in order to prevent in original image f (x, y) noise spot to be exaggerated, therefore can not directly to make
Use αk(x, y) is used as weight coefficient.αkThe noise intensity of the point is bigger in the bigger explanation image of (x, y) value, in order to suppress noise
The weight coefficient value of point, utilizes formulaInBy the weights of noise spot
Coefficient has carried out inverse proportion function change, it is much smaller than the corresponding weight coefficient of non-noise point.
Step 4 (S4)：Image f after original image f (x, y) is subtracted smoothlyc(x, y), is produced anti-
(x5,y)
Unsharp mask image fmask(x, y)=f (x, y)-fc (x, y).
Step 5 (S5)：Unsharp masking image f is added on original imagemask(x, y) weight portion g (x, y)=f (x,
y)+K*fmask(x, y) * KW (x, y), obtain enhanced image.
Fig. 2 is the primitive oral cavity CBCT panorama sketch before the enhancing of the present embodiment.
Fig. 3 is the direct utilization enhanced oral cavity CBCT panorama sketch of unsharp masking operator in the present embodiment.
Fig. 4 is to utilize the enhanced oral cavity CBCT panorama sketch of CBCT panorama sketch sharpening enhancement methods in the present embodiment.
As shown in Figures 2 to 4, it is directly excellent using the contrast of the enhanced oral cavity CBCT panorama sketch of unsharp masking operator
In the primitive oral cavity CBCT panorama sketch before enhancing.Using the enhanced image of the method for the present embodiment in the enhancing effect of soft tissue and whole
Better than directly strengthen the effect of image method in the contrast of individual image using unsharp masking.
Embodiment is acted on and effect
The invention provides a kind of non-linear sharpening enhancement method of the CBCT panorama sketch based on neighborhood information and gaussian filtering,
Comprise the following steps：A width CBCT original image f (x, y) are read, the image after being obtained after process of convolution smoothly, artwork are carried out to it
Picture and it is smooth after image make the difference and obtain unsharp mask image, original image f (x, y) is traveled through afterwards, weight coefficient KW is calculated
(x, y), finally plus unsharp mask image and weight coefficient KW (x, y) the unsharp mask image constituted on original image
Weight portion, obtains enhanced image.Compared with CBCT panorama sketch sharpening enhancement method of the prior art, in the present invention
Weight coefficient KW (x, y) is provided with CBCT panorama sketch sharpening enhancement methods, can effectively suppress noise spot in original image f (x, y)
Soft-tissue image and image boundary tissue enhancing effect caused by being exaggerated in CBCT original images be not obvious, and influences
The phenomenon of CBCT image overall contrasts occurs, meanwhile, the algorithm of this method is simple, and arithmetic speed is fast, and has well
Robustness.
Claims (6)
1. a kind of CBCT panorama sketch sharpening enhancement methods based on neighborhood information and gaussian filtering, for improving the CBCT images
Overall contrast, it is characterised in that comprise the following steps：
Step one, a width CBCT original image f (x, y) are read；
Step 2, uses radius for R, and standard deviation is carried out at convolution for σ gauss low frequency filter to the CBCT original images
Reason, the image f after obtaining smoothlyc(x,y)；
Step 3, travels through the original image f (x, y), with each pixel XkCentered on, calculate the average value of pixel in its m*n neighborhood
μ (x, y) and meansquaredeviationσ (x, y), and by formula
Weight coefficient KW (x, y) is calculated,
Wherein, the α (x, y) is noise intensity coefficient；
Step 4, the original image f (x, y) in the step one is subtracted the image f after smooth in the step 2c(x, y),
Obtain unsharp masking image fmask(x, y)=f (x, y)-fc (x, y)；
Step 5, adds the unsharp masking image f on the CBCT original imagesmaskThe weight portion g (x, y) of (x, y)=
f(x,y)+K*fmask(x, y) * KW (x, y), obtain enhanced image.
2. the CBCT panorama sketch sharpening enhancement methods according to claim 1 based on neighborhood information and gaussian filtering, it is special
Levy and be：
Wherein, in step 2, the value of the radius R is preferably 3, and the value of the standard deviation is preferably 20.
3. the CBCT panorama sketch sharpening enhancement methods according to claim 1 based on neighborhood information and gaussian filtering, it is special
Levy and be：
Wherein, in step 2, the gauss low frequency filter formula isWherein, HLP(u, v) is height
This low-pass filter function；D (u, v) arrives the distance of filter center for each point (u, v) in picture frequency domain, and σ is standard deviation.
4. the CBCT panorama sketch sharpening enhancement methods according to claim 1 based on neighborhood information and gaussian filtering, it is special
Levy and be：
Wherein, in step 2, the 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 methods according to claim 1 based on neighborhood information and gaussian filtering, it is special
Levy and be：
Wherein, the preferred 3*3 neighborhoods of the m*n neighborhoods.
6. the CBCT panorama sketch sharpening enhancement methods according to claim 1 based on neighborhood information and gaussian filtering, it is special
Levy and be：
Wherein, the scopes of K values described in step 5 are 1~4.
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| CN106911904B (en) * | 2015-12-17 | 2020-04-21 | 通用电气公司 | Image processing method, image processing system and imaging system |
| CN105894444B (en) * | 2016-03-31 | 2018-12-21 | 深圳市菲森科技有限公司 | A kind of method and device based on CBCT video generation panoramic dental image |
| 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 |
| 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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| CN103391392A (en) * | 2012-05-11 | 2013-11-13 | 索尼公司 | Image enhancement apparatus and method |
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| JP5537164B2 (en) * | 2010-01-08 | 2014-07-02 | 株式会社日立メディコ | Image reconstruction apparatus and X-ray CT apparatus |
| CN103391392A (en) * | 2012-05-11 | 2013-11-13 | 索尼公司 | Image enhancement apparatus and method |
| CN103473748A (en) * | 2013-09-23 | 2013-12-25 | 中国矿业大学(北京) | Method for enhancing underground coal mine image |
| CN103530854A (en) * | 2013-10-15 | 2014-01-22 | 南通市牧井微电科技发展有限公司 | Spatial-domain filtering-based CBCT (Cone Beam Computed Tomography) panoramic image sharpening method |
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