CN102456222A - Method and device for organized equalization in image - Google Patents
Method and device for organized equalization in image Download PDFInfo
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- CN102456222A CN102456222A CN2010105290341A CN201010529034A CN102456222A CN 102456222 A CN102456222 A CN 102456222A CN 2010105290341 A CN2010105290341 A CN 2010105290341A CN 201010529034 A CN201010529034 A CN 201010529034A CN 102456222 A CN102456222 A CN 102456222A
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Abstract
The invention discloses a method and a device for an organized equalization in an image, wherein the method comprises the following steps of: calculating a mask image of an image to obtain the local grey level distribution of each pixel point, and obtaining the equalization degree parameters of the whole image; automatically setting the equalization degree coefficient of each pixel point in the image based on the mask image and the equalization degree parameters of the whole image; and performing an equalization calculation on each pixel point in the image based on the mask image and the equalization degree coefficient of each pixel point in the image to obtain the equalized image. Each pixel point can be processed by different equalization degree parameters, and the local contrast of the equalized image can be enhanced by a contrast amplification coefficient to adequately keep the original information of the image via the method and the device for an organized equalization in an image disclosed by the invention.
Description
Technical field
The present invention relates to a kind of DR (Digital Radiography, Direct Digital X ray) field of diagnostic imaging, organize the method and apparatus of equalizing self-adapting in particular a kind of image.
Background technology
Image possibly occur that X ray is under-exposed or over-exposed etc. to cause image local to cross bright or dark excessively situation in the DR imaging, thereby causes texture unclear, the influence diagnosis.Therefore; We add tissue in the DR Flame Image Process balanced, suitably regulates crossing bright or dark excessively part in the image, keeping its original characteristic, do not adding under the prerequisite of noise; Make its suitable deepening or brighten, finally reach in image can clear demonstration each several part institutional framework purpose.
Usually the brightness histogram of organizing the equalization algorithm great majority to be based on image carries out equilibrium, and it is to adopt adaptive histogram homogenization that method is further arranged.These class methods are only considered image histogram, do not consider the local message contrast, lose the image local contrast information easily.The part document adopts the method for brightness mapping, and entire image is carried out the linear or nonlinear mapping of brightness, and these class methods and similar based on histogram method are not considered local contrast information, and the variation in organizing balancing procedure causes information dropout.Prior art gives based on definite figure image intensifying backoff weight such as image exposure parameter, generates mask images based on penalty coefficient and the brightness of image pixel point, and original image obtains to strengthen the back image divided by mask images.This method depends on the input exposure parameter and generates weight, and does not consider the local detail of image, may the lost part image information.Adopt multiple dimensioned method that image is carried out squelch and enhancing in addition in addition, but this method more complicated rely on bigger to parameter.
Therefore, prior art awaits to improve and development.
Summary of the invention
The object of the present invention is to provide the balanced method and apparatus of self-adaptation tissue in a kind of image, be intended to solve the existing equalization methods of organizing when image is carried out equilibrium treatment, do not consider the local message contrast, the problem of losing the image local contrast information easily.
Technical scheme of the present invention is following:
The self-adaptation of imaging system provided by the invention organizes equalization methods to comprise following concrete steps:
Step S1: obtain image through image capturing system;
Step S2: extract area-of-interest in the image, as the balanced input picture of tissue;
It specifically comprises based on image segmentation and removes invalid image-region data, and carries out processing such as brightness mapping to area-of-interest.
Step S3: whether need organize equilibrium based on the input picture adaptive judgement, need then execution in step S4, not need then execution in step S5;
Step S4: obtain and organize the balanced intensity parameter; Based on this parameter entire image being carried out equalization handles; Adopt equalization in various degree to handle to the bright dark areas of difference simultaneously, and contrast strengthen to image local based on the equalization degree, by the time the image after the equalization;
Step S5: image is carried out the adjustment of window width and window level self-adaptation, and output image.
The balanced device of self-adaptation tissue comprises in the image that the embodiment of the invention provides: image collection module, image processing module, image organizational balance module, window width and window level self-adaptation adjusting module, image display, image PACS and print module.Said image PACS is Picture Archiving and Communication Systems, image filing and communication system.Said image collection module, image processing module, the image organizational balance module, window width and window level self-adaptation adjusting module connects successively, and said window width and window level self-adaptation adjusting module connects image display respectively, and image PACS transmits and print module.
Beneficial effect of the present invention: the present invention organizes equalization methods and device through proposing a kind of brand-new self-adaptation; Whether need organize equilibrium treatment based on the input picture adaptive judgement; And the required balanced intensity parameter alpha of organizing; Based on this parameter entire image is carried out equalization; And adopt different balanced intensity parameters to handle to different bright dark areas, adopt the contrast magnificationfactor that the image local contrast is strengthened based on the equalization degree simultaneously, fully keep the original information of image.
Description of drawings
Fig. 1 is the theory diagram of the device that embodiment provides among the present invention;
Fig. 2 is the theory diagram of the image organizational balance module among Fig. 1;
Fig. 3 is the process flow diagram of organizing equalization methods that embodiment provides among the present invention;
What Fig. 4 was that embodiment provides among the present invention carries out balanced Calculation Method process flow diagram to image;
Fig. 5 is the balanced intensity function curve diagram that embodiment provides among the present invention;
Fig. 6 is the squelch function curve diagram that embodiment provides among the present invention.
Embodiment
For making the object of the invention, technical scheme and advantage clearer, clear and definite, below develop simultaneously embodiment to further explain of the present invention with reference to accompanying drawing.
See also Fig. 1; Organize the device of equalizing self-adapting to comprise in the image that the embodiment of the invention provides: image collection module, image processing module, image organizational balance module; Window width and window level self-adaptation adjusting module; Image display, image PACS (Picture Archiving and Communication Systems, image filing and communication system) and print module.Said image collection module, image processing module, image organizational balance module, window width and window level self-adaptation adjusting module connect successively, and said window width and window level self-adaptation adjusting module connects image display, image PACS and print module respectively.
Image collection module is used to obtain view data, and image processing module is used for removing based on image segmentation the data of invalid image-region, and carries out processing such as brightness mapping to area-of-interest.The appointed area that said area-of-interest will be observed by the doctor.Said image organizational balance module makes in same window width, window position, have more AP information directly to appear through organizing equalization algorithm that each several part tissue in the image of input is carried out optimizing and revising of brightness.Said window width and window level self-adaptation adjusting module is used for image is carried out the adjustment of adaptive window width, window position, then image is transferred to image display and shows, perhaps image is sent to image PACS and print module and files and print.
See also Fig. 2, said image organizational balance module specifically comprises: be used for a mask images of computed image, obtain the mask images computing module of the local gray level distribution of each pixel; Be used to obtain the entire image balanced intensity parameter determination module of the balanced intensity parameter of entire image; Be used for equalization coefficients calculation block based on the balanced intensity coefficient of each pixel of balanced intensity parameter automatic setting image of mask images and entire image; Be used for each pixel in the image being carried out equilibrium and calculate, and carry out the image organizational equalization computing module that local contrast strengthens to organizing the image after the equilibrium through the contrast amplification coefficient based on the balanced intensity coefficient of mask images and each pixel of image; Be used to set the object brightness determination module of the object brightness of image; Be used to calculate the gradation of image calculating module of the preceding gradation of image value of equalization.
Wherein, said mask images computing module is connected the equalization coefficients calculation block with entire image balanced intensity parameter determination module; Said equalization coefficients calculation block connects image organizational equalization computing module; Said gradation of image calculates module and connects image organizational equalization computing module.
Said image organizational balance module also comprises: be used for the contrast amplification coefficient computing module based on the contrast amplification coefficient of each pixel of image calculation image before mask images and the equalization; Wherein, said mask images computing module, equalization coefficients calculation block and gradation of image calculating module all are connected contrast amplification coefficient computing module; Said contrast amplification coefficient computing module connects image organizational equalization computing module.
The balanced intensity parameter of the entire image in above-mentioned entire image balanced intensity parameter determination module is through predefined, not through system's automatic setting.When needs passed through the balanced intensity parameter of system's automatic setting entire image, above-mentioned image organizational balance module also comprised: the homogeneous metric parameter computing module that is used to calculate the homogeneous metric parameter.Wherein, said homogeneous metric parameter computing module input end connects the mask images computing module, and output terminal connects entire image balanced intensity parameter determination module.At this moment, described entire image balanced intensity parameter determination module is according to the balanced intensity parameter of homogeneous metric parameter automatic setting entire image.
See also Fig. 3, the method for equalizing self-adapting of organizing provided by the invention comprises following concrete steps:
Step S1: obtain image through image capturing system;
Step S2: extract area-of-interest in the image, as the balanced input picture of tissue;
It specifically comprises based on image segmentation and removes invalid image-region data, and carries out processing such as brightness mapping to area-of-interest.
Step S3: whether need organize equilibrium based on the input picture adaptive judgement, need then execution in step S4, not need then execution in step S5;
Step S4: obtain the balanced intensity coefficient of each pixel in the image, the equalization that each pixel in the image carries out is in various degree handled, obtain the image after the equalization based on this coefficient;
Step S5: the image after the output organization equilibrium.
See also Fig. 4, step S4 specifically may further comprise the steps:
Step S100: a mask images of computed image, the local gray level that obtains each pixel distributes, and obtains the balanced intensity parameter of entire image;
A mask images of computed image is come the interior intensity profile of regional area (being neighborhood) of presentation video: M, and (x is the intensity profile in image local zone y), can be the intermediate value in a n * n mask, perhaps average; Same this mask also can be the low scalogram picture behind the multiple dimensioned down-sampling of image.
Step S200: based on the balanced intensity coefficient of each pixel in the balanced intensity parameter automatic setting image of mask images and entire image;
Step S300: based on the balanced intensity parameter of each pixel in mask images and the image each pixel in the image is carried out equilibrium and calculate, obtain the image after the equalization;
Step S400:, and strengthen organizing image after the equilibrium to carry out local contrast through this coefficient based on the contrast amplification coefficient of each pixel in the image calculation image before mask images and the equalization.
For step S100, the balanced intensity parameter of said entire image is through preestablishing in system, or system is based on homogeneous metric parameter automatic setting.
Definite method of said homogeneous metric parameter is:
Step a100: the average of all local intensity profile was called gray scale row average during each was gone in the calculating mask images;
Step a200: each pixel in the image is calculated the gray variance in its neighborhood, and the average of obtaining all gray variances in each row of image according to gray variance is called variance row average;
The local gray level that calculates each pixel distribute with gray variance after, (x, y) (x, the average of y) obtaining all local intensity profile in each row is called gray scale row average, remembers and makes M with gray variance S according to local gray level distribution M
Mid(x, y), the average of obtaining all gray variances in each row is called variance row average, and note is made S
Mid(x, y).
The average of obtaining all local intensity profile in each row is called gray scale row average, is equivalent to entire image is carried out smoothing processing with behavior unit.For example for the image of a N*N size, the average gray value of each pixel is M in its certain delegation
1(x, y), M
2(x, y), M
3(x, y) ..., M
N(x, y), the average M of all pixels in this row
Mid(x y) is: [M
1(x, y)+...+M
N(x, y)]/N.
Step a300: gray scale row average maximum in the entire image is deducted minimum gray scale row average, draw the difference of the gray average of entire image; All variance row averages are asked on average, drawn the average variance of entire image;
Utilization deducts minimum gray scale row average with gray scale row average maximum in the entire image, draws the difference of the gray average of entire image, and note is made Δ M
Mid, and to all variance row average S
Mid(x y) asks average, draws the average variance of entire image, and note is done
Step a400: the homogeneous metric parameter that the difference of the gray-scale value of entire image is drawn image than the average variance of last entire image.
Difference DELTA M with the gray-scale value of entire image
MidAverage variance than last entire image
Draw the homogeneous metric parameter A of image, promptly
Image all once parameter can be the variance of integral image average divided by image; Can also be that maximal value deducts the average of minimum value divided by local variance in the image local average, all can based on the representation of image local average and variance.
The equalization algorithm of organizing provided by the invention can be shown following formula by simple table:
g(xy)＝α[DM-M(x，y)]+M(x，y)+[I(x，y)-M(x，y)]×β...............(1)
Wherein, (x y) is certain any gray-scale value in the image after handling to g; (x y) is certain any gray-scale value in the image before handling to I; DM is an object brightness, can be set at the gray average of entire image, perhaps the function of average; (x is that the regional bright dark distribution of image local is that local gray level distributes y) to M, can be the intermediate value in a n * n mask, perhaps average; α is that to organize the balanced intensity parameter be the equalization coefficient, and it obtains big more, and the gray scale of entire image is just leaned on closely more to DM, can make that just dark place is brighter, and bright place is darker; β is the contrast amplification coefficient, and its meaning is to increase in right amount local contrast, the loss of the detail textures of bringing during compensating equalization.
When β gets 1, promptly the local contrast of image is not strengthened, the expression formula of this moment is following:
g(x，y)＝α[DM-M(x，y)]+I(x，y)。
Formula (1) has good reinforced effects for uneven image.But its shortcoming is that the contrast magnificationfactor is non-adjustable in contrast enhancing process, causes in overall enhanced, can not finely tune to topography.In order to address the above problem, need adjust above-mentioned algorithm.
That organizes balanced intensity parameter alpha and contrast magnificationfactor confirms it is the core of whole algorithm, the balanced intensity that different parametric representations is different.These two parameters all are set to constant in the existing technology, and promptly whole compression factor is identical.The present invention's image local characteristic of giving chapter and verse is carried out the weight that self-adaptation is regulated partial equilibriumization.Simultaneously before image is organized equilibrium, calculate the index of organizing balanced intensity in the image to entire image, i.e. the homogeneous metric parameter A of image organizes the balanced intensity parameter alpha based on what this parameter A was set entire image.
Draw the homogeneous metric parameter A of image through said method after, this parameter A promptly capable of using is confirmed the maximal value α that organizes the balanced intensity parameter alpha of image
MaxIt is the balanced intensity parameter of entire image.Referring to Fig. 5, α in the present invention
MaxBe the power function form of parameter A, note is made α
Max=h (A), wherein balanced intensity function h is a power function, its codomain be [0, C
D], C
DExpression is maximum organizes the balanced intensity parameter, can be arbitrary small number between 0 to 1.
For different luminance areas are adopted equilibrium treatment in various degree, then according to local mean value M (x, y) and the self-adaptation that concerns between object brightness DM confirm the balanced intensity parameter alpha of each pixel, the formula of its adaptive approach is following:
α(x，y)＝f(M(x，y)-DM)·α
max
Wherein, (x y) is the balanced intensity of zones of different in the image to α; ((x y)-DM) is a codomain in [0,1] in interval function is that (x y) confirms with object brightness DM by image local intensity profile M to M to f; α
MaxIt is the maximal value of organizing the balanced intensity parameter of image.
F (M (x y)-DM) is a codomain in [0,1] in interval function, and be that (x y) confirms with object brightness DM by image local intensity profile M.Present embodiment provides following two kinds of functional forms, for example:
Perhaps f (M (and x, y)-DM)=1-e
-λ | and M (x, y)-DM|,
Wherein λ is enough big constant.
In the image organizational equilibrium, the equalization extent index is big more, and the loss of detail textures is just many more, so the local contrast that adopts parameter beta to come enlarged image.Contrast magnificationfactor and α (x, function y) is correlated with, and definite formula of β is:
β＝1+φ(α)×w
Wherein, φ (α) may be defined as:
C
βExpression β handles coefficient, and this coefficient is a positive number.Because α >=0, so φ (α) >=0, promptly φ (α) * w is expressed as has a weighting (β=1 o'clock represent that local contrast is constant) to incremental portion.Wherein, squelch function w may be defined as:
w＝Ψ(I(x，y)，M(x，y))，
And the codomain of w is [0,1], and promptly w is directly proportional with local contrast.W is illustrated in has only the detail textures part just need amplify contrast in the topography, smooth processes hardly.Be illustrated in figure 3 as a kind of value curve synoptic diagram of the w that present embodiment provides.W=Ψ (I (x, y), the obtaining value method of M (x, y)) function can be the following several kinds of arbitrary forms that similarly satisfy definite condition:
Or w=Ψ (I (x, y), M (x, y))=1-e
-λ | f (x, y)-M (x, y) |,
Wherein λ is enough big constant.
Through the above-mentioned equilibrium treatment of organizing; Make brightness of image integral body to object brightness DM near; The overall dynamic range of downscaled images makes and under certain window width and window level, can see more effective informations, and the part contrast enhancing based on equalizing coefficient simultaneously can guarantee local grain information.
Utilization at last organizes balanced intensity parameter alpha and contrast magnificationfactor that image is organized equilibrium, and adopts the contrast amplification coefficient that the image local contrast is strengthened based on the equalization degree simultaneously, fully keeps the original information of image.
Should be understood that application of the present invention is not limited to above-mentioned giving an example, concerning those of ordinary skills, can improve or conversion that all these improvement and conversion all should belong to the protection domain of accompanying claims of the present invention according to above-mentioned explanation.The present invention not only is applied to the DR image, also can be applied to the brightness of other any images, the equilibrium treatment of color.
Claims (24)
1. organize balanced method in an image, it is characterized in that, may further comprise the steps:
S1: obtain image through image capturing system;
S4: obtain the balanced intensity coefficient of each pixel in the image, the equalization that each pixel in the image carries out is in various degree handled, obtain the image after the equalization based on this coefficient;
S5: the image after the output organization equilibrium;
Wherein, said step S4 specifically comprises:
A mask images of computed image, the local gray level that obtains each pixel distributes, and obtains the balanced intensity parameter of entire image;
Balanced intensity coefficient based on each pixel in the balanced intensity parameter automatic setting image of mask images and entire image;
Based on the balanced intensity coefficient of each pixel in mask images and the image each pixel in the image is carried out equilibrium and calculate, obtain the image after the equalization.
2. the balanced method of tissue in the image according to claim 1 is characterized in that said method also comprises:
S2: extract area-of-interest in the image, as the balanced input picture of tissue.
3. the balanced method of tissue in the image according to claim 1 and 2 is characterized in that said method also comprises:
S3: image is carried out adaptive judgement, and whether decision need organize equilibrium, is execution in step S4 then, otherwise execution in step S5.
4. the balanced method of tissue is characterized in that in the image according to claim 1, and said step S5 also comprises the image after the equalization is carried out the adjustment of window width and window level self-adaptation.
5. the balanced method of tissue in the image according to claim 1 is characterized in that said step S4 also comprises:
Based on the contrast amplification coefficient of each pixel in the image calculation image before mask images and the equalization, and strengthen organizing image after the equilibrium to carry out local contrast through this coefficient.
6. according to the balanced method of tissue in claim 1 or the 5 described images, it is characterized in that the balanced intensity parameter of said entire image is through preestablishing in system.
7. the balanced method of tissue is characterized in that the balanced intensity parameter of said entire image is that system is based on homogeneous metric parameter automatic setting in the image according to claim 1.
8. the balanced method of tissue is characterized in that the balanced intensity parameter of said entire image is that system passes through based on homogeneous metric parameter automatic setting in the image according to claim 5.
9. according to the balanced method of tissue in claim 7 or the 8 described images, it is characterized in that definite method of said homogeneous metric parameter is:
The average of all local intensity profile was called gray scale row average during each was gone in the calculating mask images;
Each pixel in the image is calculated the gray variance in its neighborhood, and the average of obtaining all gray variances in each row of image according to gray variance is called variance row average;
Gray scale row average maximum in the entire image is deducted minimum gray scale row average, draw the difference of the gray average of entire image; To all variance row averages ask average, draw the average variance of entire image;
The difference of the gray-scale value of entire image is drawn the homogeneous metric parameter of image than the average variance of last entire image.
10. according to the balanced method of tissue in claim 7 or the 8 described images, it is characterized in that definite method of said homogeneous metric parameter is: with the average of integral image variance divided by integral image.
11., it is characterized in that the method for said balanced intensity parameter based on homogeneous metric parameter automatic setting entire image is according to the balanced method of tissue in claim 7 or the 8 described images:
α
max＝h(A)
Wherein, α
MaxBe the balanced intensity parameter of entire image, function h is a power function, its codomain be [0, C
D], C
DExpression is maximum organizes the balanced intensity parameter, is arbitrary small number between 0 to 1.
12. the balanced method of tissue in the image according to claim 1 is characterized in that said method based on the balanced intensity coefficient of each pixel in the balanced intensity parameter automatic setting image of mask images and entire image is:
α(x，y)＝f(M(x，y)-DM)·α
max
Wherein, (x y) is the balanced intensity coefficient of each pixel in the image to α; ((function that x y)-DM) is a codomain in [0,1] in interval is that (x is y) with the object brightness DM decision of image by image local intensity profile M to M to f; α
MaxIt is the balanced intensity parameter of entire image.
13. the balanced method of tissue is characterized in that in the image according to claim 12, said function f (M (x, the formula that embodies y)-DM) is:
Wherein, (x y) is the image local intensity profile to M; DM is the object brightness of image; λ is enough big constant.
14. the balanced method of tissue in the image according to claim 1; It is characterized in that; A mask images of said computed image; The local gray level that obtains each pixel distributes, and wherein the local gray level of each pixel is distributed as the intermediate value of this pixel all gray values of pixel points in the neighborhood of its n*n.
15. the balanced method of tissue in the image according to claim 1; It is characterized in that; A mask images of said computed image; The local gray level that obtains each pixel distributes, and wherein the local gray level of each pixel is distributed as the average of this pixel all gray values of pixel points in the neighborhood of its n*n.
16. the balanced method of tissue is characterized in that in the image according to claim 1, said balanced intensity coefficient based on each pixel in mask images and the image carries out the balanced formula that calculates to each pixel in the image and is:
g(x，y)＝α[DM-M(x，y)]+I(x，y)
Wherein, (x y) is certain any gray-scale value in the image after equalization is handled to g; (x y) is certain any gray-scale value in the image before equalization is handled to I; DM is an object brightness; (x y) is the image local intensity profile to M; α is the balanced intensity coefficient of each pixel in the image.
17. the balanced method of tissue in the image according to claim 5 is characterized in that said method based on the contrast amplification coefficient of each pixel in the image calculation image before mask images and the equalization is:
β＝1+φ(α)×w
Wherein, β is the contrast amplification coefficient of each pixel in the image; φ (α) is defined as:
C
βThe processing coefficient of expression β is positive number, and φ (α)>=0; Function w is defined as: w=Ψ (I (x, y), M (x, y)), and the codomain of w is 0 to 1.
18. the balanced method of tissue in the image according to claim 17 is characterized in that the formula that embodies of said function w is:
Wherein, (x y) is the image local intensity profile to M; (x y) is certain any gray-scale value in the image before equalization is handled to I; λ is enough big constant.
19. the balanced method of tissue is characterized in that in the image according to claim 5, saidly through the contrast amplification coefficient to organizing image after the equilibrium to carry out the method that local contrast strengthens is:
g(x，y)＝α[DM-M(x，y)]+M(x，y)+[I(x，y)-M(x，y)]×β
Wherein, (x y) is certain any gray-scale value in the image after equalization is handled to g; (x y) is certain any gray-scale value in the image before equalization is handled to I; DM is an object brightness; (x y) is the image local intensity profile to M; α is the balanced intensity coefficient of each pixel in the image; β is the contrast amplification coefficient of each pixel in the image.
20. the balanced device of tissue is characterized in that in the image, comprising:
Be used to obtain the image collection module of view data;
Be used for adopting equalization in various degree to handle each pixel in the image of input based on its neighborhood information, obtain the image after the equalization through organizing equalization algorithm and organizing the balanced intensity coefficient, the image organizational balance module;
Be used to show, transmit the perhaps image output module of print image;
Wherein, said image collection module, image organizational balance module, image output module connect successively;
Said image organizational balance module specifically comprises:
A mask images that is used for computed image obtains the mask images computing module that the local gray level of each pixel distributes;
Be used to obtain the entire image balanced intensity parameter determination module of the balanced intensity parameter of entire image;
Be used for equalization coefficients calculation block based on the balanced intensity coefficient of each pixel of balanced intensity parameter automatic setting image of mask images and entire image;
Be used for each pixel in the image being carried out equilibrium and calculate, and carry out the image organizational equalization computing module that local contrast strengthens to organizing the image after the equilibrium through the contrast amplification coefficient based on the balanced intensity coefficient of mask images and each pixel of image;
Be used to set the object brightness determination module of the object brightness of image;
Be used to calculate the gradation of image calculating module of the preceding gradation of image value of equalization,
Wherein, said mask images computing module is connected the equalization coefficients calculation block with entire image balanced intensity parameter determination module; Said equalization coefficients calculation block connects image organizational equalization computing module; Said gradation of image calculates module and connects image organizational equalization computing module.
21. the balanced device of tissue in the image according to claim 20; It is characterized in that; Said device also comprises: be used for removing invalid image-region data based on image segmentation; And be directed against the process image processing module that area-of-interest carries out the brightness mapping treatment, and the input end of said image processing module connects image collection module, and output terminal connects the image organizational balance module.
22. the balanced device of tissue in the image according to claim 20 is characterized in that said device also comprises:
Be used for image is presented at the image display on the screen;
Be used for image is filed and images printed PACS and print module,
Wherein, said image display and image PACS and print module are connected to window width and window level self-adaptation adjusting module respectively.
23. the balanced device of tissue in the image according to claim 20 is characterized in that said image organizational balance module also comprises:
Be used for contrast amplification coefficient computing module based on the contrast amplification coefficient of each pixel of image calculation image before mask images and the equalization,
Wherein, said mask images computing module, equalization coefficients calculation block and gradation of image calculating module all are connected contrast amplification coefficient computing module; Said contrast amplification coefficient computing module connects image organizational equalization computing module.
24. the balanced device of tissue in the image according to claim 23 is characterized in that said image organizational balance module also comprises:
Be used to calculate the homogeneous metric parameter computing module of homogeneous metric parameter,
Wherein, said homogeneous metric parameter computing module input end connects the mask images computing module, and output terminal connects entire image balanced intensity parameter determination module.
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| CN104243824A (en) * | 2014-09-19 | 2014-12-24 | 深圳市安健科技有限公司 | Method and system for nonlinearly adjusting image brightness |
| CN104574361A (en) * | 2014-11-27 | 2015-04-29 | 沈阳东软医疗系统有限公司 | Image processing method and device for mammary peripheral tissue equalization |
| CN105046674A (en) * | 2015-07-14 | 2015-11-11 | 中国科学院电子学研究所 | Nonuniformity correction method of multi-pixel parallel scanning infrared CCD images |
| CN104091309B (en) * | 2014-06-19 | 2017-02-15 | 华南理工大学 | Balanced display method and system for flat-plate X-ray image |
| CN108242049A (en) * | 2016-12-26 | 2018-07-03 | 河北天地智慧医疗设备股份有限公司 | A kind of full size DR Imaging enhanced processing methods for GPU optimizations |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102982536A (en) * | 2012-11-05 | 2013-03-20 | 华为技术有限公司 | Image processing method and device |
| WO2014067494A1 (en) * | 2012-11-05 | 2014-05-08 | 华为技术有限公司 | Method and device for processing image |
| CN102982536B (en) * | 2012-11-05 | 2015-07-22 | 华为技术有限公司 | Image processing method and device |
| CN104091309B (en) * | 2014-06-19 | 2017-02-15 | 华南理工大学 | Balanced display method and system for flat-plate X-ray image |
| CN104243824A (en) * | 2014-09-19 | 2014-12-24 | 深圳市安健科技有限公司 | Method and system for nonlinearly adjusting image brightness |
| CN104574361A (en) * | 2014-11-27 | 2015-04-29 | 沈阳东软医疗系统有限公司 | Image processing method and device for mammary peripheral tissue equalization |
| CN104574361B (en) * | 2014-11-27 | 2017-12-29 | 沈阳东软医疗系统有限公司 | A kind of mammary gland peripheral tissues balanced image processing method and device |
| CN105046674A (en) * | 2015-07-14 | 2015-11-11 | 中国科学院电子学研究所 | Nonuniformity correction method of multi-pixel parallel scanning infrared CCD images |
| CN108242049A (en) * | 2016-12-26 | 2018-07-03 | 河北天地智慧医疗设备股份有限公司 | A kind of full size DR Imaging enhanced processing methods for GPU optimizations |
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