CN106485665B - A kind of low dosage DR image processing method and its device - Google Patents
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Abstract
The invention discloses a kind of low dosage DR image processing method and its devices, which comprises noise of detector inhibits；Organizational information compartment equalization and multipole fusion；Image enhancement noise reduction.Described device includes noise of detector suppression module, the processing of organizational information compartment equalization and multipole Fusion Module, image enhancement noise reduction module.The present invention uses a kind of low dosage DR image processing method, can effectively reduce picture noise when exposing using low dosage condition, make full use of display space to promote tissue contrast's difference, enhance the display capabilities of textual details, promote picture quality.It is particularly suitable for being promoted the picture quality when shooting of infant's low dosage.
Description
Technical field
The present invention relates to X-ray digital image processing technology fields, more particularly to a kind of low dosage DR image processing method
Method, and the low dosage DR image processing apparatus realized using this method.
Background technique
DR is the digitized image equipment that X-ray photographic is carried out using digital flat-panel detector.With common X-ray image documentation equipment phase
Than the advantages that big with high resolution, dynamic range, dosage is low, and organizational information enriches.X-ray is from bulb when being shot
It sets out, is received after tissue by flat panel detector, and be converted into digital signal, form digitized video.Number at this time
Although image includes the necessary information for clinical examination, its organisational level difference is small, and noise is big, can not be directly used in
Clinical diagnosis.Increase organisational level information, a kind of most direct mode for reducing noise is exactly to increase exposure dose, and exposure dose
Increase bigger radiation injury will necessarily be brought to patient.When especially infant shoots, since its histoorgan is not yet sent out
It educates completely, radiation injury injures it particularly evident.Common method is to radiate it in picture quality and patient in clinic at present
Between weighed, finally obtain one under exposure dose appropriate obtain be used for clinical diagnosis DR image.
Image enhancement is generallyd use to the processing of DR image in current product and adds image noise reduction, and then reaches and is enhancing
Inhibit the noise of image while textual details.Such processing mode can obtain preferable processing effect under exposure dose appropriate
Fruit, but with the reduction of exposure dose, treatment effect also will worse and worse.
Summary of the invention
Aiming at the defects existing in the prior art, the object of the present invention is to provide at a kind of low dosage DR image
Reason method, this method can effectively handle the DR image under low dosage；And provide a kind of low dosage realized using this method
DR image processing apparatus.
Present invention technical solution used for the above purpose is: a kind of low dosage DR image processing method, including
Following steps:
Noise of detector inhibits: creation noise of detector sharing data table and detector signal a reference value utilize detector
Noise sharing data table and detector signal a reference value inhibit the noise of detector component in image；
Organizational information compartment equalization and multipole fusion: the discrete extreme value point of image after compacting noise of detector inhibits, then
It carries out multi-layer compartmentalization image reconstruction and image co-registration and multipole is organized to merge；
Image enhancement noise reduction: carrying out enhancing processing to the detailed information in image, and reduce the noise in image, generates most
Whole image.
The creation noise of detector sharing data table, specifically:
Acquire N1 group detector base data: D10、D11、……、D1N1-1, N1 is artificial preset value；
Generate noise sharing data table:
The creation detector signal a reference value, specifically:
Acquire N2 group detector base data: D20、D21、……、D2N2-1, N2 is artificial preset value；
Generate detector signal a reference value:
Image after the noise of detector inhibits are as follows:
Wherein, IoriFor initial pictures, i.e. noise of detector inhibits object, DmapFor noise sharing data table, DbaseTo visit
Device signal criterion value is surveyed, N1 is the group number of detector base data in noise sharing data table.
The discrete extreme value point of image after the compacting noise of detector inhibits, comprising the following steps:
Image carries out statistics with histogram after inhibiting to noise of detector；
With calculating extreme point threshold value according to statistics with histogram result:
Tdh=imax×rh
Wherein, imaxFor image pixel point value maximum in statistics with histogram result, rhRatio is counted for default extreme point；
In statistics with histogram result, to gray value i >=TdhValue sum, and if value be more than or equal to preset threshold,
Then it is determined as extreme point without exception；Otherwise carry out extreme point compression processing, i.e., by image between TdhAnd imaxBetween pixel
Point is compressed to TdhWith preset value idstBetween.
The multi-layer compartmentalization image reconstruction and image co-registration the following steps are included:
According to the dimension of preset current the included isolated area of level and the dimension of plate pixel, each is calculated
The corresponding plate pixel dimension of isolated area；According to preset bigoted degree, horizontal and vertical two dimensions to isolated area into
Line displacement forms bigoted region；
Space stretching is carried out to each level isolated area and bigoted region；
Create same level isolated area and bigoted region blend curve；
Same level isolated area is carried out to merge with bigoted region；
Image co-registration between progress level.
The space stretch the following steps are included:
Pixel value mapping function is created first:
Wherein, j I0The gray value of middle pixel, njThe pixel for being j for grey scale pixel value in isolated area and bigoted region
Number, N is the pixel number that isolated area and bigoted region include, idstTo preset gray value, iupFor maximum regional value tune
The whole upper limit, idnLower limit, the pixel number that N is isolated area and bigoted region includes are adjusted for maximum regional value；
By function f by same I0Grey scale pixel value in corresponding isolated area and bigoted region is adjusted to f (j), I from j0
For the image exported after compacting discrete extreme value point.
Tissue multipole fusion the following steps are included:
Create tissue weighting curve:
Wherein, i is that corresponding gray value is organized in image, and c is tracing pattern regulation coefficient；
Using tissue weighting curve lut (i) by the image I after noise of detector inhibitstIt is empty with organizational information is passed through
Between image I after equilibrium0It is merged:
I (r, c)=lut (It(r,c))×It(r,c)+(1-lut(It(r,c)))×I0(r,c)
Wherein, r is the row serial number where pixel, and c is the column serial number where pixel；
It will be by treated image I (r, c) with the image I after noise of detector inhibits0(r, c) carries out line
Property fusion:
Im(r, c)=wlin×I(r,c)+(1-wlin)×I0(r,c)
Wherein, ImFor spatial information Tissue Equalization Techniques and the fused image of multipole, wlinFor the weight coefficient of linear fusion.
The detailed information in image carries out enhancing processing, comprising the following steps:
By N grades of iterative filterings from ImIn extract the smoothed image of N kind rankWith it is corresponding
Detail pictures
The substrate curve of details enhancing curve is created based on organizing weighting curve:
lutenhb(i)=lut (i) * (max (lut (i))-lut (i))
Wherein, lut (i) is tissue weighting curve；
Corresponding curved section on the substrate curve of details enhancing curve, line amplitude of going forward side by side adjustment are taken according to actual needs
Enhancing curve lut is generated afterwardsenh(i)；
Utilize enhancing curve lutenh(i) and smoothed imageIn grayscale information to textual details
Enhanced.To detail picturesThe enhancing of details iteration is carried out, the enhanced image I of details is generatedenh。
Noise in the reduction image, comprising the following steps:
Carry out the form of noise analysis: to smoothed imagePicture noise morphological analysis is carried out, and creates noise suppressed song
Line:
Wherein, i is smoothed imageMiddle to organize corresponding gray value, c is tracing pattern regulation coefficient；
According to analyze come the form of noise carry out noise suppressed: using noise suppression curve lutnoisp(i) details is increased
Image I after strongenhImage denoising is carried out, final image is generated.
A kind of low dosage DR image processing apparatus, comprising:
Noise of detector suppression module: for creating noise of detector sharing data table and detector signal a reference value, benefit
Inhibit the noise of detector component in image with noise of detector sharing data table and detector signal a reference value；
Organizational information compartment equalization and multipole Fusion Module: for suppressing the discrete extreme value of image after noise of detector inhibits
Point carries out multi-layer compartmentalization image reconstruction and image co-registration and multipole is organized to merge；
Image enhancement noise reduction module: for carrying out enhancing processing to the detailed information in tissue multipole fused image, and
The noise in image is reduced, final image is generated.
The noise of detector suppression module includes:
Noise Sharing model creating unit: for creating noise of detector sharing data table；
Signal criterion value creating unit: for creating detector signal a reference value；
Noise of detector inhibits unit: for being inhibited using noise of detector sharing data table and detector signal a reference value
Noise of detector component in image.
The organizational information compartment equalization and multipole Fusion Module include:
Discrete extreme value point compression unit: for carrying out statistics with histogram by image after inhibiting to noise of detector and calculating
Extreme point threshold value suppresses the discrete extreme value point in image；
Multi-layer compartmentalization image reconstruction unit: the pressed image of discrete extreme value point is carried out for hierarchical subregion
Reconstruct；
Multi-layer compartmentalization image fusion unit: the image for reconstructing to hierarchical subregion merges；
Organize multipole integrated unit: for carrying out multipole fusion to fused image.
Described image enhances noise reduction module
Image enhancing unit: enhance for the textual details to multipole fused image；
Image noise reduction unit: for carrying out noise reduction to enhanced image.
Method and apparatus of the present invention are by inhibiting noise of detector, organizational information compartment equalization, fusion, increasing
By force, the processing such as noise reduction reduces picture noise while promoting organisational level, and reduction low dosage condition shoots bring organized layer
The problem that secondary difference is unobvious and picture noise is big promotes the picture quality under low dosage shooting condition, is guaranteeing clinical diagnosis
While reduce patient's exposure dose.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is that noise of detector inhibits model product process block diagram；
Fig. 3 is the device of the invention block diagram；
Fig. 4 is that noise of detector inhibits flow diagram；
Fig. 5 is that extreme point suppresses flow diagram；
Fig. 6 is organizational information compartment equalization image product process block diagram；
Fig. 7 is that tissue multipole merges flow diagram；
Fig. 8 is details enhancing and noise suppressed flow diagram；
Fig. 9 is the DR image obtained using this method and device.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and embodiments.
Low dosage DR image processing method process of the present invention is as shown in Figure 1:
A. noise of detector inhibits；
B. organizational information compartment equalization and multipole fusion；
C. image enhancement noise reduction.
It needs first to create before carrying out noise of detector inhibition for carrying out model used in noise suppressed and a reference value,
Its process is as shown in Figure 2:
Step 201. acquires N1 group detector base data: D10、D11、……、D1N1-1。
Step 202. generates noise sharing data table:
Step 203. acquires N2 group detector base data: D20、D21、……、D2N2-1。
Step 204. generates detector signal benchmark:
After having created noise of detector sharing data table and signal criterion, noise of detector inhibition, algorithm flow are carried out
It is as shown in Figure 4:
It is I that step 401., which defines initial pictures,ori, to IoriNoise of detector inhibition is carried out, noise of detector is generated and inhibits
Image I afterwardsd:
It is as shown in Figure 5 that extreme point suppresses process.
Step 501. is to the image I after noise of detector inhibitiondStatistics with histogram is carried out, result Hist (i):
Hist (i)=Hist (i)+1, Id(r, c)=i
Step 502. calculates extreme point threshold value, takes maximum image pixel point value i in Hist (i)max, preset extreme point system
Count ratio rh, the product for defining the two is extreme point threshold value: Tdh=imax×rh。
Step 503. counts extreme point number, to i >=Td in Hist (i)hValue sum, and if value be more than or equal to it is pre-
If threshold value, then it is determined as extreme point without exception, otherwise executes step 504.
Step 504. extreme point compression, by image between TdhAnd imaxBetween pixel using linearly or nonlinearly pressing
It is reduced to TdhWith preset value idstBetween.
The image definition that step 503 and step 504 export are as follows: I0。
Organizational information compartment equalization image product process, i.e. multi-layer compartmentalization image reconstruction and image co-registration process are as schemed
Shown in 6.
Step 601. determines current level isolated area dimension and bigoted degree, includes independent zones according to preset current level
The dimension in domain and the dimension of plate pixel calculate the corresponding plate pixel dimension of each isolated area, phase between isolated area
It is adjacent but be not overlapped.According to preset bigoted degree, offset is carried out to isolated area in horizontal and vertical two dimensions and forms bigoted area
Domain.
Step 602. determines the space bound of region adjustment, is segmented according to maximum value in region and minimum value and determines region
Maximum regional value is adjusted upper limit i by the bound of adjustment, this methodupIt is divided into following 3 ranks:
1. maximum value is less than or equal to i in isolated area and bigoted regiondstWhen × 0.5, the maximum value adjustment upper limit is set as
idst×0.7；
2. maximum value is greater than i in isolated area and bigoted regiondst× 0.5, and it is less than or equal to idstIt, will most when × 0.7
The big value adjustment upper limit is set as idst×0.87；
3. isolated area and bigoted maximum regional value are greater than idstWhen × 0.7, the maximum value adjustment upper limit is set as idst。
Region minimum value is adjusted into lower limit idnIt is divided into following 3 ranks:
1. minimum value is more than or equal to i in isolated area and bigoted regiondstWhen × 0.7, it is by minimum value adjustment lower limit set
idst×0.5；
2. minimum value is less than i in isolated area and bigoted regiondst× 0.7, and it is more than or equal to idstWhen × 0.5, by it
It is i that minimum value, which adjusts lower limit set,dst×0.37；
3. isolated area and bigoted region minimum value are less than idstIt is 0 by minimum value adjustment lower limit set when × 0.5.
Step 603. carries out regional space stretching, according to the adjustment bound that step 602 determines, by isolated area and bigoted
Pixel in region carries out histogram equalization stretching.Pixel value mapping function is created first:
Wherein, j I0The gray value of middle pixel, njThe pixel for being j for grey scale pixel value in isolated area and bigoted region
Number, N is the pixel number that isolated area and bigoted region include.
By function f by same I0Grey scale pixel value in corresponding isolated area and bigoted region is adjusted to f (j) from j.
Step 604. creates isolated area and bigoted region blend curve, and blend curve is divided into horizontal blend curve and vertical
Blend curve.The preferred linear fusion curve of this method, form are as follows:
Wherein, l (k) is bigoted area pixel point fusion weighting curve, l'(k) be isolated area corresponding part pixel
Merge weighting curve, k0It is the isolated area initial position Chong Die with bigoted region, k1It is that isolated area is be overlapped with bigoted region
End position, k are current pixel point position, wupIt is the fusion weight upper limit, wdnIt is fusion weight lower limit.
Step 605. isolated area is merged with bigoted region, takes isolated area and the corresponding picture of bigoted region same position k
Vegetarian refreshments ps(k) and pd(k) and corresponding bigoted zone level merges weight lh(k), weight l is vertically merged in bigoted regionv(k),
Isolated area level merges weight l'h(k), isolated area vertically merges weight lv' (k), it is merged as follows:
P (k)=(pd(k)×lh(k)+ps(k)×l'h(k))×lv(k)+(pd(k)×lh(k)+ps(k)×l'h(k))×
lv'(k)
P (k) is fused image pixel value.Image after completion generates current level space after merging stretches, then
Step 601~605 successively are executed to other levels, generate image I after the space stretching of all levelss(i)。
Step 606. carries out image co-registration between level, and image co-registration takes the mode of weighted accumulation between level:
It=∑ w (i) × Is(i)
Wherein, ItFor fused image, w (i) is the weighted value for carrying out blending image, Is(i) it is stretched for each level space
Image afterwards, w (i) can be configured according to expected image effect.
Organize multipole fusion process as shown in Figure 7.
Step 701. creates tissue weighting curve, and this method creates tissue weighting curve in the following way:
Wherein, lut (i) is tissue weighting curve, and i is that corresponding gray value is organized in image, and c is tracing pattern adjustment system
Number.
Step 702. image co-registration carries out I using lut (i)tAnd I0Between image co-registration, such as following formula:
I (r, c)=lut (It(r,c))×It(r,c)+(1-lut(It(r,c)))×I0(r,c)
Step 703. linear fusion, by I (r, c) and I0(r, c) carries out linear fusion, such as following formula:
Im(r, c)=wlin×I(r,c)+(1-wlin)×I0(r,c)
Wherein, ImFor spatial information Tissue Equalization Techniques and the fused image of multipole, wlinFor the weight coefficient of linear fusion.
Details enhancing and noise suppressed process are as shown in Figure 8.
Step 801. extracts textual details and smoothed image, and the smoothed image of N kind rank is extracted by N grades of iterative filterings
With corresponding detail pictures, smoothed image is respectively defined asDetail pictures are respectively defined as
Step 802. creation details is competed curve, further creates details based on the curve of step 701 method creation
Enhance the substrate curve of curve, such as following formula:
lutenhb(i)=lut (i) * (max (lut (i))-lut (i))
Wherein, lutenhbIt (i) is enhancing curve substrate, lut (i) is the curve created by method in step 701.With this
Corresponding curved section on curve is taken based on curve according to actual needs, generates final enhancing after line amplitude of going forward side by side adjustment
Curve is defined as lutenh(i)。
The enhancing of step 803. details, uses lutenh(i) andIn grayscale information pairThe enhancing of details iteration is carried out, image I after details enhancing is generatedenh。
The analysis of step 804. the form of noise, usesThe form of noise of image is analyzed, and is created using method in step 701
Build noise suppression curve, such as following formula:
Wherein, i is smoothed imageMiddle to organize corresponding gray value, c is tracing pattern regulation coefficient.
Step 805. noise suppressed, uses lutnoisp(i) to IenhImage denoising is carried out, final image is generated.Such as
Shown in Fig. 9, as using this method treated image.
As shown in figure 3, method in each module and the working method and embodiment of the method for unit in apparatus of the present invention embodiment
Operating procedure is corresponding, and which is not described herein again.
Module described in the embodiment of the present invention and unit may or may not be it is physically separate, can basis
It is actual to need that some or all of modules is selected to achieve the purpose that this embodiment scheme.Above-mentioned step according to the invention
Suddenly, those skilled in the art can understand and implement without creative efforts.
Claims (13)
1. a kind of low dosage DR image processing method, which comprises the following steps:
Noise of detector inhibits: creation noise of detector sharing data table and detector signal a reference value utilize noise of detector
Sharing data table and detector signal a reference value inhibit the noise of detector component in image；
Organizational information compartment equalization and multipole fusion: then the discrete extreme value point of image after compacting noise of detector inhibits carries out
Multi-layer compartmentalization image reconstruction and image co-registration and tissue multipole fusion；The multi-layer compartmentalization image reconstruction and image
Fusion is counted the following steps are included: according to the dimension of preset current the included isolated area of level and the dimension of plate pixel
Calculate the corresponding plate pixel dimension of each isolated area；According to preset bigoted degree, in horizontal and vertical two dimensions to only
Vertical region carries out offset and forms bigoted region；Space stretching is carried out to each level isolated area and bigoted region；Create same layer
Grade isolated area and bigoted region blend curve；Same level isolated area is carried out to merge with bigoted region；Scheme between level
As fusion；
Image enhancement noise reduction: enhancing processing is carried out to the detailed information in image, and reduces the noise in image, is generated final
Image.
2. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the creation detector is made an uproar
Sound sharing data table, specifically:
Acquire N1 group detector base data: D10、D11、……、D1N1-1, N1 is artificial preset value；
Generate noise sharing data table:
3. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the creation detector letter
Number a reference value, specifically:
Acquire N2 group detector base data: D20、D21、……、D2N2-1, N2 is artificial preset value；
Generate detector signal a reference value:
4. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the noise of detector suppression
Image after system are as follows:
Wherein, IoriFor initial pictures, i.e. noise of detector inhibits object, DmapFor noise sharing data table, DbaseFor detector letter
Number a reference value, N1 are the group number of detector base data in noise sharing data table.
5. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the compacting detector is made an uproar
The discrete extreme value point of image after sound inhibits, comprising the following steps:
Image carries out statistics with histogram after inhibiting to noise of detector；
With calculating extreme point threshold value according to statistics with histogram result:
Tdh=imax×rh
Wherein, imaxFor image pixel point value maximum in statistics with histogram result, rhRatio is counted for default extreme point；
In statistics with histogram result, to gray value i >=TdhValue sum, and if value be more than or equal to preset threshold, sentence
It is set to extreme point without exception；Otherwise carry out extreme point compression processing, i.e., by image between TdhAnd imaxBetween pixel press
It is reduced to TdhWith preset value idstBetween.
6. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the space, which stretches, includes
Following steps:
Pixel value mapping function is created first:
Wherein, j I0The gray value of middle pixel, njFor the number for the pixel that grey scale pixel value in isolated area and bigoted region is j
Mesh, the pixel number that N is isolated area and bigoted region includes, idstTo preset gray value, iupFor in maximum regional value adjustment
Limit, idnLower limit is adjusted for maximum regional value；
By function f by same I0Grey scale pixel value in corresponding isolated area and bigoted region is adjusted to f (j), I from j0For pressure
The image exported after discrete extreme value point processed.
7. a kind of low dosage DR image processing method according to claim 1, which is characterized in that the tissue multipole fusion
The following steps are included:
Create tissue weighting curve:
Wherein, i is that corresponding gray value is organized in image, and w is tracing pattern regulation coefficient；
Using tissue weighting curve lut (i) by the image I after noise of detector inhibitstIt is (r, c) and empty by organizational information
Between image I after equilibrium0(r, c) is merged:
I (r, c)=lut (It(r,c))×It(r,c)+(1-lut(It(r,c)))×I0(r,c)
Wherein, r is the row serial number where pixel, and c is the column serial number where pixel；
It will be by treated image I (r, c) with the image I after noise of detector inhibitst(r, c) is linearly melted
It closes:
Im(r, c)=wlin×I(r,c)+(1-wlin)×It(r,c)
Wherein, ImFor organizational information compartment equalization and the fused image of multipole, wlinFor the weight coefficient of linear fusion.
8. a kind of low dosage DR image processing method according to claim 7, which is characterized in that described to thin in image
Section information carries out enhancing processing, comprising the following steps:
By N grades of iterative filterings from ImIn extract the smoothed image of N kind rankWith corresponding detail view
Picture
The substrate curve of details enhancing curve is created based on organizing weighting curve:
lutenhb(i)=lut (i) * (max (lut (i))-lut (i))
Wherein, lut (i) is tissue weighting curve；
Corresponding curved section on the substrate curve of details enhancing curve is taken according to actual needs, it is raw after line amplitude of going forward side by side adjustment
At enhancing curve lutenh(i)；
Utilize enhancing curve lutenh(i) and smoothed imageIn grayscale information to textual details carry out
Enhancing；To detail picturesThe enhancing of details iteration is carried out, the enhanced image I of details is generatedenh。
9. a kind of low dosage DR image processing method according to claim 1, which is characterized in that in the reduction image
Noise, comprising the following steps:
Carry out the form of noise analysis: to smoothed imagePicture noise morphological analysis is carried out, and creates noise suppression curve:
Wherein, i is smoothed imageMiddle to organize corresponding gray value, c is tracing pattern regulation coefficient；
According to analyze come the form of noise carry out noise suppressed: using noise suppression curve lutnoisp(i) after to details enhancing
Image IenhImage denoising is carried out, final image is generated.
10. a kind of using a kind of device of low dosage DR image processing method described in claim 1 characterized by comprising
Noise of detector suppression module: for creating noise of detector sharing data table and detector signal a reference value, spy is utilized
Survey the noise of detector component in device noise sharing data table and detector signal a reference value inhibition image；
Organizational information compartment equalization and multipole Fusion Module: for suppressing the discrete extreme value point of image after noise of detector inhibits,
Carry out multi-layer compartmentalization image reconstruction and image co-registration and tissue multipole fusion；The multi-layer compartmentalization image reconstruction and
Image co-registration is the following steps are included: according to the dimension of preset current the included isolated area of level and the dimension of plate pixel
Number, calculates the corresponding plate pixel dimension of each isolated area；According to preset bigoted degree, in horizontal and vertical two dimensions
Offset is carried out to isolated area and forms bigoted region；Space stretching is carried out to each level isolated area and bigoted region；Creation is same
One level isolated area and bigoted region blend curve；Same level isolated area is carried out to merge with bigoted region；Carry out level
Between image co-registration；
Image enhancement noise reduction module: it for carrying out enhancing processing to the detailed information in tissue multipole fused image, and reduces
Noise in image generates final image.
11. a kind of device according to claim 10, which is characterized in that the noise of detector suppression module includes:
Noise Sharing model creating unit: for creating noise of detector sharing data table；
Signal criterion value creating unit: for creating detector signal a reference value；
Noise of detector inhibits unit: for inhibiting image using noise of detector sharing data table and detector signal a reference value
In noise of detector component.
12. a kind of device according to claim 10, which is characterized in that the organizational information compartment equalization and multipole fusion
Module includes:
Discrete extreme value point compression unit: for carrying out statistics with histogram by image after inhibiting to noise of detector and calculating extreme value
Point threshold value suppresses the discrete extreme value point in image；
Multi-layer compartmentalization image reconstruction unit: weight is carried out to the pressed image of discrete extreme value point for hierarchical subregion
Structure；
Multi-layer compartmentalization image fusion unit: the image for reconstructing to hierarchical subregion merges；
Organize multipole integrated unit: for carrying out multipole fusion to fused image.
13. a kind of device according to claim 12, which is characterized in that described image enhances noise reduction module and includes:
Image enhancing unit: enhance for the textual details to multipole fused image；
Image noise reduction unit: for carrying out noise reduction to enhanced image.
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| CN101727658A (en) * | 2008-10-14 | 2010-06-09 | 深圳迈瑞生物医疗电子股份有限公司 | Image processing method and device |
| CN101779962A (en) * | 2010-01-19 | 2010-07-21 | 西安华海医疗信息技术股份有限公司 | Method for enhancing medical X-ray image display effect |
| CN102262775A (en) * | 2010-05-26 | 2011-11-30 | 深圳迈瑞生物医疗电子股份有限公司 | Image denoising method and device and image processing equipment |
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| CN101727658A (en) * | 2008-10-14 | 2010-06-09 | 深圳迈瑞生物医疗电子股份有限公司 | Image processing method and device |
| CN101779962A (en) * | 2010-01-19 | 2010-07-21 | 西安华海医疗信息技术股份有限公司 | Method for enhancing medical X-ray image display effect |
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