CN103971339B - A kind of nuclear magnetic resonance image dividing method and equipment based on parametric method - Google Patents
A kind of nuclear magnetic resonance image dividing method and equipment based on parametric method Download PDFInfo
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Abstract
The invention discloses a kind of nuclear magnetic resonance image dividing method based on parametric method and equipment, belong to technical field of image processing.The dividing method of the present invention is on the basis of existing parametric method target function model, to introduce a new regularization term：Make iteration result for biased fieldInitialization it is insensitive, for many minimums of original target function model, the regularization term newly increased can promote biased field1 or so is converged in, iteration can be made, which finally accurately to stop at, to be made at true picture and image to be split most close minimum, obtains preferable segmentation result.Based on the dividing method of the present invention, the invention also discloses a kind of magnetic resonance imaging device, can for biased field corresponding parameter arbitrary initial in the case of be accurately partitioned into required region, the brain tissue nuclear magnetic resonance image of outputting high quality.
Description
Technical field
The invention belongs to technical field of image processing, more particularly to the nuclear magnetic resonance image to brain tissue image segmentation portion
Reason technology.
Background technology
In the development of the medical science and technology in modern age, diagnostic image serves a vital effect.(nuclear-magnetism is common by MRI
Shake imaging), the purposes of the medical imaging technology in medical science such as CT (computer tomography) increasingly popularize, with image procossing
The progress of technology, the medical image collected to medical imaging device is all often after further image procossing,
Corresponding medical image is exported to use for analysis.Nuclear magnetic resonance image such as on brain tissue, due to the original of Magnetic resonance imaging
Reason, due to the inhomogeneities by radiofrequency field, MR (nuclear magnetic resonance) equipment in itself and detected life entity head different tissues
Between otherness and brain tissue the influence such as volume effect, acquired image gray scale is slowly varying along a direction, causes
(i.e. image exists inclined same tissue (such as white matter, grey matter or cerebrospinal fluid) the uneven situation of pixel of Brain Tissues Image gathered
Move field), uneven or there is the image of weak boundary for this many gray scales, accurately and rapidly image segmentation is often relatively more tired
It is difficult.Therefore, be corrected uneven to the gray scale of image is usually needed before graphical analysis is carried out.
Currently, the bearing calibration on the biased field of the nuclear magnetic resonance image of brain tissue is broadly divided into two major classes, is respectively
Precorrection and later stage correction.The method of precorrection acts on the acquisition phase of image, and ash is avoided with special hardware and sequence
That spends is uneven.Although this method can correct uneven as the gray scale caused by nuclear magnetic resonane scanne well, not
It can correct because otherness and the volume effect of brain tissue between human brain different tissues etc. influences the gray scale produced uneven
Image problem；By contrast, later stage bearing calibration is then completely dependent on gathered original image, then can handle various originals well
Gray scale caused by is uneven.
In later stage correction method, the more commonly used method is that the correction of biased field is carried out on the basis of image segmentation, point
Cut during iteration to intersect with correcting offset and carry out, promote mutually, be finally reached accurate result.Wherein comparing has
Representational is parametric method model.In parametric method model, biased field and noise are regard as uneven main of MR gradation of images
Reason, thus obtains gathering the fit term of image：Wherein, I (x) is pending image I (collections
Original image) pixel value at pixel x,It is unknown biased fieldRepresent picture of the biased field at pixel x
Element value),Represent true picture to be restoredRepresent pixel value of the true picture at pixel x), n (x) is pixel
Point x additive noise, according to Magnetic resonance imaging principle, is set with a series of (M) basic function gk(x) biased field is constituted's
Fit termWherein real number wk(k=1 ..., M) is the coefficient of each basic function, gk(x) to be a series of
The basic function of orthogonal polynomial, and meetδ during wherein k1 ≠ k2k1k2=0,
δ during k1=k2k1k2=1, Ω represent whole image region；True pictureThe constant collection J as a segmentation is regarded as, wherein belonging to
The image-region Ω of i-th class brain tissueiConstant c can be usediRepresent, obtainFit termWherein, N
Value is determined that it is 3 that usual brain tissue, which includes white matter, grey matter and the class loading of cerebrospinal fluid three, i.e. N value, by the construction of brain,
Conditional parameterAndThen target error function of the definable on Brain Tissues ImageIt is minimum in object function by the iterative target error function
When, eventually arrive at object boundary, realize that image is split.But in parametric method model, due to the target error letter of its structure
Number there may be more than one minimum point, for biased fieldDifferent initialization can make object function not in desired pole
Small value everywhere convergent, so that making segmentation result not reach expected effect, or even obtains the segmentation result of mistake.
The content of the invention
The goal of the invention of the present invention is：For above-mentioned problem, there is provided one kind on the basis of parametric method model
The quick of nuclear magnetic resonance image, the image partition method of performance stabilization for brain tissue, can join for the correspondence of biased field
Required region is accurately partitioned into the case of number arbitrary initial.
The nuclear magnetic resonance image dividing method based on parametric method of the present invention comprises the following steps：
Step 1：Input the nuclear magnetic resonance image I of the brain tissue of collection；
Step 2：Based on parametric method modelDetermine parameter w=(w1,...,wM)T, c=
(c1,...,cN)TWith U (x)=(u1(x),...,uN(x)) T values, wherein I (x) represent pictures of the described image I at pixel x
Element value, biased fieldPixel value at pixel xRepresent orthogonal multinomial at pixel x
Formula basic function, parameter wkIt is and M basic function gk(x) corresponding coefficient, pixel value of the true picture at pixel xN represents the classification number of brain tissue, ciRepresent the image-region Ω where the i-th class brain tissueiEach pixel
Value is ciIf pixel x belongs to image-region Ωi, then parameter ui(x)=1, otherwise parameter ui(x)=0：
Step 201：Initiation parameter w, c, U (x)；
Step 202：The current value of any two parameter in three parameter w, c and U (x) is chosen, according to object functionAsk for current unselected
Minimizer corresponding to parameter, wherein G (x)=(g1(x),...,gM(x))T；
Each parameter is only performed once minimizer calculating, and by parameter w, c and U (x) value be updated to pair
The minimizer answered；
Step 203：The picture of each pixel of image calculating parameter w, c and U (x) corresponding before and after current value updates
Plain value difference is different and sums, if the order of magnitude of result of calculation is less than predetermined threshold value, the current value based on parameter w, c and U (x) is held
Row step 3；Otherwise step 202 is repeated；
Step 3：Current value based on parameter w, c, U (x), exports the true picture of the nuclear magnetic resonance image IWith
Biased field
On the basis of the nuclear magnetic resonance image dividing method of the present invention, the invention also discloses a kind of Magnetic resonance imaging
Equipment, i.e., the dividing method based on the present invention sets graphics processing unit module, is positioned in magnetic resonance imaging device,
The dividing processing of the nuclear magnetic resonance image of the brain tissue to being gathered is completed, the picture after dividing processing is directly exported to user,
Nuclear magnetic resonance image collecting unit, graphics processing unit, output unit and power subsystem are specifically included, the power subsystem is used for
For magnetic image collecting unit, graphics processing unit, output unit is powered；The nuclear magnetic resonance image collecting unit is used to gather brain
The nuclear magnetic resonance image I of tissue, and the nuclear magnetic resonance image I is sent to graphics processing unit, described image processing unit
Nuclear magnetic resonance image dividing method based on the present invention performs dividing processing to current nuclear magnetic resonance image I, and by dividing processing
The nuclear magnetic resonance image I obtained afterwards true pictureAnd biased fieldTransmit to output unit, the output unit is deposited
Store up biased fieldAnd true picture described in output display
In summary, by adopting the above-described technical solution, the beneficial effects of the invention are as follows：Segmentation result is to biased field
Relevant parameter Initialize installation is insensitive, and the iterative calculation of dividing processing can be made, which finally accurately to stop at, makes true picture with treating
At segmentation figure picture most close minimum, so as to obtain preferable segmentation result.
Brief description of the drawings
Fig. 1 is the flow chart of the dividing method of the present invention；
Fig. 2 is the result schematic diagram of the magnetic resonance imaging device of the present invention；
Fig. 3 is the segmentation contrast effect figure of embodiment 1；
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, with reference to embodiment and accompanying drawing, to this hair
It is bright to be described in further detail.
The present invention is when the nuclear magnetic resonance image of the brain tissue to collecting carries out image dividing processing, in existing target
Error functionOn the basis of, in order that the convergence of target error function
To biased fieldInitiation parameter set (refer specifically to orthogonal orthogonal polynomial basic function gk(x) coefficient wk, because gk(x) it is
Relative set is carried out based on empirical value) it is insensitive, existing target error function is improved, specifically such as formula (1) institute
Show：
For the scalar constant c in above-mentioned formula (1)1,...,cNAnd w1,...,wM, function u1,...,uNAnd g1,...,
gM, represented with vector, i.e. c=(c1,...,cN)T, w=(w1,...,wM)T, U (x)=(u1(x),...,uN(x))T, G (x)
=(g1(x),...,gM(x))T, then formula (1) can be write as：
According toAndUnderstand, as x ∈ ΩiWhen, cTU (x)=ci, then, and above-mentioned formula
(2) it can be further converted into：
In order that the minimization of object function shown in formula (3), object function can during iteration respectively about
Each variable reaches minimum, obtains the minimization of the variable of each in object function, i.e., for three parameter w, c and U (x), point
Not Gu Ding two of which value, by mathematic calculation, formula (3) is asked for not fixed corresponding to the parameter of value
Minimizer, respectively obtains the minimizer corresponding to three parameter w, c and U (x)WithSuch as：
Fixed c and w (i.e. the current value based on c and w), obtains the minimizer corresponding to parameter U (x)WhereinAnd
Fixed U (x) and w (the current value i.e. based on each U (x) He w), obtain the minimizer corresponding to parameter cWherein
Fixed U (x) and c (the current value i.e. based on each U (x) He w), are obtained And J (x)=cTU, for linear equationThere is unique solution
With reference to Fig. 1, the processing procedure of dividing method of the invention is specially：
Step 1：Input the nuclear magnetic resonance image I of brain tissue；
Step 2：Initiation parameter c, w and U (x)；
Step 4：Based on the object function shown in formula (3), the minimizer corresponding to each parameter is asked forWithAnd undated parameter c, w and U (x) value, during managing in this place, it can be taken based on the current of parameter c, w and U (x)
Value, is calculating corresponding minimizerWithAfterwards, then c, w and U (x) assignment is updated；Can also obtain
After any one minimizer, the assignment of correspondence parameter is just updated, such as, based on the current value of parameter c and w, obtains parameter U
(x) minimizer corresponding toOrderCurrent value again based on U (x) and w, obtains parameter c institutes right
The minimizer answeredThe like, minimizer calculating is only performed once to each parameter, execution sequence can arbitrarily be set
Put, complete to update parameter c, w and U (x) value.
Step 5：In order to obtain parameter c, w and U (x) appropriate value, it is necessary to which the renewal described in the execution step 4 repeated is grasped
Make, the condition (condition of convergence) that iteration terminates is：Parameter c, w and U (x) value before the update after corresponding each pixel
In predetermined threshold value e, (specific value is set the order of magnitude of the cumulative sum of the deviation of pixel value based on practical application request, generally
Can be set within 0.01) within, that is, judgeThe order of magnitude whether within predetermined threshold value e, be based on
FormulaCalculateWherein ItBefore representing t (t for integer) more than 0 secondary iteration
Image (i.e. based on parameter c, w and U (x) not the t times perform step 4 when current exploitation It(x)),For the t times repeatedly
Image (the current exploitation after performing step 4 at the t times based on parameter c, w and U (x) instead of obtained afterwards), if
It is then to stop the iteration described by step 4, perform step 6；If it is not, step 4 is then continued executing with, the calculating before and after renewal
As a result without significant change；
Meanwhile, in order to improve arithmetic speed, can also setting iterations threshold value T, (usual threshold value T span can be set
It is set to 8~15) carrys out repeating for end step 4, i.e., a counter for being initialized as 0 is set in step 4, one is often performed
Secondary step 4, then Counter Value add 1, when counter value be more than or equal to threshold value T when, perform step 6.
Step 6：Current value based on parameter w, c, U (x), exports the true picture of the nuclear magnetic resonance image IWith
Biased field
The dividing processing of the present invention is that can be completed on single PC, can also be arranged to graphics processing unit module,
It is integrated in magnetic resonance imaging device, the magnetic resonance imaging device with image-capable is constituted, such as Fig. 2 institutes
Show, it includes nuclear magnetic resonance image collecting unit, graphics processing unit, output unit and power subsystem, the power subsystem is used
In for magnetic image collecting unit, graphics processing unit, output unit is powered；The nuclear magnetic resonance image collecting unit is used to gather
The nuclear magnetic resonance image I of brain tissue, and the nuclear magnetic resonance image I is sent to graphics processing unit, described image processing is single
Nuclear magnetic resonance image dividing method of the member based on the present invention performs dividing processing to current nuclear magnetic resonance image I, and by segmentation portion
The nuclear magnetic resonance image I obtained after reason true pictureAnd biased fieldTransmit to output unit, the output unit is deposited
Store up biased fieldAnd true picture described in output display
The present invention introduces new regularization term (formula (1) institute on the basis of existing parametric method target function model
ShowMake iteration result for biased fieldInitialization it is insensitive.For original target letter
Many minimums of exponential model, the regularization term newly increased can promote biased field1 or so is converged in, iteration can be made finally accurate
Stop at and make at true picture and image to be split most close minimum, obtain preferable segmentation result.
Embodiment
Using the method for the present invention, program is write first by Matlab language；Then the brain tissue nuclear-magnetism of collection is total to
Handled in the program that the image that shakes is input on PC platforms as source data.Segmentation result and initial parameter method model are carried out
Compare, as a result (Fig. 3-a are the true figure split of initial parameter method model, and Fig. 3-b are split for the present invention as shown in Figure 3
True figure out)：It is can be seen that from Fig. 3-a to biased fieldRandom initializtion is done, the segmentation result after obtained iteration
It is far with expected difference, and be can be seen that from Fig. 3-b with method of the invention, it is also arbitrarily rightInitialized, will
Obtain being expected relatively good segmentation effect.In summary, method of the invention takes full advantage of the regularization term newly added to convergence
Last minimum creates a diversion, so as to realize quick sane segmentation to image.
The foregoing is only a specific embodiment of the invention, any feature disclosed in this specification, except non-specifically
Narration, can alternative features equivalent by other or with similar purpose replaced；Disclosed all features or all sides
Method or during the step of, in addition to mutually exclusive feature and/or step, can be combined in any way.
Claims (5)
1. a kind of nuclear magnetic resonance image dividing method based on parametric method, it is characterised in that comprise the following steps：
Step 1：Input the nuclear magnetic resonance image I of the brain tissue of collection；
Step 2：Based on parametric method modelDetermine parameter w=(w1,...,wM)T, c=(c1,...,
cN)TWith U (x)=(u1(x),...,uN(x))TValue, wherein I (x) represents pixel values of the described image I at pixel x,
Biased fieldPixel value at pixel xgk(x) orthogonal polynomial at pixel x is represented
Function, parameter wkIt is and M basic function gk(x) corresponding coefficient, pixel value of the true picture at pixel xN represents the classification number of brain tissue, ciRepresent the image-region Ω where the i-th class brain tissueiEach pixel
Value, if pixel x belongs to image-region Ωi, then parameter ui(x)=1, otherwise parameter ui(x)=0, n (x) is represented at pixel x
Additive noise：
Step 201：Initiation parameter w, c, U (x)；
Step 202：The current value of any two parameter in three parameter w, c and U (x) is chosen, according to object function F (U
(x), c, w)=∫Ω|I(x)-(wTG(x))(cTU(x))|2dx+∫Ω|I(x)-(cTU(x))|2Dx, is asked for current unselected
Minimizer corresponding to parameter, wherein G (x)=(g1(x),...,gM(x))T；
It is only performed once minimizer calculating to each parameter, and parameter w, c and U (x) value is updated to corresponding
Minimizer；
Step 203：The pixel value of each pixel of image calculating parameter w, c and U (x) corresponding before and after current value updates
Difference is simultaneously summed, if the order of magnitude of result of calculation is less than predetermined threshold value e, the current value based on parameter w, c and U (x) is performed
Step 3；Otherwise step 202 is repeated；
Step 3：Current value based on parameter w, c, U (x), exports the true picture of the nuclear magnetic resonance image IAnd biased field
2. the method as described in claim 1, it is characterised in that the step 202,203 are respectively：
Step 202：The current value of any two parameter in three parameter w, c and U (x) is chosen respectively, according to object function F
(U (x), c, w)=∫Ω|I(x)-(wTG(x))(cTU(x))|2dx+∫Ω|I(x)-(cTU(x))|2Dx asks for current unselected
Minimizer corresponding to parameter, respectively obtains the minimizer corresponding to three parameter w, c and U (x)With
Wherein G (x)=(g1(x),...,gM(x))T；
Step 203：Calculating parameter w, c, U (x) are current value and take corresponding minimizerWithWhen, institute is right
The value differences of each pixel for the image answered and summation, if the order of magnitude of result of calculation is less than predetermined threshold value, makeAnd perform step 3；Otherwise makeAnd return to execution
Step 202.
3. method as claimed in claim 1 or 2, it is characterised in that parameter w, c, U (x) iteration is updated in step 203
Iteration termination condition can also be：
Count value t is set, 0 is initialized as, step 202 often performs completion once, then count value t adds 1；
Judge whether count value t is more than or equal to predetermined threshold value, if so, then terminating that parameter w, c, U (x) iteration are updated and held
Row step 3.
4. method as claimed in claim 1 or 2, it is characterised in that in the step 203, the quantity of predetermined threshold value e value
Level is 10-2Within.
5. a kind of magnetic resonance imaging device, it is characterised in that including nuclear magnetic resonance image collecting unit, graphics processing unit,
Output unit and power subsystem, the power subsystem are used for for nuclear magnetic resonance image collecting unit, graphics processing unit, output list
Member power supply；The nuclear magnetic resonance image collecting unit is used to gathering the nuclear magnetic resonance image I of brain tissue, and by the nuclear magnetic resonance
Image I is sent to graphics processing unit, and described image processing unit is based on the dividing method described in claim 1 or 2 to current
Nuclear magnetic resonance image I performs dividing processing, and by the nuclear magnetic resonance image I obtained after dividing processing true pictureWith
Biased fieldTransmit to output unit, the output unit stores biased fieldAnd true picture described in output display
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Address after: No.3, 11th floor, building 6, no.599, shijicheng South Road, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan 610041 Patentee after: Houpu clean energy (Group) Co.,Ltd. Address before: No.3, 11th floor, building 6, no.599, shijicheng South Road, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan 610041 Patentee before: Houpu clean energy Co.,Ltd. |