CN102499711A - Three-dimensional or four-dimensional automatic ultrasound image optimization and adjustment method - Google Patents

Three-dimensional or four-dimensional automatic ultrasound image optimization and adjustment method Download PDF

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CN102499711A
CN102499711A CN2011103023529A CN201110302352A CN102499711A CN 102499711 A CN102499711 A CN 102499711A CN 2011103023529 A CN2011103023529 A CN 2011103023529A CN 201110302352 A CN201110302352 A CN 201110302352A CN 102499711 A CN102499711 A CN 102499711A
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image
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function
weights
soft
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CN102499711B (en
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赵丹华
许冠明
赵明昌
陆坚
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Wuxi Chison Medical Technologies Co Ltd
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XIANGSHENG MEDICAL IMAGE CO Ltd WUXI
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Abstract

The invention discloses a three-dimensional and four-dimensional automatic ultrasound image optimization and adjustment method. The method comprises the following steps: inputting image data; dividing soft tissue image areas; fitting a gain plane; and carrying out three-dimensional and four-dimensional gain compensation on the images. The method has the following advantages: the weights carried by pixels of the envelop data before logarithmic compression or image data after logarithmic compression are computed according to the statistical parameters, or the soft tissue areas and the non-soft tissue areas are separated by utilizing the signal to noise ratio, then gain plane fitting is carried out on the areas, the gain compensation value is computed and the gain compensation value is used for carrying out three-dimensional and four-dimensional uniformity adjustment on the ultrasound images, thus ensuring the image brightness to be uniform and consistent.

Description

Three-dimensional or four-dimensional ultrasound image Automatic Optimal control method
Technical field
The present invention relates to a kind of ultrasonoscopy gain optimization method, especially a kind of three-dimensional or four-dimensional ultrasound image Automatic Optimal control method.
Background technology
When ultrasound wave is propagated in human body; Intensification along with propagation distance; The ultrasonic signal that reflects can reduce, and makes ultrasonoscopy present the phenomenon of brightness irregularities along depth direction, and this makes soft tissue that diagnosis is played an important role also often can not clearly show exactly.Be convenient to diagnosis in order to obtain fine ultrasonoscopy, we need take the gain compensation measure, and the image gain of loss is compensated to reach an even brightness ultrasonoscopy preferably.
In ultrasonic diagnostic equipment,, generally be to compensate, be called DGC (Depth Gain Compensation on the depth direction to the compensation of image gain; Depth gain compensation); Because along with the increase of the degree of depth, also correspondingly increase sweep time, so be also referred to as TGC (Time Gain Compensation; Time gain compensation), in the narration of back, all explain with TGC.TGC commonly used regulates the general amplification that adopts multistage (include but are not limited to 8 sections, 16 sections, 24 sections etc.) potentiometer adjustment different depth.With 8 sections TGC is example, and its TGC curve is spliced by 7 sections straight lines, and the endpoint value of its 8 breaks can be by 8 sections TGC potentiometer value decisions on the control panel, and intermediate value is obtained through linear interpolation by the two ends yield value.The doctor can change gain curve through regulator potentiometer, and then image is compensated.
Automatic gain control method commonly used at present at first need carry out piecemeal to image to be handled, and then utilizes whether the sub-piece of previously selected threshold decision is soft-tissue image, and then calculated gains compensating parameter value and then optimize image quality.In these class methods, for the pixel in the sub-piece behind the image block, usually adopting pre-set threshold to judge whether is the pixel of soft-tissue image; Greater than given in advance threshold value, then be the pixel of soft-tissue image, with 1 value labelling; Otherwise be the pixel of non-soft-tissue image; With 0 value labelling, the shortcoming of doing existence like this is: soft-tissue image's area pixel point value corresponding is 1 value after the image segmentation, and the pixel value corresponding in non-soft-tissue image zone (being the noise near field, far field and the cyst in the soft tissue etc.) is 0 value; Make the entire image pixel value behind the piecemeal discontinuous; Do not have suitable transition for defining between soft-tissue image and the non-soft-tissue image, only rely on 0 value and 1 value to distinguish merely, be easy to like this whether being that soft-tissue image produces wrong judgement.If not accurate enough, then can occur making correct diagnosis thereby influence the doctor to being the phenomenon of the part undercompensation of soft-tissue image originally to soft-tissue image's judgement.
No matter be DGC or TGC; All be to compensate to the gain reduction of ultrasonoscopy on depth direction; But often each two field picture is also different for different ultrasonic echo intensity on certain depth, at this time only regulates TGC and is difficult to make the soft-tissue image's regional luminance in the image even.At present, proposed ultrasonoscopy is carried out in a lateral direction the method for gain compensation, be also referred to as LGC (Lateral Gain Compensation, lateral gain compensation) to gain compensating method.Existing method need be calculated horizontal and vertical gain compensation curve respectively according to the average of soft-tissue image, carries out image optimization according to transverse and longitudinal gain compensation curve then, thereby makes image reach the luminance proportion on the horizontal vertical both direction.Though this method make image not only on depth direction brightness more even; And also uniformity of brightness in a lateral direction; But because brightness is not merely along with depth direction and horizontal direction both direction are the variation tendency that weakens; But possibly only on both direction, carry out gain compensation and still can not compensate all sidedly along any direction non-uniform change of image along with increasing the gain loss that brings sweep time.
Summary of the invention
The purpose of this invention is to provide a kind of three-dimensional or four-dimensional ultrasound image Automatic Optimal control method, overcome present medical supersonic equipment and manually accomplished the TGC parameter regulation and can only regulate the shortcoming that gain brought on one or two direction.
According to technical scheme provided by the invention, said three-dimensional or four-dimensional ultrasound image Automatic Optimal control method may further comprise the steps:
1) input ultrasound image data, said ultrasound image data are envelope data or in the data after the logarithmic compression any before the logarithmic compression;
2) cut apart soft-tissue image, distinguish soft-tissue image and non-soft-tissue image in the ultrasonoscopy;
3) each dimension data that direction comprises of ultrasonoscopy are obtained gain curves do match;
4) and then calculate the gain compensation parameters value, ultrasonoscopy is carried out the multidimensional Automatic Optimal regulate.
Said three-dimensional is meant any three-dimensional in depth direction, horizontal direction, longitudinal direction and the time orientation four-dimension.
Said four-dimensional Automatic Optimal is regulated and is comprised the adjusting on depth direction, horizontal direction, longitudinal direction and the time orientation.
The method of cutting apart soft-tissue image described in the step 2 is distinguished soft-tissue image zone and non-soft-tissue image zone for calculate weights through Gauss distribution.Saidly calculate weights through Gauss distribution and distinguish the step in soft-tissue image zone and non-soft-tissue image zone and comprise:
1) according to pixel average μ that organizes in the ultrasonoscopy and variance δ 2, for the more any pixel value Oimg in the former ultrasonoscopy (i, j) utilize Gauss distribution calculate weights Weight (i, j), as shown in the formula:
Weight ( i , j ) = 1 2 π σ e - ( Oimg ( i , j ) - μ ) 2 2 σ 2
Obtain weights image Weight, wherein (i is that position in the weights image is (i, j) corresponding weights j) to Weight;
2) all weights in the said weights image of traversal search out maximum weights Weight Max
3) utilizing said maximum weights that all weights are carried out normalization calculates:
Weight normal ( i , j ) = Weight ( i , j ) Weight max
Obtain the weights image after the normalization, wherein Weight Normal(i is after the weights image passes through normalization j), and the image meta is changed to (i, j) weights of correspondence;
4) according to the image after weights image after the said normalization and the said input picture calculating weighting:
Dimg(i，j)＝Oimg(i，j)·Weight normal(i，j)
Wherein, (i is that the weighted image meta is changed to (i, j) corresponding pixel value j) to Dimg.
5) image after utilizing pre-set threshold THr to said weighting carries out normalization and calculates:
Dimgnormal ( i , j ) = Dimg ( i , j ) THr
Wherein, (i is that the image meta was changed to (i, j) corresponding pixel value after weighted image passed through normalization j) to Dimgnormal.
The method of cutting apart soft-tissue image described in the step 2 is to utilize the difference of signal to noise ratio snr to distinguish soft-tissue image zone and non-soft-tissue image zone.
During three-dimensional Automatic Optimal on comprising depth direction, horizontal direction and time orientation was regulated, the function of the match gain curves described in the step 3 was:
I=f (D i, L j, T k), D i, L j, T kRepresent respectively along the independent variable of depth direction, horizontal direction and time orientation, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
During three-dimensional Automatic Optimal on comprising depth direction, horizontal direction and longitudinal direction was regulated, the function of the match gain curves described in the step 3 was:
I=f (D i, L j, E k), D i, L j, E kRepresent respectively along the independent variable of depth direction, horizontal direction and longitudinal direction, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
Four-dimensional Automatic Optimal is regulated, and comprises promptly in the adjusting on depth direction, horizontal direction, longitudinal direction and the time orientation that the function of the match gain curves described in the step 3 is: I=f (D i, L j, T k, E m), D i, L j, T k, E mRepresent respectively along the independent variable of depth direction, horizontal direction, time orientation and longitudinal direction, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
Advantage of the present invention is: the present invention provides a kind of method that can carry out three-dimensional or four-dimensional Gain Automatic optimization; Whole process need not the manual adjustments parameter; Not only overcome the loaded down with trivial details shortcoming of traditional operation, significantly reduced the time of diagnosis, and can carry out gain compensation any three-dimensional in the four-dimensional direction that comprises depth direction, horizontal direction, longitudinal direction, time orientation or four-dimensional direction; Make entire image brightness even, thereby improved the accuracy rate of ultrasonic diagnosis.
Description of drawings
Fig. 1 is the system block diagram of the ultrasonic diagnostic equipment that the present invention relates to.
Fig. 2 is a flow chart of the present invention.
Fig. 3 is cut apart flow chart for the ultrasonoscopy soft-tissue image of the embodiment of the invention.
Fig. 4 is the ultrasonoscopy matrix sketch map of the embodiment of the invention.
Fig. 5 is the weights image array sketch map of the embodiment of the invention.
Fig. 6 is the image array sketch map after the weighting of the embodiment of the invention.
Fig. 7 is that a kind of three-dimensional Automatic Optimal of the embodiment of the invention is regulated sketch map.
Fig. 8 is that the another kind of three-dimensional Automatic Optimal of the embodiment of the invention is regulated sketch map.
Fig. 9 is that the four-dimensional Automatic Optimal of the embodiment of the invention is regulated sketch map.
The specific embodiment
Specify each related in the technical scheme of the present invention detailed problem below in conjunction with accompanying drawing and embodiment.
As shown in Figure 1, the ultrasonic diagnostic equipment system comprises: controller, radiating circuit, transducer, receiving circuit, wave beam are synthetic, the signal processing image forms, keyboard (or soft keyboard) and display.At first keyboard (or soft keyboard) is the user input of controller; It is mutual to come with controller for a kind of means easily of user; Transducer (also being probe) is hyperacoustic device that transmits and receives, and can convert electrical energy into acoustic energy, also can convert acoustic energy into electric energy; At first radiating circuit is under the coordination of controller; Send the signal of telecommunication to transducer, be converted into ultrasonic emitting by transducer and go out, receiving circuit is responsible for receiving transducer and is passed the echo signal (converting the signal of telecommunication into by transducer) of coming; And with its amplify, processing such as digital to analog conversion; Wave beam is synthetic to carry out dynamic focusing and dynamic aperture is handled to the echo signal on the different directions, and it is synthetic together, and the signal after signal processing and image form wave beam synthesized then carries out processing such as noise suppressed, envelope detection, logarithmic compression and finally on display, shows.The ultrasonoscopy that is shown comprises between the dead space, soft-tissue image zone (comprising institute images displayed zones such as skin, tegumentary nerve, shallow blood vessel) and strong reflection zone (zone that is shown such as bone, skull etc.).What the present invention paid close attention to is soft-tissue image zone wherein, also is to diagnosing acting zone in the ultrasonic examination.
Shown in Figure 2 is three-dimensional or four-dimensional ultrasound image Automatic Optimal control method flow chart.At first import ultrasound image data, this ultrasound image data can be preceding envelope data of logarithmic compression or any data in the data after the logarithmic compression; Distinguish then diagnosing significant soft-tissue image; At last depth direction, horizontal direction, longitudinal direction and data that time orientation comprises are obtained gain curves do match; Its space coordinates include but not limited to a plurality of directions such as depth direction, horizontal direction, time orientation, longitudinal direction; In practical implementation; Can be to be optimized on the three dimensions; Also can be to be optimized on the space-time, for example, the optimization on the three dimensions can be to carrying out match along each independent variable on depth direction, horizontal direction and the time orientation; Another embodiment can be to carrying out match along each independent variable on depth direction, horizontal direction and the longitudinal direction; Further, the optimization on the space-time can be to carrying out match along each independent variable on depth direction, horizontal direction, time orientation and the longitudinal direction, and then calculate the gain compensation parameters value and carry out the image multi-dimensional Automatic Optimal and regulate.
For the dividing method of soft-tissue image of the present invention further is described; In a preferred embodiment of the present invention; Explanation for three-dimensional or four-dimensional ultrasound image Automatic Optimal control method is as shown in Figure 3, and in step 32, at first wanting clear and definite pending data is through envelope data before the logarithmic compression or the data after the logarithmic compression; For these two kinds of different data; The method of the image after the calculated gains compensation is also inequality, those skilled in the art will readily appreciate that these two kinds of different pieces of informations realize that optimization can reach identical effect, can't cause the problem that any announcement is insufficient or announcement is fuzzy.
In step 33, at first the pixel average μ of known tissue and variance δ 2, then to each pixel in the ultrasonoscopy calculate its in image shared weights Weight (i, j) and constitute the weights image Weight with the equal size of original image.The average of said tissue (tissue) pixel and variance are meant tissue that current ultrasonic device checks such as average that corresponding image calculated and variances such as liver, heart, kidney, lung and muscle, uterus, and these two values are known.
(establishing former ultrasonoscopy is Oimg for i, calculating j), and size is M * N, and its corresponding tissue pixels average is μ, and variance is δ for weights Weight 2, for the more any pixel value Oimg in the former ultrasonoscopy (i, j), utilize Gauss distribution calculate weights Weight (i, j):
Weight ( i , j ) = 1 2 π σ e - ( Oimg ( i , j ) - μ ) 2 2 σ 2 ;
Known in Gauss distribution, this distributes by two parameters---average value mu and variance δ 2Decision, its probability density function curve is symmetrical center line with average μ, variance δ 2More little, distributing concentrates near the average μ more.In the present invention; The pixel average of tissue has maximum Gauss distribution value; So the rest of pixels point in the image is along with more and more far away apart from the tissue pixels average; Its corresponding weights are also more and more littler, and the benefit of doing like this is: soft-tissue image in the ultrasonoscopy and non-soft-tissue image (such as being included in the inner zones such as cyst of soft tissue) are effectively distinguished, because theoretically; The distribution value of each pixel is all non-vanishing in the image; That is to say and need each pixel in the ultrasonoscopy be calculated, make like this Fuzzy Processing has been carried out in the differentiation of soft-tissue image and non-soft-tissue image, in other words; It is to judge the soft-tissue image zone more exactly in order to guarantee that each pixel in the image all has distribution value, has avoided the simple threshold value that relies on judges whether it is that soft-tissue image brings inaccurate problem in image segmentation.Here need to prove; To those skilled in the art; That realizes the method for weighting and be not limited among the present invention being disclosed utilizes the method for Gauss distribution calculating weights according to tissue pixels average and variance; Such as utilizing other implementation methods to realize the calculating etc. of weightings, can think distortion of the present invention, in sum according to a lot of similarly statistical parameters (such as do parameter after the corresponding conversion etc. by average or variance); As long as every method that image is computed weighted of being used for all is applicable to the present invention.
In step 34, at first travel through whole weights image, the weights that search is maximum carry out normalization according to the weights of maximum to whole weights image to the weights image that in step 33, calculates then and calculate, and weights all are between 0 value to 1 value.
If Weight MaxBe wherein maximum weights, Weight Normal(i j) is weights after calculating through normalization, then to each the weights Weight among the weights image Weight (i, j) carry out normalization and calculate according to following formula:
Weight normal ( i , j ) = Weight ( i , j ) Weight max .
Because in Gauss distribution, near average, corresponding probability-distribution function value is big more, otherwise corresponding probability-distribution function value is more little.If certain any weights Weight Normal(i j) more near 1 value, then represent this pixel corresponding pixel value the closer to the tissue pixels average, and then it is big more to judge that this pixel belongs to the probability of soft-tissue image, otherwise, if certain some weights Weight Normal(i; J) distance 1 value is far away more; Promptly, then represent this pixel corresponding pixel value more away from the tissue pixels average the closer to 0 value, so this pixel to belong to the probability of soft-tissue image more little; And it is big more such as the probability of similar cyst or noise in the soft tissue to belong to non-soft-tissue image, and this has also been provided sketch map in Fig. 5.The judgement that whether belongs to soft-tissue image described here is just in order to explain whether the corresponding weights size of some pixels belongs to soft-tissue image with this some substantial connection is arranged; In actual implementation procedure, do not cut apart soft-tissue image and non-soft-tissue image to the size of weights as the threshold value of judging soft-tissue image and with 1 value and 0 value.
In step 35, according to the normalized weights image Weight of process in step 34 NormalAnd the image Dimg after the former ultrasonoscopy Oimg calculating weighting, computing formula is following:
Dimg＝Oimg·Weight normal
Two matrixes of the expression here carry out dot product.
That is to say that the weights that each pixel is corresponding act on this pixel, further distinguish soft-tissue image zone and non-soft-tissue image zone.If the pixel value of part pixel is near the tissue pixels average in the image after the weighting; Then represent this part pixel to belong to soft-tissue image; If pixel value is the decimal near 0 value, then represent this part pixel to belong to non-soft-tissue image, in Fig. 6, also provided sketch map.
In step 36, in order to reduce the operand of match, all pixels among the image Dimg after the weighting to be carried out normalization according to pre-set threshold THr handle, its computational methods are identical with the method that in step 34, discloses:
Dimgnormal ( i , j ) = Dimg ( i , j ) THr
Wherein (i j) carries out the numerical value after the normalization for the image after the weighting to Dimgnormal.
Need to prove; In order to guarantee that (i j) is between 0 value to 1 value, and then pre-set threshold can be the pixel value of the maximum in the original image for Dimgnormal after the normalization; Certainly; Pre-set threshold is not limited in this, and the user can thinking according to the present invention do corresponding conversion, does not have any specific restriction.
Fig. 4, Fig. 5 and Fig. 6 further describe the method through weighting differentiation soft-tissue image in the above-mentioned steps.
Fig. 4 is a ultrasonoscopy matrix sketch map.If the pixel average of current organization is 63; The pixel value that then can find out the pixel of soft-tissue image region concentrates near the 60-70; And the pixel value of all the other regional pixels and tissue pixels average differ bigger; This shows, can utilize the tissue pixels average as the standard of judging soft-tissue image.
It is as shown in Figure 5 to utilize Gauss distribution to calculate the corresponding weight matrix of former ultrasonoscopy according to the pixel average of tissue; Because the characteristic of Gauss distribution; The probability density function values corresponding the closer to the pixel of average is big more, and promptly so-called weights are big more, otherwise; More little away from the corresponding probability density function values of the pixel of average more; So contrast ultrasonoscopy shown in Figure 4 and weights image shown in Figure 5 can be found out, be higher than the corresponding weights of pixel that those differ greatly with the tissue pixels average far away with the approaching corresponding weights of pixel of tissue pixels average, and differ and reach two to three one magnitude; Then the weights of each pixel that is calculated are weighted on the corresponding pixel, obtain the image after the weighting as shown in Figure 6.
As shown in Figure 6, can find out that the pixel value of the image after the weighting distributes clearly; The pixel value of the partial pixel point after the process weighting and the pixel average of basic stitch are approaching; Think that then these and the approaching pixel of tissue pixels average belong to soft-tissue image, and the pixel average of the partial pixel point after the weighting and basic stitch differs in two to three one magnitude, then think right and wrong soft-tissue image; Can find out; Image after the weighting distinguishes the soft-tissue image that diagnosis is played an important role with non-soft-tissue image mutually, and all pixels in the image all have corresponding pixel value, and this point is to be different from prior art; In the prior art; Usually adopt 0 value and 1 value simply soft-tissue image and non-soft-tissue image to be cut apart, though this method is simple, making the corresponding value of handling in the image of back of any pixel is not that 0 value then is 1 value; But also have weak point: the image array after cutting apart, its quality of cutting apart depends on previously selected threshold value to a great extent.If choosing when being not suitable for of segmentation threshold; Very easily produce the phenomenon of the false judgment when cutting apart; Being labeled as with 0 value such as the improper pixel with certain part in the soft-tissue image owing to selection of threshold is invalid pixel, and this has lost a part of information that diagnosis is had important value beyond doubt concerning the doctor; To a certain extent; Cause the doctor to make not accurate enough diagnosis probably, this can cause very big infringement to the patient in the diagnostic procedure of reality, in the prior art in order to make the entire image pixel value continuous relatively; Also need do like this and making the amount of calculation increase undoubtedly according to the soft-tissue image zone at the effective row and utilize interpolation algorithm that 0 value point is carried out interpolation arithmetic of the shared percentage calculation of the same degree of depth.In order to overcome this problem; The technology that discloses among the present invention with of the prior art to cutting apart of image effective coverage or even sub-piece different be: as shown in Figure 6; Each pixel in the image array after the weighting all has numerical value; Before the calculated gains compensation, need not do any interpolation arithmetic and just realize Fuzzy Processing, the gain-adjusted of the integral image utilized is arranged.
Fig. 4, Fig. 5, Fig. 6 provide is the sketch map that each pixel in the image after the logarithmic compression carries out weighted calculation, shown in size and each pixel value in the image only play the effect of explaining.
For cutting apart of soft-tissue image, it includes but are not limited to the method through above-mentioned weighting, in another embodiment of the present invention, can also utilize signal to noise ratio snr as judging whether it is the standard of soft-tissue image.The computing formula of SNR is following:
SNR = μ σ
Wherein μ and σ are present image mean value of areas and variance.
Usually, in ultrasonoscopy, if its Rayleigh distributed, then the SNR value generally about 1.9, this also difference according to the difference of system and to some extent certainly.
Can find out according to above-mentioned formula; The SNR value is relevant with the average and the variance of current region image; Also all differences of the corresponding SNR value in its each zone then, this just can be used as the standard of distinguishing soft-tissue image zone and other zones (regional such as noise region and strong reflection).Be divided into three zones such as current ultrasonoscopy: strong reflection district, soft-tissue image district and noise range, its corresponding respectively signal to noise ratio is designated as: SNR 0, SNR 1And SNR 2, for given SNR Th, if meet the following conditions
|SNR i-SNR th|≤m
I=0 wherein, 1,2, threshold value m can be by User Defined, such as 0.1,0.2 etc.
Think that then this signal to noise ratio The corresponding area is the soft-tissue image zone.
Such as; In ultrasonoscopy if Rayleigh distributed; Then corresponding signal to noise ratio snr is generally about 1.9, if the absolute value of the difference of certain regional SNR value and ideal value 1.9 is in given range, i.e. and its more approaching desirable SNR value; Then this region decision is that the probability of soft-tissue image is big more, otherwise is that the probability of strong reflection zone or noise region is big more.It is more stable that this method is carried out soft-tissue image's area judging than above-mentioned method through weighting; Because its average and variance with ultrasonoscopy itself is relevant, but owing to need to calculate the variance of each subregion, so comparatively speaking; Amount of calculation is bigger; In real process, the user can oneself balance and is selected to think that proper method carries out soft-tissue image and distinguish, and does not have particular restriction.
After distinguishing soft-tissue image effectively through weighting, the gain curves match in will regulating three-dimensional and four-dimensional Automatic Optimal through several concrete optimization embodiment describe respectively.
Three-dimensional Automatic Optimal is regulated embodiment A:
Consider that because the decay of echo-signal, each frame ultrasonoscopy not only weakens along the brightness of depth direction and horizontal direction to some extent, and along with the increase of sweep time, between the two continuous frames image in the brightness of same position also difference to some extent.
In order to compensate the gain reduction of ultrasonoscopy along depth direction D (depth), horizontal direction L (lateral) and time orientation T (time) better, (Dimagnormal) is designated as I with ultrasonoscopy, then sets up following equation:
I＝f(D i，L j，T k)
Wherein, D i, L j, T kRepresent respectively along the independent variable of depth direction, horizontal direction and time orientation; That is to say pixel value I (i, j among the ultrasonoscopy I; K) be function about these three independent variables of picture depth, horizontal direction and sweep time, it satisfies following three conditions:
(1) this function is continuous at whole interval of definition;
(2) this function can be led in whole interval of definition;
(3) this function is a smooth function;
Wherein depth direction D, horizontal direction L and T sweep time x axle, y axle and t axle as shown in Figure 7.Along with the variation of independent variable, its corresponding functional value also changes.
In order to illustrate further above-mentioned functional relationship, in one embodiment of the invention, can adopt the fitting of a polynomial gain curves:
I = Σ i M 1 Σ j M 2 Σ k M 3 a i , j , k D i L j T k
A wherein I, j, kBe undetermined coefficient, M 1, M 2And M 3Can identical size, also can be different.Such as working as M 1=M 2=M 3=2 o'clock, what match obtained was a secondary gain curves.
Confirm coefficient a through separating the overdetermined equation group then I, j, kAnd then definite gain curves.
The above-mentioned match that comprises the space-time of depth direction, horizontal direction, time orientation and pixel value is included but are not limited to the method described in the embodiment A; In the practical implementation process, the user can adopt several different methods to accomplish the match of gain curves according to present case, and it includes but are not limited to the multinomial method that adopts; Be the logarithmic function distribution trend such as current change in gain; Then can adopt logarithmic function to carry out match, such as shape such as alog (x+b), if current change in gain is exponential trend; Then can adopt exponential function to carry out match, such as shape such as y=e Ax+ b can also be methods such as nonlinear multivariable surface fitting, polynary least square fitting, in a word, can adopt multiple mode to realize to this, does not have specific limited.Every approximating method that the ultrasonoscopy yield value is tried to achieve in above-mentioned space-time match is all within protection domain of the present invention.
Three-dimensional Automatic Optimal Embodiment B:
Being used for the real-time three-dimensional imaging mode at present and including but not limited to utilize the two dimensional phased array transducer, wave linear array transducer and wave convex array transducer generation three-dimensional data, can also be modes such as Mechanical Driven scanning or the scanning of magnetic field space positioning free arm.With the two dimensional phased array transducer is example, and its crystal wafer is rectangular to be arranged, and by vertical, horizontal multi-thread evenly cutting, forms the little lattice of a plurality of miniature squares.Carrying out the orientation by the phased array mode along the y direction of principal axis during probe emission velocity of sound turns to; Form two dimensional image; Carry out the three-dimensional elevation angle and turn to along fan-shaped move of z direction of principal axis again, to form the pyramid data base, to carry out space orientation for the two dimensional image of a series of separations of gathering; And to the space between the adjacent tangent plane carry out pixel interpolation level and smooth after; Form the 3 D stereo data base, in a scan period, can produce a plurality of such three-dimensional data bases, then the pixel value of the same position of the adjacent three-dimensional data base in this cycle also there are differences; And this difference is relevant with the interval between its three-dimensional data base; So at this moment the brightness value of ultrasonoscopy is not only relevant with picture depth direction and horizontal direction, also relevant, as shown in Figure 8 z direction of principal axis with variation along the longitudinal direction; So the pixel value of ultrasonoscopy is the function about depth direction D (depth), horizontal direction L (lateral) and longitudinal direction E (elevation), the direction shown in depth direction D, horizontal direction L and longitudinal direction E x axle, y axle and the z axle as shown in Figure 8 wherein.
In order to compensate the gain reduction of ultrasonoscopy along depth direction D (depth), horizontal direction L (lateral) and longitudinal direction E (elevation) better, (Dimagnormal) is designated as I with ultrasonoscopy, then sets up following equation:
I＝f(D i，L j，E k)
Wherein, D i, L j, E kRepresent respectively along the independent variable of depth direction, horizontal direction and longitudinal direction.
Different with embodiment A is; Pixel value I (i among the ultrasonoscopy I; J; K) be function, but its function characteristic still satisfies three conditions described in embodiment A, wherein depth direction D, horizontal direction L and longitudinal direction E x axle, y axle and z axle as shown in Figure 8 about these three independent variables of picture depth, horizontal direction and longitudinal direction.
In order to illustrate further above-mentioned functional relationship, in one embodiment of the invention, can adopt multinomial to come the match gain curves:
I = Σ i M 1 Σ j M 2 Σ k M 3 a i , j , k D i L j E k
A wherein I, j, kBe undetermined coefficient, M 1, M 2And M 3Can identical size, also can be different, confirm coefficient a through separating the overdetermined equation group then I, j, kAnd then definite gain curves.
The above-mentioned match that comprises the space-time of depth direction, horizontal direction, longitudinal direction and pixel value is included but are not limited to the method described in embodiment A and the Embodiment B; In the practical implementation process; The user can adopt multiple mode to accomplish the match of gain curves according to present case, and it is not limited only to adopt the multinomial method, is the logarithmic function distribution trend such as current change in gain; Then can adopt the logarithmic function match; If current change in gain is exponential trend, then can adopt the exponential function match, can also be methods such as nonlinear multivariable surface fitting, polynary least square fitting; Can adopt multiple mode to realize, not have specific limited.Every approximating method that the ultrasonoscopy yield value is tried to achieve in above-mentioned space-time match is all within protection domain of the present invention.
Four-dimensional Automatic Optimal Embodiment C:
The foregoing description A can be to comprising that depth direction, horizontal direction and time orientation carry out Automatic Optimal and regulate; Can be in Embodiment B to comprising that depth direction, horizontal direction and longitudinal direction carry out Automatic Optimal and regulate; But four-dimensional color ultra only on three directions, the adjusting for present can't reach sufficient optimization; Reason is; The four-dimensional ultrasound technology is to adopt three-dimensional ultrasound pattern to add the time dimension parameter; The stereo-picture of the different time in the three-dimensional ultrasound pattern being shown according to the sequencing of time cycle continuously, formed the real-time and dynamic 3-D view, obviously is inadequate so in Embodiment B, only considered depth direction, horizontal direction and longitudinal direction; Also need consider the variation of ultrasonoscopy pixel value on time dimension; So in four-dimensional ultrasound, the pixel value of its ultrasonoscopy is the function about depth direction D (depth), horizontal direction L (lateral), longitudinal direction E (elevation) and time orientation T (time), the direction shown in depth direction D, horizontal direction L, longitudinal direction E and time orientation T x axle, y axle, z axle and the t axle as shown in Figure 9 wherein.
In order to compensate the gain reduction of ultrasonoscopy along depth direction D (depth), horizontal direction L (lateral), longitudinal direction E (elevation) and time orientation T (time) better, (Dimagnormal) is designated as I with ultrasonoscopy, then sets up following equation:
I＝f(D i，L j，T k，E m)
Wherein, D i, L j, T k, E mRepresent respectively along the independent variable of depth direction, horizontal direction, time orientation and longitudinal direction.
That is to say; Pixel value I (i among the ultrasonoscopy I; J, k is about picture depth, horizontal direction, sweep time and the vertical function of these four independent variables of time m); Its function characteristic satisfies three conditions described in embodiment A, wherein depth direction D, horizontal direction L, time orientation T and longitudinal direction E x axle, y axle, z axle and t axle as shown in Figure 8.
In order to illustrate further above-mentioned functional relationship, still coming the match gain curves with multinomial is example:
I = Σ i M 1 Σ j M 2 Σ k M 3 Σ m M 4 a i , j , k , m D i L j T k E m
A wherein I, j, k, mBe undetermined coefficient, M 1, M 2, M 3And M 4Can identical size, also can be different, confirm coefficient a through separating the overdetermined equation group then I, j, k, mAnd then definite gain curves.
The above-mentioned match that comprises the quintuple space of depth direction, horizontal direction, vertical time, time orientation and pixel value is included but are not limited to the method described in the embodiment A; In the practical implementation process; The user can adopt multiple mode to accomplish the match of gain curves according to present case, and it includes but are not limited to and adopts the multinomial method, is the logarithmic function distribution trend such as current change in gain; Then can adopt the logarithmic function match; If current change in gain is exponential trend, then can adopt the exponential function match, can also be methods such as nonlinear multivariable surface fitting, polynary least square fitting; Can adopt multiple mode to realize, not have specific limited.Every approximating method that the ultrasonoscopy yield value is tried to achieve in above-mentioned quintuple space match is all within protection domain of the present invention.
At last, calculate gain compensation value, use it for three-dimensional or four-dimensional ultrasound image Automatic Optimal and regulate according to pre-set threshold.
In an embodiment of the present invention; Carrying out surface fitting again after normalization is handled to the process of the image array after the weighting is in order to reduce operand; Also can directly carry out surface fitting to the image array after the weighting; This dual mode all can reach identical technique effect, and is not limited to the mode described in the present invention, and this point is readily appreciated that to those skilled in the art.
The concrete steps of the foregoing description and the explanation of relevant indicators are comparatively concrete; Can not therefore think restriction to scope of patent protection of the present invention; Every having used computes weighted and utilizes the method for fitting algorithm calculated gains parameter value image, all should be within protection scope of the present invention.

Claims (9)

1. three-dimensional or four-dimensional ultrasound image Automatic Optimal control method is characterized in that, may further comprise the steps:
1) input ultrasound image data, said ultrasound image data are envelope data or in the data after the logarithmic compression any before the logarithmic compression;
2) cut apart soft-tissue image, distinguish soft-tissue image and non-soft-tissue image in the ultrasonoscopy;
3) each dimension data that direction comprises of ultrasonoscopy are obtained gain curves do match;
4) and then calculate the gain compensation parameters value, ultrasonoscopy is carried out the multidimensional Automatic Optimal regulate.
2. three-dimensional as claimed in claim 1 or four-dimensional ultrasound image Automatic Optimal control method is characterized in that said three-dimensional is meant any three-dimensional in depth direction, horizontal direction, longitudinal direction and the time orientation four-dimension.
3. three-dimensional as claimed in claim 1 or four-dimensional ultrasound image Automatic Optimal control method is characterized in that, said four-dimensional Automatic Optimal is regulated and comprised the adjusting on depth direction, horizontal direction, longitudinal direction and the time orientation.
4. three-dimensional according to claim 1 or four-dimensional ultrasound image Automatic Optimal control method; It is characterized in that the method for cutting apart soft-tissue image described in the step 2 is distinguished soft-tissue image zone and non-soft-tissue image zone for calculate weights through Gauss distribution.
5. three-dimensional according to claim 4 or four-dimensional ultrasound image Automatic Optimal control method is characterized in that, saidly calculate weights through Gauss distribution and distinguish the step in soft-tissue image zone and non-soft-tissue image zone and comprise:
1) according to pixel average μ that organizes in the ultrasonoscopy and variance δ 2, for the more any pixel value Oimg in the former ultrasonoscopy (i, j) utilize Gauss distribution calculate weights Weight (i, j), as shown in the formula:
Weight ( i , j ) = 1 2 π σ e - ( Oimg ( i , j ) - μ ) 2 2 σ 2
Obtain weights image Weight, wherein (i is that position in the weights image is (i, j) corresponding weights j) to Weight;
2) all weights in the said weights image of traversal search out maximum weights Weight Max
3) utilizing said maximum weights that all weights are carried out normalization calculates:
Weight normal ( i , j ) = Weight ( i , j ) Weight max
Obtain the weights image after the normalization, wherein Weight Normal(i is after the weights image passes through normalization j), and the image meta is changed to (i, j) weights of correspondence;
4) according to the image after weights image after the said normalization and the said input picture calculating weighting:
Dimg(i，j)＝Oimg(i，j)·Weight normal(i，j)
Wherein, (i is that the weighted image meta is changed to (i, j) corresponding pixel value j) to Dimg.
5) image after utilizing pre-set threshold THr to said weighting carries out normalization and calculates:
Dimgnormal ( i , j ) = Dimg ( i , j ) THr
Wherein, (i is that the image meta was changed to (i, j) corresponding pixel value after weighted image passed through normalization j) to Dimgnormal.
6. three-dimensional according to claim 1 or four-dimensional ultrasound image Automatic Optimal control method is characterized in that, the method for cutting apart soft-tissue image described in the step 2 is to utilize the difference of signal to noise ratio snr to distinguish soft-tissue image zone and non-soft-tissue image zone.
7. three-dimensional according to claim 1 or four-dimensional ultrasound image Automatic Optimal control method; It is characterized in that; During three-dimensional Automatic Optimal on comprising depth direction, horizontal direction and time orientation was regulated, the function of the match gain curves described in the step 3 was:
I=f (D i, L j, T k), D i, L j, T kRepresent respectively along the independent variable of depth direction, horizontal direction and time orientation, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
8. three-dimensional according to claim 1 or four-dimensional ultrasound image Automatic Optimal control method; It is characterized in that; During three-dimensional Automatic Optimal on comprising depth direction, horizontal direction and longitudinal direction was regulated, the function of the match gain curves described in the step 3 was:
I=f (D i, L j, E k), D i, L j, E kRepresent respectively along the independent variable of depth direction, horizontal direction and longitudinal direction, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
9. three-dimensional according to claim 1 or four-dimensional ultrasound image Automatic Optimal control method; It is characterized in that; Four-dimensional Automatic Optimal is regulated; Comprise promptly in the adjusting on depth direction, horizontal direction, longitudinal direction and the time orientation that the function of the match gain curves described in the step 3 is: I=f (D i, L j, T k, E m), D i, L j, T k, E mRepresent respectively along the independent variable of depth direction, horizontal direction, time orientation and longitudinal direction, this function satisfies following three conditions:
1) this function is continuous at whole interval of definition;
2) this function can be led in whole interval of definition;
3) this function is a smooth function.
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