CN102499711B - 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 PDFInfo
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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
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 for the ultrasonoscopy that obtains high-quality, we need take the gain compensation measure, make the image gain of loss be compensated to reach an even brightness ultrasonoscopy preferably.
In ultrasonic diagnostic equipment to the compensation of image gain, generally be at compensating on the depth direction, be called DGC (Depth Gain Compensation, 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 general multistage (include but are not limited to 8 sections, 16 sections, the 24 sections etc.) potentiometer that adopts and adjusts the amplification of different depth.Be example with 8 sections TGC, its TGC curve is spliced by 7 sections straight lines, and the endpoint value of its 8 breaks can determine that intermediate value is obtained by linear interpolation by the two ends yield value by 8 sections TGC potentiometer values on the control panel.The doctor can change gain curve by regulator potentiometer, and then image is compensated.
Automatic gain control method commonly used at present at first needs image is carried out the piecemeal processing, 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 optimization picture 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 threshold value given in advance, it then is 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, non-soft-tissue image zone (is the near field, the noise in far field and the cyst in the soft tissue etc.) the pixel value corresponding be 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 to soft-tissue image's judgement, then can occur making correct diagnosis to being the phenomenon of the part undercompensation of soft-tissue image originally thereby influence the doctor.
No matter be DGC or TGC, all be that the gain reduction on depth direction compensates at ultrasonoscopy, 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, propose ultrasonoscopy is carried out in a lateral direction the method for gain compensation at gain compensating method, be also referred to as LGC (Lateral Gain Compensation, lateral gain compensation).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 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 may only carry out gain compensation at both direction and still can not compensate all sidedly along with increasing the gain loss that brings sweep time along any direction non-uniform change of image.
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 finished the adjusting of TGC parameter and can only regulate the shortcoming that the gain on one or two direction brings.
According to technical scheme provided by the invention, described three-dimensional or four-dimensional ultrasound image Automatic Optimal control method may further comprise the steps:
1) input ultrasound image data, described 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.
Described three-dimensional refers to any three-dimensional in depth direction, horizontal direction, longitudinal direction and the time orientation four-dimension.
Described four-dimensional Automatic Optimal is regulated the adjusting that comprises 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 by Gauss distribution.Describedly calculate weights by Gauss distribution and distinguish the step in soft-tissue image zone and non-soft-tissue image zone and comprise:
1) according to the 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:
Obtain weights image Weight, wherein (i is that position in the weights image is for (i, j) Dui Ying weights j) to Weight;
2) all weights in the described weights image of traversal search out maximum weights Weight
Max
3) utilizing described maximum weights that all weights are carried out normalization calculates:
Obtain the weights image after the normalization, wherein Weight
Normal(i is that the image meta was set to (i, j) Dui Ying weights after the weights image passed through normalization j);
4) according to the image after the weights image after the described normalization and the described input picture calculating weighting:
Dimg(i，j)＝Oimg(i，j)·Weight
normal(i，j)
Wherein, (i is that the weighted image meta is set to (i, j) corresponding pixel value j) to Dimg.
5) image after utilizing pre-set threshold THr to described weighting carries out normalization and calculates:
Wherein, (i is that the image meta was set 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 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 smooth function.
Four-dimensional Automatic Optimal is regulated, and comprises namely 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 smooth function.
Advantage of the present invention is: the 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 to 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 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
Describe each related detailed problem in the technical solution of the present invention in detail below in conjunction with drawings and Examples.
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, can convert electrical energy into acoustic energy, also acoustic energy can be converted to electric energy, at first radiating circuit is under the coordination of controller, send the signal of telecommunication to transducer, being converted into ultrasonic emitting by transducer goes out, receiving circuit is responsible for receiving transducer and is passed the echo signal (being converted to the signal of telecommunication by transducer) of coming, and it is amplified, processing such as digital to analog conversion, wave beam is synthetic to carry out dynamic focusing and dynamic aperture processing to the echo signal on the different directions, it is synthesized together, and the signal that forms after synthetic to wave beam of signal processing and image carries out noise suppressed then, envelope detection, processing such as logarithmic compression finally show at display.Shown ultrasonoscopy comprises between the dead space, soft-tissue image zone (comprising shown image-regions such as skin, tegumentary nerve, shallow blood vessel) and strong reflection zone (such as shown zones such as bone, skulls).The present invention pays close attention to is wherein soft-tissue image zone, also is the zone of in the ultrasonic examination diagnosis being worked.
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 the 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 to depth direction, horizontal direction, longitudinal direction and data that time orientation comprises obtain gain curves do match, its space coordinates include but not limited to depth direction, horizontal direction, time orientation, a plurality of directions such as longitudinal direction, in concrete enforcement, can be to be optimized on the three dimensions, also can be to be optimized on the space-time, for example, optimization on the three dimensions can be to along depth direction, each independent variable on horizontal direction and the time orientation carries out match, another embodiment can be to along depth direction, each independent variable on horizontal direction and the longitudinal direction carries out match, further, optimization on the space-time can be to along depth direction, horizontal direction, each independent variable on time orientation and the longitudinal direction carries out match, and then calculates 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, for the explanation of three-dimensional or four-dimensional ultrasound image Automatic Optimal control method as shown in Figure 3, in step 32, at first wanting clear and definite pending data is through the 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 weights image Weight with the equal size of original image.The average of described tissue (tissue) pixel and variance refer to average and the variance that tissue that current ultrasonic device checks calculates such as corresponding images 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):
Known in Gauss distribution, this distributes by two parameters---average value mu and variance δ
2Determine that its probability density function curve is symmetrical center line with average μ, variance δ
2More little, distributing more concentrates near the average μ.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 distance tissue pixels average is more and more far away, its corresponding weights are also more and more littler, the benefit of doing like this is: the soft-tissue image in the ultrasonoscopy and non-soft-tissue image (such as the zones such as cyst that are included in soft tissue inside) 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 the 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, realize the method for weighting and be not limited to disclose among the present invention utilize Gauss distribution to calculate the method for weights according to tissue pixels average and variance, such as utilizing other implementation methods to realize the calculating etc. of weightings according to a lot of similarly statistical parameters (such as done parameter after the corresponding conversion etc. by average or variance), can think distortion of the present invention, in sum, 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 calculates then and calculate in step 33, 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 according to following formula and calculate:
Because in Gauss distribution, near average, corresponding probability-distribution function value is more big, 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 more big 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 more far away, namely the closer to 0 value, then represent this pixel corresponding pixel value more away from the tissue pixels average, and then this pixel to belong to the probability of soft-tissue image more little, and it is more big 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 just has substantial connection for whether the weights size that some pixel correspondences are described belongs to soft-tissue image with this point, in actual implementation procedure, the size of weights is not cut apart soft-tissue image and non-soft-tissue image 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 as follows:
Dimg＝Oimg·Weight
normal
Two matrixes of the expression here carry out dot product.
That is to say that the weights with each pixel correspondence 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 and belong to soft-tissue image, if pixel value is the decimal near 0 value, then represent this part pixel and 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 are carried out normalized according to pre-set threshold THr, its computational methods are identical with the method that discloses in step 34:
Wherein (i j) carries out numerical value after the normalization for the image after the weighting to Dimgnormal.
Need to prove, in order to guarantee the Dimgnormal (i after the normalization, j) be between 0 value to 1 value, then pre-set threshold can be the pixel value of the maximum in the original image, certainly, pre-set threshold is not limited in this, and the user can thinking according to the present invention do corresponding conversion, there is no any specific restriction.
Fig. 4, Fig. 5 and Fig. 6 further describe the method by weighting differentiation soft-tissue image in the above-mentioned steps.
Fig. 4 is ultrasonoscopy matrix sketch map.If the pixel average of current organization is 63, then the pixel value of the pixel of soft-tissue image region concentrates near the 60-70 as can be seen, 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.
Utilize weight matrix that Gauss distribution calculates former ultrasonoscopy correspondence as shown in Figure 5 according to the pixel average of tissue, because the characteristic of Gauss distribution, probability density function values the closer to the pixel correspondence of average is more big, be that so-called weights are more big, otherwise, more the probability density function values away from the pixel correspondence of average is more little, so contrast ultrasonoscopy shown in Figure 4 and weights image shown in Figure 5 as can be seen, the weights that the pixel that approaches with the tissue pixels average is corresponding are higher than the corresponding weights of those pixels that differ greatly with the tissue pixels average far away, and differ and reach two to three orders of magnitude, weights with each pixel of calculating are weighted on the corresponding pixel then, obtain the image after the weighting as shown in Figure 6.
As shown in Figure 6, as can be seen, the pixel value of the image after the weighting distributes clearly, approach through the pixel value of the partial pixel point after the weighting and the pixel average of basic stitch, think that then the pixel that these and tissue pixels average approach belongs to soft-tissue image, and the pixel average of the partial pixel point after the weighting and basic stitch differs at two to three orders of magnitude, then think right and wrong soft-tissue image, as can be seen, image after the weighting distinguishes the soft-tissue image that diagnosis is played an important role and non-soft-tissue image mutually, and all pixels in the image have corresponding pixel value, 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 value of handling any pixel correspondence in the image of back 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 of choosing owing to threshold value with certain part in the soft-tissue image is invalid pixel, this is concerning the doctor, lost a part of information that diagnosis is had important value beyond doubt, 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 according to the soft-tissue image zone to do like this and making the amount of calculation increase undoubtedly 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, do not need to do any interpolation arithmetic and just realized 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 is weighted calculating, 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 by 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 as follows:
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.
According to above-mentioned formula as can be seen, the SNR value is relevant with 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 (such as noise region and strong reflection zone).Be divided into three zones such as current ultrasonoscopy: strong reflection district, soft-tissue image district and noise range, its respectively corresponding signal to noise ratio be 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 Dui Ying 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, be that it is more near desirable SNR value, then this region decision is that the probability of soft-tissue image is more big, otherwise is that the probability of strong reflection zone or noise region is more big.It is more stable that this method is carried out soft-tissue image's area judging than above-mentioned method by 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 there is no particular restriction.
After distinguishing soft-tissue image effectively by weighting, the gain curves match in will regulating three-dimensional and four-dimensional Automatic Optimal by 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 to some extent along the brightness of depth direction and horizontal direction, 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 ultrasonoscopy better along the gain reduction of depth direction D (depth), horizontal direction L (lateral) and time orientation T (time), (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 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:
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.
Determine coefficient a by separating the overdetermined equation group then
I, j, kAnd then definite gain curves.
To the above-mentioned depth direction that comprises, horizontal direction, the match of the space-time of time orientation and pixel value includes but are not limited to the method described in the embodiment A, in specific implementation process, the user can adopt several different methods to finish the match of gain curves according to present case, 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, there is no specific limited.Every above-mentioned space-time match is tried to achieve the approximating method of ultrasonoscopy yield value 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.Be example with the two dimensional phased array transducer, 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, carrying out the three-dimensional elevation angle along the fan-shaped movement of z direction of principal axis again turns to, form the pyramid data base, carry out space orientation for a series of discrete two dimensional image 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 with variation along the longitudinal direction, z direction of principal axis as shown in Figure 8, so the pixel value of ultrasonoscopy is about depth direction D (depth), the function of horizontal direction L (lateral) and longitudinal direction E (elevation), wherein depth direction D, horizontal direction L and longitudinal direction E x axle as shown in Figure 8, direction shown in y axle and the z axle.
In order to compensate ultrasonoscopy better along the gain reduction of depth direction D (depth), horizontal direction L (lateral) and longitudinal direction E (elevation), (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 about these three independent variables of picture depth, horizontal direction and longitudinal direction, 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.
In order to illustrate further above-mentioned functional relationship, in one embodiment of the invention, can adopt multinomial to come the match gain curves:
A wherein
I, j, kBe undetermined coefficient, M
1, M
2And M
3Can identical size, also can be different, determine coefficient a by separating the overdetermined equation group then
I, j, kAnd then definite gain curves.
To the above-mentioned depth direction that comprises, horizontal direction, the match of the space-time of longitudinal direction and pixel value includes but are not limited to the method described in embodiment A and the Embodiment B, in specific implementation process, the user can adopt multiple mode to finish the match of gain curves according to present case, it is not limited only to adopt the multinomial method, be 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, it can also be the nonlinear multivariable surface fitting, methods such as polynary least square fitting, can adopt multiple mode to realize, there is no specific limited.Every above-mentioned space-time match is tried to achieve the approximating method of ultrasonoscopy yield value all within protection domain of the present invention.
Four-dimensional Automatic Optimal Embodiment C:
Above-described embodiment A can be to comprising depth direction, horizontal direction and time orientation carry out Automatic Optimal to be regulated, can be to comprising depth direction in Embodiment B, horizontal direction and longitudinal direction carry out Automatic Optimal to be regulated, but only regulate in three directions for present four-dimensional color ultrasound and 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 is shown continuously according to the sequencing of time cycle, form the real-time and dynamic 3-D view, so in Embodiment B, only considered depth direction, horizontal direction and longitudinal direction obviously are inadequate, also need to consider the variation of ultrasonoscopy pixel value on time dimension, so in four-dimensional ultrasound, the pixel value of its ultrasonoscopy is about depth direction D (depth), horizontal direction L (lateral), the function of longitudinal direction E (elevation) and time orientation T (time), wherein depth direction D, horizontal direction L, longitudinal direction E and time orientation T x axle as shown in Figure 9, the y axle, direction shown in z axle and the t axle.
In order to compensate ultrasonoscopy better along the gain reduction of depth direction D (depth), horizontal direction L (lateral), longitudinal direction E (elevation) and time orientation T (time), (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, m) be about picture depth, horizontal direction, sweep time and the vertical function of these four independent variables of time, 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:
A wherein
I, j, k, mBe undetermined coefficient, M
1, M
2, M
3And M
4Can identical size, also can be different, determine coefficient a by separating the overdetermined equation group then
I, j, k, mAnd then definite gain curves.
To the above-mentioned depth direction that comprises, horizontal direction, vertical time, the match of the quintuple space of time orientation and pixel value includes but are not limited to the method described in the embodiment A, in specific implementation process, the user can adopt multiple mode to finish the match of gain curves according to present case, 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 the logarithmic function match, if current change in gain is exponential trend, then can adopt the exponential function match, it can also be the nonlinear multivariable surface fitting, methods such as polynary least square fitting, can adopt multiple mode to realize, there is no specific limited.Every above-mentioned quintuple space match is tried to achieve the approximating method of ultrasonoscopy yield value all within protection domain of the present invention.
At last, calculate gain compensation value according to pre-set threshold, use it for three-dimensional or four-dimensional ultrasound image Automatic Optimal and regulate.
In an embodiment of the present invention, be in order to reduce operand to the image array after the weighting through carrying out surface fitting again after the normalized, also can directly carry out surface fitting to the image array after the weighting, this dual mode all can reach identical technique effect, and being not limited to the mode described in the present invention, this point is readily appreciated that to those skilled in the art.
The concrete steps of above-described embodiment and the explanation of relevant indicators are comparatively concrete; can not therefore think the 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 (6)
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, described 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;
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 smooth function;
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 by Gauss distribution;
Describedly calculate weights by Gauss distribution and distinguish the step in soft-tissue image zone and non-soft-tissue image zone and comprise:
1) according to the 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:
Obtain weights image Weight, wherein (i is that position in the weights image is for (i, j) Dui Ying weights j) to Weight;
2) all weights in the described weights image of traversal search out maximum weights Weight
Max
3) utilizing described maximum weights that all weights are carried out normalization calculates:
Obtain the weights image after the normalization, wherein Weight
Normal(i is that the image meta was set to (i, j) Dui Ying weights after the weights image passed through normalization j);
4) according to the image after the weights image after the described normalization and the described input picture calculating weighting:
Dimg（i,j）=Oimg(i,j)·Weight
normal(i,j)
Wherein, (i is that the weighted image meta is set to (i, j) corresponding pixel value j) to Dimg;
5) image after utilizing pre-set threshold THr to described weighting carries out normalization and calculates:
Wherein, (i is that the image meta was set to (i, j) corresponding pixel value after weighted image passed through normalization j) to Dimgnormal.
2. three-dimensional as claimed in claim 1 or four-dimensional ultrasound image Automatic Optimal control method is characterized in that described three-dimensional refers to 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, described four-dimensional Automatic Optimal is regulated the adjusting that comprises 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 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.
5. 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 smooth function.
6. 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 namely 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 smooth function.
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| CN103845077B (en) * | 2012-12-05 | 2016-01-20 | 深圳迈瑞生物医疗电子股份有限公司 | Ultrasonoscopy gain optimization method and the Gain Automatic optimization device of ultra sonic imaging |
| CN104537621B (en) * | 2014-12-31 | 2017-12-12 | 深圳开立生物医疗科技股份有限公司 | A kind of method, apparatus and system of two-way Automatic Optimal |
| CN105559829A (en) * | 2016-01-29 | 2016-05-11 | 任冰冰 | Ultrasonic diagnosis and imaging method thereof |
| CN106618632B (en) * | 2016-12-14 | 2020-08-07 | 无锡祥生医疗科技股份有限公司 | Automatic optimized ultrasonic imaging system and method |
| CN106725612B (en) * | 2016-12-23 | 2020-10-23 | 深圳开立生物医疗科技股份有限公司 | Four-dimensional ultrasonic image optimization method and system |
| CN108198158B (en) * | 2017-12-28 | 2021-06-01 | 江苏师范大学 | An Ultrasound and Magnetic Resonance Imaging Fusion Method Based on Rayleigh Noise Distribution |
| CN108553763B (en) * | 2018-01-19 | 2020-02-18 | 北京工业大学 | A microwave hyperthermia monitoring method based on ultrasonic echo decorrelation imaging technology |
| WO2019145188A1 (en) * | 2018-01-24 | 2019-08-01 | Koninklijke Philips N.V. | Ultrasound system with improved noise performance by persistence processing |
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| CN108877902B (en) * | 2018-08-02 | 2020-12-04 | 飞依诺科技(苏州)有限公司 | Ultrasound image brightness adjustment method and adjustment system |
| CN109242794B (en) * | 2018-08-29 | 2021-05-11 | Oppo广东移动通信有限公司 | Image processing method, apparatus, electronic device, and computer-readable storage medium |
| CN109512457B (en) * | 2018-10-15 | 2021-06-29 | 东软医疗系统股份有限公司 | Method, device and equipment for adjusting gain compensation of ultrasonic image and storage medium |
| CN110148090B (en) * | 2019-01-19 | 2023-04-07 | 深圳蓝影医学科技股份有限公司 | Automatic optimization method and device for B-type image gain |
| CN110327076B (en) * | 2019-07-05 | 2022-08-16 | 深圳开立生物医疗科技股份有限公司 | Blood flow gain adjusting method, device, equipment and readable storage medium |
| CN112788997B (en) * | 2019-08-23 | 2024-08-06 | 深圳迈瑞生物医疗电子股份有限公司 | Liver and kidney echo contrast measuring method, equipment, medical system and storage medium |
| CN114690120B (en) * | 2021-01-06 | 2024-11-22 | 杭州嘉澜创新科技有限公司 | A positioning method, device and system, and computer-readable storage medium |
| CN113014827B (en) * | 2021-03-05 | 2022-12-06 | 深圳英美达医疗技术有限公司 | Imaging automatic gain compensation method, system, storage medium and ultrasonic endoscope |
| CN116400490B (en) * | 2023-06-08 | 2023-08-25 | 杭州华得森生物技术有限公司 | Fluorescence microscopic imaging system and method thereof |
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