CN103202713A - Image optimization method by blending of ultrasound fundamental wave and harmonic wave - Google Patents

Abstract

The invention discloses an image optimization method by blending of ultrasound fundamental wave and harmonic wave. Based on a method of covariance coefficient of texture analysis and Yin Yang normalization, the invention combines the logarithmic compressed ultrasound fundamental wave and the texture harmonic wave, and then a blending image is obtained through scanning and transformation, so that the advantages of both can be utilized and the image quality can be improved.

Description

The image optimization method that a kind of medical supersonic first-harmonic and harmonic wave merge

Technical field

The invention belongs to medical ultrasound image Display Technique field, relate to the image optimization method that a kind of medical supersonic first-harmonic and harmonic wave merge.

Background technology

The medical ultrasonic image technology is one of the most frequently used clinically medical science influence, because of its safety, real-time, painless advantage such as radiationless and cheap, is widely used in clinical examination and diagnosis, extremely numerous medical personnels and patient's welcome.The ultrasonic Fundamental Imaging FI of B pattern (fundamental image) is substantially the most also to be the most frequently used imaging technique, it can provide the anatomical structure of organ for clinical diagnosis, be widely used in each section office, for example: monitoring fetal state and valvular diagnosis etc. in abdomen organ's diagnosis, the obstetrics.But B pattern ultrasonic imaging technique also has a lot of limitation, and relatively and other equipment such as CT, ultrasonoscopy resolution is lower, and imaging region is limited etc.

Tissue harmonic imaging THI (tissue harmonic image) technology is a kind of new ultrasonic imaging technique and more and more is used widely.In the process that ultrasound wave is propagated in tissue, nonlinear interaction because of tissue, produce frequency gradually for sending the harmonic signal of wave frequency integral multiple, the signal filtering that the tissue harmonic imaging interface differential technique is received obtains second harmonic, utilizes this second harmonic to form image.So harmonic frequency signal improves longitudinal resolution and improves, the main lobe of harmonic signal is narrower with respect to the main lobe of fundamental signal, so the harmonic image lateral resolution is better, the secondary lobe of harmonic signal is lower, so the noise of harmonic image reduces, the contrast resolution improves, in addition, the grating phenomenon of harmonic signal weakens, so the pseudo-shadow of harmonic image weakens.As Fig. 1, with respect to fundamental signal, the signal of tissue harmonic has narrower main lobe and lower secondary lobe.

But harmonic signal is that sound wave produces in the communication process in tissue gradually, so in the near field, not or more weak harmonic signal is arranged, and electronic noise is not stronger relatively; Harmonic signal strengthens with the degree of depth, but can decay gradually again after obtaining to a certain degree, so in the far field, harmonic signal dies down, electronic noise strengthens relatively.As Fig. 2, tissue harmonic signal intensity is different with the degree of depth, harmonic signal near-field signals a little less than, produce harmonic signal gradually with degree of depth increase, so middle field signal is stronger, but the degree of depth deepens can decay rapidly again after to a certain degree; And fundamental signal intensity is successively decreased with the degree of depth and is higher than harmonic signal.

Therefore, the tissue harmonic image has the better image quality in the midfield, and near field and far field, because the more weak picture quality of signal reduces, ultrasonic fundamental signal is then stronger relatively.As seen, ultrasonic first-harmonic image and harmonic image respectively have pluses and minuses.The mode that is used alone clinically is difficult to obtain high quality graphic, and the combination of both imaging techniques has necessity, and feasibility is also arranged.

Prior art merges ultrasonic first-harmonic image and radiography harmonic image.Radiography harmonic imaging technology is by inject the signal that acoustic contrast agent strengthens blood in human body in stream position in vein, be conducive to observe the thin blood vessel of human body internal hair, prior art is can strengthen focus at the radiography harmonic imaging, Fundamental Imaging can provide the characteristics of organizational boundary clearly, propose to utilize the ultrasonic contrast first-harmonic gathered under the same frequency and harmonic image to carry out image interfusion method based on the Curvele conversion, strengthened the organization edge in the harmonic image.This method is at first carried out the Curvelet conversion to ultrasonic contrast first-harmonic and harmonic image, Curvelet coefficient after decomposing is merged, fusion rule is: to the low frequency part weighted average, the HFS absolute value is got maximum method, and then pass through the image after the Curvelet inverse transformation obtains merging.This technology obtains all ultrasonoscopys clearly of organizational boundary and vascular detail, realizes the location of focal zone and organizational boundary.

The shortcoming of prior art:

Prior art is that first-harmonic image and radiography harmonic image merge, radiography harmonic imaging technology is the signal imaging that utilizes between the acoustic contrast agent reflection, the acoustic contrast agent signal is higher than about 4 times of fundamental signal, so the organizational structure information of non-angiosomes is very weak in the contrast agent imaging, it merges purpose is that the first-harmonic image is combined with vessel information.The present invention is the first-harmonic image and has higher details resolution and contrast resolution's tissue harmonic image co-registration, the tissue harmonic imaging technology is the Second Harmonic Imaging of utilizing sound wave to produce owing to nonlinear interaction in the communication process in tissue, details resolution and the contrast resolution of tissue image have been improved, with respect to the first-harmonic image, picture quality has a distinct increment.

The fusion method of prior art is not suitable for ultrasonic first-harmonic and tissue harmonic image co-registration, the method that prior art utilizes Curvelet to decompose, but near-field region and far-field region for the tissue harmonic image, signal a little less than, electronic noise is just bigger relatively, this method can not be distinguished the lower zone of signal to noise ratio, and fusion results comprises more electronic noise.The method based on normalized covariance coefficient and texture analysis that the present invention proposes has been considered the factors such as signal to noise ratio, resolution, contrast of tissue harmonic image to comprise less noise in the fusion results.

Summary of the invention

The image optimization method that the object of the present invention is to provide a kind of medical supersonic first-harmonic and harmonic wave to merge, to the ultrasonoscopy quality optimization, it is low to have overcome ultrasonic first-harmonic image detail resolution and contrast resolution, pseudo-shadow, the low low problem of signal to noise ratio that causes a little less than tissue harmonic image near field and the far-field signal that reaches of the picture quality that factors such as grating cause, utilize the method for texture analysis and normalized covariance coefficient, analyze both details resolution, contrast, signal to noise ratio, thereby utilize the texture factor and normalization coefficient to weigh both picture qualities and determine both weights, fundamental signal and tissue harmonic signal combination.For convex array probe and phased array probe, the coordinate relative position can change before and after the scan conversion, and the present invention is merged the image after scan conversion obtains merging then to the envelope signal before the surface sweeping conversion.

Its technical scheme is:

The image optimization method that a kind of medical supersonic first-harmonic and harmonic wave merge comprises the steps:

1) obtains the harmonious wave envelope data of first-harmonic

Probe alternately dispatching centre frequency is 2f ₀And f ₀Signal is for mid frequency 2f ₀Signal, directly adopting mid frequency is 2f ₀Fundamental signal is handled and is obtained envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression, is f for mid frequency ₀Signal, what probe received is that mid frequency is 2f ₀Signal, adopting high pass filter to filter mid frequency is f ₀The first-harmonic composition, reserve frequency is 2f ₀Harmonic components, and then handle and obtain envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression.

2) form characteristic (the image focusing degree of depth, mid frequency, point spread function (Point Spread Function, PSF), the visually-perceptible minimum is distinguished contrast size etc.), the size of setting window according to ultrasonoscopy;

According to the depth of focus of current images acquired default, supersonic frequency, calculate the speckle cell size:

S _cx＝0.87λz/D

S _cz＝0.91C ₀/Δf

In the formula, Scx, Scz is respectively lateral cell size and the vertical size of unit, and λ is wavelength, λ=C ₀/ Fc, z are the depth of focus, the probe aperture that D activates when being ultrasonic beam formation, C ₀Be the velocity of sound, Fc is mid frequency, Fc=2f ₀, Δ f calculates by Fc * bandwidth, and " bandwidth " is bandwidth, and based on a mark of probe tranmitting frequency, window size is 2 * 2 speckle unit when calculating texture analysis;

3) ask the covariance of tissue harmonic signal, calculate SNR

For envelope data, the SNR positive correlation of covariance coefficient and envelope data, and during SNR=0DB, the covariance coefficient is ncc=0.35, get adjacent two frame tissue harmonic envelope data, each is asked normalized covariance coefficient to sliding window, if ncc＜0.35, then the tissue harmonic picture signal too a little less than, do not participate in merging; If ncc＞0.35 then enters next step;

The covariance computing formula: $\mathrm{ncc}(x,y)=\frac{<x-\stackrel{\&OverBar;}{x},y-\stackrel{\&OverBar;}{y}>}{\sqrt{<x-\stackrel{\&OverBar;}{x},x-\stackrel{\&OverBar;}{x}>\&times;<y-\stackrel{\&OverBar;}{y},y-\stackrel{\&OverBar;}{y}>}}$

Wherein, $<x,y>=\underset{i=i_0-R}{\overset{i=i_0+R}{\mathrm{\&Sigma;}}}\underset{j=j_0-R}{\overset{j=j_0+R}{\mathrm{\&Sigma;}}}x(i,j)\&CenterDot;y(i,j)$

4) to tissue harmonic signal and fundamental signal texture analysis, the weights of difference computation organization's harmonic signal and fundamental signal

To the window of each slip, obtain fundamental signal and harmonic signal respectively with the difference rectangular histogram, obtain then and differ from histogrammic texture factor E (energy), I (entropy), C (contrast) asks the weights of fundamental signal and harmonic signal and differs from rectangular histogram according to the factor again

$H_s(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}+y_{k+d_{x}l+d_y}=i\}$

$H_d(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}-y_{k+d_{x}l+d_y}=i\}$

$N=\mathrm{Card}\left\{D\right\}=\underset{i}{\mathrm{\&Sigma;}}{H}_s\left(i\right)=\underset{j}{\mathrm{\&Sigma;}}{H}_d\left(j\right)$

${\hat{P}}_s\left(i\right)=H_s\left(i\right)/N;$ i＝(2，...2G)

${\hat{P}}_d\left(i\right)=H_d\left(i\right)/N;$ i＝(-G+1，...G-1)

Get the size of sliding window and try to achieve 2S by second step _Cx* 2S _CzAnd difference histogrammic be 0 ° apart from d=2 and the histogrammic direction of difference, 45 °, 90 ° and 135 °, every pair of sliding window, calculate four direction respectively with the difference rectangular histogram, all directions and poor histogrammic texture factor pair should be averaging;

The texture factor

E＝∑ _iP _s(i) ²∑ _jP _d(j) ²

I＝-∑ _iP _s(i)logP _s-∑ _jP _d(j)logP _d

C＝∑ _jj ²P _d(j)

According to the weights of texture factor calculating fundamental signal and tissue harmonic signal, with being subordinate to the sigmf function to weighing straight reinforcement, can regulate sigmf function parameters a and select the difference of weights to strengthen degree then, the weights of fundamental signal and harmonic signal satisfy W _FI+ W _THI=1;

${\hat{W}}_{\mathrm{FI}}=\left\{\begin{array}{cc}{w}_{i}s.t|w_i-0.5|=\underset{i}{\max }\left(\right|W_i-0.5\left|\right)& \mathrm{ncc}>\mathrm{\&mu;}\\ 1& \mathrm{ncc}\&le;\mathrm{\&mu;}\end{array}\right.$

$W_i{|}_{i=1}=W_{\mathrm{FI}\_E}=\frac{E_{\mathrm{THI}}}{E_{\mathrm{FI}}+E_{\mathrm{THI}}}$

$W_i{|}_{i=2}{W}_{\mathrm{FI}\_C}=\frac{C_{\mathrm{FI}}}{C_{\mathrm{FI}}+C_{\mathrm{THI}}}$

$W_i{|}_{i=3}{W}_{\mathrm{FI}\_I}=\frac{I_{\mathrm{FI}}}{I_{\mathrm{FI}}+I_{\mathrm{THI}}}$

5) fusion and scan conversion

The fundamental signal that calculates according to step 4) and the weights of harmonic signal merge envelope to first-harmonic and tissue harmonic envelope _Fuston=W _FI* envelope _FI+ W _THI* envelpe _THI, pass through scan conversion again, the image after obtaining merging.

The present invention's beneficial effect compared with prior art

The image optimization method that medical supersonic first-harmonic of the present invention and harmonic wave merge utilizes the better feature of secondary structure harmonic image midfield picture quality, methods analyst picture quality with texture analysis and normalized covariance coefficient, and then fundamental signal and harmonic wave signal fused, traditional first-harmonic image midfield quality is improved.

Description of drawings

Fig. 1 is that first-harmonic and 10 times of harmonic waves are at the cross direction profiles curve;

Fig. 2 is that fundamental signal and harmonic wave signal intensity are with the change in depth curve;

Fig. 3 is the image optimization method flow chart that medical supersonic first-harmonic of the present invention and harmonic wave merge;

Fig. 4 is the image of liver, and wherein Fig. 4 a is the first-harmonic image, and Fig. 4 b is the tissue harmonic image, and Fig. 4 c is the image after merging;

Fig. 5 is the image of kidney, and wherein Fig. 5 a is the first-harmonic image, and Fig. 5 b is the tissue harmonic image, and Fig. 5 c is the image after merging.

The specific embodiment

Below in conjunction with the drawings and specific embodiments technical scheme of the present invention is described further.

The image optimization method that a kind of medical supersonic first-harmonic and harmonic wave merge comprises the steps:

1) obtains the harmonious wave envelope data of first-harmonic

Probe alternately dispatching centre frequency is 2f ₀And f ₀Signal is for mid frequency 2f ₀Signal, directly adopting mid frequency is 2f ₀Fundamental signal is handled and is obtained envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression, in. frequency of heart is f ₀Signal, what probe received is that mid frequency is 2f ₀Signal, adopting high pass filter to filter mid frequency is f ₀The first-harmonic composition, reserve frequency is 2f ₀Harmonic components, and then handle and obtain envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression; Because of the relative fundamental signal of tissue harmonic signal a little less than, when logarithmic compression, the gain compensation of tissue harmonic signal than the gain compensation big 10 of fundamental signal to 15DB.

2) form characteristic (the image focusing degree of depth, mid frequency, point spread function (Point Spread Function, PSF), the visually-perceptible minimum is distinguished contrast size etc.), the size of setting window according to ultrasonoscopy;

According to the depth of focus of current images acquired default, supersonic frequency, calculate the speckle cell size:

S _cx＝0.87λz/D

S _cz＝0.91C ₀/Δf

In the formula, Scx, Scz is respectively lateral cell size and the vertical size of unit, and λ is wavelength, λ=C ₀/ Fc, z are the depth of focus, the probe aperture that D activates when being ultrasonic beam formation, C ₀Be the velocity of sound, (for example, 1.54mm/ μ s), Fc is mid frequency, Fc=2f ₀, Δ f calculates by Fc * bandwidth, and " bandwidth " is bandwidth, and based on a mark of probe tranmitting frequency (for example 80%), window size is 2 * 2 speckle unit during the calculating texture analysis;

3) ask the covariance of tissue harmonic signal, calculate SNR

According to experimental results show that, for envelope data, the SNR positive correlation of covariance coefficient and envelope data, and during SNR=0DB, covariance coefficient ncc=0.35 gets adjacent two frame tissue harmonic envelope data, each is asked normalized covariance coefficient to sliding window, if ncc＜0.35, then the tissue harmonic picture signal too a little less than, do not participate in merging; If ncc＞0.35 then enters next step;

The covariance computing formula: $\mathrm{ncc}(x,y)=\frac{<x-\stackrel{\&OverBar;}{x},y-\stackrel{\&OverBar;}{y}>}{\sqrt{<x-\stackrel{\&OverBar;}{x},x-\stackrel{\&OverBar;}{x}>\&times;<y-\stackrel{\&OverBar;}{y},y-\stackrel{\&OverBar;}{y}>}}$

Wherein, $<x,y>=\underset{i=i_0-R}{\overset{i=i_0+R}{\mathrm{\&Sigma;}}}\underset{j=j_0-R}{\overset{j=j_0+R}{\mathrm{\&Sigma;}}}x(i,j)\&CenterDot;y(i,j)$

4) to tissue harmonic signal and fundamental signal texture analysis, the weights of difference computation organization's harmonic signal and fundamental signal

According to the reflection picture quality that experiment showed, that the texture factor can be stable.Use among the present invention and differ from histogrammic texture analysis method.To the window of each slip, obtain fundamental signal and harmonic signal respectively with the difference rectangular histogram, obtain then and differ from histogrammic texture factor E (energy), I (entropy), C (contrast) asks the weights of fundamental signal and harmonic signal again according to the factor

With the difference rectangular histogram

$H_s(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}+y_{k+d_{x}l+d_y}=i\}$

$H_d(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}-y_{k+d_{x}l+d_y}=i\}$

$N=\mathrm{Card}\left\{D\right\}=\underset{i}{\mathrm{\&Sigma;}}{H}_s\left(i\right)=\underset{j}{\mathrm{\&Sigma;}}{H}_d\left(j\right)$

${\hat{P}}_s\left(i\right)=H_s\left(i\right)/N;$ i＝(2，...2G)

${\hat{P}}_d\left(i\right)=H_d\left(i\right)/N;$ i＝(-G+1，...G-1)

Among the present invention, get the size of sliding window and try to achieve 2S by second step _Cx* 2S _CzAnd difference histogrammic be 0 ° apart from d=2 and the histogrammic direction of difference, 45 °, 90 ° and 135 °, every pair of sliding window, calculate four direction respectively with the difference rectangular histogram, all directions and poor histogrammic texture factor pair should be averaging;

The texture factor

E＝∑ _iP _s(i) ²∑ _jP _d(j) ²

I＝-∑ _iP _s(i)logP _s-∑ _jP _d(j)logP _d

C＝∑ _jj ²P _d(j)

According to the weights of texture factor calculating fundamental signal and tissue harmonic signal, with being subordinate to the sigmf function weights are being strengthened then, can regulate sigmf function parameters a and select the difference of weights to strengthen degree, the weights of fundamental signal and harmonic signal satisfy W _FI+ W _THI=1;

${\hat{W}}_{\mathrm{FI}}=\left\{\begin{array}{cc}{w}_{i}s.t|w_i-0.5|=\underset{i}{\max }\left(\right|W_i-0.5\left|\right)& \mathrm{ncc}>\mathrm{\&mu;}\\ 1& \mathrm{ncc}\&le;\mathrm{\&mu;}\end{array}\right.$

$W_i{|}_{i=1}=W_{\mathrm{FI}\_E}=\frac{E_{\mathrm{THI}}}{E_{\mathrm{FI}}+E_{\mathrm{THI}}}$

$W_i{|}_{i=2}{W}_{\mathrm{FI}\_C}=\frac{C_{\mathrm{FI}}}{C_{\mathrm{FI}}+C_{\mathrm{THI}}}$

$W_i{|}_{i=3}{W}_{\mathrm{FI}\_I}=\frac{I_{\mathrm{FI}}}{I_{\mathrm{FI}}+I_{\mathrm{THI}}}$

5) fusion and scan conversion

The fundamental signal that calculates according to step 4) and the weights of harmonic signal merge envelope to first-harmonic and tissue harmonic envelope _Fuston=W _FI* envelope _FI+ W _THI* envelpe _THI, pass through scan conversion again, the image after obtaining merging.

Part at technical solution of the present invention also has following two kinds of replacement schemes

Scheme one, obtain part at first-harmonic in the step 1) and harmonic signal, can use ultrasonic probe to send a mid frequency and be f ₀Signal, obtaining frequency respectively by different wave filter is f ₀Fundamental signal and frequency be 2f ₀Harmonic signal, utilize these two kinds of signals to enter next step fusion.This scheme has improved frame frequency.

Scheme two, at the part of obtaining of harmonic signal, replacement scheme is: double transmission frequency is f ₀Signal plus to obtain frequency be 2f ₀Harmonic signal, wherein the frequency of twice transmission is f ₀The signal phase phase difference of pi, 2f after the addition ₀Harmonic signal strengthen, frequency is f ₀First-harmonic part and the positive and negative counteracting of odd harmonic part.The advantage of this scheme is that the raising of harmonic signal signal to noise ratio and removal first-harmonic part are cleaner, and shortcoming is just can obtain a frame harmonic signal because send twice, so frame frequency reduces.

The above; only be the preferable specific embodiment of the present invention; protection scope of the present invention is not limited thereto; anyly be familiar with those skilled in the art; in the technical scope that the present invention discloses, the simple transformation of the technical scheme that can obtain apparently or equivalence are replaced and are all fallen within the scope of protection of the present invention.

Claims (1)

1. the image optimization method that merges of a medical supersonic first-harmonic and harmonic wave is characterized in that, comprises the steps:

1) obtains the harmonious wave envelope data of first-harmonic

Probe alternately dispatching centre frequency is 2f ₀And f ₀Signal is for mid frequency 2f ₀Signal, directly adopting mid frequency is 2f ₀Fundamental signal is handled and is obtained envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression, is f for mid frequency ₀Signal, what probe received is that mid frequency is 2f ₀Signal, adopting high pass filter to filter mid frequency is f ₀The first-harmonic composition, reserve frequency is 2f ₀Harmonic components, and then handle and obtain envelope signal through orthogonal modulation, low-pass filtering, envelope detected, log compression.

2) form characteristic according to ultrasonoscopy: the image focusing degree of depth, mid frequency, point spread function (Point Spread Function, PSF), the minimum contrast size of distinguishing of visually-perceptible, the size of setting window;

According to the depth of focus of gathering the present image default, supersonic frequency, calculate the speckle cell size:

S _cx＝0.87λz/D

S _cz＝0.91C ₀/Δf

In the formula, Scx, Scz is respectively lateral cell size and the vertical size of unit, and λ is wavelength, λ=C ₀/ Fc, z are the depth of focus, the probe aperture that D activates when being ultrasonic beam formation, C ₀Be the velocity of sound, Fc is mid frequency, Fc=2f ₀, Δ f calculates by Fc * bandwidth, and " bandwidth " is bandwidth, and based on a mark of probe tranmitting frequency, window size is 2 * 2 speckle unit when calculating texture analysis;

3) ask the covariance of tissue harmonic signal, calculate SNR

For envelope data, the SNR positive correlation of covariance coefficient and envelope data, and during SNR=0DB, the covariance coefficient is ncc=0.35, get adjacent two frame tissue harmonic envelope data, each is asked normalized covariance coefficient to sliding window, if ncc＜0.35, then the tissue harmonic picture signal too a little less than, do not participate in merging; If ncc＞0.35 then enters next step;

The covariance computing formula: $\mathrm{ncc}(x,y)=\frac{<x-\stackrel{\&OverBar;}{x},y-\stackrel{\&OverBar;}{y}>}{\sqrt{<x-\stackrel{\&OverBar;}{x},x-\stackrel{\&OverBar;}{x}>\&times;<y-\stackrel{\&OverBar;}{y},y-\stackrel{\&OverBar;}{y}>}}$

Wherein, $<x,y>=\underset{i=i_0-R}{\overset{i=i_0+R}{\mathrm{\&Sigma;}}}\underset{j=j_0-R}{\overset{j=j_0+R}{\mathrm{\&Sigma;}}}x(i,j)\&CenterDot;y(i,j)$

4) to tissue harmonic signal and fundamental signal texture analysis, the weights of difference computation organization's harmonic signal and fundamental signal

To the window of each slip, obtain fundamental signal and harmonic signal respectively with the difference rectangular histogram, obtain then and differ from histogrammic texture factor E (energy), I (entropy), C (contrast) asks the weights of fundamental signal and harmonic signal and differs from rectangular histogram according to the factor again

$H_s(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}+y_{k+d_{x}l+d_y}=i\}$

$H_d(i,d_x,d_y)=\mathrm{Card}\left\{(k,l)\right|y_{k,l}-y_{k+d_{x}l+d_y}=i\}$

$N=\mathrm{Card}\left\{D\right\}=\underset{i}{\mathrm{\&Sigma;}}{H}_s\left(i\right)=\underset{j}{\mathrm{\&Sigma;}}{H}_d\left(j\right)$

${\hat{P}}_s\left(i\right)=H_s\left(i\right)/N;$ i＝(2，...2G)

${\hat{P}}_d\left(i\right)=H_d\left(i\right)/N;$ i＝(-G+1，...G-1)

Get the size of sliding window and try to achieve 2S by second step _Cx* 2S _CzAnd difference histogrammic be 0 ° apart from d=2 and the histogrammic direction of difference, 45 °, 90 ° and 135 °, every pair of sliding window, calculate four direction respectively with the difference rectangular histogram, all directions and poor histogrammic texture factor pair should be averaging;

The texture factor

E＝∑ _iP _s(i) ²∑ _jP _d(j) ²

I＝-∑ _iP _s(i)logP _s-∑ _jP _d(j)logP _d

C＝∑ _ij ²P _d(j)

According to the weights of texture factor calculating fundamental signal and tissue harmonic signal, with being subordinate to the sigmf function weights are being strengthened then, can regulate sigmf function parameters a and select the difference of weights to strengthen degree, the weights of fundamental signal and harmonic signal satisfy W _FI+ W _THI=1;

${\hat{W}}_{\mathrm{FI}}=\left\{\begin{array}{cc}{w}_{i}s.t|w_i-0.5|=\underset{i}{\max }\left(\right|W_i-0.5\left|\right)& \mathrm{ncc}>\mathrm{\&mu;}\\ 1& \mathrm{ncc}\&le;\mathrm{\&mu;}\end{array}\right.$

$W_i{|}_{i=1}=W_{\mathrm{FI}\_E}=\frac{E_{\mathrm{THI}}}{E_{\mathrm{FI}}+E_{\mathrm{THI}}}$

$W_i{|}_{i=2}{W}_{\mathrm{FI}\_C}=\frac{C_{\mathrm{FI}}}{C_{\mathrm{FI}}+C_{\mathrm{THI}}}$

$W_i{|}_{i=3}{W}_{\mathrm{FI}\_I}=\frac{I_{\mathrm{FI}}}{I_{\mathrm{FI}}+I_{\mathrm{THI}}}$

5) fusion and scan conversion

The fundamental signal that calculates according to step 4) and the weights of harmonic signal merge envelope to first-harmonic and tissue harmonic envelope _Fuston=W _FI* envelope _FI+ W _THI* envelpe _THI, pass through scan conversion again, the image after obtaining merging.

Images