CN103784164A - Method and system for processing ultrasonic signals - Google Patents
Method and system for processing ultrasonic signals Download PDFInfo
- Publication number
- CN103784164A CN103784164A CN201410054451.3A CN201410054451A CN103784164A CN 103784164 A CN103784164 A CN 103784164A CN 201410054451 A CN201410054451 A CN 201410054451A CN 103784164 A CN103784164 A CN 103784164A
- Authority
- CN
- China
- Prior art keywords
- signal
- echo
- bar
- noise
- ultrasonic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention provides a method and system for processing ultrasonic signals. The method includes the following steps that N pieces of echo signals are received, and M pieces of echo signals are randomly extracted from the N pieces of echo signals to be processed so as to calculate a system function, wherein M is smaller than N; linear interpolation calculation is performed on the received N pieces of echo signals so that the N pieces of echo signals can be increased to P pieces of echo signals, wherein P is larger than N; on the basis of the calculated system function and the P pieces of echo signals, deconvolution is performed; a target image is acquired. According to the method and system for processing the ultrasonic signals, before the target image is acquired, the echo signals are processed in the deconvolution mode, noise in the echo signals is removed, original echo signals are reserved to the maximum extent, the contrast of the target image acquired finally is remarkably enhanced, and therefore the resolution ratio of the target image is improved, and the tissue texture of the acquired target image is more delicate and clearer.
Description
Technical field
The invention belongs to ultrasonic diagnostic imaging field, relate to a kind of preprocess method and system of ultrasonic signal.
Background technology
Along with improving constantly that the develop rapidly of microelectric technique, computer technology and the mankind require self living standard, become one of Medical Technology field with fastest developing speed as the medical ultrasound diagnosis imaging technique of clinical diagnosis.
Wherein, be widely used with the medical supersonic instrument that obtains target image by receiving, process echo-signal, contemporary medical supersonic instrument, no matter be linear scan or sector scanning, the received echo-signal of ultrasonic transducer all comprises three partial informations conventionally: Part I part is the useful information (also referred to as tissue texture) of tested tissue structure; Part II is due to the obfuscation information to tissue texture that reason causes such as system imaging resolution is limited; In addition also have the random phase stack producing because of hyperacoustic scattering, and the random noise that produces such as instrument oneself factor, environmental unit factor.
Ultra sonic imaging gained image is the convolution of Part I information and Part II information, adds Part III information.The result of convolution declines the image resolution ratio of measured signal; Measured object is read shape and is distorted; Noise declines contrast; Meanwhile, the system function of ultrasonic image-forming system is hidden in the signal of seizing, and is difficult to separate, and this is the inherent limitation of any one signal detection system.
Current ultrasonic device, the signal that scanning is obtained generally can receive in control, signal and process, and ultra sonic imaging completes to obtain target image, the target image obtaining is processed afterwards again, be referred to as " post processing ", i.e. processed offline (Off Line); Concrete, after ultra sonic imaging completes, every subgoal image is all estimated to system function separately, and do not consider whether several target images are generated by same system.Therefore, the system function of every subgoal image is different, and it is relevant with tested tissue that this situation is called system function, and this has run counter to " system uniqueness " this basic norm of measurement; Meanwhile, adopt this processing method to obtain in the process of target image, acquisition speed and efficiency are all very low.
Summary of the invention
In order to address the above problem, the invention provides a kind of target image system organization texture preprocess method more careful, ultrasonic signal clearly and system of getting of making.
Accordingly, the preprocess method of a kind of ultrasonic signal of the present invention, said method comprising the steps of:
S1, receive N bar echo-signal, randomly draw echo-signal described in M bar and process from echo-signal described in the N bar receiving, to calculate system function, wherein, M is less than N;
S2, echo-signal described in the N bar receiving is carried out to linear interpolation calculating, so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N;
S3, take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing;
S4, obtain target image.
As a further improvement on the present invention, described step S1 specifically comprises: receive N bar echo-signal, randomly draw echo-signal described in M bar from echo-signal described in the N bar receiving, and adopt two spectrum identification processing to process, to calculate system function, wherein, M is less than N.
As a further improvement on the present invention, described deconvolution is treated to the long-pending processing of Fourier-small echo regular deconvolution.
As a further improvement on the present invention, described step S3 specifically comprises: remove the noise in described echo-signal.
As a further improvement on the present invention, " removing the noise in described echo-signal " step comprises: echo-signal described in employing Fourier-Tikhonov shrink process, and to weaken the noise in described echo-signal.
As a further improvement on the present invention, " removing the noise in described echo-signal " step also comprises: echo-signal described in employing small echo hard-threshold shrink process, again decompose described echo-signal, and described echo-signal secondary is removed to noise.
As a further improvement on the present invention, step also comprises " to remove the noise in described echo-signal ": adopt Wavelet Domain Wiener Filtering to process described echo-signal.
Accordingly, the pretreatment system of a kind of ultrasonic signal of the present invention, described system comprises:
Communication unit, for receiving N bar echo-signal;
Processing unit, randomly draws echo-signal described in M bar for echo-signal described in the N bar from receiving and processes, and to calculate system function, wherein, M is less than N;
Echo-signal described in the N bar receiving is carried out to linear interpolation calculating, and so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N;
Take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing;
Obtain target image.
As a further improvement on the present invention, described processing unit is also for receiving N bar echo-signal, randomly draws echo-signal described in M bar from echo-signal described in the N bar receiving, and adopt two spectrum identification processing to process, to calculate system function, wherein, M is less than N.
As a further improvement on the present invention, described deconvolution is treated to the long-pending processing of Fourier-small echo regular deconvolution.
As a further improvement on the present invention, described processing unit is also for removing the noise of described echo-signal.
As a further improvement on the present invention, described processing unit is also for adopting echo-signal described in Fourier-Tikhonov shrink process, to weaken the noise in described echo-signal; Echo-signal described in employing small echo hard-threshold shrink process, decomposes described echo-signal again, and described echo-signal secondary is removed to noise; Adopt Wavelet Domain Wiener Filtering to process described echo-signal.
Compared with prior art, the preprocess method of ultrasonic signal of the present invention and system, before obtaining target image, adopt deconvolution to process described echo-signal, remove the noise in described echo-signal, and at utmost retain original described echo-signal, the target image contrast of finally obtaining is significantly strengthened, and then promoted the resolution of target image, make the tissue texture of the target image obtaining also more careful, clear simultaneously.
Accompanying drawing explanation
Fig. 1 is the flow chart of the processing method of the ultrasonic signal that provides in prior art;
Fig. 2 is the flow chart of the processing method of the ultrasonic signal that provides in embodiment of the present invention;
Fig. 3 is the flow chart of the preprocess method of the ultrasonic signal that provides in embodiment of the present invention;
Fig. 4 is the module diagram of the pretreatment system of the ultrasonic signal that provides of embodiment of the present invention.
The specific embodiment
Describe the present invention below with reference to embodiment shown in the drawings.But embodiment does not limit the present invention, the conversion in structure, method or function that those of ordinary skill in the art makes according to these embodiments is all included in protection scope of the present invention.
As shown in Figure 1, the processing method of ultrasonic signal in prior art, the method mainly comprises the following steps:
M1, launch scanning linear scan destination organization by Ultrasound Instrument, so that original image is sampled;
M2, receive described echo-signal;
M3, complete ultra sonic imaging according to described echo-signal, obtain target image;
M4, finally to described destination organization estimating system function, so that described target image is done to further processing.
What above process adopted is post-processing technology, on the basis that obtains target image, further processes, so that the target image finally obtaining is more outstanding; But, owing to adopting said method, when the different target image of medical supersonic instrument scanning, the system function difference of at every turn obtaining.Run counter to " system uniqueness " this basic norm of measurement, and then caused image processing process low speed, poor efficiency.
As shown in Figure 2, the processing method of ultrasonic signal of the present invention, the method mainly comprises the following steps:
P1, launch scanning linear scan destination organization by Ultrasound Instrument, so that original image is sampled;
P2, receive all echo-signals;
P3, described echo-signal is carried out to pretreatment;
P4, complete ultra sonic imaging according to described echo-signal, obtain target image;
The difference part of the ultrasonic signal processing method of ultrasonic signal processing method of the present invention and prior art is: before completing ultra sonic imaging according to described echo-signal, described method also comprises, described ultrasonic signal is carried out to pretreatment, again according to completing ultra sonic imaging through pretreated described echo-signal, obtain target image afterwards.The target image now obtaining is the target image that finally will obtain.And no longer adopt post-processing technology to process to described target image.
As shown in Figure 3, Fig. 3 is the flow chart of the preprocess method of the ultrasonic signal that provides in embodiment of the present invention; Be the concrete introduction of step P3 in Fig. 2, concrete, the preprocess method of a kind of ultrasonic signal of the present invention, said method comprising the steps of:
S1, receive N bar echo-signal, randomly draw echo-signal described in M bar and process from echo-signal described in the N bar receiving, to calculate system function, wherein, M is less than N;
S2, echo-signal described in the N bar receiving is carried out to linear interpolation calculating, so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N;
S3, take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing;
S4, obtain target image.
Accordingly, in an embodiment of the present invention, because described system function is unknown, therefore, before carrying out described deconvolution processing, first carry out system identification, i.e. blind deconvolution (blind deconvolution).
Concrete, described step S1 specifically comprises: receive N bar echo-signal, randomly draw echo-signal described in M bar, and adopt two spectrum identification processing to process from echo-signal described in the N bar receiving, to calculate system function, wherein, M is less than N.The described system function that adopts this kind of method to calculate, can be according to ambient temperature, and the impact of the extraneous factors such as humidity variation, calculates suitable described system function in real time.
The method of selecting higher-order spectrum to analyze, for gaussian signal, all Higher Order Cumulants are all zero, can eliminate the interference of white Gaussian noise and coloured noise in non-Gaussian Systems, improve signal to noise ratio; In non-minimum phase system, the phase property that higher-order spectrum method can stick signal.
Concrete, adopt the identification of two spectrum to process to calculate system function, its implementation is as follows:
The three rank cumulants of zero average signal y (n) are defined as:
In formula,
it is zero average signal
three rank cumulants, be a constant;
represent assembly average.
Afterwards, successively use (3) (1) (2) step in following formula can estimate system function;
Further, two spectrums of falling are that above formula is done to transform, after getting natural logrithm, then do inverse transformation, obtain:
The cepstrum of note h (n) is:
In formula,
represent two-dimension fourier transform.
By above-mentioned two spectra system identification algorithms that fall, utilize MatLab higher-order spectrum calculation procedure, the described echo-signal receiving is processed, choose optimum described system function by estimation.
Accordingly, step S2 carries out linear interpolation calculating to echo-signal described in the N bar receiving, and so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N.
Adopting the algorithm of linear interpolation that N bar echo-signal is increased to P bar echo-signal, is general knowledge known in this field, is not described in detail at this.
Accordingly, step S3, take the system function that calculates and P bar echo-signal as basis, carries out deconvolution processing; S4, obtain target image.
Concrete, described deconvolution is treated to the long-pending processing of Fourier-small echo regular deconvolution, removes the noise in described echo-signal simultaneously.Fourier-small echo regular deconvolution integration method has very strong robustness, has high resistance and makes performance, can significantly improve the caused resolution degradation phenomenon of convolution, strengthens picture contrast, reduces noise.
Accordingly, adopt echo-signal described in Fourier-Tikhonov shrink process, to weaken the noise in described echo-signal.
The concrete methods of realizing that adopts Fourier-Tikhonov to shrink is as follows:
The mathematical model of ultra sonic imaging is:
In formula: y (n) represents the described echo-signal observing; X (n) represents the signal of tested tissue, is a Gauss, zero average, stable signal; H (n) is and asks in step S1, represents the described system function of Ultrasound Instrument, is that a length is the FIR filter system of M, definite, cause and effect;
represent additive gaussian noise; * represent convolution.
Above formula is done to discrete Fourier transform to be obtained:
Adopt inverse operator to obtain the estimated value of X:
If
, deconvolution becomes morbid state.
In order to solve the problem of ill deconvolution, the impact of the method attenuating noise shrinking with Fourier, will
be multiplied by a contraction
Obtain:
Above step inverse operator and Fourier shrink and carry out simultaneously, are called the long-pending method of Fourier's regular deconvolution.
Wherein, the long-pending constriction coefficient of Fourier's regular deconvolution is:
for regular terms, the regular terms of the different long-pending technology of Fourier's regular deconvolution is different.
If σ
2for noise variance, α is regular parameter (α >0), and the regular terms of Tikhonov deconvolution is
it is the meansigma methods of y;
If get regular parameter
, can obtain signal noisy without inclined to one side estimation;
If get regular parameter
, can suppress noise completely, but also lose the information of former described echo-signal k item simultaneously.
Choose optimum regular parameter, will make the signal that finally estimates and the mean square deviation of former described echo-signal
minimum, due to former described echo-signal the unknown, so MSE cannot directly obtain.
The present invention uses the cost function based on observing to be similar to MSE:
The α that makes above formula value minimum is optimum regular parameter α.
In actual ultrasonic system, noise variance is unknown; Select classical Donoho to ask the method for noise variance herein,, on the fine dimension of wavelet decomposition, the intermediate value of signal wavelet coefficient is divided by 0.6745;
If make the signal after Fourier-Tikhonov shrinks be
, wherein,
fourier transformation.
Shrink after described echo-signal by Fourier Tikhonov, the noise contribution having amplified is cut down greatly, but has also lost a part of signal component simultaneously, and due to the existence on border, signal exists distortion.In order to improve the quality of signal, need be at wavelet field decomposed signal signal is carried out to denoising again.
Accordingly, adopt echo-signal described in small echo hard-threshold shrink process, again decompose described echo-signal, described echo-signal secondary is removed to noise.
The concrete methods of realizing that adopts small echo hard-threshold to shrink is as follows:
Described in employing Fourier-Tikhonov contraction, small echo hard-threshold shrink process, after echo-signal, recycling Wavelet Domain Wiener Filtering obtains final estimated value
Compared with prior art, the preprocess method of ultrasonic signal of the present invention, before obtaining target image, when overcoming the different target image of same Ultrasound Instrument scanning, the system function difference of at every turn obtaining, adopts the identification of two spectrum to process to calculate system function; Afterwards, adopt the described echo-signal of the long-pending processing of Fourier-small echo regular deconvolution, remove the noise in described echo-signal, and at utmost retain original described echo-signal, the target image contrast of finally obtaining is significantly strengthened, and then promoted the resolution of target image, simultaneously, make the tissue texture of the target image obtaining also more careful, clear.
As shown in Figure 4, Fig. 4 is the module diagram of the pretreatment system of ultrasonic signal in an embodiment of the present invention.
Accordingly, described system comprises: communication unit 100, for receiving N bar echo-signal; Processing unit 200, randomly draws echo-signal described in M bar for echo-signal described in the N bar from receiving and processes, and to calculate system function, wherein, M is less than N; Echo-signal described in the N bar receiving is carried out to linear interpolation calculating, and so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N; Take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing; Obtain target image.
Accordingly, want ultrasonic signal to process to obtain target image, first need to receive echo-signal by communication unit 100, by processing unit 200, described echo-signal is processed again afterwards, to obtain described target image.
Accordingly, in an embodiment of the present invention, because described system function is unknown, therefore, before carrying out described deconvolution processing, first by processing unit 200, system is carried out to identification, i.e. blind deconvolution (blind deconvolution).
Accordingly, described processing unit 200 is for receiving N bar echo-signal, randomly draws echo-signal described in M bar, and adopt two spectrum identification processing to process from echo-signal described in the N bar receiving, and to calculate system function, wherein, M is less than N.The described system function that adopts this kind of method to calculate, can be according to ambient temperature, and the impact of the extraneous factors such as humidity variation, calculates suitable described system function in real time.
The method that described processing unit 200 selects higher-order spectrum to analyze, for gaussian signal, all Higher Order Cumulants are all zero, can eliminate the interference of white Gaussian noise and coloured noise in non-Gaussian Systems, improve signal to noise ratio; In non-minimum phase system, the phase property that higher-order spectrum method can stick signal.
Concrete, described processing unit 200 adopts the identification of two spectrum to process to calculate system function, and its implementation is as follows:
The three rank cumulants of zero average signal y (n) are defined as:
In formula,
being the three rank cumulants of zero average signal x (n), is a constant; E{} represents assembly average.
Afterwards, successively use (3) (1) (2) step in following formula can estimate system function;
Further, two spectrums of falling are that above formula is done to transform, after getting natural logrithm, then do inverse transformation, obtain:
In formula, F[] expression two-dimension fourier transform.
By above-mentioned two spectra system identification algorithms that fall, utilize MatLab higher-order spectrum calculation procedure, the described echo-signal receiving is processed, choose optimum described system function by estimation.
Accordingly, described processing unit 200 also carries out linear interpolation calculating for echo-signal described in the N bar to receiving, and so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N.
Described processing unit 200 adopts the algorithm of linear interpolation that N bar echo-signal is increased to P bar echo-signal, is general knowledge known in this field, is not described in detail at this.
Accordingly, described processing unit 200 also for the system function to calculate and P bar echo-signal as basis, carry out deconvolution processing; Obtain target image.
Concrete, described processing unit 200 adopts the described echo-signal of the long-pending processing of Fourier-small echo regular deconvolution, removes the noise in described echo-signal simultaneously.Fourier-small echo regular deconvolution integration method has very strong robustness, has high resistance and makes performance, can significantly improve the caused resolution degradation phenomenon of convolution, strengthens picture contrast, reduces noise.
Accordingly, described processing unit 200 adopts echo-signal described in Fourier-Tikhonov shrink process, to weaken the noise in described echo-signal.
The concrete methods of realizing that described processing unit 200 adopts Fourier-Tikhonov to shrink is as follows:
The mathematical model of ultra sonic imaging is:
In formula: y (n) represents the described echo-signal observing; X (n) represents the signal of tested tissue, is a Gauss, zero average, stable signal; H (n) is and asks in step S1, represents the described system function of Ultrasound Instrument, is that a length is the FIR filter system of M, definite, cause and effect; γ (n) represents additive gaussian noise; * represent convolution.
Above formula is done to discrete Fourier transform to be obtained:
Adopt inverse operator to obtain the estimated value of X:
Adopt inverse operator to obtain the estimated value of X:
If
, deconvolution becomes morbid state.
In order to solve the problem of ill deconvolution, the impact of the method attenuating noise shrinking with Fourier, will
be multiplied by a constriction coefficient
Obtain:
Above step inverse operator and Fourier shrink and carry out simultaneously, are called the long-pending method of Fourier's regular deconvolution.
Wherein, the long-pending constriction coefficient of Fourier's regular deconvolution is:
for regular terms, the regular terms of the different long-pending technology of Fourier's regular deconvolution is different.
If σ
2for noise variance, α is regular parameter (α >0), and the regular terms of Tikhonov deconvolution is
If get regular parameter
, can obtain signal noisy without inclined to one side estimation;
If get regular parameter
, can suppress noise completely, but also lose the information of former described echo-signal k item simultaneously.
Choose optimum regular parameter, will make the signal that finally estimates and the mean square deviation of described echo original signal
minimum, due to former described echo-signal the unknown, so MSE cannot directly obtain.
The present invention uses the cost function based on observing to be similar to MSE:
The α that makes above formula value minimum is optimum regular parameter α.
In actual ultrasonic system, noise variance is unknown; Select classical Donoho to ask the method for noise variance herein,, on the fine dimension of wavelet decomposition, the intermediate value of signal wavelet coefficient is divided by 0.6745;
If make the signal after Fourier-Tikhonov shrinks be
?
, wherein,
fourier transformation.
Described processing unit 200 shrinks after described echo-signal by Fourier Tikhonov, and the noise contribution having amplified is cut down greatly, but also lost a part of signal component simultaneously, and due to the existence on border, signal exists distortion.In order to improve the quality of signal, need be at wavelet field decomposed signal signal is carried out to denoising again.
Accordingly, described processing unit 200 adopts echo-signal described in small echo hard-threshold shrink process, again decomposes described echo-signal, and described echo-signal secondary is removed to noise.
The concrete methods of realizing that described processing unit 200 adopts small echo hard-threshold to shrink is as follows:
Described processing unit 200 adopts that Fourier-Tikhonov shrinks, described in small echo hard-threshold shrink process after echo-signal, recycling Wavelet Domain Wiener Filtering obtains final estimated value
;
Compared with prior art, the preprocess method of ultrasonic signal of the present invention and system, before obtaining target image, when overcoming the different target image of same Ultrasound Instrument scanning, the system function difference of at every turn obtaining; Adopt the identification of two spectrum to process to calculate system function; Afterwards, adopt the described echo-signal of the long-pending processing of Fourier-small echo regular deconvolution, remove the noise in described echo-signal, and at utmost retain former described beginning echo-signal, the target image contrast of finally obtaining is significantly strengthened, and then promoted the resolution of target image, simultaneously, make the tissue texture of the target image obtaining also more careful, clear.
For convenience of description, while describing above device, being divided into various modules with function describes respectively.Certainly, in the time implementing the application, the function of each module can be realized in same or multiple software and/or hardware.
As seen through the above description of the embodiments, those skilled in the art can be well understood to the mode that the application can add essential general hardware platform by software and realizes.Based on such understanding, the part that the application's technical scheme contributes to prior art in essence in other words can embody with the form of software product, this computer software product can be kept in Protector, as ROM/RAM, magnetic disc, CD etc., comprise that some instructions (can be personal computers in order to make a computer equipment, Information Push Server, or the network equipment etc.) carry out the method described in some part of each embodiment of the application or embodiment.
Device embodiments described above is only schematic, the wherein said module as separating component explanation can or can not be also physically to separate, the parts that show as module can be or can not be also physical modules, can be positioned at a place, or also can be distributed on multiple mixed-media network modules mixed-medias.Can select according to the actual needs some or all of module wherein to realize the object of present embodiment scheme.Those of ordinary skills, in the situation that not paying creative work, are appreciated that and implement.
The application can be used in numerous general or special purpose computingasystem environment or configuration.For example: personal computer, Information Push Server computer, handheld device or portable set, plate equipment, multi-processing module system, system, set top box, programmable consumer-elcetronics devices, network PC, minicomputer, mainframe computer based on micro treatment module, comprise distributed computing environment of above any system or equipment etc.
The application can describe in the general context of the computer executable instructions of being carried out by computer, for example program module.Usually, program module comprises and carries out particular task or realize routine, program, object, assembly, data structure of particular abstract data type etc.Also can in distributed computing environment, put into practice the application, in these distributed computing environment, be executed the task by the teleprocessing equipment being connected by communication network.In distributed computing environment, program module can be arranged in the local and remote computer Protector including preservation equipment.
Be to be understood that, although this description is described according to embodiment, but be not that each embodiment only comprises an independently technical scheme, this narrating mode of description is only for clarity sake, those skilled in the art should make description as a whole, technical scheme in each embodiment also can, through appropriately combined, form other embodiments that it will be appreciated by those skilled in the art that.
Listed a series of detailed description is above only illustrating for feasibility embodiment of the present invention; they are not in order to limit the scope of the invention, all do not depart from the equivalent embodiment that skill spirit of the present invention does or change and all should be included in protection scope of the present invention within.
Claims (12)
1. a preprocess method for ultrasonic signal, is characterized in that, said method comprising the steps of:
S1, receive N bar echo-signal, randomly draw echo-signal described in M bar and process from echo-signal described in the N bar receiving, to calculate system function, wherein, M is less than N;
S2, echo-signal described in the N bar receiving is carried out to linear interpolation calculating, so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N;
S3, take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing;
S4, obtain target image.
2. the preprocess method of ultrasonic signal according to claim 1, is characterized in that, described step S1 specifically comprises:
Receive N bar echo-signal, randomly draw echo-signal described in M bar, and adopt two spectrum identification processing to process from echo-signal described in the N bar receiving, to calculate system function, wherein, M is less than N.
3. the preprocess method of ultrasonic signal according to claim 1, is characterized in that, described deconvolution is treated to the long-pending processing of Fourier-small echo regular deconvolution.
4. the preprocess method of ultrasonic signal according to claim 1, is characterized in that, described step S3 specifically comprises: remove the noise in described echo-signal.
5. the preprocess method of ultrasonic signal according to claim 4, is characterized in that, " removing the noise in described echo-signal " step comprises:
Echo-signal described in employing Fourier-Tikhonov shrink process, to weaken the noise in described echo-signal.
6. the preprocess method of ultrasonic signal according to claim 5, is characterized in that, " removing the noise in described echo-signal " step also comprises:
Echo-signal described in employing small echo hard-threshold shrink process, decomposes described echo-signal again, and described echo-signal secondary is removed to noise.
7. the preprocess method of ultrasonic signal according to claim 6, is characterized in that, " removing the noise in described echo-signal " step also comprises:
Adopt Wavelet Domain Wiener Filtering to process described echo-signal.
8. a pretreatment system for ultrasonic signal, is characterized in that, described system comprises:
Communication unit, for receiving N bar echo-signal;
Processing unit, randomly draws echo-signal described in M bar for echo-signal described in the N bar from receiving and processes, and to calculate system function, wherein, M is less than N;
Echo-signal described in the N bar receiving is carried out to linear interpolation calculating, and so that N bar echo-signal is increased to P bar echo-signal, wherein, P is greater than N;
Take the system function that calculates and P bar echo-signal as basis, carry out deconvolution processing;
Obtain target image.
9. the pretreatment system of ultrasonic signal according to claim 8, it is characterized in that, described processing unit is also for receiving N bar echo-signal, from echo-signal described in the N bar receiving, randomly draw echo-signal described in M bar, and adopt two spectrum identification processing to process, to calculate system function, wherein, M is less than N.
10. the pretreatment system of ultrasonic signal according to claim 8, is characterized in that, described deconvolution is treated to the long-pending processing of Fourier-small echo regular deconvolution.
The pretreatment system of 11. ultrasonic signals according to claim 8, is characterized in that, described processing unit is also for removing the noise of described echo-signal.
The pretreatment system of 12. ultrasonic signals according to claim 11, is characterized in that, described processing unit is also for adopting echo-signal described in Fourier-Tikhonov shrink process, to weaken the noise in described echo-signal; Echo-signal described in employing small echo hard-threshold shrink process, decomposes described echo-signal again, and described echo-signal secondary is removed to noise; Adopt Wavelet Domain Wiener Filtering to process described echo-signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410054451.3A CN103784164B (en) | 2014-02-18 | 2014-02-18 | The preprocess method of ultrasonic signal and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410054451.3A CN103784164B (en) | 2014-02-18 | 2014-02-18 | The preprocess method of ultrasonic signal and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103784164A true CN103784164A (en) | 2014-05-14 |
| CN103784164B CN103784164B (en) | 2015-10-21 |
Family
ID=50660525
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410054451.3A Active CN103784164B (en) | 2014-02-18 | 2014-02-18 | The preprocess method of ultrasonic signal and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103784164B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104808643A (en) * | 2015-02-15 | 2015-07-29 | 浙江大学 | Control circuit nonlinearity detection method based on improved bi-cepstrum analysis |
| CN104905784A (en) * | 2015-06-25 | 2015-09-16 | 南京大学 | A method for reducing bioelectricity sampling wave form distortion |
| CN106055841A (en) * | 2016-06-30 | 2016-10-26 | 张升泽 | Noise preprocessing method and system of electronic chip |
| CN109350118A (en) * | 2018-12-05 | 2019-02-19 | 宁波可凡电器有限公司 | Ultrasonic scanning aid in treatment mechanism |
| CN116584977A (en) * | 2023-06-30 | 2023-08-15 | 南京澳思泰生物科技有限公司 | Computer-aided diagnosis system and method for analyzing cerebrovascular diseases |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005245479A (en) * | 2004-03-01 | 2005-09-15 | Fuji Photo Film Co Ltd | Ultrasonic diagnostic apparatus |
| JP2006255256A (en) * | 2005-03-18 | 2006-09-28 | Matsushita Electric Ind Co Ltd | Ultrasonic diagnostic system |
| US20070167780A1 (en) * | 2005-11-25 | 2007-07-19 | Tomohisa Imamura | Ultrasonic diagnostic scanner and method for processing ultrasonic signal |
| CN101380237A (en) * | 2007-09-04 | 2009-03-11 | 深圳迈瑞生物医疗电子股份有限公司 | Scanning transform method and device for ultrasonic image-forming |
| JP2012096095A (en) * | 2005-11-25 | 2012-05-24 | Toshiba Corp | Ultrasonic diagnostic scanner, ultrasonic diagnostic scanner-controlling program, and method for processing ultrasonic signal |
-
2014
- 2014-02-18 CN CN201410054451.3A patent/CN103784164B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005245479A (en) * | 2004-03-01 | 2005-09-15 | Fuji Photo Film Co Ltd | Ultrasonic diagnostic apparatus |
| JP2006255256A (en) * | 2005-03-18 | 2006-09-28 | Matsushita Electric Ind Co Ltd | Ultrasonic diagnostic system |
| US20070167780A1 (en) * | 2005-11-25 | 2007-07-19 | Tomohisa Imamura | Ultrasonic diagnostic scanner and method for processing ultrasonic signal |
| JP2012096095A (en) * | 2005-11-25 | 2012-05-24 | Toshiba Corp | Ultrasonic diagnostic scanner, ultrasonic diagnostic scanner-controlling program, and method for processing ultrasonic signal |
| CN101380237A (en) * | 2007-09-04 | 2009-03-11 | 深圳迈瑞生物医疗电子股份有限公司 | Scanning transform method and device for ultrasonic image-forming |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104808643A (en) * | 2015-02-15 | 2015-07-29 | 浙江大学 | Control circuit nonlinearity detection method based on improved bi-cepstrum analysis |
| CN104808643B (en) * | 2015-02-15 | 2017-08-15 | 浙江大学 | A kind of control loop non-linear detection method based on improved double-spectrum analysis |
| CN104905784A (en) * | 2015-06-25 | 2015-09-16 | 南京大学 | A method for reducing bioelectricity sampling wave form distortion |
| CN106055841A (en) * | 2016-06-30 | 2016-10-26 | 张升泽 | Noise preprocessing method and system of electronic chip |
| CN109350118A (en) * | 2018-12-05 | 2019-02-19 | 宁波可凡电器有限公司 | Ultrasonic scanning aid in treatment mechanism |
| CN116584977A (en) * | 2023-06-30 | 2023-08-15 | 南京澳思泰生物科技有限公司 | Computer-aided diagnosis system and method for analyzing cerebrovascular diseases |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103784164B (en) | 2015-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dini et al. | Class of widely linear complex Kalman filters | |
| Yu et al. | A blind deconvolution approach to ultrasound imaging | |
| Varghese et al. | Video denoising based on a spatiotemporal Gaussian scale mixture model | |
| Tania et al. | A comparative study of various image filtering techniques for removing various noisy pixels in aerial image | |
| JP6057881B2 (en) | Method for removing noise from input image consisting of pixels containing noise | |
| Dass | Speckle noise reduction of ultrasound images using BFO cascaded with wiener filter and discrete wavelet transform in homomorphic region | |
| CN103784164B (en) | The preprocess method of ultrasonic signal and system | |
| Zhang et al. | Multicomponent microseismic data denoising by 3D shearlet transform | |
| Han et al. | Adaptive fission particle filter for seismic random noise attenuation | |
| Kaur et al. | Survey of de-noising methods using filters and fast wavelet transform | |
| Sheybani | An Algorithm for Real-Time Blind Image Quality Comparison and Assessment | |
| WO2018003158A1 (en) | Correlation function generation device, correlation function generation method, correlation function generation program, and wave source direction estimation device | |
| Zhang et al. | A reverberation noise suppression method of sonar image based on shearlet transform | |
| Xin et al. | An improved time-frequency representation based on nonlinear mode decomposition and adaptive optimal kernel | |
| Boopathi Rajan et al. | Time delay estimation using wavelet denoising maximum likelihood method for underwater reverberant environment | |
| Gupta | A review and comprehensive comparison of image denoising techniques | |
| WO2016197629A1 (en) | System and method for frequency estimation | |
| CN101930605B (en) | Synthetic Aperture Radar (SAR) image target extraction method and system based on two-dimensional mixing transform | |
| Sheta | Restoration of medical images using genetic algorithms | |
| Tan et al. | An integrated denoising method for sensor mixed noises based on wavelet packet transform and energy-correlation analysis | |
| Ma et al. | SVMD coupled with dual-threshold criteria of correlation coefficient: A self-adaptive denoising method for ship-radiated noise signal | |
| CN104156925A (en) | Processing method and system used for carrying out speckle removing and boundary enhancing on ultrasound image | |
| Baselice et al. | Enhanced wiener filter for desplecking ultra-sound images | |
| Wang et al. | A novel algorithm for image denoising based on unscented Kalman filtering | |
| Attlas et al. | Wavelet based techniques for speckle noise reduction in ultrasound images |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 215123 5th floor, building a, 4th floor, building C, No. 27, Xinfa Road, Suzhou Industrial Park, Jiangsu Province Patentee after: Feiyinuo Technology Co.,Ltd. Address before: 215123 5th floor, building a, 4th floor, building C, No. 27, Xinfa Road, Suzhou Industrial Park, Jiangsu Province Patentee before: Feiyinuo Technology (Suzhou) Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder | ||
| CP03 | Change of name, title or address |
Address after: 215123 5th floor, building a, 4th floor, building C, No. 27, Xinfa Road, Suzhou Industrial Park, Jiangsu Province Patentee after: Feiyinuo Technology (Suzhou) Co.,Ltd. Address before: 215123 unit 501, building C8, bio nano Park, No. 218, Xinghu street, industrial park, Suzhou, Jiangsu Province Patentee before: VINNO TECHNOLOGY (SUZHOU) Co.,Ltd. |
|
| CP03 | Change of name, title or address |