CN109190677A - The control system and control method of inserting needle equipment in single ubarachnoid block art - Google Patents
The control system and control method of inserting needle equipment in single ubarachnoid block art Download PDFInfo
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Abstract
The invention belongs to field of medical technology, the control system and control method of inserting needle equipment in a kind of single ubarachnoid block art are disclosed, control system includes: image capture module, parameter configuration module, central control module, image data processing module, anesthesia module, locating module, display module.The present invention can be shown the cavum subarachnoidale position for being identified as abnormal signal by image data processing module, it is referred to for medical worker, accurate data is provided for the diagnosis of medical worker, subarachnoid hemorrhage is accurately determined with auxiliary, can reduce mistaken diagnosis/rate of missed diagnosis of subarachnoid hemorrhage；Subarachnoid nerve block anesthesia plane is measured using infrared thermal imagery by anesthesia module simultaneously, change and uses tactile and cold and hot outmoded measurement method in the past, keep measurement means more scientific, more objective, more acurrate, safer, so that patient is performed the operation after being anesthetized safer with anaesthesia process.
Description
Technical field
The invention belongs to a kind of controls of inserting needle equipment in field of medical technology more particularly to single ubarachnoid block art
System and control method processed.
Background technique
Currently, the prior art commonly used in the trade is such that
Arachnoid is to constitute tissue by very thin connective tissue, is one layer of translucent film, and it is deep to be located at endocranium
Portion, having potentiality lacuna therebetween is cavum subdurale.It is intracavitary to contain a small amount of liquid.Arachnoid crosses over brain, is coated on the surface of brain, with
There are biggish gap, referred to as nethike embrane cavity of resorption between pia mater, it is intracavitary to be full of cerebrospinal fluid.At certain position, cavum subarachnoidale extension
And deepen, become cisternae subarachnoideales.Maximum is cisterna magna, it passes through median aperture and front-side holes and fourth ventricle's phase
Logical: bridge pond is located at pons veutro: interpeduncular cistern is recessed between foot；Cistern of chiasma is located in front of optic chiasma.However, existing for amount of bleeding
Smaller and bleeding is difficult to carry out accurate hemorrhagic areas segmentation in the subarachnoid hemorrhage of Dispersed precipitate and bleeding determines；Together
When, the measurement of existing subarachnoid block anesthesia plane is clinically often used needle point method, tactile method and temperature sense and obtains method；
Make patient itself since tolerance difference with reactive different have differences is unable to accurate judgement pain sensation sterilization pill, though needle point method
Block scope and effect can be predicted, but syringe needle used may puncture skin and bring wound or infection to patient.
In conclusion problem of the existing technology is:
Existing smaller for amount of bleeding and bleeding is difficult to carry out accurate bleeding in the subarachnoid hemorrhage of Dispersed precipitate
Region segmentation and bleeding determine；Meanwhile the measurement of existing subarachnoid block anesthesia plane, be clinically often used needle point method,
Tactile method and temperature sense obtain method；Make patient itself since tolerance difference cannot accurately be sentenced with reactive different have differences
Disconnected pain sensation sterilization pill, though needle point method can predict block scope and effect, syringe needle used may puncture skin to patient with
Come wound or infection.
Prior art image information processing accuracy is poor.Classification accuracy is low in conventional images Modulation recognition method asks
Topic.
Summary of the invention
In view of the problems of the existing technology, the present invention provides inserting needle equipment in a kind of single ubarachnoid block art
Control system and control method.
The invention is realized in this way in a kind of single ubarachnoid block art inserting needle equipment control method, it is described
The control method of inserting needle equipment includes: in single ubarachnoid block art
Acquire CT image data information；Configure inserting needle equipment parameters；
CT image is detected with the Pulse-coupled Neural Network Model of suitable processing biological tissue's class image information；CT
Image is handled by the lesser impulsive noise pollution of density by adaptive weighted filter；CT image is by the biggish pulse of density
Noise pollution is using the introducing binode constitutive element mathematical morphology progress secondary filtering for keeping edge detail information；
It is suitble to the Pulse-coupled Neural Network Model of processing biological tissue's class image information:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
The method of CT image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, chooses filter window M, it is right
Image polluted by noise fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fij
To correspond to the output valve of window center position after filtering:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are exhausted
To mean value, max is maximizing symbol；
Identifying processing is carried out to the CT image of acquisition；The original CT signal got is pre-processed, to reduce artefact
Interference；Filter is created, by pretreated CT signal filtering to required frequency range；
Using Phase synchronization analysis method, phase of the CT signal of each frequency range between various time points every two channel is calculated
Position relationship, obtains dynamic function connection matrix；The time domain entropy for calculating phase relation value between two channels one by one, obtains each edge
Comentropy, to measure the complexity of each side time-domain of CT functional network；
Using the dynamic function connection entropy of each frequency range respectively as the characteristic of division of CT functional network, training is adaptive to be improved
Classifier obtains multiple adaptive raising classifiers and corresponding classification accuracy rate；
Classification is combined to sample in the way of voting by trained multiple adaptive raising classifiers；
The creation method of filter are as follows: CT signal is five frequency ranges, i.e. δ (1-10Hz), θ (11- using WAVELET PACKET DECOMPOSITION
20Hz),α(21-35Hz),β(36-55Hz)γ(56-70Hz)；
Phase of the CT signal of each frequency range in various time points between every two channel is calculated using PGC demodulation value PLV
Position relationship, specific calculation formula are as follows:
PLV=| < exp (j { Фi(t)-Фj(t)})>|；
Wherein, Фi(t) and Фj(t) be respectively electrode i and j instantaneous phase；
The phase value of signal can be calculated using Hilbert transform, specific formula is as follows:
xi(τ) is the continuous time signal of electrode i, and τ is a time variable, and t indicates time point, and PV is Cauchy's principal value；
Instantaneous phase is calculated as follows:
Similarly, instantaneous phase Ф is calculatedj(t)；
If selected CT port number is M, it is T that the CT time, which counts, and different channels pair is constructed using channel two-by-two, calculates institute
There is the PLV value in channel pair, obtain M × M × T three-dimensional matrice K at this time, wherein M × M is the upper triangle at a time point
Matrix:
Each element K of KijtFor the PLV value on t time point between i-th of electrode and j-th of electrode, which is
State function connects matrix, it not only contains the phase relation of the different channels CT between any two, further comprises the space in the channel CT
Information and temporal information；
Anesthetized area is determined using infrared thermal imaging inspection apparatus acquisition arachnoid thermal map；
Inserting needle position is positioned according to the arachnoid thermal image of acquisition；
Show the inserting needle location drawing picture information of acquisition.
Further, it chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is:
Further, obtaining the optimal adaptive detailed process for improving classifier includes: to given sample (x1,
y1) ..., (xm, ym), wherein xi∈ X, yi∈ Y=(- 1,1), X are training characteristics, and Y is subject's classification, and initialization first is every
The weight of a training sample set isP iteration, D are carried out later1It (i) is i.e. p=1 each training when initializing
The weight of sample set, iterative process are as follows: variable p is initially increased to P from 1, and each iteration calculates each Weak Classifier h firstp
The error in classification classified to training sample set
Wherein, hp(xi) it is the tag along sort value that p-th of Weak Classifier obtains sample classification, DpIt (i) is pth time iteration
When each training sample set weight, then calculate sorting sequence weightThe each trained sample of final updating
The weight of this collectionWherein, D+1It (i) is each updated each training
The weight of this collection, ZpFor normalization factor,It is the weight in order to adjust sample set, when classification divides right, update
WeightThe weight of sample will reduce；When classification misclassification, weight is updatedSample
This weight will improve；
P Weak Classifier h under the frequency range is obtained after P iterationp, finally most by P Weak Classifier combination building
Whole classifier is optimal adaptive raising classifier:
Then the optimal adaptive classification accuracy rate for improving classifier under each frequency range is calculated separately.
Further, after trained multiple adaptive raising classifiers are combined in the way of voting, sample is divided
Class:
Further, anesthetized area determines that method includes:
Firstly, acquiring infrared signal using infrared thermal imaging inspection apparatus；
Then, to the infrared signal of acquisition, thermal map is formed after being analyzed relatively；
Finally, changing by the light and shade that thermal map is shown, the range of subarachnoid nerve block anesthesia plane is determined.
Inserting needle equipment in the single ubarachnoid block art is realized another object of the present invention is to provide a kind of
The computer program of control method.
Inserting needle equipment in the single ubarachnoid block art is realized another object of the present invention is to provide a kind of
The computer of control method.
Another object of the present invention is to provide a kind of computer readable storage mediums, including instruction, when it is in computer
When upper operation, so that computer executes the control method of inserting needle equipment in the single ubarachnoid block art.
Another object of the present invention is to provide a kind of control systems of inserting needle equipment in single ubarachnoid block art
Include:
Image capture module is connect with central control module, for acquiring brain CT image data information；
Parameter configuration module is connect with central control module, for configuring inserting needle equipment work in ubarachnoid block art
Make parameter；
Central control module, with image capture module, parameter configuration module, image data processing module, anesthesia module, fixed
Position module, display module connection, work normally for controlling modules；
Image data processing module is connect with central control module, for carrying out identifying processing to the image of acquisition；
Module is anaesthetized, is connect with central control module, for acquiring arachnoid thermal map by infrared thermal imaging inspection apparatus
Determine anesthetized area；
Locating module is connect with central control module, for the image by acquisition to single ubarachnoid block art
Middle inserting needle position is positioned；
Display module is connect with central control module, for the image information by display screen display acquisition.
Further, image data processing module includes region estimation module, characteristic extracting module, abnormal signal identification mould
Block；
Region estimation module carries out cavum subarachnoidale for receiving the brain CT image, and to the brain CT image
The estimation of area-of-interest；
Characteristic extracting module, for receiving the brain CT image after interesting region estimating, and to the area-of-interest
Brain CT image after estimation carries out feature extraction, obtains characteristic value；
Abnormal signal identification module, for receiving the brain CT image after the interesting region estimating and characteristic value, and
Using the method for pattern-recognition, identify whether there is abnormal signal in the area-of-interest according to the characteristic value, and will identification
As a result the display module is sent to.
Advantages of the present invention and good effect are as follows:
The present invention can be shown the cavum subarachnoidale position for being identified as abnormal signal by image data processing module
Out, it is referred to for medical worker, provides accurate data for the diagnosis of medical worker, subarachnoid hemorrhage is carried out with auxiliary
It is accurate to determine, it can reduce mistaken diagnosis/rate of missed diagnosis of subarachnoid hemorrhage；It is measured simultaneously by anesthesia module using infrared thermal imagery
Subarachnoid nerve block anesthesia plane changes in the past with needle thorn, tactile and cold and hot outmoded measurement method, makes measurement means more
It is scientific, more objective, more acurrate, safer, so that patient is performed the operation after being anesthetized safer with anaesthesia process.
The present invention is by improvement Pulse Coupled Neural Network without detecting single automatically in the case where setting detection threshold value
The noise in area's micro-image is anaesthetized in ubarachnoid block art, and the removal of noise is completed using multistage combination filter,
The information such as image edge detailss are protected while effectively filtering out noise jamming well.The present invention has the effect that
It 1) is research of the Image Information Processing technology to Chinese Traditional Medicine progress modern measure identification pretreatment application technology,
It is combined for the information age " tradition " with " modern times " analysis detection and completely new technical thought and method is provided, to modern not damaged letter
Breath, which tests and analyzes, carries out beneficial exploration；
2) in the micro-image impulse noise detection stage, the present invention utilizes the lock-out pulse granting of Pulse Coupled Neural Network
Characteristic distinguishes position pulse noise spot and signal pixels point position, relatively traditional to be examined based on intermediate value detection or related intermediate value of improving
Survey method has higher noise detection performance, relative to other threshold value noise detection methods；The present invention is without setting detection threshold
Value, noise fallout ratio and omission factor are low, and noise measuring precision is higher；Meanwhile relative to other noise iteration detection methods；This hair
Bright method detection time is short, and automaticity is strong；
There is presently no any impulse noise correction methods to apply the micro-image arteries and veins in single ubarachnoid block art
It rushes in the detection of noise；
3) filter out the stage in micro-image impulsive noise, the present invention first according to the above-mentioned noise detected and signaling point,
Classification processing is carried out to image pixel；Place only is filtered to the noise spot of detection when using first order adaptive weighted filter
Reason, protects signaling point information relative to the methods of other median filterings, Wiener filtering while effectively filtering out noise；?
It is to carry out supplement auxiliary to the related noise missed in prime filtering to filter out when second level mathematical morphology filter, while denoising
Noise jamming can be not only effectively filtered out, and the information such as image edge detailss can be protected well；
With stronger subjective vision effect and index is objectively evaluated, noise removal capability is strong, signal-to-noise ratio is high and adaptability is good, special
It is not to anesthesia area's micro-image in the single ubarachnoid block art by serious noise pollution, it is shown that bigger filtering is excellent
More property.
CT image classification method of the invention by using Phase synchronization analysis method, comentropy method, adaptively mention
High (adaboost) sorting algorithm, multi-categorizer ballot combined method, realize and dynamic function connection are described, thus greatly
Width improves classification accuracy.The present invention efficiently solves that traditional images signal data classification method classification accuracy is low to ask
Topic is suitable for CT image signal data and classifies.
Detailed description of the invention
Fig. 1 is the control method flow chart that the present invention implements inserting needle equipment in the single ubarachnoid block art provided.
Fig. 2 is the Control system architecture frame that the present invention implements inserting needle equipment in the single ubarachnoid block art provided
Figure.
In figure: 1, image capture module；2, parameter configuration module；3, central control module；4, image data processing module；
5, module is anaesthetized；6, locating module；7, display module.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
With reference to the accompanying drawing and specific embodiment is further described application principle of the invention.
As shown in Figure 1, in a kind of single ubarachnoid block art provided by the invention the control system of inserting needle equipment and
Its control method the following steps are included:
S101 acquires brain CT image data information by image capture module；Spider web is configured by parameter configuration module
Hypostegal cavity blocks inserting needle equipment parameters in art；
S102, central control module carry out identifying processing by image of the image data processing module to acquisition；
S103 acquires arachnoid thermal map using infrared thermal imaging inspection apparatus by anesthesia module and determines anesthetized area；
S104 determines inserting needle position in single ubarachnoid block art according to the image of acquisition by locating module
Position；
S105 passes through the image information of display module display acquisition.
As shown in Fig. 2, the control system of inserting needle equipment includes: figure in single ubarachnoid block art provided by the invention
As acquisition module 1, parameter configuration module 2, central control module 3, image data processing module 4, anesthesia module 5, locating module
6, display module 7.
Image capture module 1 is connect with central control module 3, for acquiring brain CT image data information；
Parameter configuration module 2 is connect with central control module 3, for configuring inserting needle equipment in ubarachnoid block art
Running parameter；
Central control module 3, with image capture module 1, parameter configuration module 2, image data processing module 4, anesthesia mould
Block 5, locating module 6, display module 7 connect, and work normally for controlling modules；
Image data processing module 4 is connect with central control module 3, for carrying out identifying processing to the image of acquisition；
Module 5 is anaesthetized, is connect with central control module 3, for acquiring arachnoid heat by infrared thermal imaging inspection apparatus
Scheme to determine anesthetized area；
Locating module 6 is connect with central control module 3, for the image by acquisition to single ubarachnoid block
Inserting needle position is positioned in art；
Display module 7 is connect with central control module 3, for the image information by display screen display acquisition.
Image data processing module 4 provided by the invention includes region estimation module, characteristic extracting module, abnormal signal knowledge
Other module；
Region estimation module carries out cavum subarachnoidale for receiving the brain CT image, and to the brain CT image
The estimation of area-of-interest；
Characteristic extracting module, for receiving the brain CT image after interesting region estimating, and to the area-of-interest
Brain CT image after estimation carries out feature extraction, obtains characteristic value；
Abnormal signal identification module, for receiving the brain CT image after the interesting region estimating and characteristic value, and
Using the method for pattern-recognition, identify whether there is abnormal signal in the area-of-interest according to the characteristic value, and will identification
As a result the display module is sent to.
The anesthetized area of anesthesia module 5 provided by the invention determines that method is as follows:
Firstly, acquiring infrared signal using infrared thermal imaging inspection apparatus；
Then, to the infrared signal of acquisition, thermal map is formed after being analyzed relatively；
Finally, changing by the light and shade that thermal map is shown, the range of subarachnoid nerve block anesthesia plane is determined.
Below with reference to concrete analysis, the invention will be further described.
The control method of inserting needle equipment in single ubarachnoid block art provided in an embodiment of the present invention, comprising:
Acquire CT image data information；Configure inserting needle equipment parameters；
CT image is detected with the Pulse-coupled Neural Network Model of suitable processing biological tissue's class image information；CT
Image is handled by the lesser impulsive noise pollution of density by adaptive weighted filter；CT image is by the biggish pulse of density
Noise pollution is using the introducing binode constitutive element mathematical morphology progress secondary filtering for keeping edge detail information；
It is suitble to the Pulse-coupled Neural Network Model of processing biological tissue's class image information:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
The method of CT image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, chooses filter window M, it is right
Image polluted by noise fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fij
To correspond to the output valve of window center position after filtering:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are exhausted
To mean value, max is maximizing symbol；
Identifying processing is carried out to the CT image of acquisition；The original CT signal got is pre-processed, to reduce artefact
Interference；Filter is created, by pretreated CT signal filtering to required frequency range；
Using Phase synchronization analysis method, phase of the CT signal of each frequency range between various time points every two channel is calculated
Position relationship, obtains dynamic function connection matrix；The time domain entropy for calculating phase relation value between two channels one by one, obtains each edge
Comentropy, to measure the complexity of each side time-domain of CT functional network；
Using the dynamic function connection entropy of each frequency range respectively as the characteristic of division of CT functional network, training is adaptive to be improved
Classifier obtains multiple adaptive raising classifiers and corresponding classification accuracy rate；
Classification is combined to sample in the way of voting by trained multiple adaptive raising classifiers；
The creation method of filter are as follows: CT signal is five frequency ranges, i.e. δ (1-10Hz), θ (11- using WAVELET PACKET DECOMPOSITION
20Hz),α(21-35Hz),β(36-55Hz)γ(56-70Hz)；
Phase of the CT signal of each frequency range in various time points between every two channel is calculated using PGC demodulation value PLV
Position relationship, specific calculation formula are as follows:
PLV=| < exp (j { Фi(t)-Фj(t)})>|；
Wherein, Фi(t) and Фj(t) be respectively electrode i and j instantaneous phase；
The phase value of signal can be calculated using Hilbert transform, specific formula is as follows:
xi(τ) is the continuous time signal of electrode i, and τ is a time variable, and t indicates time point, and PV is Cauchy's principal value；
Instantaneous phase is calculated as follows:
Similarly, instantaneous phase Ф is calculatedj(t)；
If selected CT port number is M, it is T that the CT time, which counts, and different channels pair is constructed using channel two-by-two, calculates institute
There is the PLV value in channel pair, obtain M × M × T three-dimensional matrice K at this time, wherein M × M is the upper triangle at a time point
Matrix:
Each element K of KijtFor the PLV value on t time point between i-th of electrode and j-th of electrode, which is
State function connects matrix, it not only contains the phase relation of the different channels CT between any two, further comprises the space in the channel CT
Information and temporal information；
Anesthetized area is determined using infrared thermal imaging inspection apparatus acquisition arachnoid thermal map；
Inserting needle position is positioned according to the arachnoid thermal image of acquisition；
Show the inserting needle location drawing picture information of acquisition.
It chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is:
Obtaining the optimal adaptive detailed process for improving classifier includes: to given sample (x1, y1) ..., (xm,
ym), wherein xi∈ X, yi∈ Y=(- 1,1), X are training characteristics, and Y is subject's classification, initialize each training sample set first
Weight beP iteration, D are carried out later1(i) be initialization when each training sample set of i.e. p=1 weight, repeatedly
As follows for process: variable p is initially increased to P from 1, and each iteration calculates each Weak Classifier h firstpTraining sample set is carried out
The error in classification that classification obtains
Wherein, hp(xi) it is the tag along sort value that p-th of Weak Classifier obtains sample classification, DpIt (i) is pth time iteration
When each training sample set weight, then calculate sorting sequence weightThe each trained sample of final updating
The weight of this collectionWherein, D+1It (i) is each updated each training
The weight of this collection, ZpFor normalization factor,It is the weight in order to adjust sample set, when classification divides right, update
WeightThe weight of sample will reduce；When classification misclassification, weight is updated
Sample weights will improve；
P Weak Classifier h under the frequency range is obtained after P iterationp, finally most by P Weak Classifier combination building
Whole classifier is optimal adaptive raising classifier:
Then the optimal adaptive classification accuracy rate for improving classifier under each frequency range is calculated separately.
After trained multiple adaptive raising classifiers are combined in the way of voting, classify to sample:
Anesthetized area determines that method includes:
Firstly, acquiring infrared signal using infrared thermal imaging inspection apparatus；
Then, to the infrared signal of acquisition, thermal map is formed after being analyzed relatively；
Finally, changing by the light and shade that thermal map is shown, the range of subarachnoid nerve block anesthesia plane is determined.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When using entirely or partly realizing in the form of a computer program product, the computer program product include one or
Multiple computer instructions.When loading on computers or executing the computer program instructions, entirely or partly generate according to
Process described in the embodiment of the present invention or function.The computer can be general purpose computer, special purpose computer, computer network
Network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or from one
Computer readable storage medium is transmitted to another computer readable storage medium, for example, the computer instruction can be from one
A web-site, computer, server or data center pass through wired (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)
Or wireless (such as infrared, wireless, microwave etc.) mode is carried out to another web-site, computer, server or data center
Transmission).The computer-readable storage medium can be any usable medium or include one that computer can access
The data storage devices such as a or multiple usable mediums integrated server, data center.The usable medium can be magnetic Jie
Matter, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk Solid
State Disk (SSD)) etc..
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (10)
1. the control method of inserting needle equipment in a kind of single ubarachnoid block art, which is characterized in that the single arachnoid
The control method of inserting needle equipment includes: in cavity of resorption retardance art
Acquire CT image data information；Configure inserting needle equipment parameters；
CT image is detected with the Pulse-coupled Neural Network Model of suitable processing biological tissue's class image information；CT image
It is handled by the lesser impulsive noise pollution of density by adaptive weighted filter；CT image is by the biggish impulsive noise of density
Pollution is using the introducing binode constitutive element mathematical morphology progress secondary filtering for keeping edge detail information；
It is suitble to the Pulse-coupled Neural Network Model of processing biological tissue's class image information:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
θij[n]=θ0e-αθ(n-1)；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, internal activity
Item and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
The method of CT image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, filter window M is chosen, to noise dirt
Contaminate image fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fijFor filter
The output valve of window center position is corresponded to after wave:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are absolutely equal
Value, max are maximizing symbol；
Identifying processing is carried out to the CT image of acquisition；The original CT signal got is pre-processed, to reduce artifacts；
Filter is created, by pretreated CT signal filtering to required frequency range；
Using Phase synchronization analysis method, calculates phase of the CT signal of each frequency range between various time points every two channel and close
System obtains dynamic function connection matrix；The time domain entropy for calculating phase relation value between two channels one by one, obtains the letter of each edge
Entropy is ceased, to measure the complexity of each side time-domain of CT functional network；
Using the dynamic function connection entropy of each frequency range respectively as the characteristic of division of CT functional network, training is adaptive to improve classification
Device obtains multiple adaptive raising classifiers and corresponding classification accuracy rate；
Classification is combined to sample in the way of voting by trained multiple adaptive raising classifiers；
The creation method of filter are as follows: CT signal using WAVELET PACKET DECOMPOSITION be five frequency ranges, i.e. δ (1-10Hz), θ (11-20Hz),
α(21-35Hz),β(36-55Hz)γ(56-70Hz)；
Phase of the CT signal of each frequency range in various time points between every two channel is calculated using PGC demodulation value PLV to close
System, specific calculation formula are as follows:
PLV=| < exp (j { Φi(t)-Φj(t)})>|；
Wherein, Φi(t) and Φj(t) be respectively electrode i and j instantaneous phase；
The phase value of signal can be calculated using Hilbert transform, specific formula is as follows:
xi(τ) is the continuous time signal of electrode i, and τ is a time variable, and t indicates time point, and PV is Cauchy's principal value；
Instantaneous phase is calculated as follows:
Similarly, instantaneous phase Φ is calculatedj(t)；
If selected CT port number is M, it is T that the CT time, which counts, and different channels pair is constructed using channel two-by-two, is calculated all logical
The PLV value in road pair obtains M × M × T three-dimensional matrice K at this time, and wherein M × M is the upper triangular matrix at a time point:
Each element K of KijtFor the PLV value on t time point between i-th of electrode and j-th of electrode, which is dynamic function
Energy connection matrix, it not only contains the phase relation of the different channels CT between any two, further comprises the spatial information in the channel CT
And temporal information；
Anesthetized area is determined using infrared thermal imaging inspection apparatus acquisition arachnoid thermal map；
Inserting needle position is positioned according to the arachnoid thermal image of acquisition；
Show the inserting needle location drawing picture information of acquisition.
2. the control method of inserting needle equipment in single ubarachnoid block art as described in claim 1, which is characterized in that
It chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is:
3. the control method of inserting needle equipment in single ubarachnoid block art as described in claim 1, which is characterized in that
Obtaining the optimal adaptive detailed process for improving classifier includes: to given sample (x1, y1) ..., (xm, ym), wherein
xi∈ X, yi∈ Y=(- 1,1), X are training characteristics, and Y is subject's classification, and the weight for initializing each training sample set first isP iteration, D are carried out later1(i) be initialization when each training sample set of i.e. p=1 weight, iterative process is such as
Under: variable p is initially increased to P from 1, and each iteration calculates each Weak Classifier h firstpTraining sample set is classified to obtain
Error in classification εp=∑ Dp(i), hp(xi)≠yi,
Wherein, hp(xi) it is the tag along sort value that p-th of Weak Classifier obtains sample classification, Dp(i) every when being pth time iteration
Then the weight of a training sample set calculates sorting sequence weightThe each training sample of final updating
The weight of collectionWherein, D+1It (i) is each updated each training book
The weight of collection, ZpFor normalization factor,It is the weight in order to adjust sample set, when classification point is right, update is weighed
WeightThe weight of sample will reduce；When classification misclassification, weight is updatedSample
This weight will improve；
P Weak Classifier h under the frequency range is obtained after P iterationp, finally by P Weak Classifier combination building final classification
Device is optimal adaptive raising classifier:
Then the optimal adaptive classification accuracy rate for improving classifier under each frequency range is calculated separately.
4. the control method of inserting needle equipment in single ubarachnoid block art as described in claim 1, which is characterized in that
After trained multiple adaptive raising classifiers are combined in the way of voting, classify to sample:
5. the control method of inserting needle equipment in single ubarachnoid block art as described in claim 1, which is characterized in that fiber crops
Liquor-saturated area determination method includes:
Firstly, acquiring infrared signal using infrared thermal imaging inspection apparatus；
Then, to the infrared signal of acquisition, thermal map is formed after being analyzed relatively；
Finally, changing by the light and shade that thermal map is shown, the range of subarachnoid nerve block anesthesia plane is determined.
6. a kind of controlling party for realizing inserting needle equipment in single ubarachnoid block art described in Claims 1 to 5 any one
The computer program of method.
7. a kind of controlling party for realizing inserting needle equipment in single ubarachnoid block art described in Claims 1 to 5 any one
The computer of method.
8. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer is executed as weighed
Benefit requires the control method of inserting needle equipment in single ubarachnoid block art described in 1-5 any one.
9. a kind of single spider web for realizing the control method of inserting needle equipment in single ubarachnoid block art described in claim 1
Hypostegal cavity blocks the control system of inserting needle equipment in art, which is characterized in that inserting needle is set in the single ubarachnoid block art
Standby control system includes:
Image capture module is connect with central control module, for acquiring brain CT image data information；
Parameter configuration module is connect with central control module, for configuring inserting needle equipment work ginseng in ubarachnoid block art
Number；
Central control module, with image capture module, parameter configuration module, image data processing module, anesthesia module, positioning mould
Block, display module connection, work normally for controlling modules；
Image data processing module is connect with central control module, for carrying out identifying processing to the image of acquisition；
Module is anaesthetized, is connect with central control module, is determined for acquiring arachnoid thermal map by infrared thermal imaging inspection apparatus
Anesthetized area；
Locating module is connect with central control module, for by acquisition image in single ubarachnoid block art into
Pin position is positioned；
Display module is connect with central control module, for the image information by display screen display acquisition.
10. the control system of inserting needle equipment in single ubarachnoid block art as claimed in claim 9, which is characterized in that
Image data processing module includes region estimation module, characteristic extracting module, abnormal signal identification module；
Region estimation module, for receiving the brain CT image, and it is emerging to carry out cavum subarachnoidale sense to the brain CT image
The estimation in interesting region；
Characteristic extracting module, for receiving the brain CT image after interesting region estimating, and to the interesting region estimating
Brain CT image afterwards carries out feature extraction, obtains characteristic value；
Abnormal signal identification module for receiving the brain CT image after the interesting region estimating and characteristic value, and uses
The method of pattern-recognition identifies whether there is abnormal signal in the area-of-interest according to the characteristic value, and by recognition result
Send the display module to.
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