CN108618840A - A kind of fracture internal fixation hollow nail take out in stress information acquisition system - Google Patents
A kind of fracture internal fixation hollow nail take out in stress information acquisition system Download PDFInfo
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- CN108618840A CN108618840A CN201810258854.8A CN201810258854A CN108618840A CN 108618840 A CN108618840 A CN 108618840A CN 201810258854 A CN201810258854 A CN 201810258854A CN 108618840 A CN108618840 A CN 108618840A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B17/00—Surgical instruments, devices or methods, e.g. tourniquets
- A61B17/56—Surgical instruments or methods for treatment of bones or joints; Devices specially adapted therefor
- A61B17/58—Surgical instruments or methods for treatment of bones or joints; Devices specially adapted therefor for osteosynthesis, e.g. bone plates, screws, setting implements or the like
- A61B17/88—Osteosynthesis instruments; Methods or means for implanting or extracting internal or external fixation devices
- A61B17/92—Impactors or extractors, e.g. for removing intramedullary devices
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
- A61B90/36—Image-producing devices or illumination devices not otherwise provided for
- A61B90/361—Image-producing devices, e.g. surgical cameras
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
- A61B90/36—Image-producing devices or illumination devices not otherwise provided for
- A61B90/37—Surgical systems with images on a monitor during operation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
- A61B90/36—Image-producing devices or illumination devices not otherwise provided for
- A61B90/37—Surgical systems with images on a monitor during operation
- A61B2090/376—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy
Abstract
The invention belongs to field of medical technology, the stress information acquisition system during a kind of fracture internal fixation hollow nail takes out is disclosed, the stress information acquisition system during the fracture internal fixation hollow nail takes out includes：Photographing module, pull force calculation module, vital signs detection module, single chip control module, anesthesia module, remove module, sterilization module, fault pre-alarming module, display module.The present invention can carry out disinfection to the hollow nail of taking-up by sterilization module, the health of guarantee environment；Simultaneously by fault pre-alarming module before taking out hollow nail, self-test is carried out to equipment, alarms at once if detection there are abnormal conditions, greatly improves the safety that equipment uses, ensure and remove smoothly completing for hollow tack operation.
Description
Technical field
Being adopted by force information in being taken out the invention belongs to field of medical technology more particularly to a kind of fracture internal fixation hollow nail
Collecting system.
Background technology
Currently, the prior art commonly used in the trade is such：
Fracture is that the continuity of bone structure is broken completely or partially.Be more common in children and the elderly, young people also when
There is generation.Patient is often-a position fracture, and minority is multiple fracture.Through appropriate processing in time, most patients can restore former
The function of coming, a few patients can leave different degrees of sequelae.However, it is existing during taking out hollow nail if there is
Equipment fault then leads to operative failure, seriously threatens patient safety；The hollow nail taken out simultaneously directly throws away easy to carry germ,
It is unfavorable for health.
In conclusion problem of the existing technology is：
It is existing then to lead to operative failure if there is equipment fault during taking out hollow nail, seriously threaten patient's peace
Entirely；The hollow nail taken out simultaneously directly throws away easy to carry germ, is unfavorable for health.
In existing bone image processing, it is unfavorable for automatic noise detection existing for micro-image noise filtering, adaptively
Energy force difference, there is contradiction in noise remove and filtering performance.
Invention content
In view of the problems of the existing technology, the stress letter in being taken out the present invention provides a kind of fracture internal fixation hollow nail
Cease acquisition system.
The invention is realized in this way the stress information acquisition system in a kind of fracture internal fixation hollow nail taking-up, described
Fracture internal fixation hollow nail take out in stress information acquisition system include：
Photographing module is connect with single chip control module, image and processing for obtaining skeleton by x-ray；
Photographing module is handled the image of the skeleton of acquisition by integrated image processing module, specific to wrap
It includes：
Bone micro-image is examined using the Pulse-coupled Neural Network Model of suitable processing bone class image information
It surveys；Bone micro-image is handled by the smaller impulsive noise pollution of density by adaptive weighted filter；Bone micro-image
By the larger impulsive noise pollution of density using the introducing binode constitutive element mathematical morphology progress for keeping edge detail information
Secondary filtering；
It is suitble to the Pulse-coupled Neural Network Model of processing bone 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, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected pending window W, Δ is adjustment factor, chooses 1~3；
Pull force calculation module, connect with single chip control module, for removing hollow nail by pulling force sensor detection
When stress data；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of pull force calculation module is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) conjugation for indicating x (t), as x (t)
For real signal when, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x*
(t)；
Vital signs detection module, connect with single chip control module, for detecting the blood pressure of patient, heart rate, pulse
Life-information；
The detection signal model of vital signs detection module is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length；
Single chip control module, with photographing module, pull force calculation module, vital signs detection module, anesthesia module, removal
Module, sterilization module, fault pre-alarming module, display module connection, for controlling scheduling modules normal work；
Module is anaesthetized, is connect with single chip control module, for being anaesthetized to patient；
Remove module is connect with single chip control module, for being removed to hollow nail by mechanical arm；
Sterilization module is connect with single chip control module, for carrying out disinfection to the hollow nail after removal；
Fault pre-alarming module, connect with single chip control module, for being detected to equipment, is sent out in time if abnormal
Go out alarm；
Display module is connect with single chip control module, for showing detection data information.
Further, when Pulse-coupled Neural Network Model is detected bone micro-image, make ash using network characteristic
Degree is SijmaxPixel igniting activation, then second of Pulse Coupled Neural Network iterative processing is carried out, between [Sij max/1+
βijLij,Sij max] between pixel capture activation, make the corresponding Y of the pixel activated twiceijOutput is 1；Then to the dirt of former noise
Dye image highlights processing, then to treated image SijIt is iterated processing by aforementioned, and makes corresponding output Yij=1, it utilizes
Picture noise pixel and surrounding pixel correlation are small, the big characteristic of gray scale difference, where the excitation of a neuron does not cause
When the excitation of the most of neurons of areas adjacent, just illustrate that the neuron respective pixel may be noise spot；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of bone micro-image, is protected；To YijOutput
It is counted within the scope of 3*3 templates B to export Y for 1 pixelijThe number N that neighborhood element value is 1 centered on=1YDifferentiation is returned
Class：1≤NY≤ 8, it is noise spot, works as NY=9, it is determined as image slices vegetarian refreshments；
The implementation method of bone micro-image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to choose filter window M when being 1 number in 3*3 templates B, it is right
Image polluted by noise fijAdaptive-filtering, filtering equations are：
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；
It chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is：
The specific method of binode constitutive element mathematical morphology second level filtering：
The bone micro-image of residual impulse noise is f, and E is structural element SE, then expansion has following relational expression：
In formulaIt is accorded with for dilation operation, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, and boundary is made to be expanded to outside
Process, fill up the hole in object；
Above formula Θ accords with for erosion operation, and corrosion is to eliminate boundary point, and boundary is internally shunk, while in the base of corrosion expansion
On plinth, in conjunction with morphologic opening and closing operation：
Bone micro-image is examined using the Pulse-coupled Neural Network Model of suitable processing bone class image information
Survey needs to input Noise bone micro-image, and carries out colour-gradation conversion pretreatment.
Further, the photographing module image capture method is as follows：
First, the basic point position altitude information at the basic point position of diagnosis object is obtained；
Then, the direction parameter of diagnosis object detected part, the orientation are obtained based on basic point position altitude information
Parameter includes the position height data of the detected part；
Finally, the direction parameter based on the detected part, control X ray image filming apparatus X-ray emitter and/
Or detector is moved to the camera site of the detected part.
Further, basic point position altitude information includes the height data and shoulder height data for diagnosing object.
Further, the direction parameter packet that diagnosis object detected part is obtained based on basic point position altitude information
It includes：Proportionate relationship based on basic point position altitude information and partes corporis humani position is to obtain diagnosis object detected part
Position height data.
Another object of the present invention is to provide the stress letters in a kind of taking-up equipped with the fracture internal fixation hollow nail
Cease the information processing terminal of acquisition system.
Advantages of the present invention and good effect are：
The present invention can carry out disinfection to the hollow nail of taking-up by sterilization module, the health of guarantee environment；Pass through simultaneously
Fault pre-alarming module carries out self-test before taking out hollow nail, to equipment, alarms at once if detection has abnormal conditions,
The safety that equipment uses is greatly improved, ensures and removes smoothly completing for hollow tack operation.
The Image Information Processing technology of the present invention carries out modern measure identification pretreatment application technology to traditional bone image
Research, analyze to detect with " modern times " for information age bone " tradition " the completely new technical thought and method of offer be provided, give
Modern bone is contactless, harmless information detection and analysis carry out beneficial exploration, to improve bone mass detection, identification in next step
Basis early period is established with identification；
Meanwhile there is important theory significance and application value with analysis and research for China's bone detection identification；
In the bone micro-image impulse noise detection stage, the present invention is sent out using the lock-out pulse of Pulse Coupled Neural Network
It puts characteristic and distinguishes position pulse noise spot and signal pixels point position, it is relatively traditional that intermediate value is improved based on intermediate value detection or correlation
Detection method has higher noise detection performance, relative to other threshold value noise detection methods；The present invention is detected without setting
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
Inventive method detection time is short, and automaticity is strong；
There is presently no any impulse noise correction methods to apply in the detection of bone micro-image impulsive noise；
The stage is filtered out in bone micro-image impulsive noise, the present invention is first according to the above-mentioned noise detected and signal
Point carries out classification processing to image pixel；Only the noise spot of detection is filtered when using first order adaptive weighted filter
Wave processing, signaling point information is protected relative to the methods of other medium 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 in second level mathematical morphology filter, in denoising
Noise jamming can be not only effectively filtered out simultaneously, and the information such as image edge detailss can be protected well；
With stronger subjective vision effect and objective evaluation index, noise removal capability is strong, signal-to-noise ratio is high and adaptability is good, special
It is not to the bone micro-image by serious noise pollution, it is shown that the filtering superiority of bigger.
The fractional lower-order ambiguity function of the digital modulation signals x (t) of pull force calculation module of the present invention is expressed as：
Vital signs detection module, connect with single chip control module, for detecting the blood pressure of patient, heart rate, pulse
Life-information；
The detection signal model of vital signs detection module is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
The signal data accuracy of detection improves nearly 6 percentage points compared with the prior art.
Description of the drawings
Fig. 1 is the stress information acquisition system structural frames during fracture internal fixation hollow nail provided in an embodiment of the present invention takes out
Figure.
In figure：1, photographing module；2, pull force calculation module；3, vital signs detection module；4, single chip control module；5、
Anaesthetize module；6, remove module；7, sterilization module；8, fault pre-alarming module；9, display module.
Specific implementation mode
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and coordinate attached drawing
Detailed description are as follows.
The structure of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, the stress information acquisition system during fracture internal fixation hollow nail provided by the invention takes out includes：It takes the photograph
As module 1, pull force calculation module 2, vital signs detection module 3, single chip control module 4, anesthesia module 5, remove module 6,
Sterilization module 7, fault pre-alarming module 8, display module 9.
Photographing module 1 is connect with single chip control module 4, is shot to skeleton for passing through x-ray；
Pull force calculation module 2 is connect with single chip control module 4, for hollow in removal by pulling force sensor detection
Stress data when nail；
Vital signs detection module 3 is connect with single chip control module 4, blood pressure, heart rate, pulse for detecting patient
Equal life-informations；
Single chip control module 4, with photographing module 1, pull force calculation module 2, vital signs detection module 3, anesthesia module
5, remove module 6, sterilization module 7, fault pre-alarming module 8, display module 9 connect, for controlling the scheduling normal work of modules
Make；
Module 5 is anaesthetized, is connect with single chip control module 4, for being anaesthetized to patient；
Remove module 6 is connect with single chip control module 4, for being removed to hollow nail by mechanical arm；
Sterilization module 7 is connect with single chip control module 4, for carrying out disinfection to the hollow nail after removal；
Fault pre-alarming module 8 is connect with single chip control module 4, timely if abnormal for being detected to equipment
Send out alarm；
Display module 9 is connect with single chip control module 4, for showing detection data information.
1 image capture method of photographing module provided by the invention is as follows：
First, the basic point position altitude information at the basic point position of diagnosis object is obtained；
Then, the direction parameter of diagnosis object detected part, the orientation are obtained based on basic point position altitude information
Parameter includes the position height data of the detected part；
Finally, the direction parameter based on the detected part, control X ray image filming apparatus X-ray emitter and/
Or detector is moved to the camera site of the detected part.
Basic point position provided by the invention altitude information includes the height data and shoulder height data for diagnosing object.
The direction parameter packet provided by the invention that diagnosis object detected part is obtained based on basic point position altitude information
It includes：Proportionate relationship based on basic point position altitude information and partes corporis humani position is to obtain diagnosis object detected part
Position height data.
When the present invention works, skeleton is shot by photographing module 1, determines the position of hollow nail；Pass through drawing
Power detection module 2 detects the stress data when removing hollow nail；By vital signs detection module 3 detect patient blood pressure,
The life-informations such as heart rate, pulse；Single chip control module 4 starts anesthesia module 5 and is anaesthetized to patient；Then by removing mould
Block 6 removes hollow nail；Then, it is carried out disinfection to the hollow nail after removal by sterilization module 7；Pass through fault pre-alarming module 8
Equipment is detected, alarm is sent out in time if abnormal；Finally, detection data information is shown by display module 9.
With reference to concrete analysis, the invention will be further described.
Photographing module is handled the image of the skeleton of acquisition by integrated image processing module, specific to wrap
It includes：
Bone micro-image is examined using the Pulse-coupled Neural Network Model of suitable processing bone class image information
It surveys；Bone micro-image is handled by the smaller impulsive noise pollution of density by adaptive weighted filter；Bone micro-image
By the larger impulsive noise pollution of density using the introducing binode constitutive element mathematical morphology progress for keeping edge detail information
Secondary filtering；
It is suitble to the Pulse-coupled Neural Network Model of processing bone 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, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected pending window W, Δ is adjustment factor, chooses 1~3；
Pull force calculation module, connect with single chip control module, for removing hollow nail by pulling force sensor detection
When stress data；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of pull force calculation module is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) conjugation for indicating x (t), as x (t)
For real signal when, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x*
(t)；
Vital signs detection module, connect with single chip control module, for detecting the blood pressure of patient, heart rate, pulse
Life-information；
The detection signal model of vital signs detection module is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length；
Single chip control module, with photographing module, pull force calculation module, vital signs detection module, anesthesia module, removal
Module, sterilization module, fault pre-alarming module, display module connection, for controlling scheduling modules normal work；
When Pulse-coupled Neural Network Model is detected bone micro-image, gray scale is set to be S using network characteristicijmax
Pixel igniting activation, then second of Pulse Coupled Neural Network iterative processing is carried out, between [Sij max/1+βijLij,
Sij max] between pixel capture activation, make the corresponding Yi of the pixel activated twicejOutput is 1；Then to former noise pollution figure
As highlighting processing, then to treated image SijIt is iterated processing by aforementioned, and makes corresponding output Yij=1, utilize image
Noise pixel and surrounding pixel correlation are small, the big characteristic of gray scale difference, when the excitation of a neuron does not cause region
When the excitation of neighbouring most of neurons, just illustrate that the neuron respective pixel may be noise spot；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of bone micro-image, is protected；To YijOutput
It is counted within the scope of 3*3 templates B to export Yi for 1 pixeljThe number N that neighborhood element value is 1 centered on=1YDifferentiation is returned
Class：1≤NY≤ 8, it is noise spot, works as NY=9, it is determined as image slices vegetarian refreshments；
The implementation method of bone micro-image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to choose filter window M when being 1 number in 3*3 templates B, it is right
Image polluted by noise fijAdaptive-filtering, filtering equations are：
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；
It chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is：
The specific method of binode constitutive element mathematical morphology second level filtering：
The bone micro-image of residual impulse noise is f, and E is structural element SE, then expansion has following relational expression：
In formulaIt is accorded with for dilation operation, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, and boundary is made to be expanded to outside
Process, fill up the hole in object；
Above formula Θ accords with for erosion operation, and corrosion is to eliminate boundary point, and boundary is internally shunk, while in the base of corrosion expansion
On plinth, in conjunction with morphologic opening and closing operation：
Bone micro-image is examined using the Pulse-coupled Neural Network Model of suitable processing bone class image information
Survey needs to input Noise bone micro-image, and carries out colour-gradation conversion pretreatment.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Every any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (6)
1. the stress information acquisition system in a kind of fracture internal fixation hollow nail taking-up, which is characterized in that the Fracture internal fixaiion
Hollow nail take out in stress information acquisition system include：
Photographing module is connect with single chip control module, image and processing for obtaining skeleton by x-ray；
Photographing module is handled the image of the skeleton of acquisition by integrated image processing module, is specifically included：
Bone micro-image is detected using the Pulse-coupled Neural Network Model of suitable processing bone class image information；Bone
Bone micro-image is handled by the smaller impulsive noise pollution of density by adaptive weighted filter；Bone micro-image is by close
Larger impulsive noise pollution is spent using the secondary filter of introducing binode constitutive element mathematical morphology progress for keeping edge detail information
Wave；
It is suitble to the Pulse-coupled Neural Network Model of processing bone 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 pending window W, Δ is adjustment factor, chooses 1~3；
Pull force calculation module, connect with single chip control module, for being detected by pulling force sensor when removing hollow nail
Stress data；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of pull force calculation module is expressed as：
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 ＜ a, b ＜ α/2, x*(t) conjugation for indicating x (t), when x (t) is real
When signal, x (t)＜ p ＞=| x (t) |＜ p ＞sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]＜ p ＞=| x (t) |p-1x*(t)；
Vital signs detection module, connect with single chip control module, for detecting the blood pressure of patient, the life of heart rate, pulse
Information；
The detection signal model of vital signs detection module is expressed as：
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xi(t) it is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency overlapping letter
The number of number component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiExist for i-th of signal
The amplitude at k moment, TsiFor Baud Length；
Single chip control module, with photographing module, pull force calculation module, vital signs detection module, anesthesia module, removal mould
Block, sterilization module, fault pre-alarming module, display module connection, for controlling scheduling modules normal work；
Module is anaesthetized, is connect with single chip control module, for being anaesthetized to patient；
Remove module is connect with single chip control module, for being removed to hollow nail by mechanical arm；
Sterilization module is connect with single chip control module, for carrying out disinfection to the hollow nail after removal；
Fault pre-alarming module, connect with single chip control module, and for being detected to equipment, report is sent out in time if abnormal
Alert sound；
Display module is connect with single chip control module, for showing detection data information.
2. the stress information acquisition system in fracture internal fixation hollow nail taking-up as described in claim 1, which is characterized in that pulse
When coupled neural network model is detected bone micro-image, gray scale is set to be S using network characteristicijmaxPixel igniting swash
It is living, then second of Pulse Coupled Neural Network iterative processing is carried out, between [Sijmax/1+βijLij,Sijmax] between pixel capture
Activation, makes the corresponding Y of the pixel activated twiceijOutput is 1；Then processing is highlighted to former image polluted by noise, then to processing
Image S afterwardsijIt is iterated processing by aforementioned, and makes corresponding output Yij=1, utilize picture noise pixel and surrounding pixel
Correlation is small, the big characteristic of gray scale difference, when the excitation of a neuron does not cause most of neurons near region
When excitation, just illustrate that the neuron respective pixel may be noise spot；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of bone micro-image, is protected；To YijOutput is 1
Pixel counts within the scope of 3*3 templates B to export YijThe number N that neighborhood element value is 1 centered on=1YDifferentiate and sorts out：1≤
NY≤ 8, it is noise spot, works as NY=9, it is determined as image slices vegetarian refreshments；
The implementation method of bone micro-image adaptive weighting filter noise filtering；
When pulse exports Yij=1 and NY=1~8, NYIt is to work as in 3*3 templates B for 1 number, filter window M is chosen, to noise dirt
Contaminate image fijAdaptive-filtering, filtering equations are：
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；
It chooses filter window M and chooses the filter window M that size is m*m, the selection principle of window size is：
The specific method of binode constitutive element mathematical morphology second level filtering：
The bone micro-image of residual impulse noise is f, and E is structural element SE, then expansion has following relational expression：
In formulaIt is accorded with for dilation operation, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, makes boundary to the mistake of outside expansion
Journey fills up the hole in object；
Above formulaIt is accorded with for erosion operation, corrosion is to eliminate boundary point, and boundary is internally shunk, while on the basis of corrosion expansion
On, in conjunction with morphologic opening and closing operation：
Being detected to bone micro-image using the Pulse-coupled Neural Network Model of suitable processing bone class image information is needed
Noise bone micro-image is inputted, and carries out colour-gradation conversion pretreatment.
3. the stress information acquisition system in fracture internal fixation hollow nail taking-up as described in claim 1, which is characterized in that described
Photographing module image capture method is as follows：
First, the basic point position altitude information at the basic point position of diagnosis object is obtained；
Then, the direction parameter of diagnosis object detected part, the direction parameter are obtained based on basic point position altitude information
Position height data including the detected part；
Finally, the direction parameter based on the detected part controls X-ray emitter and/or the spy of X ray image filming apparatus
Survey the camera site that device is moved to the detected part.
4. the stress information acquisition system in fracture internal fixation hollow nail taking-up as claimed in claim 3, which is characterized in that described
Basic point position altitude information includes the height data and shoulder height data for diagnosing object.
5. the stress information acquisition system in fracture internal fixation hollow nail taking-up as claimed in claim 3, which is characterized in that described
Based on basic point position altitude information obtain diagnosis object detected part direction parameter include：Based on basic point position height
Degrees of data and the proportionate relationship of partes corporis humani position are to obtain the position height data of diagnosis object detected part.
6. a kind of stress information collection system equipped in fracture internal fixation hollow nail taking-up described in claim 1-5 any one
The information processing terminal of system.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113331931A (en) * | 2021-05-25 | 2021-09-03 | 长沙年轮骨科医院有限公司 | Fracture intramedullary fixation device and method |
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