CN102357033B - Laser speckle blood stream imaging processing system and method - Google Patents
Laser speckle blood stream imaging processing system and method Download PDFInfo
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- CN102357033B CN102357033B CN 201110300757 CN201110300757A CN102357033B CN 102357033 B CN102357033 B CN 102357033B CN 201110300757 CN201110300757 CN 201110300757 CN 201110300757 A CN201110300757 A CN 201110300757A CN 102357033 B CN102357033 B CN 102357033B
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Abstract
The invention discloses a laser speckle blood stream imaging processing system and method based on SOC (System on Chip)/IP (Internet Protocol) solution. The process of acquiring, processing and displaying a laser speckle blood stream image is completely implemented by a hardware circuit; and a multistage production line technique and a parallel processing unit are introduced into the design of the processing core, so that the system can implement laser speckle blood stream imaging real-time processing at video rate at lower clock frequency (about 50MHz). The scheme greatly reduces the power consumption and volume of the laser speckle blood stream imaging system, and provides a design method of a portable laser speckle blood stream imaging instrument. The scheme is implemented by an IP core designed according to hardware description language, and can be implemented by a discrete circuit element, an FPGA (field programmable gate array) and an ASIC (application-specific integrated circuit) chip.
Description
Technical field
The present invention relates to a kind of processing system and method for laser speckle blood current imaging, the two-dimentional blood flow that is applied to real-time video speed distributes and monitors.
Background technology
Blood flow rate is one of very important functional metabolism parameter in the vital movement process.The change in time and space of dynamic monitoring blood flow rate is for postgraduate's fabric texture physiological change process, and functional activity and evaluating drug effect etc. is significant.
In biological tissue's blood flow detection, all there are many deficiencies in existent method at present.Indirect measurement method, as local organization impedance type volume pulse wave graphy method, photoelectric reflection formula volume pulse wave graphy method, the spatial resolution of multipoint temperature measuring method is lower.Direct measuring method, as hemocyte fluorescent tracing vivo observation method, the hydrion dilution method, radiological predication fallout plot playing skill art all needs to introduce exogenous material, can produce certain influence to biological tissue's physiological parameter, the micro tv method can not real-time continuous ground measurement flow rate change, and the ultrasound Doppler's method spatial resolution is lower.Laser-Doppler blood flow imaging method will realize two-dimentional blood flow velocity measurement based on single-point doppler shift effect measurement flow rate, need take the mode of spot scan or a plurality of probe and line scanning, and its time resolution is generally several minutes.
The laser speckle blood current imaging method with its fast, high-resolution, non-intruding and need not to scan can realize on a large scale in the characteristics of the rapid-result picture of two-dimensional flow, in biological tissue's blood flow detection such as cerebral tissue, skin, optical fundus retina, joint and mesentery, obtained important application, for reflection biological tissue functional activity, announcement major disease generation mechanism and evaluating drug effect provide important research tool.Under many circumstances, the real-time that blood flow is monitored requires will reach the detection of video rate than higher, also is about per second 24 vertical frame dimension image in different resolution.Huge like this disposal ability is far from that CPU on the ordinary PC can finish.Therefore, generally need to introduce some special devices and quicken the processing of laser speckle blood current imaging with powerful disposal ability.Reported method has two kinds:
1. use the GPU(Graphics Processing unit that can be applied to general-purpose computations, image processor)
Along with the development of GPU technology, present GPU not only can be applied to traditional image and play up, and can be used for quickening general general-purpose computations.The reason that present GPU can quicken general-purpose computations is: at first, GPU was the multinuclear framework originally, the chip of general GPU the inside is integrated tens to up to a hundred processors, have the ability of parallel processing; Secondly, present GPU is programmable, and the user can utilize and oneself define the task that processor will be finished among the GPU, and the tissue of task is to realize with the threads of a large amount of operations simultaneously.Like this, the user just can be high some bulk densities but algorithm that have a concurrency improved, be loaded into GPU and go up operation, utilize the powerful computation capability of GPU to reach the purpose of acceleration.The Flame Image Process of laser speckle just can adopt GPU to quicken.Earlier a two field picture is divided into a lot of little subimages that equate, each subimage is given corresponding thread block (block), a thread in each piece is responsible for calculating a corresponding little sliding window, obtains a pixel in the flow velocity image.After treating that all subimages all are converted into the subimage of corresponding flow velocity figure, these flow velocitys figure subimage is being merged, just obtaining blood flow distributed image that a frame is complete.The shortcoming of GPU scheme must cooperate with PC exactly, and volume and power consumption are all very big like this, is unfavorable for developing portable laser speckle blood current imaging instrument.
2. adopt DSP(Digital Signal Processor, digital signal processor)
Digital signal processor is the same with traditional processor, also is to finish calculation task by the operation of program.But, to compare with common processor, it has adopted some more optimal designs, improves computing capability.Such as: traditional processor generally uses the Feng Ruoyiman framework, a memory space of program code and data sharing and a cover address-data bus, DSP generally then adopts Harvard structure, program code and data independent distribution are in different memory spaces, have bus structures separately, so DSP can improve the bandwidth of date processing greatly; Secondly, DSP is integrated, and hardware multiplication device specially finishes multiplication calculates, and can finish complicated multiply operation in the single cycle; In addition, most of dsp processor has zero and consumes the specialised hardware of loop control, and zero to consume circulation be that need not the take time value of test loop enumerator of finger processor just can be carried out one group of circulation of instructing, and hardware is finished the decay of cycling jump and cycle counter; In addition, also have many other to be specifically designed to the hardware cell of some operation in the optimizer.
The algorithm that the laser speckle blood-stream image is handled is to move on DSP in the mode of program language equally.Exactly because DSP has the special hardware unit of these faster procedure operations and data computation, so can reach the purpose of real-time processing on some high-performance digital signal processor.The shortcoming of DSP scheme is exactly the disposal ability of DSP to be required very high, and the High Performance DSP that clock frequency will reach about 800MHZ can be competent at, so cost and power consumption are all than higher.
Summary of the invention
Technical problem to be solved by this invention has provided a kind of processing system and method thereof of laser speckle blood current imaging, to have reduced the power consumption and the volume of laser speckle blood current imaging system.
For solving the problems of the technologies described above, the present invention proposes a kind of laser speckle blood current imaging processing system, comprise the CCD acquisition control module, original image cache module, original image read module, the laser speckle image is handled kernel module and display control module, it is characterized in that described CCD acquisition control module, original image cache module, the original image read module, the laser speckle image is handled kernel module and display control module is integrated on the SOC framework; Described laser speckle image is handled integrated control unit and stream treatment unit on the kernel module, described stream treatment unit comprises first accumulator module, first square of module, second accumulator module, second square of module, subtractor, divider, if the sliding window size N=i * i of described laser speckle blood current imaging initial data, the value of i is one of 3,5,7,9;
Described original image read module is used to read the i * i pixel value of described sliding window;
Described first accumulator module is used for the pixel value of described sliding window is added up, and obtains first accumulated value,
Described first square of module is used for its square value of calculated for pixel values to described sliding window, obtains the first square value vector;
Described second square of module is used for described first accumulated value is carried out square operation, obtains second square value;
Described second accumulator module is used for the described first square value vector is carried out accumulating operation, obtains second accumulated value;
Described subtractor is used for described second accumulated value is deducted described second square value, obtains the 3rd value;
Described divider is used for described second square value obtaining the flow speed value of described laser speckle blood flow divided by described the 3rd value;
Described control unit is used to control the data that described original image read module reads sliding window continuously, delivers to unitary first accumulator module of described stream treatment, first square of module.
Preferably, described divider quantity is (d+a) * 2+2+m)/i, if this formula result is not an integer, then gets one bigger in the adjacent integer; The bit wide of divider is (d+a) * 2+m; Wherein, d is the binary system bit wide of input pixel value; The value of i was respectively 3,5,7,9 o'clock, and the value of a corresponds to 4,5,6,7 respectively; M is the decimal digits (binary system) that will keep; The value of i is got the sliding window size of 3,5,7,9 difference corresponding 3 * 3,5 * 5,7 * 7,9 * 9.That is, the quantity of described divider and bit wide depend on 3 factors: the binary system bit wide of input pixel value, sliding window size size and result of calculation is kept the required precision of decimal digits.
This laser speckle blood current imaging processing system also comprises first buffer, second buffer, described first buffer connects described first accumulator module, be used for storing temporarily the described sliding window that described first accumulator module calculates the 2nd～i row pixel value add up and; Described second buffer connects described second accumulator module, be used for storing temporarily the described sliding window that described second accumulator module calculates the 2nd～i row pixel square value add up and.
Described control unit also is used for selecting and dispatch described divider by the mode that the time wheel changes.
The invention allows for a kind of processing method, it is characterized in that, may further comprise the steps according to described laser speckle blood current imaging processing system:
Step 1, described original image read module read the i * i pixel value of first described sliding window,
Step 2, described first accumulator module add up to the pixel value of described sliding window, obtain first accumulated value, and described first accumulated value is sent into described second square of module; Concurrently,
Described first square of module obtains the first square value vector to its square value of calculated for pixel values of described sliding window, and the described first square value vector is sent into described second accumulator module;
Step 3, described second square of module are carried out square operation with described first accumulated value, obtain second square value, and described second square value is sent into described subtractor; Concurrently,
Described second accumulator module is carried out accumulating operation with the described first square value vector, obtains second accumulated value, and described second accumulated value is sent into described subtractor;
Step 4, described subtractor deduct described second square value with described second accumulated value, obtain the 3rd value;
Step 5, described divider receive described second square value in the described buffer, and are worth divided by the described the 3rd with described second square value, obtain the flow speed value of current sliding window;
Step 6, described control unit are selected next described sliding window, if there is next sliding window, then described original image read module reads the last string pixel value of next sliding window, the last string pixel value of this sliding window and 2～i row pixel value of a last sliding window are formed the i * i pixel value of this sliding window, return step 2; If not, then finish, obtain the flow speed value of described laser speckle blood flow.
More optimal technical scheme is:
In described step 2, when described first accumulated value is sent into described second square of module, the 2nd～i row pixel value of the current sliding window of the described first buffer buffer memory add up and;
In described step 3, when described second accumulated value is sent into described subtractor, described second this sliding window of buffer buffer memory the 2nd～i row pixel square value add up and;
Through described step 6, carry out
Step 7, described first accumulator module adds up to the last string pixel value of current sliding window and 2～i row pixel value of a last sliding window, obtains first accumulated value, and described first accumulated value is sent into described second square of module; Concurrently,
Described first square of module obtains the first square value vector to its square value of calculated for pixel values of described sliding window, and the described first square value vector is sent into described second accumulator module;
Described second square of module carried out square operation with described first accumulated value, obtains second square value, and described second square value is sent into described subtractor; Concurrently,
Described second accumulator module is carried out accumulating operation with the described first square value vector, obtains second accumulated value, and described second accumulated value is sent into described subtractor;
Described subtractor deducts described second square value with described second accumulated value, obtains described the 3rd value;
Described divider receives described second square value in the described buffer, and is worth divided by the described the 3rd with described second square value, obtains the flow speed value of current sliding window; Return step 6.
Than traditional scheme based on GPU and the execution of DSP program, the present invention propose based on SOC/IP solution (System On chip/Intellectual Property, the processing system and the method for laser speckle blood current imaging SOC(system on a chip)/the have integrated circuit modules of specific function), the omnidistance processing all is to adopt hardware circuit to realize, the system that makes can be issued to the processing capability in real time of video rate (under higher clock in lower power consumption and clock frequency (for example 50MHZ), can handle more original image, when clock was 130MHZ, per second can be handled 80 frames approximately).The original image collection of this method, Processing Algorithm and demonstration all are to realize with the IP kernel (integrated circuit modules with specific function) of hardware description language design, can be by discrete circuit element, FPGA(field programmable gate array) or the ASIC(special IC) realize concrete physical computing circuit.Wherein, on FPGA and ASIC platform, can realize SOC(System On Chip easily, SOC(system on a chip)), the original image acquisition module, memory module, processing module and display module are integrated on the chip, can design a kind of low-power consumption, miniature portable laser speckle blood current imaging instrument.
The present invention adopts the hardware circuit control logic to come operational store, make the buffer memory of initial data carry out with reading in the mode of high-speed data-flow, each clock can buffer memory or is read in an effective original image pixels value, so greatly increased the bandwidth of data read, for example under the clock frequency of 50M, system can the per second buffer memory or reads 50M beginning pixel value.
In order to tackle multiply operation abundant in the algorithm, the present invention has used the hardware multiplication circuit in design, and these hardware multipliers can be finished a multiply operation in the single clock cycle.The parallel connection of a plurality of multipliers is used can finish a plurality of multiplication in a clock cycle.
For further enhancement process ability, the present invention program has adopted the mode of streamline to come deal with data.When the next stage processing unit when handling will finishing of task at the corresponding levels, the upper level processing unit is being handled the data that top-ranking unit biography is come simultaneously, has so greatly increased the disposal ability and the data throughout of system.
In the present invention program, the mode that divider is to use displacement to subtract each other realizes.Realize a divide operations, need finish with the clock pulses number that dividend and divisor binary system bit wide equate.Obviously, the data throughput capabilities of divider front stage circuits has surpassed the disposal ability of divider.Therefore, adopted a plurality of divider units to mate the processing speed of previous stage circuit in the scheme.The data output of upper level is assigned on these dividers by the mode that the time wheel changes.The value of these divider outputs is reintegrated together, and according to pixels order is exported flow velocity figure.
Description of drawings
Fig. 1 is a laser speckle blood current imaging system construction drawing involved in the present invention, comprises the laser lighting part, and image acquisition is handled and the display part.
Fig. 2 is the collection of laser speckle blood current imaging based on SOC described in the invention, and storage is handled and block diagram.
Fig. 3 is the process block diagram of streams data direction in the Processing Algorithm of the present invention.
Fig. 4 is the processing sequential sketch map of streamline first order output front end in the Processing Algorithm of the present invention.
The specific embodiment
By shown in Figure 1, the structured flowchart of laser speckle blood current imaging system involved in the present invention has simply been described, by the laser lighting part, image acquisition is handled and display module is formed.Laser illumination system is by helium neon laser, expander lens, and the sample platform is formed.The laser that laser instrument sends shines on the sample platform after expanding bundle through expander lens.Image is buffered in the memorizer after being gathered by CCD, handles the core simultaneously and reads the original image processing from memorizer, and the flow velocity image that obtains after the processing is delivered to the display part and shown.
Fig. 2 has described the laser speckle blood current imaging SOC system that the present invention is based on FPGA or ASIC.Can be on a FPGA or asic chip the acquisition controlling unit of integrated ccd image sensor, the original image caching control unit, laser speckle blood current imaging is handled kernel unit and LCD display controller unit etc.The direction representative data of arrow mobile direction between each integrated circuit modules.
The insider will be appreciated that the ultimate principle of laser speckle blood current imaging.Do concise and to the point description at this.At first, the laser irradiation behind a branch of expansion bundle is (sample can be a skin, retina, cortex etc.) to sample, and the CCD camera is gathered sample image.Owing to be coherent light illumination, the image that CCD obtains is that a width of cloth is by interfering the speckle pattern that causes at random.Primary speckle pattern is taked following processing method: from first pixel of the image upper left corner, get a square wicket (size generally chooses 5 * 5 or 7 * 7), pixel value in the window is taken statistics, calculate the ratio of the standard deviation and the meansigma methods of these pixels, we are called this ratio and contrast; Allow this wicket with the stepping of a pixel from left to right, slide from top to bottom, calculate the value of contrasting of each sliding window, these are new contrasts the image that value forms and just can reflect that speckle is by the degree of bluring.Do not have blood flow on the sample or the place of denier blood flow is arranged, the speckle fog-level is low, and it is high to contrast value, and flow velocity is low, and in the place that blood flow is arranged, speckle fog-level height causes and contrasts value decline, flow velocity height.We just can describe the variation of blood flow on the sample according to the variation of contrasting value like this.Industry is general variation (relative velocity value) of measuring flow velocity with the inverse that contrasts value square on engineering is handled.
It is as follows that the basic handling formula (1) of value is contrasted in calculating:
Wherein,
CExpression is contrasted,
σ I The standard deviation of representing all pixel values in the sliding window,
The mean intensity of representing all pixel values in the sliding window,
iThe index value of pixel in the expression sliding window,
I i Expression the
iThe intensity of individual pixel,
NThe size (generally choosing the square of 5 * 5 or 7 * 7 pixels) of expression sliding window.
With the approximate N * (N-1) (this approximate almost can ignore) that replaces in the following formula molecule of N * N, and carry out simple conversion, calculate the formula (2) (flow speed value promptly contrasts the inverse of value square) of flow speed value below can obtaining result's influence:
Algorithm block diagram shown in Figure 3 is based on above-mentioned formula (2).At first, initial data is read out from high-speed cache and, and enters the module of the diagram streamline first order, adds up in accumulator module, the pixel value that obtains a subwindow add up and, obtain square value at a square module application hardware multiplication circuit.Enter streamline second level module then, accumulated value can by square, and the pixel square value of previous stage can be added up, and has obtained molecule in the following formula (2) and the minuend in the denominator and subtrahend (noticing that molecule partly is identical with subtrahend in the denominator) so respectively.The streamline third level, two data of second level output can be subtracted each other the denominator that obtains in the following formula (2), and molecule (be pixel accumulated value square) also can buffer memory simultaneously, exports simultaneously with denominator then.Because divider is the strategy of taking to be shifted and subtracting each other, so finish the division of molecule denominator, can consume a plurality of clocks, need a plurality of dividers to work simultaneously, could be complementary with the output speed of previous stage.Can comprise the control unit that does not mark among a Fig. 3 in total circuit design.Whether controller also is responsible for monitoring has had an effective sliding window corresponding data from the output of the streamline third level, if having, then selects an idle divider for these data from the divider array, if do not have, then ignores this dateout.When the 3rd level subtrator was exported effective molecule and denominator, control unit can select an idle divider to finish divide operations.Control unit carries out the mode that the scheduling of divider takes time wheel to change, and guarantees that all dividers are all doing effective work.The value of the output of these dividers staggers on time coordinate, so only need a simple circuit unit to combine, order output is used (such as storage or demonstration) for other modules between delaying time then.In sum, all simultaneously concurrent independent execution of each module on the streamline, in current flowing water the cycle, the data that back one-level module is being handled are data results of previous stage module pro-one flowing water period treatment, and the previous stage module is being handled the data that newly enter its module at current flowing water in the cycle.
Equaling 5 * 5 sliding window with N is example, according to prior art, reads one 5 * 5 sliding window, and totally 25 pixel values need 25 clocks.And as in the preceding three class pipeline shown in Figure 3 of the present invention, single treatment is finished in each unit will only consume 5 clocks.As shown in Figure 4, be example with the unitary part that adds up of first order flowing water, per 5 clocks can read in 5 pixels of vertically arranging in the sliding window successively.From the 1st row at first, read the 5th row after, at this moment the pixel value unit that adds up can be exported effectively adding up of first sliding window and give next stage, the 2nd, 3,4 of buffer memory sliding window is understood in this unit that adds up simultaneously, 5 row add up and, when read in the 6th row after, the 6th row add up and can with the 2nd of buffer memory, 3,4,5 four row add up and addition, export that the 2nd sliding window effectively adds up and, buffer memory the 3rd, 4 simultaneously, 5,6 row add up and.In like manner, read in the 7th row after, can and the 3rd, 4 of buffer memory, 5,6 four row add up and export the 3rd sliding window add up and.According to this reason, the adding up and be worth of a new sliding window of every calculating, only need read in 5 pixels of new string adds 20 pixels of 4 row of buffer memory.Like this, remove first window of every row need 25 clocks just can obtain one effectively add up and, back to back each sliding window only needs 5 clocks.This method avoids internal memory to repeat read operation to a certain extent, has improved data throughout and treatment effeciency.
Unitary square of part of first order flowing water just read in 5 pixels at every turn simply, and square output, do not do any buffer memory.Like this, can accomplish that per 5 clocks just can export the square value of the string pixel value correspondence of 5 * 5 sliding windows, give the unit that adds up of second level flowing water.The unitary operation principle that adds up of the 2nd grade of flowing water is similar to the unit that adds up of the first order, also be back 4 row by the last sliding window of buffer memory add up and, thereby the pixel value of accomplishing a sliding window of per 5 clocks output square adds up and.Other parts are equally also followed per 5 water operations that clock is an one-period.
In general, preceding three class pipeline is concurrent working, when back one level production line when handling current data, the previous stage streamline can be handled new data.For the sliding window of 5x5, can guarantee that before the divider array per 5 clocks just can be exported the molecule and the denominator about following formula of a sliding window.
In order to guarantee that circuit has higher operating frequency, divider is that the mode that adopts displacement to subtract each other designs.If the size of sliding window is 5 * 5, then the division in the following formula needs 30 clocks under the situation that only keeps integer.For adapt with front streamline output speed (per 5 clocks will be exported molecule and denominator in the following formula), should use 6 dividers.Except that first result of division output has the time-delay of several clocks, and then per 5 clocks just have an effective division value output.
Foregoing has been described the processing of the sliding window of original image the 1st row correspondence, and ensuing other row are handled identical therewith.When needs enter a new line, directly read the pixel column in first sliding window of next line, be sent in the preceding three class pipeline, but need at this time to forbid that corresponding divider receives invalid molecule and denominator, up to the streamline 3rd level output of effective value of calculation from Fig. 3 of first sliding window of this delegation by the time, just enable divider work.
The flow speed value of divider output is normalized to pixel value (0-255) afterwards, writes the high speed video memory.Lcd controller takes out image from video memory, be presented at LCD then for user's monitoring.
Above-mentioned design was done test on the Cyclone of Altera II FPGA, operating frequency reaches per second 80 frames 640 * 480 images, 5 * 5 sliding windows up to the 130MHZ(disposal ability).Under the 50MHZ frequency, sliding window size chooses 5 * 5, can handle 33 frames, 640 * 480 pixel size images by per second, has reached the requirement of real-time video processing speed fully.In addition, the same sliding window of supporting to comprise 3 * 3,7 * 7 different sizes such as grade of the design.
It should be noted last that, the above specific embodiment is only unrestricted in order to technical scheme of the present invention to be described, although the present invention is had been described in detail with reference to preferred embodiment, those of ordinary skill in the art is to be understood that, can make amendment or be equal to replacement technical scheme of the present invention, and not breaking away from the spirit and scope of technical solution of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (6)
1. laser speckle blood current imaging processing system, comprise the CCD acquisition control module, the original image cache module, the original image read module, the laser speckle image is handled kernel module and display control module, it is characterized in that, described CCD acquisition control module, the original image cache module, the original image read module, the laser speckle image is handled kernel module and display control module is integrated on the SOC framework; Described laser speckle image is handled integrated control unit and stream treatment unit on the kernel module, described stream treatment unit comprises first accumulator module, first square of module, second accumulator module, second square of module, subtractor, divider, if the sliding window size N=i * i of laser speckle blood current imaging initial data, the value of i is one of 3,5,7,9;
Described original image read module is used to read the i * i pixel value of described sliding window;
Described first accumulator module is used for the pixel value of described sliding window is added up, and obtains first accumulated value,
Described first square of module is used for its square value of calculated for pixel values to described sliding window, obtains the first square value vector;
Described second square of module is used for described first accumulated value is carried out square operation, obtains second square value;
Described second accumulator module is used for the described first square value vector is carried out accumulating operation, obtains second accumulated value;
Described subtractor is used for described second accumulated value is deducted described second square value, obtains the 3rd value;
Described divider is used for described second square value obtaining the flow speed value of described laser speckle blood flow divided by described the 3rd value;
Described control unit is used to control the data that described original image read module reads sliding window continuously, delivers to unitary first accumulator module of described stream treatment, first square of module.
2. laser speckle blood current imaging processing system according to claim 1 is characterized in that, described divider quantity is that ((d+a) * 2+2+m)/i if this formula result is not an integer, then gets one bigger in the adjacent integer; The bit wide of divider is (d+a) * 2+m; Wherein, d is the binary system bit wide of input pixel value; The value of i was respectively 3,5,7,9 o'clock, and the value of a corresponds to 4,5,6,7 respectively; M is the binary fraction figure place that will keep; The value of i is got the sliding window size of 3,5,7,9 difference corresponding 3 * 3,5 * 5,7 * 7,9 * 9.
3. laser speckle blood current imaging processing system according to claim 2, it is characterized in that, also comprise first buffer, second buffer, described first buffer connects described first accumulator module, be used for storing temporarily the described sliding window that described first accumulator module calculates the 2nd～i row pixel value add up and; Described second buffer connects described second accumulator module, be used for storing temporarily the described sliding window that described second accumulator module calculates the 2nd～i row pixel square value add up and.
4. laser speckle blood current imaging processing system according to claim 3 is characterized in that, described control unit also is used for selecting and dispatch an idle described divider by the mode that the time wheel changes.
5. the processing method of laser speckle blood current imaging processing system according to claim 4 is characterized in that, may further comprise the steps:
Step 1, described original image read module read the i * i pixel value of first described sliding window,
Step 2, described first accumulator module add up to the pixel value of described sliding window, obtain first accumulated value, and described first accumulated value is sent into described second square of module; Concurrently,
Described first square of module obtains the first square value vector to its square value of calculated for pixel values of described sliding window, and the described first square value vector is sent into described second accumulator module;
Step 3, described second square of module are carried out square operation with described first accumulated value, obtain second square value, and described second square value is sent into described subtractor; Concurrently,
Described second accumulator module is carried out accumulating operation with the described first square value vector, obtains second accumulated value, and described second accumulated value is sent into described subtractor;
Step 4, described subtractor deduct described second square value with described second accumulated value, obtain the 3rd value;
Step 5, described divider receive described second square value in the described buffer, and are worth divided by the described the 3rd with described second square value, obtain the flow speed value of current sliding window;
Step 6, described control unit are selected next described sliding window, if there is next sliding window, then described original image read module reads the last string pixel value of next sliding window, the last string pixel value of this sliding window and 2～i row pixel value of a last sliding window are formed the i * i pixel value of this sliding window, return step 2; If not, then finish, obtain the flow speed value of described laser speckle blood flow.
6. the processing method of laser speckle blood current imaging processing system according to claim 5 is characterized in that,
In described step 2, when described first accumulated value is sent into described second square of module, the 2nd～i row pixel value of the current sliding window of the described first buffer buffer memory add up and;
In described step 3, when described second accumulated value is sent into described subtractor, described second this sliding window of buffer buffer memory the 2nd～i row pixel square value add up and;
Through described step 6, carry out
Step 7, described first accumulator module adds up to the last string pixel value of current sliding window and 2～i row pixel value of a last sliding window, obtains first accumulated value, and described first accumulated value is sent into described second square of module; Concurrently,
Described first square of module obtains the first square value vector to its square value of calculated for pixel values of described sliding window, and the described first square value vector is sent into described second accumulator module;
Described second square of module carried out square operation with described first accumulated value, obtains second square value, and described second square value is sent into described subtractor; Concurrently,
Described second accumulator module is carried out accumulating operation with the described first square value vector, obtains second accumulated value, and described second accumulated value is sent into described subtractor;
Described subtractor deducts described second square value with described second accumulated value, obtains described the 3rd value;
Described divider receives described second square value in the described buffer, and is worth divided by the described the 3rd with described second square value, obtains the flow speed value of current sliding window;
Return step 6.
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| CN103300841B (en) * | 2013-06-13 | 2014-11-05 | 上海理工大学 | Fast laser speckle blood imaging system and method |
| CN103330557B (en) * | 2013-06-25 | 2014-11-26 | 上海理工大学 | Exposure time determination-based laser speckle blood flow imaging method |
| CN107260155A (en) * | 2016-04-07 | 2017-10-20 | 动心医电股份有限公司 | Detect the method and its device of velocity of blood flow |
| CN106384337A (en) * | 2016-09-07 | 2017-02-08 | 上海理工大学 | Laser speckle blood flow imaging enhancement method |
| CN106353534A (en) * | 2016-09-30 | 2017-01-25 | 天津市誉航润铭科技发展有限公司 | Novel pipeline flow speed detection sensor |
| EP3626160A1 (en) | 2018-09-24 | 2020-03-25 | Dianova A/S | A handheld imaging element with a movement sensor |
| CN110141208A (en) * | 2019-04-12 | 2019-08-20 | 上海健康医学院 | A kind of flow imaging system that dynamic image combines and method |
| CN110991609B (en) * | 2019-11-27 | 2023-12-26 | 天津大学 | Line buffer for data transmission |
| CN111083202B (en) * | 2019-11-28 | 2021-03-26 | 中科驭数（北京）科技有限公司 | Multi-connection interaction method and device for TCP/IP accelerator |
| EP3836154A1 (en) | 2019-12-13 | 2021-06-16 | Morten Bo Søndergaard Svendsen | A method and an apparatus for imaging curved surfaces |
| EP3839883A1 (en) | 2019-12-18 | 2021-06-23 | Morten Bo Søndergaard Svendsen | A method and a system for obtaining image data of an object |
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| WO2009008745A2 (en) * | 2007-07-06 | 2009-01-15 | Industrial Research Limited | Laser speckle imaging systems and methods |
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