Patent Publication Number: US-9424631-B2

Title: Device and method for calculating dynamic variation of object

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of Korean Patent Application No. 10-2012-0115644 filed on Oct. 17, 2012 and PCT Application No. PCT/KR2013/000062 filed on Jan. 4, 2013, the entire disclosures of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The embodiments described herein pertain generally to a device and a method for calculating dynamic variation of an object. 
     BACKGROUND ART 
     In recent, there has been known a technology that measures and visualizes velocity of fluid by using a particle image velocimetry (PIV) method. A PIV device visually represents flow of fluid, and is a technology that can measure, for example, instantaneous velocity of a tracer particle in a cross section of fluid. In this case, a tracer particle is dispersed in fluid, which is an object for velocity measurement, and a size of the tracer particle is small enough to sufficiently follow flow of the fluid. Meanwhile, the technology applied to the PIV device may be divided into a method that measures velocity based on an image photographed through a CCD camera and an echo PIV method that uses ultrasonic waves, according to an optical method. 
     For example, the echo PIV device radiates an ultrasonic beam to target fluid, and determines gradation of an ultrasonic image based on intensity of an echo signal radiated by an ultrasonic contrast agent dispersed in the target fluid. In addition, a position of a scatterer in the traveling direction of the ultrasonic beam can be identified from a period of time from radiation of an ultrasonic signal to reception of an echo signal. 
     However, there may be the case where it is difficult to exactly measure velocity of fluid depending on an environment where the PIV method is used, e.g., characteristics of the fluid. 
     For example, there may be difficulty in measuring blood flow velocity within the left ventricle by using the echo PIV device. That is, if any tissue of the left ventricle radiates an echo signal having similar intensity to that of the tracer particle, it is difficult to clearly discriminate an acquired ultrasonic image, and there may be the possibility that an error will occur in measuring the velocity. 
     In this regard, Korean Patent No. 1136814 (Title of Invention: A Method for Estimating a Hemorheological Factor of Blood Flow by Using a Red Blood Cell as a Tracer Particle) describes: a first stage of photographing and storing two (2) blood flow images for a red blood cell flowing within a blood vessel at a certain time interval by using particle image velocimetry (PIV); a second stage of calculating an instantaneous velocity field from displacement of the position of the red blood cell observed in each of the images photographed in the first stage and the time interval for photographing the two (2) images; a third stage of determining a velocity field distribution function, which is a radial coordinate function of the blood vessel, after curve fitting from an average velocity field, which is an average of a multiple number of the instantaneous velocity fields calculated in the second stage; a fourth stage of determining a shear rate from the velocity field distribution function; and a stage of determining a hemorheological characteristic from the shear rate determined in the fourth stage so as to predict a disease. 
     SUMMARY 
     In view of the foregoing problems, example embodiments provide a device and a method for calculating dynamic variation of an object, which are capable of clearly discriminating a tracer particle and a peripheral image so as to minimize an error in velocity measurement. 
     In accordance with a first aspect of example embodiments, there is provided a device for calculating dynamic variation of an object, comprising: an enhancement processing unit that adjusts brightness of a pixel determined to indicate a particle dispersed in the object in at least two (2) images generated for the object at different time points; and a calculating unit that calculates dynamic variation of the object based on correlation of the different two (2) images processed by the enhancement processing unit. Herein, the enhancement processing unit sets brightness of a pixel having brightness with a threshold value or higher for each of the images to reference brightness higher than the threshold value. 
     Further, the device for calculating dynamic variation of an object may comprise a pre-processing unit that implements pre-processing for the at least two (2) images generated at the different time points. Herein, the enhancement processing unit adjusts brightness of a pixel for the images that have been subject to the pre-processing. 
     Further, the device for calculating dynamic variation of an object may comprise an information providing unit that visually represents and provides the calculated dynamic variation in the images. 
     Especially, the enhancement processing unit may produce a curve graph through curve fitting for a histogram graph obtained by accumulating the number of pixels according to brightness intensity of the pixels included in the images, and set the threshold value based on the curve graph. 
     Especially, the enhancement processing unit may set the threshold value to an intensity value corresponding to a cross point between a straight line contacting with a starting point of the curve graph and an X axis. 
     Especially, the enhancement processing unit may set the threshold value to a value of an X axis of a quadrangle having a Y axis value of the starting point of the curve graph as height, and the threshold value may be set to a value of the X axis when a first part surrounded by an upper portion of the curve graph and the quadrangle and a second part positioned in a lower portion of the curve graph in an outside area of the quadrangle have the same area. 
     Herein, the reference brightness may be maximum brightness. 
     In accordance with second first aspect of example embodiments, there is provided a method for calculating dynamic variation of an object by using the device for calculating dynamic variation of an object, the method comprising: adjusting brightness of a pixel determined to indicate a particle dispersed in an object in at least two (2) images generated for the object at different time points; and calculating dynamic variation of the object based on correlation of the different two (2) images that have been subject to the adjustment of the brightness. Herein, the adjustment of the brightness sets brightness of a pixel having brightness with a threshold value or higher for each of the images to reference brightness with the threshold value or higher. 
     Especially, the adjustment of the brightness may comprise: producing a curve graph through curve fitting for a histogram graph obtained by accumulating the number of pixels according to brightness intensity of the pixels included in the images; and setting the threshold value based on the curve graph. 
     Especially, the setting of the threshold value based on the curve graph may set the threshold value to an intensity value corresponding to a cross point between a straight line contacting with a starting point of the curve graph and an X axis. 
     Especially, the setting of the threshold value based on the curve graph may set the threshold value to a value of an X axis of a quadrangle having a Y axis value of the starting point of the curve graph as height, and the threshold value may be set to a value of the X axis when a first part surrounded by an upper portion of the curve graph and the quadrangle and a second part positioned in a lower portion of the curve graph in an outside area of the quadrangle have the same area. 
     Herein, the reference brightness may be maximum brightness. 
     In accordance with the example embodiments, the dynamic variation calculating device can more exactly calculate dynamic variation of an object, by using displacement of a tracer particle between different two (2) images. That is, in a relation between a tracer particle included in an image, which is a reference for velocity calculation, and a peripheral image, the dynamic variation calculating device can clearly calculate displacement of a tracer particle in different two (2) images, through enhancement processing that sets brightness of the tracer particle to reference brightness or higher. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a dynamic variation calculating device in accordance with an example embodiment. 
         FIG. 2  is a flow chart showing a particle enhancement processing method in accordance with an example embodiment. 
         FIGS. 3A-3C  show a particle enhancement processing algorithm. 
         FIG. 4  shows a velocity calculating process in accordance with an example embodiment. 
         FIGS. 5A-5C  show a velocity information providing screen in accordance with an example embodiment. 
         FIGS. 6A and 6B  show a velocity information providing screen in accordance with an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, example embodiments will be described in detail with reference to the accompanying drawings so that inventive concept may be readily implemented by those skilled in the art. However, it is to be noted that the present disclosure is not limited to the example embodiments, but can be realized in various other ways. In the drawings, certain parts not directly relevant to the description are omitted to enhance the clarity of the drawings, and like reference numerals denote like parts throughout the whole document. 
     Throughout the whole document, the terms “connected to” or “coupled to” are used to designate a connection or coupling of one element to another element and include both a case where an element is “directly connected or coupled to” another element and a case where an element is “electronically connected or coupled to” another element via still another element. Further, the term “comprises or includes” and/or “comprising or including” means that one or more other components, steps, operations, and/or the existence or addition of elements are not excluded in addition to the described components, steps, operations and/or elements. 
       FIG. 1  shows a dynamic variation calculating device in accordance with an example embodiment. 
     The dynamic variation calculating device  10  includes an output unit  110 , an image receiving unit  120 , an image pre-processing unit  130 , an enhancement processing unit  140 , a calculating unit  150  and an information providing unit  160 . 
     The output unit  110  outputs a laser or ultrasonic signal to an object to be photographed  20 . For example, the output unit  110  may emit laser beam sheet at each of different first and second time points like an optical PIV device, or output an ultrasonic signal at each of first and second time points like an echo PIV device. In addition, the output unit  110  may output an X ray used in a computed tomography (CT) device or a certain range of a high frequency signal used in a magnetic resonance imaging (MRI) device. 
     The image receiving unit  120  receives images generated at different time points for the object to be photographed  20 . For example, the image receiving unit  120  receives images generated at different time points, like a first image generated at a first time point and a second image generated at a second time point. In this case, the first and second time points may be continuous or discrete with a certain time interval. 
     For example, the image receiving unit  120  provides a CCD camera in a direction perpendicular to the laser beam sheet like the optical PIV device, and receives an image acquired through the CCD camera. Also, the image receiving unit  120  receives an ultrasonic image generated based on an ultrasonic signal like the echo PIV device. In addition, the image receiving unit  120  may receive an image generated based on an X ray like a CT device, or an image generated based on a high frequency signal like an MRI device. 
     The image pre-processing unit  130  implements pre-processing for the received images. 
     For example, the image pre-processing unit  130  may eliminate noise through a Gaussian smoothing filter or a median filter, or implement bilinear interpolation. 
     The Gaussian smoothing filter is used for processing of noise included in the received images, and convolutes each pixel of the input images and a Gaussian kernel with each other to generate a resulting image. Especially, it is possible to change a degree of smoothing through setting of standard deviation of a Gaussian distribution function. 
     Math Formula 1 below presents the Gaussian distribution function, and Math Formula 2 below presents an example for a two-dimensional Gaussian smoothing filter. 
     
       
         
           
             
               
                 
                   
                     
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     This pre-processing process can decrease a noise signal, and is effective in enhancing only a pixel indicating a tracer particle in a process for implementing enhancement processing later. Meanwhile, the pre-processing process may be omitted or modified according to selection of a performer. 
     The enhancement processing unit  140  enhances signal intensity of the tracer particle included in the received images, to increase a difference in signal intensity between the tracer particle and a part other than the tracer particle. Especially, the enhancement processing unit  140  implements enhancement processing to brighten a pixel determined to indicate a particle dispersed in an object in at least two (2) images generated at different time points. 
     When the received images are ultrasonic images, or the Gaussian smoothing filter is applied through the image pre-processing unit  130 , if discrimination between the tracer particle and the part other than the tracer particle is unclear due to noise signals included in other received images or others, calculating dynamic variation of the tracer particle through the calculating unit  150  may be difficult. More specifically, the calculating unit  150  regards points having the highest brightness intensity as tracer particles, and calculates dynamic variation by using displacement of the tracer particles; however, in the case where there is a point that is not an actual tracer particle but is similar to a tracer particle in terms of intensity of brightness, there is a possibility that an error will occur in calculating the dynamic variation. To solve this problem, the example embodiments regard image data meeting certain requirements as tracer particles, and implement enhancement processing to set brightness of the tracer particles to the maximum. 
     Meanwhile, the tracer particle defined in the example embodiments is dispersed in an object to be traced in order to identify fluid flow or dynamic variation of the object to be traced, and for example, substance like a contrast agent may function as a tracer particle. For example, a bubble shape may appear in the received images, by the tracer particle forming the contrast agent dispersed in the object to be traced, and a pixel forming the bubble shape may be a pixel indicating each tracer particle. 
       FIG. 2  is a flow chart showing a particle enhancement processing method in accordance with an example embodiment, and  FIGS. 3A-3C  show a particle enhancement processing algorithm. 
     First, a histogram graph is produced based on brightness intensity of pixels included in the received images (S 120 ). As illustrated in  FIG. 3A , the histogram graph may be produced by a method that accumulates the number of pixels according to brightness intensity of each of the pixels. From the illustrated graph, it can be identified that as brightness intensity of the pixels increases, the number of the pixels having the corresponding brightness intensity decreases. 
     Next, a curve graph  30  is produced through curve fitting, based on the histogram graph (S 220 ). Through the curve fitting, the curved graph  30  in the form illustrated in  FIG. 3A  may be produced. 
     The curve fitting work for the histogram graph produced in S 210  is implemented, and an interpolation or smoothing process may be additionally implemented. In addition, for example, the curve fitting may be implemented by using a least square exponential function. 
     For example, it is assumed that a curve graph represented by an exponential function like y=ae bx  is produced. By taking a log on both sides of the exponential function, math formulas like Math Formula 4 below may be obtained. A function of the curve graph may be calculated in the manner that a and b are calculated, by using Math Formula 5 for each of the math formulas. 
     
       
         
           
             
               
                 
                   
                     
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     Next, a probability density function corresponding to the curve graph produced through S 220  is specified (S 230 ). 
     That is, a normal distribution function like Math Formula 6 is specified, by specifying an average (μ) and a standard deviation (σ). 
     
       
         
           
             
               
                 
                   
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     Next, an intensity scale, which is a reference that can determine luminescence intensity of a pixel corresponding to a tracer particle, is detected (S 240 ). That is, a pixel having luminescence intensity in an intensity scale corresponding to a threshold value or higher is determined as a tracer particle. 
     In the example embodiments, the intensity scale corresponding to the threshold value is detected largely by two (2) methods. 
     As to the first method, as illustrated in  FIG. 3B , the threshold value is set to an intensity value corresponding to a cross point P 1  between a straight line  40  contacting with a starting point of the curve graph  30  and a Y axis. 
     As to the second method, as illustrated in  FIG. 3C , an X axis of a quadrangle  60 , which has a Y axis value of the starting point of the curve graph  30  as height, is set as a cross point P 2 . In this case, the quadrangle  60  contacts with the Y or X axis of the curve graph. The threshold value is set to an intensity value corresponding to the cross point P 2  when a first part  50  surrounded by the quadrangle  60  and the cure graph  30  and a second part  52  have the same area. That is, as illustrated, the threshold value is set to an value of an X axis when a value obtained from integrating the area of the first part  50  surrounded by an upper portion of the curve graph  30  and the quadrangle  60  while changing the value of the X axis of the quadrangle, and a value obtained from integrating the area of the second part  52  positioned in a lower portion of the curve graph  30  in the outside area of the quadrangle  60  while changing the value of the X axis of the quadrangle are the same. 
     Next, enhancement processing is implemented to enable the pixel having the intensity with the threshold value or higher as detected in S 240  to have the intensity of the reference brightness higher than the threshold value (S 250 ). 
     For example, the reference brightness may be set as maximum brightness. Assuming that a bit value of a pixel having intensity of the maximum brightness is 255, a bit value of the pixel having the intensity with the threshold value or higher as detected in S 240  may be set to 255. 
     In this way, it is possible to more clearly enhance the tracer particle prior to implementing a velocity calculating process so as to exactly calculate velocity. 
     Returning to  FIG. 1 , the calculating unit  150  calculates displacement of the tracer particle through a cross-correlation function based on the image photographed at the first time point and the image photographed at the second time point. 
       FIG. 4  shows a fluid flow calculating process in accordance with an example embodiment. 
     As illustrated, interrogation areas  412  and  422  are set for an image  410  photographed at the first time point and an image  420  photographed at the second time point, respectively, among the images received through the image receiving unit  120 , and displacement is calculated for a tracer particle included in each of the interrogation areas. This velocity calculation is implemented in the unit of each of the interrogation areas, and repeated until all two-dimensional vector fields are produced. For example, the interrogation areas may have a square shape, and a size ranging from 16*16 pixels to 128*128 pixels. 
     The cross-correlation function may be calculated through fast Fourier transform (FFT). The fast Fourier transform represents components of an image in a frequency domain. First, FFT for the first interrogation area and FFT for the second interrogation area are obtained, and the FFTs are multiplied by each other to evaluate the cross-correlation. In addition, a value where the cross-correlation is largest is found, and inverse transform for the value is implemented. The FFT is implemented through Math Formula 7 below, and f(m,n) refers to a pixel value, and F(x,y) refers to a value in a frequency domain, and M and N refer to a size of an image. 
     
       
         
           
             
               
                 
                   
                     
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     After implementing the cross-correlation processing, the point where the cross-correlation is largest indicates the displacement of the tracer particle. That is, as illustrated in  FIG. 4 , a position of a peak based on an original point is the displacement of the tracer particle. In order to estimate the position where the cross-correlation is largest, interpolation may be used. 
     As a method for sub-pixel analysis, a Gaussian peak fitting method may be used. Displacement of a sub-pixel for the peak point (x0) may be obtained as in Math Formula 8. 
     
       
         
           
             
               
                 
                   
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     In addition, for the sub-pixel interpolation, three (3)-point estimation may be used, and applies Math Formulas 9 and 10. In this case, Zx refers to a maximum point, and Zx−1 and Zx+1 refer to values for neighboring points. 
     
       
         
           
             
               
                 
                   
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     Next, after the maximum point is found, smoothing processing may be implemented. 
     In this way, the calculating unit  150  may implement velocity calculation by setting the interrogation areas for the images generated at different time points, and calculating the displacement of the tracer particles within the interrogation areas. 
     Meanwhile,  FIG. 4  shows a process of calculating velocity among physical quantity showing fluid flow. Based on the calculated velocity, various types of physical quantity that can show flow of fluid like vortex or turbulence can be calculated. 
     Returning to  FIG. 1 , the information providing unit  160  may visually represent the velocity information calculated through the calculating unit  150  to provide the information to a user. 
       FIGS. 5A-5C  show a velocity information providing screen in accordance with an example embodiment. 
     First, the drawing illustrated in  FIG. 5A  relates to implementing particle enhancement processing for a conventional ultrasonic screen, and may set intensity of a pixel determined as a tracer particle to the maximum to represent the pixel to a user. 
     Meanwhile, the information providing unit  160  may represent a velocity component of a tracer particle generated by an arrow, as shown in  FIG. 5B  or  FIG. 5C . In this case, a size component of velocity may be represented by length of the arrow, and a direction component of velocity may be represented in the direction of the arrow. Meanwhile,  FIG. 5B  represents a velocity component calculated based on a conventional technology, i.e., an image, which has not been subject to particle enhancement processing, and  FIG. 5C  represents a velocity component calculated based on an image, which has been subject to particle enhancement processing in accordance with an example embodiment. 
     The corresponding images have been obtained from photographing blood flow in the left ventricle, and it can be identified that the velocity component represented in the image of  FIG. 5C , compared to the image of  FIG. 5B , is more consistent with actual blood flow in terms of the size or direction of the velocity. 
       FIGS. 6A and 6B  show a velocity information providing screen in accordance with an example embodiment. 
     Based on the velocity component calculated in accordance with an example embodiment, vorticity of fluid may be visually represented. That is, as illustrated in  FIG. 6B , color of an area where spinning arrows are concentrated is represented in a different color from that of other areas. For example, by representing an area where the arrows are maintained in a straight line direction and an area where the arrows spin in different colors, it is possible to enable a user to more easily identify the point where a vortex phenomenon of fluid occurs. 
     As described, since the particle image velocity device or the particle image velocity method in accordance with an example embodiment is included in an ultrasonic image analyzer, an optical particle image velocity device or others, it is possible to provide convenience in enabling a user to identify movement of fluid or liquid. 
     For reference, the components illustrated in  FIG. 1  in accordance with an example embodiment may imply software or hardware such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), and carry out predetermined functions. 
     However, the “components” are not limited to the software or the hardware, and each of the components may be stored in an addressable storage medium or may be configured to implement one or more processors. 
     Accordingly, the components may include, for example, software, object-oriented software, classes, tasks, processes, functions, attributes, procedures, sub-routines, segments of program codes, drivers, firmware, micro codes, circuits, data, database, data structures, tables, arrays, variables and the like. 
     The components and functions thereof can be combined with each other or can be divided. 
     Example embodiments can be embodied in a storage medium including instruction codes executable by a computer or processor such as a program module executed by the computer or processor. A computer readable medium can be any usable medium which can be accessed by the computer and includes all volatile/nonvolatile and removable/non-removable media. Further, the computer readable medium may include all computer storage and communication media. The computer storage medium includes all volatile/nonvolatile and removable/non-removable media embodied by a certain method or technology for storing information such as computer readable instruction code, a data structure, a program module or other data. The communication medium typically includes the computer readable instruction code, the data structure, the program module, or other data of a modulated data signal such as a carrier wave, or other transmission mechanism, and includes information transmission mediums. 
     The above description of the example embodiments is provided for the purpose of illustration, and it would be understood by those skilled in the art that various changes and modifications may be made without changing technical conception and essential features of the example embodiments. Thus, it is clear that the above-described example embodiments are illustrative in all aspects and do not limit the present disclosure. For example, each component described to be of a single type can be implemented in a distributed manner. Likewise, components described to be distributed can be implemented in a combined manner. 
     The scope of the inventive concept is defined by the following claims and their equivalents rather than by the detailed description of the example embodiments. It shall be understood that all modifications and embodiments conceived from the meaning and scope of the claims and their equivalents are included in the scope of the inventive concept.