KR101357754B1 - Image processing method and image processing apparatus thereof - Google Patents

Abstract

Image processing method according to an embodiment of the present invention is an image processing method for measuring the myocardial performance index (MPI), based on the signal level of the input signal and output signal in the cardiac spectrum image, region of interest for measuring myocardial performance index Acquiring a plurality of marker regions each having at least one marker for measuring the myocardial performance index (MPI) based on feature values of the input signal and the output signal in the region of interest; And acquiring the at least one marker for each of the plurality of marker regions.

Description

Image processing method and image processing apparatus according thereto {IMAGE PROCESSING METHOD AND IMAGE PROCESSING APPARATUS THEREOF}

The present invention relates to an image processing method and an image processing apparatus, and more particularly, to an image processing method and an image processing apparatus for automatically measuring myocardial performance index (MPI).

Ultrasonic diagnostic apparatuses widely used for diagnosing a disease may reproduce an image signal or an audio signal corresponding to blood flow, heart rate, etc. in a human body using ultrasound. In addition, a user such as a doctor may diagnose the presence of disease in each organ by using the ultrasound image generated by the ultrasound diagnostic apparatus.

For example, in order to diagnose heart disease of the fetus or an adult, an ultrasound diagnostic device may be used to observe the movement of the heart. Specifically, the ultrasound signal is applied to the heart by contacting the probe included in the ultrasound diagnostic device with the heart region. Then, the Doppler ultrasound signal, which is a reflection signal according to the Doppler effect, may be received through the probe in response to the applied ultrasound signal. The ultrasound diagnosis apparatus may acquire an exercise image of the heart by using the received Doppler ultrasound signal.

Specifically, in order to diagnose whether the cardiac exercise is normally performed, a value such as Myocardial Performance Index (MPI) should be measured, and it should be determined whether the measured value is within the normal range. Even if an ultrasound image representing the cardiac motion is obtained using an ultrasound diagnostic device, an ultrasound image must be manually analyzed by a doctor in order to measure biometric values such as myocardial performance index (MPI). For example, a doctor may mark points necessary for measuring myocardial performance index (MPI) on an ultrasound image and calculate a myocardial performance index (MPI) using the length between the markers, which are marked points. .

Such a doctor's passive myocardial performance index (MPI) measurement will vary the accuracy of the measurement results according to the doctor's skill. In addition, when the doctor checks the marker at the wrong point, an error occurs in the myocardial performance index (MPI) value.

Accordingly, there is a need to provide a method and apparatus that can more accurately obtain biometric values, such as myocardial performance index (MPI).

An object of the present invention is to provide an image processing method capable of automatically measuring a predetermined biometric value using an ultrasound image, and an image processing apparatus according thereto. More specifically, an object of the present invention is to provide an image processing method capable of automatically measuring a myocardial performance index (MPI), which is a biological value, and an image processing apparatus according thereto.

In addition, the present invention using an ultrasound image, an image processing method and image processing apparatus that can accurately measure the myocardial performance index (MPI) by reducing the measurement error that can occur when manual measurement of the myocardial performance index (MPI) For the purpose of providing

An image processing method according to an embodiment of the present invention is an image processing method for measuring a myocardial performance index (MPI). The image processing method may include obtaining a region of interest for measuring a myocardial performance index based on a signal level of an input signal and an output signal in a heart spectrum image, and in the region of interest, a feature value of the input signal and the output signal. And obtaining a plurality of marker regions each having at least one marker for measuring myocardial performance index (MPI), and acquiring the at least one marker for each of the plurality of marker regions.

The image processing method may further include obtaining at least one of a peak value corresponding to the input signal and a peak value corresponding to the output signal as the feature value.

The acquiring of the ROI may include selecting a predetermined point in the cardiac spectral image through a user interface screen, and an input signal of one cycle in the cardiac spectrum image corresponding to the predetermined point, The method may include acquiring, as the ROI, a myocardial execution period including an output signal of one period corresponding to an input signal.

The acquiring of the ROI may include a section including a plurality of myocardial performance cycles including one cycle of an input signal in the heart spectrum image and one cycle of an output signal corresponding to the cycle of the input signal. And acquiring into an area.

The acquiring of the ROI may include sampling a myocardial cycle, which includes one cycle of an input signal and one cycle of an input signal in the cardiac spectrum image, and acquires the sampled myocardial cycle of performance as the region of interest. can do.

The acquiring of the marker region may include first and second intervals of a signal from a first peak value corresponding to the input signal to a second peak value adjacent to the first peak value and corresponding to the output signal. Acquiring the first and second marker regions in which the marker is present; and generating a signal section from the second peak value to a third peak value adjacent to the second peak value and corresponding to the input signal. And acquiring the third and fourth marker regions in which the four markers are present.

The acquiring of the feature value may include accumulating signal values in the cardiac spectrum image and acquiring at least one peak value of an input signal and an accumulated output signal accumulated in the ROI as the feature value. It may include.

The acquiring of the feature value may further include high frequency filtering the cardiac spectral image and acquiring a peak value as the feature value from the filtered cardiac spectral image.

The obtaining of the marker may include, for each of the plurality of marker regions, a click signal of the input signal and the output signal, a gradient value of the input signal and the output signal, and signal strengths of the input signal and the output signal. Based on at least one, the method may include obtaining the marker.

In addition, the image processing method according to an embodiment of the present invention may further include overlaying and displaying the markers acquired on the cardiac spectrum image.

The acquiring the region of interest may include acquiring the region of interest based on the maximum signal level of the input signal and the maximum signal level of the output signal.

In addition, the image processing method according to an embodiment of the present invention comprises the steps of obtaining a cardiac ultrasound image taken using the ultrasound Doppler signal, and cropping, shifting, and noise removal of the cardiac ultrasound image The method may further include acquiring the heart spectrum image by performing at least one image processing among noise reduction processes.

The image processing method may further include outputting a user interface screen for setting whether to automatically or manually set the ROI.

Also, in the image processing method according to an embodiment of the present invention, when the manual setting is requested through the user interface screen, a predetermined period or a predetermined point corresponding to the predetermined period in the cardiac spectrum image is selected, and the predetermined period The method may further include acquiring into the ROI.

Also, in the image processing method according to an embodiment of the present invention, when the automatic setting is requested through the user interface screen, the image processing method may include one input signal corresponding to one cycle input signal and one cycle input signal in the heart spectrum image. The method may further include acquiring, as the ROI, a section including at least one myocardial execution period including an output signal of a period.

An image processing apparatus according to an embodiment of the present invention is an image processing apparatus for measuring a myocardial performance index (MPI). The image processing apparatus obtains a region of interest for measuring a myocardial performance index based on a signal level of an input signal and an output signal in a heart spectrum image, and within the region of interest, based on feature values of the input signal and the output signal. The apparatus may include a region obtaining unit obtaining a plurality of marker regions each having at least one marker for measuring the myocardial performance index, and a marker obtaining unit obtaining the at least one marker for each of the plurality of marker regions.

The region obtaining unit may include a feature value extracting unit obtaining at least one of a peak value corresponding to the input signal and a peak value corresponding to the output signal as the feature value, and the plurality of markers based on the feature value. It may include a marker region obtaining unit for obtaining the region.

The image processing apparatus according to an embodiment of the present invention may further include a user interface unit for selecting a predetermined point in the cardiac spectrum image, and an input signal and a period of one cycle in the cardiac spectrum image corresponding to the predetermined point. The apparatus may further include an ROI acquiring unit, which acquires a myocardial execution period including an output signal of one cycle corresponding to an input signal of the ROI.

In addition, the image processing apparatus according to an embodiment of the present invention includes a plurality of myocardial performance cycles including one cycle of an input signal and one cycle of an output signal corresponding to the cycle of the input signal in the cardiac spectrum image. The apparatus may further include a region of interest obtaining unit configured to obtain a section as the region of interest.

In addition, the image processing apparatus according to an embodiment of the present invention samples a myocardial performance cycle including one cycle of an input signal and one cycle of an input signal in the cardiac spectrum image, and stores the sampled myocardial cycle of performance. The apparatus may further include an ROI acquirer that acquires the ROI.

The marker region acquiring unit includes first and second markers in a section of a signal from a first peak value corresponding to the input signal to a second peak value adjacent to the first peak value and corresponding to the output signal. Can be obtained with the first and second marker regions. Also, a signal section from the second peak value to a third peak value adjacent to the second peak value and corresponding to the input signal may be acquired as the third and fourth marker regions in which the third and fourth markers exist. Can be.

The feature value extractor may accumulate signal values in the cardiac spectrum image and obtain at least one peak value of an input signal and an output signal accumulated in the ROI as the feature value.

The feature value extractor may perform high frequency filtering on the cardiac spectrum image and obtain a signal peak value as the feature value from the filtered cardiac spectrum image.

The marker acquisition unit may include at least one of a click signal of the input signal and the output signal, a gradient value of the input signal and the output signal, and a signal strength of the input signal and the output signal for each of the plurality of marker regions. Based on this, the marker can be obtained.

The image processing apparatus may further include an input / output unit configured to overlay the markers acquired on the cardiac spectrum image and display the overlay.

In addition, the image processing apparatus according to an embodiment of the present invention obtains an echocardiogram image captured by using an ultrasound Doppler signal, and scales, shifts, and noises the echocardiogram image. The image acquisition unit may further include an image acquisition unit configured to perform at least one of a reduction process.

The image processing apparatus may further include a user interface for outputting a user interface screen for setting whether the ROI is automatically set or manually set, and an ROI acquiring unit for acquiring the ROI. Can be.

In addition, when the manual setting is requested through the UI screen, the ROI acquirer may select a predetermined period or a predetermined point corresponding to the predetermined period in the cardiac spectrum image, and acquire the predetermined period as the ROI. have.

In addition, when the automatic setting is requested through the user interface screen, the ROI acquirer performs a myocardial muscle including one cycle of an input signal in the heart spectrum image and one cycle of an output signal corresponding to the cycle of the input signal. A section including at least one period may be obtained as the ROI.

1 is a view showing a heart that is an ultrasound measurement target.
2 is a diagram illustrating a heart spectral image.
3 is a diagram illustrating an image processing method according to an exemplary embodiment of the present invention.
4 is a diagram illustrating an image processing method according to another exemplary embodiment of the present invention.
5 is a diagram illustrating an echocardiography image.
6 is another diagram illustrating a heart spectral image.
7 is a diagram for describing a converted signal of a heart spectrum image used to acquire a feature value.
8 is a view for explaining the operation of step 425 of FIG.
FIG. 9 is a diagram for describing an operation of step 427 of FIG. 4.
FIG. 10 is a diagram for describing an operation of step 430 of FIG. 4.
11 is a diagram illustrating a displayed heart spectrum image.
12 is a block diagram illustrating an image processing apparatus according to an embodiment of the present invention.
13 is a block diagram illustrating an image processing apparatus according to another exemplary embodiment of the present invention.

Hereinafter, an image processing method and an image processing apparatus according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view showing a heart that is an ultrasound measurement target.

Referring to FIG. 1, the heart 100 may be divided into a left ventricle (LV), a left atrium (LA), a right ventricle (RV), and a right atrium (RA).

In addition, the mitral valve (MV) and the aortic valve (AV: Aortic Valve) open and close, and blood flows in and out of the heart.

By using an ultrasound Doppler signal, which is a signal reflected in response to an ultrasound signal applied to the heart, a heart spectrum image capable of observing the flow of blood to and from the heart may be obtained. The doctor can diagnose whether an abnormality has occurred in the patient's heart by reading a heart spectral image showing the flow of blood corresponding to the movement of the heart.

The heart spectral image is described in detail below with reference to FIG. 2.

2 is a diagram illustrating a heart spectral image. In FIG. 2, the x axis represents time and the y axis represents the magnitude value of the signal.

Referring to FIG. 2, around the baseline 201, an inflow of blood flowing into the heart is shown at the upper end 210, and an outflow of blood flowing out of the heart is shown at the lower end 220. do. Hereinafter, an image showing both the flow of blood flowing into and out of the heart is called a heart spectrum image, and the upper portion 210 graph representing the flow of blood flowing into the heart is called an input signal, and the flow of blood flowing out of the heart The lower 220 graph indicating the output signal is referred to as.

In general, to determine whether the heart is exercising normally, the myocardial performance index (MPI), which is a biometric value, is measured, and whether the measured myocardial performance index (MPI) is within the normal range is determined. Can be. The myocardial performance index (MPI) is a value obtained by marking the start or end time points of the flow of the input signal and the flow of the output signal in the cardiac spectrum image, and quantifying the interval between the markers, which are marked points, according to a predetermined equation. A medical practitioner, such as a doctor, may determine whether the cardiac exercise ability of the subject is normally performed using a myocardial performance index (MPI) value.

Myocardial performance index (MPI) can be calculated by Equation 1 below.

[Equation 1]

Myocardial performance index (MPI) = (ICT + IRT) / ET

                    = (MCO-ET) / ET

ICT stands for Isovolumic Contraction Time (ICT). The isotropic systolic (ICT) represents the time between the closing end time t1 of the aortic valve AV and the opening start time t2 of the mitral valve MV.

IRT stands for Isovolumic relaxation time (IRT). The isotropic diastolic (IRT) represents the time between the closing end point t3 of the mitral valve MV and the opening start point t4 of the aortic valve AV.

In addition, ET is the ejection time, which is the time between the isotropic systolic (ICT) and the isotropic diastolic (IRT).

The mitral closure time (MCO) represents the interval between the end and the starting point of the incoming blood flow of the mitral valve (MV). For example, the MCO may represent one period of the input signal of FIG. 2.

For example, to measure the isotropic systolic (ICT), isotropic diastolic (IRT) and release time (ET), which are required for the calculation of myocardial performance index (MPI), the end point of closure of the aortic valve (AV), the mitral valve The start point of the opening of the (MV), the end point of the closing of the mitral valve (MV), and the start point of the opening of the aortic valve (V) should be known. In the related art, a doctor or the like manually sets the markers by marking the viewpoints on the input signal and the output signal in the cardiac spectrum image, and manually calculates the myocardial performance index (MPI) using the time points of the set markers.

T11, t12, t13, and t14 shown in Fig. 2 are the closing end point t1 of the aortic valve AV, the opening start point t2 of the mitral valve MV, and the closing end of the mitral valve MV, respectively. It corresponds to the time point t3 and the start time point t4 of the opening of the aortic valve AV.

In order to measure myocardial performance index (MPI) using an ultrasound image of the heart, a doctor typically manually places a marker for measuring myocardial performance index (MPI) on a cardiac spectrum image, which is a type of cardiac ultrasound image. The four points must be marked at the aforementioned points in time t1, t2, t3, t4.

However, manually reading and detecting the above-described points of time t1, t2, t3, t4 in the heart spectral image is likely to cause an error or error depending on the skill of the doctor, the noise component included in the image, and the like.

In the present application, the closing end point t1 of the aortic valve AV, which is the points necessary for measuring the myocardial performance index (MPI), the opening start point t2 of the mitral valve MV, and the closing end point of the mitral valve MV ( t3) and the marker region, which is the region where the opening start time point t4 of the aortic valve AV, respectively exist, are first obtained, and the myocardial performance index (MPI) is automatically detected by detecting the marker at that time within each marker region. To be measured.

3 is a diagram illustrating an image processing method according to an exemplary embodiment of the present invention.

Referring to FIG. 3, an image processing method 300 for measuring myocardial performance index (MPI) according to an embodiment of the present invention is based on signal levels of an input signal and an output signal in a heart spectrum image. In operation 310, a region of interest (ROI) for measuring myocardial performance index (MPI) is obtained.

Specifically, the cardiac spectrum image corresponds to the ultrasound image shown in FIG. 2, and the input signal and the output signal are respectively shown at the upper end 210 of the reference line 201 and the lower end 220 of the reference line 201. Corresponding signal. In addition, the region of interest represents a predetermined section of the heart spectral image sampled to measure myocardial performance index (MPI). That is, the myocardial performance index (MPI) is measured by using or analyzing the input signal and the output signal of the heart spectrum image included in the ROI.

In the region of interest acquired in operation 310, a plurality of marker regions each having a plurality of markers for measuring myocardial performance index (MPI) are obtained based on feature values of the input signal and the output signal (320). step).

In detail, the marker may indicate the location of each point in the cardiac spectral image required for calculating the myocardial performance index (MPI) or the time point of each point needed to obtain a time interval between the points. For example, the marker may be the end point of closure of the aortic valve (AV), mitral valve (MV) required to measure the isotropic systolic (ICT), isotropic diastolic (IRT) and release time (ET) described above in FIG. It may correspond to at least one of the opening start time of the mitral valve (MV), the closing end time of the mitral valve (MV) and the opening start time of the aortic valve (V).

Also, the marker region indicates an area where the marker is likely to be present. The marker region will be described in detail with reference to FIGS. 8 to 9 below.

At least one marker is acquired for each of the plurality of marker regions obtained in operation 320 (operation 330). Specifically, the marker region is first obtained by restricting and then detecting the marker in the restricted region so that the marker can be accurately detected and acquired.

4 is a diagram illustrating an image processing method according to another exemplary embodiment of the present invention. In FIG. 4, steps 410, 420, and 430 correspond to steps 310, 320, and 330 described with reference to FIG. 3, respectively. Therefore, description overlapping with that in FIG. 3 is omitted. In detail, the image processing method 400 according to another embodiment of the present invention may further include at least one of the area obtaining unit, the steps 405, 415, and 430, as compared to the image processing method 300 of FIG. 3. Can be.

Referring to FIG. 4, the image processing method 400 may further include obtaining a cardiac ultrasound image (operation 401). In detail, the cardiac ultrasound image may be an image captured based on an ultrasound Doppler signal, which is a reflection signal received by applying an ultrasound signal to the heart of the patient and may be captured by an ultrasound imaging apparatus (not shown). have. The echocardiogram image may be raw data without performing image processing such as noise reduction. The echocardiography image may be received externally. It can also be generated by itself by applying the ultrasound signal to the heart of the patient and receiving the ultrasound Doppler signal accordingly. The echocardiography image will be described in detail with reference to FIG. 5 below.

In addition, the image processing method 400 may further include a step (405) of obtaining a heart spectrum image by image processing the cardiac ultrasound image acquired in step 401.

In detail, the cardiac spectrum image may be obtained by performing at least one image processing among cropping, shifting, and noise reduction processing on the cardiac ultrasound image. When noise reduction is not performed, the accuracy of marker acquisition may decrease due to an error of signal values present in the heart spectrum image. Therefore, by reducing the signal noise through pro-processing to obtain a cardiac spectral image that accurately represents the movement of the heart, it is possible to perform marker detection on the acquired cardiac spectrum image.

5 is a diagram illustrating an echocardiography image.

Referring to FIG. 5, an ultrasound image 510 indicating a movement of the heart itself may be acquired using an ultrasound imaging apparatus (not shown). In addition, an ultrasound image 550 indicating an inflow / outflow of blood in the heart indicating the movement of the heart may be acquired.

In addition, the ultrasound image 510 and / or 550 illustrated in FIG. 5 may be the raw data described above, or may be an image signal that has performed image preprocessing. The ultrasound images 510 and / or 550 illustrated in FIG. 5 may be displayed to be viewed by a user such as a patient or a doctor through a display device (not shown). In addition, the cardiac ultrasound image 550 corresponds to the cardiac spectrum image shown in FIG. 2.

The image processing method 400 may acquire a region of interest in the heart spectrum image (step 410).

For example, the ROI may be manually set by the user, or may be set by the image processing method 400 by itself. In detail, an example operation of manually setting a region of interest by the user will be described in detail with reference to FIG. 6.

6 is another diagram illustrating a heart spectral image.

In detail, referring to FIG. 6, a user interface screen including a heart spectrum image 600 representing an inflow / outflow of blood in the heart is illustrated. In detail, the heart spectrum image 600 corresponds to the ultrasound image 550 illustrated in FIG. 5.

The region of interest acquisition 405 may include selecting a predetermined point in the cardiac spectrum image through a user interface screen, and corresponding to the input signal of one cycle and the input signal of the one cycle in the heart spectrum image, corresponding to the predetermined point. The method may include obtaining a myocardial performance cycle including the output signal of one cycle as the ROI (step not shown).

In detail, when a predetermined point 610 of the heart spectrum image 600 included in the user interface screen is selected, one myocardial cycle 620 in which the predetermined point 610 is located may be acquired as the ROI. For example, when the doctor clicks on the predetermined point 610, it may be determined that the predetermined point 610 is selected.

In addition, when a predetermined point 610 of the heart spectrum image 600 included in the UI screen is selected, a plurality of myocardial cycles 630 may be acquired as the ROI centering on the predetermined point 610. 6 illustrates an example in which four myocardial performance cycles are acquired as a region of interest around a predetermined point 610.

Also, the ROI acquisition operation may be performed by the image processing method 400 itself without a user's selection operation.

Specifically, the ROI acquiring step 410 includes a plurality of myocardial execution periods including an input signal of one cycle and an output signal of one cycle corresponding to the input signal of the one cycle in the heart spectrum image 600. Can be obtained as the ROI.

For example, when a plurality of myocardial cycles are acquired as the ROI, markers may be obtained for each myocardial cycle. In this case, the myocardial performance index (MPI) can be calculated using the markers for each cycle, and the final myocardial performance index (MPI) can be obtained as an average value of the myocardial performance index (MPI) of each of the plurality of myocardial performance cycles. have.

In addition, the ROI acquiring step 410 samples a myocardial performance period (eg, 620) including one cycle of the input signal and one cycle of the input signal in the cardiac spectrum image 600, and sampled one myocardium. The execution period may be acquired as the ROI. In this case, the myocardial performance index (MPI) may be finally obtained using the markers acquired in one cycle.

Also, the region of interest acquisition step 410 may be obtained based on the maximum signal level of the input signal and the maximum signal level of the output signal. In detail, one myocardial cycle may be determined from one point corresponding to the maximum signal level of the input signal to the next maximum signal level and the corresponding myocardial cycle may be acquired as the ROI. In addition, one myocardial cycle may be determined from one point corresponding to the maximum signal level of the output signal to the next maximum signal level as a myocardial cycle and the corresponding myocardial cycle may be acquired as the ROI.

In addition, the interested area obtaining step 410 may further include outputting a user interface screen for setting whether to automatically set or manually set the ROI. Accordingly, the user may determine whether to automatically set or manually set, and the above-described automatic set or manual set may be performed according to the user's decision. Specifically, when a manual setting is requested through the user interface screen, as described with reference to FIG. 6, a predetermined period or a predetermined point corresponding to the predetermined period in the cardiac spectrum image may be selected, and a predetermined period may be acquired as the ROI. have. In addition, when an automatic setting is requested through the user interface screen, an interval including at least one myocardial performance cycle including an input signal of one cycle and an output signal of one cycle corresponding to the input signal of one cycle in the cardiac spectrum image is interested. Can be acquired as an area.

In addition, the image processing method 400 may further include a step (415) of obtaining a plurality of feature values in the heart spectrum image. Specifically, at least one of a peak value corresponding to an input signal and a peak value corresponding to an output signal may be obtained as the feature value. The feature value obtaining operation will be described in detail below with reference to FIG. 7.

7 is a diagram for describing a converted signal of a heart spectrum image used to acquire a feature value.

Referring to FIG. 7, (a) of FIG. 7 illustrates a binarized cumulative signal obtained by binarizing an input signal and an output signal included in a heart spectrum image and accumulating each period. FIG. 7B illustrates a Morphology signal accumulated after each period after removing noise of an input signal and an output signal of a heart spectrum image. FIG. 7C illustrates a gray signal obtained by accumulating grayscale values of an input signal and an output signal of a heart spectrum image.

FIG. 7D illustrates a signal obtained by converting an input signal and an output signal, which are signals included in a heart spectrum image, into a frequency domain. In detail, the signal illustrated in FIG. 7D may be a signal obtained by converting a heart spectral image by using a transform that leaves only a high frequency component or a transform that filters only a high frequency component within a cardiac spectrum image.

Specifically, a signal obtained by Laplacian conversion of an input signal and an output signal included in a heart spectrum image is shown.

In order to obtain the feature value, the input signal and the output signal included in the heart spectrum image may use accumulated cumulative signals. Specifically, the signals shown in Figs. 7A, 7B, and 7C can be used.

For example, the signal values in the cardiac spectrum image are accumulated, and at least one peak value of the input signal (eg, 701) and the accumulated output signal (eg, 703) accumulated in the ROI are characterized. Can be obtained by. For example, as illustrated in FIG. 7A, a point or viewpoint of a heart spectrum image corresponding to the peak values 711 and 712 of the accumulated input signal may be acquired as a feature value. In addition, a point or viewpoint of the heart spectrum image corresponding to the peak values 715, 716, and 717 of the accumulated output signal may be acquired as a feature value.

In addition, in order to obtain the feature value, the input signal and the output signal included in the heart spectrum image may use a signal converted into a frequency domain or a signal of high frequency filtering. Specifically, the signal shown in (d) of FIG. 7 may be used.

That is, in step 415, the cardiac spectrum image may be subjected to high frequency filtering, and a signal peak value may be obtained as the feature value from the filtered cardiac spectrum image. For example, as illustrated in FIG. 7D, a point or a viewpoint of a heart spectrum image corresponding to the peak values 711 and 712 of the accumulated input signal may be acquired as a feature value. In addition, a point or viewpoint of the heart spectrum image corresponding to the peak values 715, 716, and 717 of the accumulated output signal may be acquired as a feature value. Also, the 711 peak value and the 712 peak value may be obtained at the same point or point as the 731 peak value and the 732 peak value, respectively, and the 715 peak value and the 717 peak value are the same point or point as the 733 peak value and the 734 peak value, respectively. Can be obtained from

Also, in the image processing method 400, acquiring a marker region 420 may include detecting an approximate marker region 425 and acquiring a detailed marker region from an approximate marker region 427. have.

For example, the closing end point t1 of the aortic valve AV, the opening start point t2 of the mitral valve MV, and the mitral valve, which are four time points necessary for measuring the myocardial performance index (MPI) described above in FIG. 2. When the closing end point t3 of the membrane MV and the opening start point t4 of the aortic valve AV are obtained as markers, four markers should be acquired in one myocardial performance cycle.

For convenience of explanation, hereinafter, a case in which four marker regions are acquired in one myocardial cycle and a marker is acquired for each marker region will be described as an example.

8 is a view for explaining the operation of step 425 of FIG. FIG. 8A illustrates peak values detected from a signal corresponding to an input signal of a heart spectrum image, for example, an accumulated signal 701 of a binarized input signal. 8B is a diagram illustrating a heart spectral image. FIG. 8C illustrates peak values detected in a signal corresponding to the output signal of the heart spectrum image, for example, the cumulative signal 703 of the binarized output signal.

Referring to FIG. 8B, the ROI may be set as a predetermined region including the illustrated 810 and 820.

Referring to FIG. 8A, the peak value point t81, the peak value point t82, and the peak value point t84 are respectively applied to the peak value 711 and the output signal corresponding to the input signal of FIG. 7. The corresponding peak value 733 and the peak value 712 corresponding to the input signal correspond to the detected point. Hereinafter, for convenience of explanation, the peak value point t81, the peak value point t82, and the peak value point t84 are referred to as first, second and third peak value points, respectively.

In detail, in operation 425, an approximate marker region may be obtained by using peak values that are characteristic values obtained in operation 415. Specifically, the interval of the signal from the first peak value point t81 corresponding to the input signal to the second peak value point t82 adjacent to the first peak value point t 81 and corresponding to the output signal is defined. The first and second marker regions 810 may be specific to the first and second markers.

In addition, the third and fourth markers exist in the signal interval from the second peak value point t82 to the third peak value point t84 adjacent to the second peak value point t82 and corresponding to the input signal. To the third and fourth marker regions 820.

After obtaining the approximate marker regions 810 and 820 in step 425, the detailed marker region may be re-acquired in step 427 for each marker region obtained in step 425.

FIG. 9 is a diagram for describing an operation of step 427 of FIG. 4. FIG. 9A corresponds to FIG. 8B and shows roughly the marker regions 910 and 920. Regions 910 and 920 correspond to regions 810 and 820 described above with reference to FIG. 8A, respectively.

FIG. 9B is a diagram illustrating a normalized summation signal 930 of the accumulated signal described with reference to FIGS. 7A, 7B, and 7C. FIG. 9C is a diagram illustrating each of the detailed marker regions. 9 (d) shows an image in which a detailed marker region is overlaid and displayed on a heart spectrum image.

Referring to FIGS. 9A to 9C, in the normalized summation signal 930, the upper limit peak values 931 and 932 and the lower limit peak values 933 and 934 are detected in detail. Marker regions can be partitioned. Specifically, based on the points t91, t92, t93, t94, and t95 where the respective peak values exist, the first and second marker regions 910 may be divided into the first marker region 950 and the second marker region ( 960). In addition, the third and fourth marker regions 920 may be divided into a third marker region 970 and a fourth marker region 980.

That is, the first and second marker regions 910 are partitioned based on the point t92 at which the lower limit peak value 933 of the normalized sum signal 930 exists, so that the first marker region 950 and the second marker region 910 are divided. The marker regions 960 may be obtained, respectively, and the first marker and the second marker may be detected in each of the first marker region 950 and the second marker region 960.

In addition, the third and fourth marker regions 920 are partitioned based on the point t94 at which the lower limit peak value 934 of the normalized summation signal 930 exists, so that the third marker region 970 and the fourth marker region 920 are divided. The marker regions 980 may be obtained, respectively, and a third marker and a fourth marker may be detected in each of the third marker region 970 and the fourth marker region 980.

 In addition, referring to FIG. 9D, an image obtained by overlaying the acquired first to fourth marker regions 950, 960, 970, and 980 on the heart spectrum image may be displayed to the user. In addition, the region of interest 990 may be additionally displayed on the displayed image.

In operation 430, a marker is acquired for each marker region acquired in operation 420.

In detail, in operation 420, for each of the acquired plurality of marker regions, click signals of input signals and output signals, gradient values of input signals and output signals, and signal strengths of input signals and output signals ( the marker may be acquired based on at least one of signal intensity).

FIG. 10 is a diagram for describing an operation of step 430 of FIG. 4.

FIG. 10A illustrates a heart spectral image including the first to fourth marker regions 1001, 1002, 1003, and 1004 obtained in step 427. FIG. 10B is a diagram illustrating a click signal existing in an image spectrum signal. FIG. 10C illustrates a marker detected by using gradient values of the click signal, the input signal, and the output signal. FIG. 10D illustrates a standardized cumulative signal of a heart spectrum image.

Referring to FIG. 2, the click signal in the heart spectrum image may occur when the mitral valve (MV) or the aortic valve (AV) is opened or closed. For example, the click signal 231 may be generated when the mitral valve MV is closed, and the click signal 232 may be generated when the aortic valve AV is opened. In addition, the click signal 233 may be generated when the aortic valve AV is closed, and the click signal 234 may be generated when the mitral valve MV is opened. In addition, referring to FIG. 10B, the click signal may be detected by detecting peak values in a graph representing blood flow.

Therefore, the click signal point detected in each of the first to fourth marker regions 1001, 1002, 1003, and 1004 may be detected as a marker point.

In detail, the first marker 1031 may be acquired at the detection point of the click signal 1011 detected in the first marker region 1001. The third marker 1033 may be acquired at the detection point of the click signal 1013 detected in the third marker region 1003.

Also, in the graph showing the normalized cumulative signal of the input signal and the output signal shown in FIG. 10 (d), the points 1021 and 1023 at which the gradient values are maximized are detected and the detected points 1021 and 1023 are detected. You can get a marker at). In detail, the second marker 1032 may be obtained at the maximum gradient point 1021 existing in the second marker region 1002. In addition, the fourth marker 1034 may be obtained at the maximum gradient point 1023 existing in the fourth marker region 1004.

In addition, the image processing method 400 may calculate a myocardial performance index (MPI) using the points of the respective markers obtained in step 430.

In addition, the image processing method 400 may further include displaying and overlaying the markers acquired on the cardiac spectrum image after step 430 (step not shown).

11 is a diagram illustrating a displayed heart spectrum image.

Referring to FIG. 11, the first marker 1110, the second marker 1120, the third marker 1140, and the fourth marker 1140 obtained in operation 430 may be displayed on the heart spectrum image and displayed to the user. Can be.

12 is a block diagram illustrating an image processing apparatus according to an embodiment of the present invention. Each configuration operation of the image processing apparatus 1200 illustrated in FIG. 12 is the same as each operation of the image processing methods 300 and 400 according to one or another embodiment of the present invention described with reference to FIGS. 1 to 11. . Therefore, description overlapping with those in FIGS. 1 to 11 will be omitted.

 Referring to FIG. 12, the image processing apparatus 1200 includes an area acquirer 1230 and a marker acquirer 1250. Hereinafter, a detailed operation of the image processing apparatus 1200 will be described with reference to the image processing method 300.

The region acquirer 1230 obtains a region of interest for measuring myocardial performance index (MPI) based on the signal level of the input signal and the output signal in the cardiac spectrum image, and, within the region of interest, the region of the input signal and the output signal. Based on the feature values, a plurality of marker regions each having at least one marker for measuring myocardial performance index (MPI) are obtained. In detail, the area acquirer 1230 performs operations 310 and 320.

The marker obtaining unit 1250 obtains at least one marker for each of the obtained plurality of marker regions. In detail, the marker acquirer 1250 performs an operation of step 330.

13 is a block diagram illustrating an image processing apparatus according to another exemplary embodiment of the present invention. FIG. 13 illustrates that in the image processing apparatus 1300 according to another exemplary embodiment, the region acquirer 1330 and the marker acquirer 1350 may be the region acquirer 1230 and the marker acquirer described with reference to FIG. 12. 1250). Therefore, description overlapping with that in FIG. 12 is omitted. In addition, operations of the image processing apparatus 1300 illustrated in FIG. 13 may include operations of the image processing methods 300 and 400 according to one or another embodiment of the present invention described with reference to FIGS. 1 to 11. same. Therefore, description overlapping with those in FIGS. 1 to 11 will be omitted.

Referring to FIG. 13, the image processing apparatus 1300 may be externally connected to the ultrasound imaging apparatus 1305. The ultrasound imaging apparatus 1305 is an apparatus for generating an ultrasound image by applying an ultrasound signal to a predetermined part of the human body.

Also, the image processing apparatus 1300 may further include at least one of an image acquisition unit 1310, an input / output unit 1370, and an ROI acquisition unit 1390, as compared to the image processing apparatus 1200.

The image acquirer 1310 acquires an echocardiography image. In detail, the image acquirer 1310 acquires a cardiac ultrasound image photographed using the ultrasound Doppler signal, and at least one of cropping, shifting, and noise reduction processing of the cardiac ultrasound image. By performing one, a heart spectrum image may be obtained. In detail, the image acquirer 1310 may include a receiver 1311 and a preprocessor 1313.

In detail, the receiver 1311 receives an echocardiogram image captured by the ultrasound imaging apparatus 1305. In detail, the receiver 1311 may perform an operation of step 401.

The preprocessor 1313 obtains a heart spectrum image by image processing the echocardiogram image transmitted from the receiver 1311. In detail, the preprocessor 1313 may perform operation 405.

The ROI acquirer 1390 acquires an ROI for measuring myocardial performance index (MPI). In detail, an ROI including at least one myocardial cycle may be acquired in the heart spectrum image. In more detail, the ROI acquirer 1390 may perform operation 410.

Also, the area acquirer 1330 may include a feature value extractor 1331 and a marker area obtainer 1333.

The feature value extractor 1331 obtains at least one of a peak value corresponding to the input signal and a peak value corresponding to the output signal as the feature value. In detail, the feature value extractor 1331 may perform an operation of step 415.

The marker region obtaining unit 1333 obtains a plurality of marker regions based on the feature values obtained by the feature value extracting unit 1331. In detail, the marker region obtaining unit 1333 may perform operation 420.

The marker acquirer 1350 acquires markers for each of the obtained plurality of marker regions. In detail, the marker acquirer 1350 may perform an operation of step 430.

The input / output unit 1370 displays a cardiac spectrum image or receives a predetermined command or request from a user. In addition, the user interface unit 1137 may be further included. The input / output unit 1370 may display a heart spectrum image in which a marker region and / or a marker is displayed.

The user interface unit 1372 is responsible for the interface with the user. In detail, a predetermined point in the heart spectrum image may be selected.

For example, when a predetermined point in the cardiac spectrum image is selected through the user interface unit 1137, at least one myocardial execution period in the cardiac spectrum image corresponding to the predetermined point may be acquired as the ROI.

Myocardial performance index (MPI) can be automatically measured by the image processing method and the image processing apparatus according to the above-described one or another embodiment of the present invention. Specifically, after limiting the marker region, the marker may be accurately obtained by obtaining the marker in the marker region obtained by being limited to the predetermined region. In addition, by reducing the error rate according to the proficiency of the doctor, etc. that occur during the manual measurement of myocardial performance index (MPI), it is possible to increase the accuracy of obtaining the marker and myocardial performance index (MPI).

In addition, the image processing method according to an embodiment of the present invention may be embodied as computer readable codes or programs on a computer readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, hard disk, floppy disk, flash memory, optical data storage, and the like. The computer readable recording medium may also be distributed over a networked computer system and stored and executed as computer readable code in a distributed manner.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be construed to include various embodiments within the scope of the claims.

1200: image processing device
1230: area acquisition unit
1250: marker acquisition unit
1300: image processing device
1305: ultrasonic imaging apparatus
1310: image acquisition unit
1311: receiver
1330: area acquisition unit
1331: feature value extraction unit
1333: marker area acquisition unit
1350: marker acquisition unit
1370: input and output unit
1371: User Interface Part
1390: region of interest acquisition unit

Claims (29)

In the image processing method for measuring myocardial performance index (MPI),
Acquiring a region of interest for measuring myocardial performance index based on the signal level of the input signal and the output signal in the cardiac spectral image;
Acquiring a plurality of marker regions in the region of interest, each of which has at least one marker for measuring the myocardial performance index (MPI) based on feature values of the input signal and the output signal; And
And acquiring the at least one marker for each of the plurality of marker regions. The method of claim 1,
And obtaining at least one of a peak value corresponding to the input signal and a peak value corresponding to the output signal as the feature value. The method of claim 1, wherein the obtaining of the ROI is performed.
Selecting a predetermined point in the cardiac spectrum image through a user interface screen; And
Acquiring, as the ROI, a myocardial performance period corresponding to the predetermined point, the myocardial period of execution including the one cycle of the input signal in the heart spectrum image and the one cycle of the output signal corresponding to the cycle of the input signal; An image processing method characterized by the above-mentioned. The method of claim 1, wherein the obtaining of the ROI is performed.
And acquiring, as the ROI, a section including a plurality of myocardial performance cycles including one cycle of an input signal in the heart spectrum image and one cycle of an output signal corresponding to the cycle of the input signal. Image processing method. The method of claim 1, wherein the obtaining of the ROI is performed.
Sampling a myocardial cycle in which the one-cycle input signal and the one-cycle input signal are included in the cardiac spectrum image and acquiring one sampled myocardial cycle in the region of interest; Treatment method. The method of claim 2, wherein obtaining the marker area
First and second markers in which the first and second markers exist in a section of a signal from a first peak value corresponding to the input signal to a second peak value adjacent to the first peak value and corresponding to the output signal. Obtaining into an area; And
Acquiring a signal section from the second peak value to a third peak value adjacent to the second peak value and corresponding to the input signal as third and fourth marker regions in which third and fourth markers exist; Image processing method comprising a. The method of claim 2, wherein obtaining the feature value
Accumulating signal values in the cardiac spectrum image, and acquiring at least one peak value of an input signal and an output signal accumulated in the ROI as the feature value. The method of claim 2, wherein obtaining the feature value
And high frequency filtering the cardiac spectral image and acquiring a peak value as the feature value from the filtered cardiac spectral image. The method of claim 1, wherein obtaining the marker
For each of the plurality of marker regions, the marker is selected based on at least one of a click signal of the input signal and the output signal, a gradient value of the input signal and the output signal, and a signal strength of the input signal and the output signal. The image processing method comprising the step of obtaining. The method of claim 1,
And overlaying and displaying the markers acquired on the cardiac spectrum image. The method of claim 1, wherein the obtaining of the ROI is performed.
And acquiring the region of interest based on the maximum signal level of the input signal and the maximum signal level of the output signal. The method of claim 1,
Acquiring a cardiac ultrasound image photographed using the ultrasound Doppler signal; And
The cardiac ultrasound image may further include acquiring the cardiac spectrum image by performing at least one of image processing such as cropping, shifting, and noise reduction. Treatment method. The method of claim 1,
And outputting a user interface screen for setting whether to automatically set or manually set the ROI. 14. The method of claim 13,
If the manual setting is requested through the user interface screen, selecting a predetermined period or a predetermined point corresponding to the predetermined period in the cardiac spectrum image, and acquiring the predetermined period as the ROI. Image processing method. 14. The method of claim 13,
When the automatic setting is requested through the user interface screen, a section including at least one myocardial cycle including at least one input signal and one output signal corresponding to the one input signal in the heart spectrum image And acquiring the data into the ROI. An image processing apparatus for measuring a myocardial performance index (MPI),
Obtain a region of interest for measuring myocardial performance index based on the signal level of the input signal and the output signal in the cardiac spectrum image, and within the region of interest, based on feature values of the input signal and the output signal, the myocardium A region obtaining unit obtaining a plurality of marker regions each having at least one marker for performing a performance index measurement; And
And a marker acquisition unit for acquiring the at least one marker for each of the plurality of marker regions. The method of claim 16, wherein the area obtaining unit
A feature value extracting unit obtaining at least one of a peak value corresponding to the input signal and a peak value corresponding to the output signal as the feature value; And
And a marker region obtaining unit obtaining the plurality of marker regions based on the feature value. 17. The method of claim 16,
A user interface unit for selecting a predetermined point in the heart spectrum image; And
The ROI acquiring unit acquires a myocardial performance cycle corresponding to the predetermined point, the myocardial execution period including an input signal of one cycle and an output signal of one cycle corresponding to the input signal of the one cycle as the ROI. Image processing apparatus comprising a. 17. The method of claim 16,
The apparatus may further include an ROI acquiring unit configured to acquire, as the ROI, a section including a plurality of myocardial execution periods including an input signal of one cycle and an output signal of one cycle corresponding to the input signal of the one cycle. An image processing apparatus, characterized in that. 17. The method of claim 16,
The apparatus may further include a region of interest acquisition unit configured to sample a myocardial cycle of performance including a cycle of an input signal and a cycle of an input signal in the cardiac spectrum image and to acquire the sampled myocardial cycle of performance as the region of interest. Image processing apparatus. The method of claim 17, wherein the marker region obtaining unit
First and second markers in which the first and second markers exist in a section of a signal from a first peak value corresponding to the input signal to a second peak value adjacent to the first peak value and corresponding to the output signal. Acquired into the realm,
Acquiring a signal section from the second peak value to a third peak value adjacent to the second peak value and corresponding to the input signal as the third and fourth marker regions in which the third and fourth markers exist. Image processing apparatus. The method of claim 17, wherein the feature value extraction unit
And accumulating signal values in the cardiac spectrum image and acquiring at least one peak value of an input signal and an output signal accumulated in the ROI as the feature value. The method of claim 17, wherein the feature value extraction unit
And high frequency filtering the cardiac spectral image and obtaining a signal peak value as the feature value from the filtered cardiac spectral image. The method of claim 16, wherein the marker acquisition unit
For each of the plurality of marker regions, the marker is selected based on at least one of a click signal of the input signal and the output signal, a gradient value of the input signal and the output signal, and a signal strength of the input signal and the output signal. And an image processing apparatus, characterized in that obtaining. 17. The method of claim 16,
And an input / output unit configured to overlay and display the markers acquired on the cardiac spectrum image. 17. The method of claim 16,
The cardiac ultrasound image may be acquired by using an ultrasound Doppler signal, and the cardiac spectrum image may be performed by performing at least one of cropping, shifting, and noise reduction processing. The image processing apparatus further comprises an image acquisition unit for acquiring. 17. The method of claim 16,
A user interface unit configured to output a user interface screen for setting whether to automatically set or manually set the ROI; And
And a region of interest obtainer configured to acquire the region of interest. The apparatus of claim 27, wherein the ROI acquirer
When the manual setting is requested through the user interface screen, the image processing apparatus may be configured to select a predetermined period or a predetermined point corresponding to the predetermined period in the cardiac spectrum image and obtain the predetermined period as the ROI. . The apparatus of claim 27, wherein the ROI acquirer
When the automatic setting is requested through the user interface screen, a section including at least one myocardial cycle including at least one input signal and one output signal corresponding to the one input signal in the heart spectrum image The image processing apparatus according to claim 1, wherein

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