TW201350084A - Image processing method, image processing apparatus and ultrasound imaging device - Google Patents

Abstract

An image processing method and an image processing apparatus are provided. The image processing method measures a myocardial performance index (MPI), the image processing method including: obtaining a region of interest (ROI) for measuring the MPI, based on signal levels of an input signal and an output signal of a heart spectrum image; obtaining a plurality of marker areas, within the obtained ROI, wherein at least one marker for measuring the MPI is located in each of the plurality of marker areas, based on at least one from among a feature value of the input signal and a feature value of the output signal; and obtaining the at least one marker for each of the plurality of marker areas.

Description

Image processing method and device [Cross-reference to related patent applications]

The present application claims priority to U.S. Provisional Application No. 61/635,425, filed on Apr. 19, 2012, to the U.S. Patent. The priority of the present application is hereby incorporated by reference.

The method and apparatus consistent with the exemplary embodiments relate to image processing methods and apparatus, and more particularly to image processing methods and apparatus for automatically measuring myocardial performance index (MPI).

An ultrasonic diagnostic apparatus widely used for diagnosing diseases can reproduce an image signal or an audio signal corresponding to a blood flow or a heart rate in a human body by using ultrasonic waves. A user such as a physician can diagnose whether a disease has occurred in an organ such as the heart by using an ultrasound image generated by the ultrasonic diagnostic apparatus.

For example, in order to diagnose a heart disease in a fetus or an adult, Ultrasonic diagnostic equipment is used to observe the movement of the fetal heart or adult heart. In detail, the ultrasonic signal is applied to the heart by contacting the heart region by placing a probe of the ultrasonic diagnostic device. Then, due to the Doppler effect, the Doppler ultrasonic signal is reflected, thereby being received via the probe in response to the applied ultrasonic signal. The ultrasonic diagnostic apparatus can acquire an image of cardiac motion by using the received Doppler ultrasonic signal.

In order to diagnose whether the heart is working properly, it is necessary to measure such as myocardial performance. The number (MPI) of the organism index and determine whether the measured index is within the normal range. Although an ultrasound image indicating cardiac motion has been acquired by using an ultrasound diagnostic apparatus, the physician needs to manually analyze the ultrasound image to measure an organism index such as MPI. For example, a physician may mark a point on the ultrasound image that is necessary to measure MPI, and may calculate the MPI by using the length between each of the marked points.

In the case of passive measurement of MPI, the accuracy of the calculated results can be rooted. It varies according to the doctor's technique. In addition, if the physician mistakenly marks the point, the MPI may be erroneously calculated.

Therefore, it is necessary to provide for more accurate acquisition or acquisition of such as MPI Method and apparatus for biological index.

The exemplary embodiments provide an image processing method and apparatus for measuring a predetermined index (eg, myocardial performance index (MPI)) of an organism by using an ultrasound image.

The illustrative embodiments are also provided for reducing the image in the self-sounding image by The image processing method and apparatus for accurately measuring MPI by measuring the measurement error occurring when MPI is measured.

Providing a measured myocardial efficacy index according to an aspect of the exemplary embodiment (MPI) image processing method, comprising: obtaining a region of interest (ROI) for measuring the MPI based on a signal level of an input signal and an output signal of a cardiac spectrum image; And obtaining, by the feature value of one of the input signal and the output signal, a plurality of mark regions in the obtained ROI, wherein at least one mark for measuring the MPI is located in the plurality of mark regions And obtaining the at least one indicia for each of the plurality of marked regions.

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

The obtaining of the ROI may include: receiving via a user interface screen Selecting a predetermined point in the cardiac spectrum image; and obtaining a myocardial performance period corresponding to the predetermined point as the ROI, wherein the obtained myocardial performance period includes one of the input signals of the cardiac spectrum image Looping and a cycle of the output signal corresponding to the one cycle of the input signal.

The obtaining of the ROI may comprise: obtaining a plurality of myocardial efficacy weeks a period interval as the ROI, wherein each of the plurality of myocardial performance cycles comprises one of the input signals of the cardiac spectrum image and the cycle corresponding to the one of the input signals A cycle of the output signal.

The obtaining the ROI may include: sampling includes one of the input signals Cycle and a cycle of myocardial performance of one of the output signals, and sampling using The myocardial performance cycle is taken as the ROI.

The obtaining of the plurality of marking regions may include: obtaining from a corresponding a first peak point of the input signal to a signal interval corresponding to the first peak point and corresponding to the second peak point of the output signal, as the first mark area and the second mark area, and the first mark and a second marker located in the first marker region and the second marker region; and a signal from the second peak point to a third peak point adjacent to the second peak point corresponding to the input signal The interval is the third mark area and the fourth mark area, and the third mark and the fourth mark are respectively located in the third mark area and the fourth mark area.

The obtaining the at least one peak as the feature value may include accumulating The input signal and the output signal of the cardiac spectrum image and a peak value of at least one of the input signal and the output signal accumulated in the ROI are used as the feature value.

The obtaining the at least one peak as the feature value may include: The cardiac spectrum image is subjected to high frequency filtering and a signal peak of the filtered cardiac spectrum image is obtained as the characteristic value.

The obtaining the at least one mark may comprise based on the following The lesser one obtains the at least one mark for each of the plurality of marked areas: a click signal of the input signal and the output signal, the input signal, and the output signal a gradient value, and a signal strength of the input signal and the output signal.

The image processing method may further include: at least one of the obtained markings Stacked and displayed on the cardiac spectrum image.

The obtaining the ROI may include: a maximum signal based on the input signal The ROI is obtained by the number level and the maximum signal level of the output signal.

The image processing method may further include: obtaining by using ultrasonic waves a cardiac ultrasound image captured by the signal; and a processed cardiac ultrasound image obtained by performing at least one of trimming, shifting, and noise reduction on the obtained cardiac ultrasound image.

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

The image processing method may further include: receiving a spectrum image of the heart a predetermined period or a selection of a predetermined point corresponding to the predetermined period of the cardiac spectrum image, and the predetermined period is obtained as the ROI when the manual setting is requested via the user interface screen.

The image processing method may further include: flashing through the user interface Receiving, in the automatic setting, an interval including at least one myocardial performance period as the ROI, the at least one myocardial performance period including a cycle of the input signal of the cardiac spectrum image and corresponding to the input signal One cycle of the output signal of the cycle.

According to another aspect of the exemplary embodiment, a method for measuring myocardial efficacy is provided An image processing device for index (MPI), the image processing device comprising: a region obtainer that obtains a note area (ROI) for measuring the MPI based on an input signal of the heart spectrum image and a signal level of the output signal, And obtaining, from the ROI, a plurality of marking regions based on the feature values of at least one of the input signal and the output signal, wherein measuring at least one of the MPIs is located in each of the plurality of marking regions And a marker obtainer that obtains the at least one marker for each of the plurality of marker regions.

The region obtainer may include: a feature value extractor obtained corresponding to At least one of a peak of the input signal and a peak corresponding to the output signal as the feature value; and a marker region obtainer that obtains the plurality of marker regions based on the feature value.

The image processing device may further include: a user interface, which receives the opposite Selecting a predetermined point in the cardiac spectrum image; and an ROI obtainer that obtains a myocardial performance period corresponding to the predetermined point as the ROI, wherein the myocardial performance period includes the input signal of the cardiac spectrum image One of the loops and a cycle of the output signal corresponding to the one cycle of the input signal.

The image processing apparatus may further include: an ROI obtainer, the ROI obtained Obtaining, as the ROI, an interval comprising a plurality of myocardial performance cycles, wherein each of the plurality of myocardial performance cycles comprises one of the input signals of the cardiac spectrum image and corresponds to the input signal a cycle of the output signal of the heart spectrum image of the cycle.

The image processing device may further include an ROI obtainer, and the ROI is obtained The device samples a cycle of myocardial performance comprising one cycle of the input signal and one of the output signals, and using the sampled myocardial performance cycle as the ROI.

The mark area obtainer is obtainable from the input signal corresponding to a first peak point to a signal interval adjacent to the first peak point and corresponding to a second peak point of the output signal as a first mark area and a second mark area, and the first mark and the second mark respectively a first marked area and a second marked area, and a signal interval obtained from the second peak point to a third peak point adjacent to the second peak point corresponding to the input signal as a third a marking area and a fourth marking area, wherein the third marking and the fourth marking are respectively located in the third marking area a domain and the fourth marked area.

The feature value extractor may accumulate the input of the cardiac spectrum image The signal and the output signal are input, and a peak value of at least one of the input signal and the output signal accumulated in the ROI may be used as the feature value.

The feature value extractor can high-frequency filter the cardiac spectrum image and can A signal peak of the filtered cardiac spectrum image is obtained as the feature value.

The tag obtainer may target the at least one of the following Each of the plurality of marked areas obtains the at least one mark: a tapping sound signal of the input signal and the output signal, a gradient value of the input signal and the output signal, and the input signal And the signal strength of the output signal.

The image processing device may further include an input/output device, the input device The /outputter overlays and displays the at least one of the obtained markers on the cardiac spectral image.

The image processing device may further include an image obtainer, and the image is obtained Obtaining a processed cardiac ultrasound by performing at least one of a heart ultrasound image captured using an ultrasonic Doppler signal and performing trimming, shifting, and noise reduction on the obtained cardiac ultrasound image image.

The image processing device may further comprise: a user interface, the output of which is used for Setting a user interface screen for automatically or manually setting the ROI; and an ROI obtainer that obtains the ROI.

When the manual setting is requested via the user interface screen, the The ROI obtainer may receive a selection of a predetermined period of the cardiac spectrum image or a predetermined point corresponding to the predetermined period, and the predetermined period may be obtained as the ROI.

When the automatic setting is requested via the user interface screen, the The ROI obtainer may obtain an interval including at least one myocardial performance period as the ROI, the at least one myocardial performance period including a cycle of the input signal of the cardiac spectrum image and the one corresponding to the input signal A cycle of the output signal of the cycle.

100‧‧‧ heart

201‧‧‧ Baseline

210‧‧‧ upper part

220‧‧‧ lower part

231~234‧‧‧Knock signal

300‧‧‧Image processing method

310, 320, 330‧‧‧ operations

400‧‧‧Image processing method

401, 405, 410, 415, 420, 425, 427, 430‧‧‧ operations

510, 550‧‧ ‧ ultrasound image

600‧‧‧Heart Spectrum Image

610‧‧‧ Reservation

620, 630‧ ‧ myocardial efficiency cycle

701‧‧‧Input signal/accumulated signal

703‧‧‧peak/cumulative signal

711, 712, 715, 717, 731~734‧‧‧ peak

810, 820‧‧‧ areas

910‧‧‧Approximate marker area/first marker area

920‧‧‧Approximate marking area

930‧‧‧Normalized sum signal

931, 932‧‧‧ upper peak

933, 934‧‧‧ lower peak

950‧‧‧First marked area

960‧‧‧Second marked area

970‧‧‧ Third marking area

980‧‧‧ fourth marked area

990‧‧‧Note Area (ROI)

1001‧‧‧First marked area

1002‧‧‧Second marked area

1003‧‧‧ third marking area

1004‧‧‧fourth marking area

1011, 1013‧‧‧ knocking signal

1021, 1023‧‧ points

1031‧‧‧ first mark

1032‧‧‧second mark

1033‧‧‧ third mark

1034‧‧‧ fourth mark

1110‧‧‧ first mark

1120‧‧‧ second mark

1130‧‧‧ third mark

1140‧‧‧ fourth mark

1200‧‧‧Image processing device

1230‧‧‧Regional acquirer

1250‧‧‧Marker Getter

1300‧‧‧Image processing device

1305‧‧‧Ultrasonic image capture device

1310‧‧‧Image Observer

1311‧‧‧ Receiver

1313‧‧‧Preprocessor

1330‧‧‧Regional acquirer

1331‧‧‧Characteristic value extractor

1333‧‧‧Marker Area Acquirer

1350‧‧‧Marker Getter

1370‧‧‧Input/Output

1371‧‧‧User interface

1390‧‧‧ROI acquirer

End time of t1‧‧‧AV closure

t2‧‧‧MV opening start time

End time of closing of t3‧‧‧MV

t4‧‧‧AV opening start time

T11, t12, t13, t14‧‧

T81, t82, t84‧‧‧ peak point

T91, t92, t93, t94, t95‧‧ points

The above and other features of the exemplary embodiments will be more apparent from the detailed description of the exemplary embodiments.

Figure 1 is a diagram illustrating the heart as a target for ultrasonic measurement.

Figure 2 is a diagram illustrating a spectrum image of the heart or a spectral image of the heart.

FIG. 3 is a flowchart illustrating an image processing method according to an exemplary embodiment.

FIG. 4 is a flowchart illustrating an image processing method according to another exemplary embodiment.

Figure 5 is a diagram illustrating a cardiac ultrasound image.

Figure 6 is a diagram illustrating the spectrum image of the heart.

7A to 7D are diagrams for explaining a conversion signal of a cardiac spectrum image for acquiring or obtaining a feature value.

8A through 8C are diagrams for explaining operation 425 of Fig. 4.

9A through 9D are diagrams for explaining operation 427 of Fig. 4.

10A through 10D are diagrams for explaining operation 430 of Fig. 4.

Figure 11 is a diagram illustrating the displayed spectrum image of the heart.

FIG. 12 is a block diagram of an image processing apparatus according to an exemplary embodiment.

FIG. 13 is a block diagram of an image processing apparatus according to another exemplary embodiment.

Hereinafter, a method and apparatus for processing an image according to an exemplary embodiment will be described in detail with reference to the accompanying drawings.

The expression "at least one of" is used to modify the entire list of elements and the individual elements of the list are not modified.

FIG. 1 is a diagram illustrating the heart 100 as a target for performing ultrasonic measurement.

Referring to FIG. 1, the heart 100 can be divided into a left ventricle (LV) 101, a left atrium (LA) 102, a right ventricle (RV) 103, and a right atrium (RA) 104.

The blood flows into and out of the heart 100 as the mitral valve (MV) 105 and the aortic valve (AV) 106 open and/or close.

The heart spectrum image or the spectral image of the heart by which the blood flow can flow into and out of the heart 100 can be obtained or obtained by using the ultrasonic Doppler signal. The signal is a signal that is reflected from the heart in response to an ultrasonic signal applied to the heart 100. A physician such as a doctor or an ultrasonic technician can diagnose whether the heart has an abnormality by analyzing a cardiac spectrum image indicating a blood flow corresponding to the movement of the heart 100.

The cardiac spectrum image is described in detail below with reference to FIG.

Figure 2 is a diagram illustrating the spectrum image of the heart. In Fig. 2, the abscissa (x-axis) indicates time and the y-axis indicates the amplitude of the ultrasonic signal.

Referring to Figure 2, the inflow of blood is illustrated in the upper portion 210 relative to the baseline 201 and the flow of blood out of the heart is illustrated in the lower portion 220. Hereinafter, an image indicating the inflow and outflow of blood is referred to as a cardiac spectrum image. Indicating blood flow The graph in the upper portion 210 of the input is referred to as an input signal, and the graph in the lower portion 220 indicating the outflow of blood is referred to as an output signal.

In general, to determine if the heart is moving normally, the Cardiac Performance Index (MPI) (ie, the body index) can be measured to determine if the measured MPI is within the normal range. The normal range can be determined based on, for example, the range determined by the medical profession. The MPI marks the start time point or the end time point on each of the input signal stream and the output signal stream in the heart spectrum image and quantizes the interval between each of the marked time points according to a predetermined equation. The value obtained. A physician such as a doctor or an ultrasonic technician can determine whether the patient's heart is moving normally by using the patient's MPI value.

The MPI can be calculated by Equation 1.

MPI=(ICT+IRT)/ET=(MCT-ET)/ET (1)

The ICT in Equation 1 represents the isovolumic contraction time (ICT). The ICT is the time from the closing end time point t1 of the AV to the opening start time point t2 of the MV.

The IRT in Equation 1 represents the isovolumic relaxation time (IRT). The IRT is the time from the closing end time t3 of the MV to the opening start time point t4 of the AV.

The ET in Equation 1 represents the ejection time (ET) and is the time between ICT and IRT.

The MCT in Equation 1 represents the mitral closure time (MCT) and is the interval between the end point of the inflowing blood flow of the MV and the starting point of the inflowing blood flow of the MV. For example, the MCT can be a period of the input signal of FIG.

For example, to measure the ICT, IRT, and ET necessary to calculate MPI, The AV closing end time point t1, the MV opening start time point t2, the MV closing end time point t3, and the AV opening start time point t4 need to be known. In the prior art, the physician manually marks the time points t1 to t4 on the input signal and the output signal of the cardiac spectrum image, and manually calculates the MPI by using the manually marked time points t1 to t4.

In Figure 2, t11, t12, t13, and t14 correspond to the closing of the AV, respectively. The end time point t1, the opening start time point t2 of the MV, the closing end time point t3 of the MV, and the opening start time point t4 of the AV.

To measure MPI by using an ultrasound image captured from the heart, In general, a physician, for example, needs to manually mark at least four time points to measure MPI. That is, the physician must mark the time points t1 to t4 on the cardiac spectrum image. The heart spectrum image is a type of cardiac ultrasound image.

Visually reading and determining the time points t1 to t4 on the spectrum image of the heart An error or error that is based on the skill level of the physician and based on the amount of noise components contained in the spectrum image of the heart.

In an exemplary embodiment, it is necessary to first obtain the measurement MPI The time zone t1, t2, t3, and t4 are located in the marked area and the MPI is measured by detecting the marks in the mark area corresponding to the time points t1, t2, t3, and t4. Based on the foregoing, the MPI can be automatically measured without requiring the physician to mark each of the time points.

FIG. 3 is a flow diagram illustrating an image processing method 300 in accordance with an exemplary embodiment. Cheng Tu.

Referring to FIG. 3, an image processing method 300 for measuring MPI includes The signal level of the input signal and the output signal in the heart spectrum image is used to obtain the amount A region of interest (ROI) of the MPI is measured (operation 310).

In detail, the heart spectrum image corresponds to the ultrasound image illustrated in Figure 2. The input signal and the output signal correspond to the signals illustrated in the upper portion 210 above the baseline 201 and the signals illustrated in the lower portion 220 below the baseline 201, respectively. Additionally, the ROI indicates a predetermined segment of the cardiac spectrum image that is sampled to measure the MPI. That is, the MPI is measured by using and/or analyzing the input and output signals contained in the ROI of the cardiac spectrum image.

Based on the input signal and output signal in the ROI obtained in operation 310 The feature value is obtained from the acquired ROI or obtained by a plurality of tag regions in which the plurality of tags for measuring the MPI are located (operation 320).

In particular, each marker can indicate a calculated MPI in the heart spectrum image. Every point necessary, or the position of each point in the heart spectrum image, is necessary to obtain the time interval between each point. For example, each mark may correspond to at least one of the following: an OFF end time point t1 of the AV, an open start time point t2 of the MV, a close end time point t3 of the MV, and an open start time point t4 of the AV. The point in time is necessary to measure ICT, IRT, and ET, as described with respect to FIG.

Each marked area indicates where the mark may be located. See Figure 8 below And the marking area is described in detail in FIG.

Respecting each of the plurality of marked regions detected in operation 320 At least one flag is taken (operation 330). In particular, after each restricted area is acquired by limiting each marked area, each mark can be accurately sensed and acquired within each restricted mark area.

FIG. 4 illustrates an image processing method 400 according to another exemplary embodiment. Flow chart.

The operations 410, 420, and 430 illustrated in FIG. 4 correspond to FIG. 3, respectively. Operations 310, 320, and 330 are described. Therefore, the description overlapping with the description of FIG. 3 is omitted. Compared with the image processing method 300 of FIG. 3, the image processing method 400 may further include at least one of operations 401, 405, and 415.

Referring to FIG. 4, the image processing method 400 may further include acquiring a cardiac ultrasound. Image (operation 401). In particular, the cardiac ultrasound image may be an image captured based on an ultrasonic Dübler signal that is a reflected signal received from the heart of the patient after the ultrasound signal is applied to the heart region. The ultrasonic signal can be applied by, for example, an ultrasonic transducer or a probe. The cardiac ultrasound image can be captured by an ultrasound image capture device (not shown). The ultrasound image capture device can be, for example, any ultrasonic machine or device that produces an ultrasound image. The cardiac ultrasound image can be the raw material containing the noise. In addition, cardiac ultrasound images can be obtained from outside. For example, a previously obtained ultrasound image can be provided. Alternatively, the cardiac ultrasound image can be generated autonomously by receiving an ultrasonic Dübler signal from the heart of the patient after transmitting the ultrasound signal to the heart region of the patient. Cardiac ultrasound images are described in more detail below with reference to FIG.

Image processing method 400 may further include obtaining by operation 401 The cardiac ultrasound image performs image processing to acquire a cardiac spectrum image (operation 405).

In detail, it can be trimmed, shifted and processed for cardiac ultrasound images. A cardiac spectrum image is acquired by at least one of noise reduction. If the noise reduction process is not performed, the mark may be inaccurately attributed due to a signal value error existing in the heart spectrum image due to noise. Therefore, after obtaining a cardiac spectrum image accurately indicating cardiac motion by reducing signal noise through preprocessing, Get a heart spectrum image to perform more accurate marker detection.

Figure 5 is a diagram illustrating a cardiac ultrasound image.

Referring to Figure 5, an ultrasonic image capturing device (not shown) can be used. An ultrasound image 510 indicating the motion of the heart itself is obtained. The ultrasound image capture device can be, for example, any ultrasonic machine or device that produces an ultrasound image. Additionally, an ultrasound image 550 can be obtained that indicates the inflow and outflow of blood in the heart, which indicates the motion of the heart.

Ultrasound image 510 and/or ultrasound image 550 illustrated in FIG. It can be the original material or the image that has been preprocessed with the image. Ultrasound image 510 and/or ultrasound image 550 can be displayed on a display device (not shown) for viewing by a user such as a patient, physician or the like. The display device may comprise, for example, a HD liquid crystal display (LCD) monitor, an LCD monitor, or a touch screen display. These display devices are examples, and the illustrative embodiments are not limited to such display devices. In addition, the ultrasound image can be displayed in black and white or in color. The ultrasound image 550 corresponds to the cardiac spectrum image illustrated in FIG.

The image processing method 400 can include acquiring an ROI from a cardiac spectrum image (operation For 410).

For example, the ROI can be manually set by the user or can be used in the image processing side. The method 400 is set autonomously. An exemplary operation of a user manually setting an ROI is described in detail below with reference to FIG.

Figure 6 is another diagram illustrating the spectrum image of the heart.

Referring to Figure 6, a user interface such as a computer monitor or monitor is illustrated. A face screen containing a heart spectrum image 600 indicative of the inflow and outflow of blood in the heart. The cardiac spectrum image 600 corresponds to the ultrasound image 550 illustrated in FIG.

Acquiring the ROI (operation 410) can include receiving a selection of a predetermined point in the cardiac spectrum image via the user interface screen, and acquiring a myocardial performance period corresponding to the predetermined point as the ROI. The selection of the predetermined point can be made by a user such as a physician. The acquired myocardial performance cycle comprises a cycle of one of the input signals of the cardiac spectrum image and a cycle of the output signal corresponding to one of the input signals. When a predetermined point 610 of the cardiac spectrum image 600 included in the user interface screen is selected, one of the myocardial performance cycles 620 at which the predetermined point 610 is located may be acquired as the ROI. For example, when a physician selects a predetermined point 610 using, for example, a mouse, keyboard, or other selection device, it may be determined that the predetermined point 610 is selected.

Additionally, when a predetermined point 610 of the cardiac spectrum image 600 included in the user interface screen is selected, a plurality of myocardial performance cycles 630 centered at the predetermined point 610 can be acquired as the ROI. Figure 6 illustrates the acquisition of four myocardial performance cycles centered at a predetermined point 610 as an example of an ROI.

The operation of acquiring the ROI can be performed in the image processing method 400 without the user's selection operation.

In the process of acquiring the ROI (operation 410), an interval including a plurality of myocardial performance cycles may be acquired as an ROI, and each myocardial performance period includes one of an input signal of the cardiac spectrum image and a cycle corresponding to the input signal. A loop of the output signal.

For example, when multiple myocardial performance cycles are acquired as ROI, markers can be acquired for each myocardial performance cycle. In this case, each MPI can be calculated for each myocardial performance cycle by using a marker, and the average of the MPIs of the plurality of myocardial performance cycles can be obtained as the final MPI.

In addition, during the process of acquiring the ROI (operation 410), one can be sampled. The myocardial performance cycle, for example, the myocardial performance cycle 620, includes a cycle of the input signal of the cardiac spectrum image 600 and a cycle of the output signal of the cardiac spectrum image 600; and the sampled myocardial performance cycle can be obtained as the ROI. In this case, the MPI can be finally obtained by using the markers obtained from the one myocardial performance cycle.

In addition, in the process of acquiring the ROI (operation 410), based on the input The ROI is obtained by the maximum signal level of the signal and the maximum signal level of the output signal. In detail, an interval from a point corresponding to a maximum level of the input signal to a point corresponding to a maximum signal level below the input signal may be determined as a myocardial performance period, and the myocardial performance period may be acquired as an ROI . In addition, an interval from a point corresponding to a maximum level of the output signal to a point corresponding to a maximum signal level below the output signal may be determined as a myocardial performance period, and the myocardial performance period may be acquired as an ROI.

Acquiring the ROI (operation 410) may further include providing a user interface screen to Used to set the ROI automatically or manually. Therefore, the user can decide to set the ROI automatically or manually, and can perform automatic setting or manual setting according to the user's decision. When a manual setting is requested via the user interface screen, as described with reference to FIG. 6, a predetermined cycle of the cardiac spectrum image or a predetermined point corresponding to a predetermined cycle may be selected, and a predetermined cycle may be acquired as the ROI. When the automatic setting is requested via the user interface screen, an interval including at least one myocardial performance period may be obtained as an ROI, the at least one myocardial performance period includes a cycle of an input signal of the cardiac spectrum image and the one corresponding to the input signal A cycle of the output signal of the loop.

The image processing method 400 can further include acquiring a plurality of special features from the cardiac spectrum image. The value is levied (operation 415). In detail, at least one of a peak corresponding to the input signal and a peak corresponding to the output signal may be acquired as the feature value. The operation of acquiring feature values is described in detail below with reference to FIGS. 7A through 7D.

7A to 7D are diagrams for explaining a heart spectrum for acquiring feature values A map of the converted signal of the image.

Figure 7A illustrates the input contained in the heart spectrum image by binarization The signal and the output signal and the binary input signal obtained by accumulating the binary input signal and the output signal for each cycle. Figure 7B illustrates a morphological signal obtained by eliminating noise in the input and output signals of the cardiac spectrum image and then accumulating the noise-reduced input and output signals for each cycle. Fig. 7C illustrates a gradation signal obtained by accumulating the input signal of the cardiac spectrum image and the gradation value of the output signal.

Figure 7D illustrates the input signal contained in the spectrum image of the heart and The output signal is converted to the signal obtained in the frequency domain. In particular, the signal illustrated in Figure 7D can be a signal obtained by converting a cardiac spectral image by using only the conversion of high frequency components in the cardiac spectral image or by using only The high frequency components in the heart spectrum image are filtered and converted.

That is, illustrated in Figure 7D by being included in the spectrum image of the heart. The input signal and the output signal are subjected to a Laplace transform signal. The Laplace transform is an integral transform that parses the function into its moment, which is a quantitative measure of the shape of a set of points.

By accumulating input signals and inputs contained in the heart spectrum image The accumulated signal obtained by the signal is used to acquire the feature value. In particular, the signals illustrated in Figures 7A, 7B, and 7C can be used to obtain feature values.

For example, the input signal and output signal of the heart spectrum image can be accumulated And can acquire a peak of at least one of the input signals accumulated in the ROI (for example, 701) and a peak of the output signal accumulated in the ROI (for example, 703) as a feature value. Therefore, the eigenvalue can be the peak of the input signal or the output signal. For example, in FIG. 7A, a point in time or a point in the heart spectrum image corresponding to the peak 711 and the peak 712 of the accumulated input signal may be acquired as a feature value. In addition, a point in time corresponding to the peak 715 and the peak 717 of the accumulated output signal or a point in the heart spectrum image can be acquired as a feature value.

By inputting the input signal and output signal contained in the spectrum image of the heart The signal obtained by converting the domain into the frequency domain or the signal obtained by high-frequency filtering the input signal and the output signal contained in the spectrum image of the heart can be used to acquire the feature value. In particular, the signals illustrated in Figure 7D can be used to obtain feature values.

That is, in operation 415, the cardiac spectrum image can be high frequency filtered, and The signal peak of the filtered cardiac spectrum image can be obtained as a feature value. For example, in FIG. 7D, a point in time or a point in the heart spectrum image corresponding to the peak 731 and the peak 732 of the accumulated input signal may be acquired as a feature value. In addition, a point in time corresponding to the peak 733 and the peak 734 of the accumulated output signal or a point in the heart spectrum image may be acquired as a feature value. The peak 731 and the peak 732 may be acquired at the same time point or point as the peak 711 and the peak 712, respectively, and the peak 733 and the peak 734 may be acquired at the same time point or point as the peak 715 and the peak 717, respectively.

In the image processing method 400, acquiring a marked area (operation 420) may A detection approximate mark area is included (operation 425) and a detailed mark area is obtained in the approximate mark area (operation 427).

For example, in order to obtain the end of the AV necessary to measure the MPI The time point t1, the opening start time point t2 of the MV, the closing end time point t3 of the MV, and the opening start time point t4 of the AV are used as marks. As described above with reference to FIG. 2, it is necessary to acquire four mark areas in one myocardial performance cycle. . The mark can be an example For example, an indicator of the closing end time point t1 of the AV, the opening start time point t2 of the MV, the closing end time point t3 of the MV, and the opening start time point t4 of the AV.

For the sake of convenience, a myocardial effect is described below with reference to FIGS. 8A to 8C. The case where four mark areas are acquired in the cycle and the mark is acquired for each mark area is taken as an example.

8A through 8C are diagrams for explaining operation 425 of Fig. 4. Figure 8A The peak detected in the signal, such as the cumulative signal 701 of the binary input signal corresponding to the input signal of the cardiac spectrum image in FIG. 7A, is illustrated. Figure 8B is a diagram illustrating a cardiac spectrum image. Figure 8C illustrates the peak detected in the signal, such as the cumulative signal 703 of the binary output signal corresponding to the output signal of the cardiac spectrum image of Figure 7A.

Referring to FIG. 8B, a predetermined area including the area 810 and the area 820 can be Set to ROI.

8A to 8C, peak point t81, peak point t82, and peak point T84 corresponds to the peak 711 corresponding to the input signal, the peak 733 corresponding to the output signal, and the peak corresponding to the peak 712 of the input signal, respectively, detected in FIGS. 7A-7D. Hereinafter, for convenience of explanation, the peak point t81, the peak point t82, and the peak point t84 are referred to as a first peak point, a second peak point, and a third peak point, respectively.

In operation 425, the features acquired for operation 415 can be utilized The peak value of the value gets the approximate marker area. In detail, a signal interval corresponding to the first peak point t81 corresponding to the input signal to the second peak point t82 adjacent to the first peak point t81 and corresponding to the output signal may be obtained as the first and second markers present in the first One and second marking areas 810.

In addition, it can be obtained from the second peak point t82 to adjacent to the second peak point t82 And the signal interval corresponding to the third peak point t84 of the input signal exists as the third and fourth marks in the third and fourth mark regions 820.

Approximate marker region 810 and approximate marker region are obtained in operation 425 After 820, a detailed marked area may be obtained for each of the marked area 810 and the marked area 820 acquired in operation 425 (operation 427).

9A through 9D are diagrams for explaining operation 427 of Fig. 4.

FIG. 9A corresponds to FIG. 8B and shows an approximate mark area 910 and an approximate mark area 920. The approximate mark area 910 and the approximate mark area 920 correspond to the approximate mark area 810 and the approximate mark area 820 illustrated in FIG. 8B, respectively.

Figure 9B is a diagram illustrating a normalized sum signal 930 of the accumulated signals described with reference to Figures 7A, 7B, and 7C. Fig. 9C is a view for explaining a detailed mark area. Figure 9D is a diagram illustrating an image displayed by overlaying a detailed marker region on a cardiac spectral image.

Referring to FIGS. 9A through 9C, detailed mark regions may be defined based on the detection of the upper peak 931 and the upper peak 932 and the lower peak 933 and the lower peak 934 in the normalized sum signal 930. In detail, based on the points t91, t92, t93, t94, and t95 at which each peak is located, the first mark region 910 may be divided into a first mark region 950 and a second mark region 960, and the second mark region 920 may be divided. The third marking area 970 and the fourth marking area 980 are formed.

That is, based on the point t92 at which the lower peak 933 of the normalized sum signal 930 is located, the first mark region 910 may be divided into the first mark region 950 and the second mark region 960. Therefore, the first mark area 950 and the second mark area 960 can be obtained, and the first mark and the second mark can be detected in the first mark area 950 and the second mark area 960, respectively.

In addition, based on the normalized sum signal 930, the peak 934 is located below At a point t94, the mark area 920 is divided into a third mark area 970 and a fourth mark area 980. Therefore, the third mark area 970 and the fourth mark area 980 can be obtained, and the third mark and the fourth mark can be detected in the third mark area 970 and the fourth mark area 980, respectively.

Referring to FIG. 9D, the first marked area 950, the second obtained by The image obtained by the marker region 960, the third marker region 970, and the fourth marker region 980 overlaid on the cardiac spectrum image is displayed to the user. The ROI 990 can additionally be displayed on the displayed image.

Next, each of the marked regions acquired in operation 420 is acquired for each A flag (operation 430).

In particular, in operation 430, based on at least one of the following: Each tag is obtained for each marked area: a tapping 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.

10A through 10D are diagrams for explaining operation 430 of Fig. 4.

FIG. 10A is a diagram illustrating a first marked area included in operation 427. A map of cardiac spectrum images of 501, second marker region 1002, third marker region 1003, and fourth marker region 1004. Figure 10B is a diagram illustrating a knocking sound signal present in an image spectrum signal. FIG. 10C is a diagram illustrating a flag detected by using a tapping sound signal and a gradient value of an input signal and an output signal. Figure 10D is a diagram illustrating a normalized cumulative signal of a cardiac spectrum image.

Referring back to Figure 2, in the heart spectrum image, open in MV or AV When it is turned off, a knocking signal can be generated. For example, it can be generated when the MV is off. The acoustic signal 231 is tapped and a knocking sound signal 232 is generated when the AV is turned on. In addition, a tapping sound signal 233 may be generated when the AV is turned off, and a tapping sound signal 234 may be generated when the MV is turned on.

Therefore, in the first marking area 1001, the second marking area 1002, A point on the tapping sound signal detected in each of the three marker areas 1003 and the fourth marker area 1004 can be detected as a marker point.

In detail, the first target can be obtained at the detection point of the tapping sound signal 1011. In 1031, the detection point is detected in the first marking area 1001. A third marker 1033 can be acquired at the detection point of the tapping sound signal 1013, the detected point being detected in the third marking area 1003.

In addition, the input signal illustrating the cardiac spectrum image illustrated in FIG. 10D In the graph of the normalized cumulative signal of the number and the output signal, the point 1021 and the point 1023 having the largest gradient value can be detected and the mark can be acquired at the detected point 1021 and the detected point 1023. In detail, the second marker 1032 can be acquired in the maximum gradient point 1021 existing in the second marker region 1002. The fourth marker 1034 can be acquired at the maximum gradient point 1023 located in the fourth marker region 1004.

In the image processing method 400, the obtained in operation 430 can be obtained by using Take the marked points to calculate the MPI.

The image processing method 400 may further include the acquired target after operation 430. Overlay and display (not shown) on the heart spectrum image.

Figure 11 is a diagram showing the heart displayed to a user on, for example, a computer monitor. A map of the spectrum image.

Referring to Figure 11, the indication in operation 430 can be obtained in the heart spectrum image. First mark 1110, second mark 1120, third mark 1130, and fourth mark 1140, and then the heart spectrum image can be displayed to a user.

FIG. 12 is a block diagram of an image processing apparatus 1200 according to an exemplary embodiment. Figure. According to an exemplary embodiment, the operation of the image processing apparatus 1200 illustrated in FIG. 12 is partially the same as the operation of the image processing method 300 or the image processing method 400 described with reference to FIGS. 1 through 11. Therefore, the description overlapping with the description of FIGS. 1 to 11 will not be repeated.

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

The region acquirer 1230 is based on an input signal and output of the heart spectrum image. The signal level of the signal acquires the ROI used to measure the MPI, and multiple marker regions are acquired from the ROI. Based on the characteristic values of the input signal and the output signal, at least one marker for measuring the MPI is located in each of the acquired plurality of marker regions. That is, the region acquirer 1230 performs operations 310 and 320 of the image processing method 300.

The tag acquirer 1250 is for each of the acquired plurality of marked regions Get at least one tag. That is, the tag acquirer 1250 performs operation 330 of the image processing method 300.

FIG. 13 is an image processing apparatus 1300 according to another exemplary embodiment. Block diagram.

Area acquirer 1330 and mark acquirer of image processing device 1300 1350 corresponds to the region acquirer 1230 and the marker obtainer 1250 described with reference to FIG. 12, respectively. Therefore, the description overlapping with the description of FIG. 12 will not be repeated. The operation of the image processing apparatus 1300 illustrated in FIG. 13 is partially the same as the operation of the image processing method 300 or the image processing method 400 described with reference to FIGS. 1 through 11. therefore, Descriptions overlapping with the descriptions of FIGS. 1 through 11 are not repeated.

Referring to FIG. 13, the image processing apparatus 1300 can be externally connected to the ultrasonic wave. Image capture device 1305. The ultrasonic image capturing device 1305 generates an ultrasonic image by applying an ultrasonic signal to a predetermined portion of the human body. The ultrasound image capture device can be, for example, any ultrasonic machine or device that produces an ultrasound image.

Compared with the image processing device 1200, the image processing device 1300 can be further packaged. At least one of an image acquirer 1310, an input/outputter 1370, and an ROI acquirer 1390 is included.

The image acquirer 1310 acquires a cardiac ultrasound image. In detail, the image The acquirer 1310 can acquire a cardiac ultrasound image captured by using an ultrasonic Doppler signal. In addition, the image acquirer 1310 can acquire the processed cardiac ultrasound image by performing at least one of trimming, shifting, and noise reduction on the cardiac ultrasound image. The image acquirer 1310 can include a receiver 1311 and a pre-processor 1313.

The receiver 1311 receives the heart captured by the ultrasonic image capturing device 1305. Dirty ultrasound image. Receiver 1311 can perform operation 401 of image processing method 400.

The preprocessor 1313 passes the heart ultrasound transmitted from the receiver 1311 The image performs image processing to obtain an image-processed cardiac ultrasound image. That is, the pre-processor 1313 can perform operation 405 of the image processing method 400.

The ROI acquirer 1390 acquires an ROI for measuring the MPI. ROI acquisition The device 1390 can acquire an ROI comprising at least one myocardial performance cycle in the cardiac spectrum image. In particular, ROI acquirer 1390 can perform operation 410 of image processing method 400.

The region acquirer 1330 may include a feature value extractor 1331 and a marked area Geter 1333.

The feature value extractor 1331 acquires a peak corresponding to the input signal and corresponds to At least one of the peaks of the output signals is taken as the feature value. In particular, feature value extractor 1331 can perform operation 415 of image processing method 400.

The mark area acquirer 1333 is based on the feature acquired by the feature value extractor 1331. The feature value is used to obtain a plurality of marked areas. In particular, the marker region acquirer 1333 can perform operation 420 of the image processing method 400.

The tag acquirer 1350 is for each of the acquired plurality of marked regions Get the tag. In particular, tag acquirer 1350 can perform operation 430 of image processing method 400.

The input/output 1370 displays a cardiac spectrum image or is received from a user Schedule a command or request. The input/output 1370 can further include a user interface 1371. The input/outputter 1370 can display the marked region and/or the cardiac spectral image on which the marker is indicated.

User interface 1371 acts as a user interface. User interface 1371 selects a predetermined point of the heart spectrum image.

For example, when the heart spectrum is selected via the user interface 1371 At a predetermined point, at least one myocardial performance period corresponding to a predetermined point of the cardiac spectrum image may be acquired as the ROI.

By using one of the exemplary embodiments according to the above description Like the processing method and image processing device, the MPI can be automatically measured. In particular, the mark can be accurately obtained from the restricted mark area. In addition, the error rate attributable to the physician's technique occurring when manually measuring the MPI can be reduced, and thus, the accuracy of acquiring the marker and the MPI can be increased.

Image processing side according to one of the exemplary embodiments described above The method can be embodied as a computer readable code or program on a non-transitory computer readable recording medium. The non-transitory computer readable recording medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of non-transitory computer readable recording media include read-only memory (ROM), random-access memory (RAM), CD-ROM, tape, hard disk, floppy disk, Flash memory, optical data storage devices, etc. The non-transitory computer readable recording medium can also be distributed over a network coupled computer system to store and execute computer readable code in a distributed manner.

While the exemplifying embodiments have been shown and described, it will be understood by those skilled in the art that the form and details may be made therein without departing from the spirit and scope of the invention as defined by the following claims. Various changes.

300‧‧‧Image processing method

310, 320, 330‧‧‧ operations

Claims (33)

An image processing method for measuring a myocardial performance index, the image processing method comprising: obtaining a attention area for measuring the myocardial performance index based on a signal level of an input signal and an output signal of a cardiac spectrum image; And at least one of an eigenvalue of the input signal and an eigenvalue of the output signal, obtaining a plurality of marker regions in the obtained attention region, wherein at least one marker for measuring the myocardial performance index is located at the plurality of markers Each of the plurality of marked regions; and the at least one marker is obtained for each of the plurality of marked regions. The image processing method of claim 1, further comprising: obtaining at least one of a peak corresponding to the input signal and a peak corresponding to the output signal as the feature value. The image processing method of claim 1, wherein the obtaining of the attention area comprises: receiving a selection of a predetermined point in the cardiac spectrum image via a user interface screen; and obtaining corresponding to the a predetermined period of myocardial performance period as the attention area, wherein the obtained myocardial performance period includes one of the input signals of the cardiac spectrum image and the output signal corresponding to the one cycle of the input signal a cycle. The image processing method of claim 1, wherein the obtaining of the attention area comprises: obtaining an interval including a plurality of myocardial performance periods as the attention area, wherein Each of the plurality of myocardial performance cycles includes one of the input signals of the cardiac spectrum image and a cycle of the output signal corresponding to the one cycle of the input signal. The image processing method of claim 1, wherein the obtaining the attention area comprises: sampling a myocardial performance period including one cycle of the input signal and one of the output signals, and a use of the The sampled myocardial performance cycle is taken as the attention zone. The image processing method of claim 2, wherein the obtaining the plurality of marking regions comprises: obtaining from a first peak point corresponding to the input signal to corresponding to the first peak point a signal interval between the second peak point of the output signal as a first mark area and a second mark area, and the first mark and the second mark are respectively located in the first mark area and the second mark area; And a signal interval obtained from the second peak point to a third peak point adjacent to the second peak point corresponding to the input signal as a third mark area and a fourth mark area, and the third mark And the fourth mark is located in the third mark area and the fourth mark area, respectively. The image processing method of claim 2, wherein the obtaining the at least one peak as the feature value comprises: accumulating the input signal and the output signal of the cardiac spectrum image, and using the same A peak value of at least one of the input signal and the output signal accumulated in the attention area is used as the feature value. The image processing method of claim 2, wherein the obtaining The at least one peak as the feature value comprises: performing high frequency filtering on the cardiac spectrum image and obtaining a signal peak of the filtered cardiac spectrum image as the feature value. The image processing method of claim 1, wherein the obtaining the at least one mark comprises obtaining the at least one for each of the plurality of mark areas based on at least one of: Marking: a tapping sound 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 of claim 1, further comprising: overlaying and displaying the obtained at least one mark on the cardiac spectrum image. The image processing method of claim 1, wherein the obtaining the attention area comprises: obtaining the attention area based on a maximum signal level of the input signal and a maximum signal level of the output signal. . The image processing method of claim 1, further comprising: obtaining a cardiac ultrasound image captured by the ultrasonic Doppler signal; and performing trimming and shifting on the obtained cardiac ultrasound image At least one of the bit and noise reduction is used to obtain a processed cardiac ultrasound image. The image processing method of claim 1, further comprising: outputting a user interface screen, wherein the user interface screen is set to automatically or manually set the attention area. The image processing method of claim 13, further comprising: receiving a predetermined period of the cardiac spectrum image or corresponding to the cardiac spectrum Selecting a predetermined point of the predetermined period of the image, and obtaining the predetermined period as the attention area when the manual setting is requested via the user interface screen. The image processing method of claim 13, further comprising: obtaining an interval including at least one myocardial performance period as the attention area when requesting the automatic setting via the user interface screen, The at least one myocardial performance cycle includes a cycle of the input signal of the cardiac spectrum image and a cycle of the output signal corresponding to the one cycle of the input signal. An image processing apparatus for measuring a myocardial performance index, the image processing apparatus comprising: a region obtainer that obtains a attention region for measuring the myocardial performance index based on an input signal of a cardiac spectrum image and a signal level of an output signal And obtaining a plurality of marker regions from the attention region based on the feature values of at least one of the input signal and the output signal, wherein at least one marker that measures the myocardial performance index is located in the plurality of markers And each of the regions; and a tag obtainer that obtains the at least one tag for each of the plurality of tag regions. The image processing device of claim 16, wherein the region obtainer comprises: a feature value extractor that obtains at least one of a peak corresponding to the input signal and a peak corresponding to the output signal As the feature value; and a mark area obtainer that obtains the plurality of mark areas based on the feature value. The image processing device of claim 16, further comprising: a user interface that receives a selection of a predetermined point in the cardiac spectrum image; Noting a region obtainer that obtains a myocardial performance period corresponding to the predetermined point as the attention region, wherein the myocardial performance period includes one of the input signals of the cardiac spectrum image circulating and corresponding to the input signal One cycle of the output signal of the cycle. The image processing device of claim 16, further comprising: an attention area obtainer, wherein the attention area obtainer obtains an interval including a plurality of myocardial performance periods as the attention area, wherein the plurality of myocardial Each of the performance cycles includes a cycle of one of the input signals of the cardiac spectrum image and a cycle of the output signal of the cardiac spectral image corresponding to the one cycle of the input signal. The image processing device of claim 16, further comprising: an attention area obtainer, the attention area obtainer sampling a myocardial performance comprising one cycle of the input signal and one of the output signals The cycle, and the sampled myocardial performance cycle is used as the attention zone. The image processing device of claim 17, wherein the mark region obtainer is obtained from a first peak point corresponding to the input signal to adjacent to the first peak point and corresponds to the output signal a signal interval of the second peak point as the first region and the second marker region, wherein the first marker and the second marker are respectively located in the first marker region and the second marker region, and obtained from the second peak point And a signal interval corresponding to the third peak point of the input signal adjacent to the second peak point as a third mark area and a fourth mark area, wherein the third mark and the fourth mark are respectively located a third marking area and the fourth marking area. The image processing device of claim 17, wherein the image processing device The feature value extractor accumulates the input signal and the output signal of the cardiac spectrum image, and uses a peak value of at least one of the input signal and the output signal accumulated in the attention area as the Eigenvalues. The image processing device of claim 17, wherein the feature value extractor performs high frequency filtering on the cardiac spectrum image and obtains a signal peak value of the filtered cardiac spectrum image as the feature value. The image processing device of claim 16, wherein the mark obtainer obtains the at least one mark for each of the plurality of mark areas based on at least one of: a tapping sound 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 device of claim 16, further comprising an input/output device that overlays and displays the obtained at least one mark on the cardiac spectrum image. The image processing device of claim 16, further comprising an image obtainer, wherein the image obtainer obtains a cardiac ultrasonic image captured by using an ultrasonic Doppler signal, and obtained by the pair The cardiac ultrasound image performs at least one of trimming, shifting, and noise reduction to obtain a processed cardiac ultrasound image. The image processing device of claim 16, further comprising: a user interface, wherein the output is used to set a user interface screen for automatically setting the attention area or manually setting the attention area; and A region obtainer that obtains the attention area. The image processing device of claim 27, wherein when the manual setting is requested via the user interface screen, the attention area obtainer receives a predetermined period of the cardiac spectrum image or corresponds to the The selection of a predetermined point of the predetermined period, and obtaining the predetermined period as the attention area. The image processing device of claim 27, wherein when the automatic setting is requested via the user interface screen, the attention area obtainer obtains an interval including at least one myocardial performance period as the attention a region, the at least one myocardial performance cycle comprising a cycle of the input signal of the cardiac spectrum image and a cycle of the output signal corresponding to the one cycle of the input signal. The image processing method of claim 1, wherein the mark for each of the plurality of mark regions indicates a point of the heart spectrum image for calculating the myocardial performance index . The image processing method of claim 1, wherein the mark for each of the plurality of mark areas is obtained without user input. The image processing method of claim 1, wherein the cardiac spectrum image comprises the input signal of the ultrasonic signal and the output signal. An ultrasonic imaging device comprising: a receiver that obtains a spectral image of the heart; a processor that obtains an area of the spectral image of the heart for measuring a myocardial performance index; and an acquirer Obtaining a plurality of marker regions in the obtained region of the spectral image of the heart, and obtaining for use in each of the plurality of marker regions At least one marker of the myocardial performance index is measured; wherein the marker for each of the plurality of marker regions is obtained without user input.