Method for analyzing an elastographic image and medical device

Abstract

The present application relates to the technical field of image processing, in particular to an elastic image analysis method and medical equipment, wherein the method comprises: obtaining a target elastic image; drawing contour lines based on the target elastic image; and analyzing the target elastic image according to the drawn contour lines. By drawing contour lines representing elastic physical quantities in the target elastic image, since the elastic image reflects the lesion information of the tissue, the contour lines drawn on the elastic image can more accurately reflect the lesion information. That is, the elastic physical quantities on the same contour line are the same, so the trend of the same contour line can be used to analyze the change trend in the target elastic image; the adjacent contour lines can be used to see the lesion, trend and softness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to an analysis method of an elastic image and a medical device. BACKGROUND

[0002] The compression type elastic imaging is also called quasi-static elastic imaging, which can qualitatively distinguish tissues with different hardness. The method mainly applies pressure to the target tissue through a handheld ultrasonic transducer, and the tissues with different hardness will have different deformations under the same pressure. By analyzing the extracted two frames of data before and after compression, the difference information of the different deformations can be extracted, and the difference is represented by different strain values; then the different strain values are pseudo-color mapped and imaged, and are fused with the tissue B image to obtain the final displayed elastic image, that is, the elastic imaging is realized.

[0003] However, in many cases, only displaying the elastic image cannot meet the needs of the user. The user not only needs to identify the target of interest, but also needs to further analyze the target of interest in detail to find more information. For example, in some reactions, the hardness of the infiltrated area around the lesion may be one of the objects that the user focuses on. SUMMARY

[0004] Therefore, the embodiments of the present application provide an analysis method of an elastic image and a medical device to solve the problem of elastic image analysis.

[0005] According to a first aspect, the embodiments of the present application provide an analysis method of an elastic image, comprising:

[0006] obtaining a target elastic image;

[0007] drawing contour lines based on the target elastic image;

[0008] analyzing the target elastic image according to the drawn contour lines.

[0009] The analysis method of the elastic image provided by the embodiments of the present application draws contour lines representing elastic physical quantities in the target elastic image. Since the elastic image reflects the lesion information of the tissue, drawing contour lines on the elastic image can more accurately reflect the lesion information. That is, the elastic physical quantities on the same contour line are the same, so the trend of the target elastic image can be analyzed by using the trend of the same contour line, and the lesion, trend and hardness can be seen by using adjacent contour lines.

[0010] In combination with the first aspect, in a first implementation manner of the first aspect, the drawing contour lines based on the target elastic image comprises:

[0011] determining a region of interest in the target elasticity image;

[0012] obtaining drawing parameters of the contour lines; wherein the drawing parameters include a number of the contour lines and a difference value of the elasticity physical quantity between adjacent contour lines;

[0013] drawing the contour lines in the region of interest based on the drawing parameters.

[0014] The elasticity image analysis method provided by the embodiments of the present application can reduce the data processing amount and ensure that the drawn contour lines better meet the user requirements.

[0015] In the second embodiment of the first aspect, in combination with the first embodiment of the first aspect, the drawing of the contour lines in the region of interest based on the drawing parameters comprises:

[0016] obtaining a preset pixel point in the region of interest;

[0017] determining a first preset contour line by using the preset pixel point;

[0018] drawing the contour lines in the region of interest based on the drawing parameters and the first preset contour line;

[0019] and / or,

[0020] obtaining a second preset contour line in the region of interest;

[0021] drawing the contour lines in the region of interest based on the drawing parameters and the second preset contour line.

[0022] In the third embodiment of the first aspect, in combination with the first aspect, the step of analyzing the target elasticity image according to the drawn contour lines further comprises:

[0023] obtaining a first target contour line determined in the drawn contour lines;

[0024] determining a parameter of outward expansion or inward contraction of the first target contour line; wherein the parameter of outward expansion or inward contraction includes a number of times and a step length;

[0025] outwardly expanding or inwardly contracting the first target contour line based on the parameter of outward expansion or inward contraction.

[0026] The elasticity image analysis method provided by the embodiments of the present application can draw rich contour lines by outwardly expanding or inwardly contracting the first target contour line.

[0027] With reference to the first aspect, in a fourth implementation form of the first aspect, the analyzing the target elasticity image according to the drawn contour line comprises:

[0028] obtaining a plurality of second target contour lines determined in the drawn contour line;

[0029] determining a plurality of closed regions formed by the plurality of second target contour lines by using the plurality of second target contour lines;

[0030] determining an analysis result of the plurality of closed regions based on elasticity physical quantities corresponding to the plurality of closed regions.

[0031] The elasticity image analysis method provided in the embodiments of the present application can quantitatively analyze the hardness of tissues in different regions and the transition information of the hardness by comparing and analyzing elasticity physical quantities corresponding to a plurality of closed regions.

[0032] With reference to the fourth implementation form of the first aspect, in a fifth implementation form of the first aspect, the determining the plurality of closed regions formed by the plurality of second target contour lines by using the plurality of second target contour lines comprises:

[0033] determining whether there is an open contour line in the plurality of second target contour lines;

[0034] when there is an open contour line in the plurality of second target contour lines, obtaining a target pixel point determined on the open contour line, and connecting the target pixel point to adjust the open contour line to a closed curve.

[0035] With reference to the first aspect, or the fourth implementation form of the first aspect, in a sixth implementation form of the first aspect, the analyzing the target elasticity image according to the drawn contour line comprises:

[0036] obtaining a reference region determined in the target elasticity image;

[0037] analyzing the target elasticity image by using the reference region and the drawn contour line.

[0038] The elasticity image analysis method provided in the embodiments of the present application can compare and analyze the hardness of tissues and the transition information of the hardness between a region formed by a contour line and a reference region by determining a reference region in a target elasticity image and using a drawn contour line.

[0039] With reference to the first aspect, in a seventh implementation form of the first aspect, the analyzing the target elasticity image according to the drawn contour line further comprises:

[0040] acquire a target gray image corresponding to the target elasticity image;

[0041] draw contour lines corresponding to contour lines on the target elasticity image on the target gray image;

[0042] analyze the target elasticity image based on the contour lines on the target elasticity image and the contour lines on the target gray image.

[0043] The elasticity image analysis method provided by the embodiments of the present application also gives the contour line information of the elasticity data in the target gray image, and the contour lines can be combined with the target gray image to further understand the lesion range of the tissue and the distribution and transition change information of the different hardness of the tissue.

[0044] In combination with the seventh embodiment of the first aspect, in an eighth embodiment of the first aspect, the analyzing the target elasticity image based on the contour lines on the target elasticity image and the contour lines on the target gray image comprises:

[0045] superimpose the target elasticity image and the target gray image;

[0046] adjust the transparency of the target elasticity image and / or the transparency of the target gray image, and analyze the target elasticity image.

[0047] The elasticity image analysis method provided by the embodiments of the present application can compare and quantify the results by superimposing and displaying the target elasticity image and the target gray image, and the target elasticity image can be analyzed intuitively.

[0048] In combination with the first aspect, in a ninth embodiment of the first aspect, the acquiring the target elasticity image comprises:

[0049] acquire multiple frames of image data;

[0050] start from a first frame of image data of the multiple frames of image data, sequentially form multiple groups of frame pair image data with adjacent preset number of image data; wherein each group of frame pair image data comprises the preset number of frame pair image data;

[0051] screen the multiple groups of frame pair image data to obtain target frame pair image data;

[0052] perform elasticity calculation on the target frame pair image data to obtain an elasticity calculation result, and store the elasticity calculation result;

[0053] form the target elasticity image based on the stored elasticity calculation result.

[0054] The elastic image analysis method provided by the embodiment of the present application can provide rich selectivity for subsequent filtering of frame pair image data by constructing frame pairs from obtained multi-frame image data, and to a certain extent, reduce the dependence on operation methods; and the formed multi-group frame pair image data is filtered before elastic calculation, so as to avoid the calculation of redundant image data, improve the drawing efficiency of the elastic image, and improve the efficiency of subsequent analysis of the target elastic image.

[0055] According to a second aspect, the embodiment of the present application further provides an elastic image analysis device, comprising:

[0056] An elastic image acquisition module is configured to acquire a target elastic image.

[0057] A drawing module is configured to draw contour lines based on the target elastic image.

[0058] An analysis module is configured to analyze the target elastic image according to the drawn contour lines.

[0059] The elastic image analysis device provided by the embodiment of the present application can draw contour lines representing elastic physical quantities in the target elastic image, and since the elastic image reflects the lesion information of the tissue, drawing contour lines on the elastic image can more accurately reflect the lesion information; that is, the elastic physical quantities on the same contour line are the same, and the trend of the target elastic image can be analyzed by using the trend of the same contour line; the lesion, trend and hardness can be observed by using adjacent contour lines.

[0060] According to a third aspect, the embodiment of the present application provides a medical device, comprising a memory and a processor, which are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the elastic image analysis method in the first aspect or any one of the embodiments of the first aspect.

[0061] According to a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores computer instructions for making the computer execute the elastic image analysis method in the first aspect or any one of the embodiments of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the specific embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0063] Figure 1 An optional system structure diagram of elastic imaging in the embodiment of the present application is shown;

[0064] Figure 2 A flow chart of the elastic image analysis method according to the embodiment of the present application is shown;

[0065] Figure 3 A flow chart of the elastic image analysis method according to the embodiment of the present application is shown;

[0066] Figure 4 A schematic diagram of contour lines in the target elastic image according to the embodiment of the present application is shown;

[0067] Figure 5 A schematic diagram of contour lines in the target elastic image according to the embodiment of the present application is shown;

[0068] Figure 6 A flow chart of the elastic image analysis method according to the embodiment of the present application is shown;

[0069] Figure 7 A schematic diagram of contour lines in the target elastic image according to the embodiment of the present application is shown;

[0070] Figure 8 A flow chart of the elastic image analysis method according to the embodiment of the present application is shown;

[0071] Figure 9 A schematic diagram of the display result of the elastic image analysis method according to the embodiment of the present application is shown;

[0072] Figure 10 A flow chart of the method for obtaining the target elastic image according to the embodiment of the present application is shown;

[0073] Figure 11 A flow chart of the method for obtaining the target elastic image according to the embodiment of the present application is shown;

[0074] Figure 12 A schematic diagram of the principle of frame pair construction according to the embodiment of the present application is shown;

[0075] Figure 13 A flow chart of the elastic imaging method according to the embodiment of the present application is shown;

[0076] Figure 14ais a fitting schematic diagram of displacement curve along the depth direction according to an embodiment of the present application;

[0077] Figure 14b is a fitting schematic diagram of displacement curve along the horizontal direction according to an embodiment of the present application;

[0078] Figure 15 is a flow chart of frame pair screening according to an embodiment of the present application;

[0079] Figure 16 is a schematic diagram of multiple elastic results compounding according to an embodiment of the present application;

[0080] Figure 17 is a structural block diagram of elastic analysis device according to an embodiment of the present application;

[0081] Figure 18 is a hardware structure schematic diagram of medical equipment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0082] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0083] The elastic image analysis method described in the embodiments of the present application is used for analyzing the obtained target elastic image, and specifically, contour lines are drawn on the target elastic image, and the target elastic image is analyzed by using the drawn contour lines.

[0084] The target elastic image can be obtained in real time or stored in the medical equipment in advance. In the following description of the present embodiment, the target elastic image obtained by the medical equipment in real time is taken as an example for description.

[0085] The target elastic image obtained by the medical equipment in real time can be sent by other electronic devices or obtained by the medical equipment through the connection with the ultrasonic probe and the processing of the ultrasonic echo data collected by the ultrasonic probe.

[0086] Please refer to Figure 1 , Figure 1 shows a structural block diagram of an ultrasonic elastic imaging system, in which Figure 1In the shown system, the medical device is connected with the ultrasonic probe from the hardware perspective, the ultrasonic probe sends the collected ultrasonic echo data to the medical device, the medical device obtains a target elasticity image after processing, and the target elasticity image is analyzed by using the elasticity image analysis method in the embodiment of the present application.

[0087] As shown in the figure, from the software implementation perspective, the software program executed by the medical device can be divided into a transmission timing control unit, a receiving storage unit, a signal processing unit, a B image processing unit, an elasticity physical quantity acquisition unit, a two-dimensional image display unit, and an elasticity data analysis unit. Figure 1 The transmission timing control unit is used for controlling the timing of the ultrasonic probe transmitting ultrasonic signals; the ultrasonic probe acts on the detected tissue according to a certain timing and transmission frequency under the action of the transmission timing control unit; the receiving storage unit realizes the receiving and storage of ultrasonic echo signals; the signal preprocessing includes beam synthesis processing, signal amplification, analog-to-digital conversion, signal detection, and quadrature demodulation, and an IQ signal containing phase information is obtained, which is sent to two parallel processing units, including the B image processing unit and the elasticity physical quantity acquisition unit; the two-dimensional image processing unit realizes the display of the processed image; finally, the elasticity information analysis unit realizes the extraction of more information of the elasticity image, and provides more useful lesion information for clinicians.

[0088] It should be noted that the software program executed by the medical device in the embodiment of the present application can only include the software program corresponding to the elasticity data analysis unit described in the Figure 1 , or can include the software program corresponding to other units in combination with the software program of the elasticity analysis unit, which is not limited here, and can be set according to actual conditions.

[0089] According to the embodiment of the present application, an elasticity image analysis method embodiment is provided, and it should be noted that the steps shown in the flowchart of the drawing can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from here.

[0090] An elasticity image analysis method is provided in the embodiment, which can be used in the medical device described above, Figure 2 The flowchart of the elasticity image analysis method according to the embodiment of the present application is shown in the figure, which includes the following steps: Figure 2

[0091] S11, obtaining a target elasticity image.

[0092] ​As shown above, the target elasticity image acquired by the medical apparatus can also be acquired from other electronic apparatuses, or can be acquired by acquiring and processing the ultrasound echo data. The acquisition method of the target elasticity image is not limited herein.

[0093] S12, contour lines are drawn based on the target elasticity image.

[0094] The elasticity physical quantity depends on the mode of detecting the tissue to be detected. When the compression detection is used, the elasticity physical quantity represented by the pixel value of each pixel point in the target elasticity image is the strain value. When the shear wave detection is used, the elasticity physical quantity represented by the pixel value of each pixel point in the target elasticity image is the shear wave speed. Of course, the elasticity physical quantity can also be the shear modulus, or the Young's modulus, etc. The elasticity physical quantity is not limited herein. However, it is necessary to note that once the detection mode of the tissue to be detected is determined, the elasticity physical quantity represented by the pixel value of each pixel point in the target elasticity image acquired by the medical apparatus is also determined. That is, the elasticity physical quantity corresponding to the target elasticity image is related to the mode of detecting the tissue to be detected by the ultrasound probe.

[0095] After the medical apparatus determines the elasticity physical quantity of each pixel point in the target elasticity image, the contour lines can be drawn by using the elasticity physical quantity. That is, the curve (contour line) with the same value of the elasticity physical quantity is outlined in the elasticity image. The curve can be a closed curve or a non-closed curve. The drawing of the contour line can be performed in the entire target elasticity image, or the contour line can be drawn in the selected region of interest, etc.

[0096] The above S12 will be described in detail below.

[0097] S13, the target elasticity image is analyzed according to the drawn contour lines.

[0098] After the medical apparatus draws the contour lines in the target elasticity image, the contour lines can be analyzed. For example, if the contour line is not a straight line, the elasticity physical quantities on both sides of the contour line are different, and the change trend can be seen based on the different changes of the elasticity physical quantities on both sides. The adjacent contour lines can be used to compare and analyze the elasticity physical quantities of the adjacent regions, etc. The specific analysis method will be described in detail below.

[0099] The elastic image analysis method provided in the embodiment can more accurately reflect the lesion information by drawing the contour line of the elastic physical quantity in the target elastic image, since the elastic image reflects the lesion information of the tissue. That is, the same contour line has the same elastic physical quantity, and thus the trend in the target elastic image can be analyzed by using the trend of the same contour line. The lesion, trend and hardness can be observed by using the adjacent contour lines.

[0100] In the embodiment, an elastic image analysis method is provided, which can be used in the medical device described above, Figure 3 is a flowchart of the elastic image analysis method according to the embodiment of the present application, as shown in the figure, the flow includes the following steps: Figure 3

[0101] S21, obtaining a target elastic image.

[0102] For details, please refer to S11 of the embodiment shown in Figure 2 , which will not be described here again.

[0103] S22, drawing a contour line based on the target elastic image.

[0104] The medical device draws the contour line in the determined region of interest. Specifically, the above S22 includes the following steps:

[0105] S221, determining a region of interest in the target elastic image.

[0106] The determination of the region of interest can be a manual operation, or can be determined by the medical device from the target elastic image according to the corresponding rules. The corresponding rules can be that the change value of the elastic physical quantity in the preset range exceeds the threshold value, etc. For the convenience of the following description, the region of interest is referred to as the ROI region.

[0107] S222, obtaining a contour line drawing parameter.

[0108] The drawing parameter includes the number of contour lines and the difference value of the elastic physical quantity between adjacent contour lines.

[0109] S223, drawing the contour line in the region of interest based on the drawing parameter.

[0110] The process of drawing the contour line in the determined ROI region by the medical device is realized by an image processing algorithm, and the extraction and calibration of the contour line can be automatically realized based on the drawing parameter.

[0111] When drawing the contour line, the following methods can be used:

[0112] (1) determining a preset pixel point in the region of interest​

[0113] When the preset pixel point is determined, the specific size of the elastic physical quantity corresponding to the preset pixel point is also determined, and then the preset pixel point can be used to draw the contour line.

[0114] (2) determining a second preset contour line in the region of interest

[0115] When the second preset contour line is determined, the contour line can be drawn based on the second preset contour line.

[0116] (3) combination of the above two methods

[0117] The above-mentioned method (1) and method (2) will be described in detail below, and the method (3) corresponds to the combination of the method (1) and the method (2).

[0118] As an optional implementation manner of the embodiment, the S223 includes the following steps:

[0119] (1.1) obtaining a preset pixel point in the region of interest.

[0120] As shown in the following formula (1), the target elastic image I, the region of interest ROI determined in the target elastic image I, before drawing the contour line, in order to make the drawn contour line more close to the effect that the user wants, a preset pixel point can be specified in the ROI region, for example, P_fix in the following formula (1). Figure 4 Figure 4 After the preset pixel point is determined, the specific value of the elastic physical quantity is specified.

[0121] The preset pixel point can be manually marked in the ROI region by the user with the help of a mouse, or the user can input the specific value of the selected elastic physical quantity, and after the specific value of the elastic physical quantity is determined, the position of the selected elastic physical quantity in the ROI region can be determined.

[0122] (1.2) determining a first preset contour line by using the preset pixel point.

[0123] As shown above, after the preset pixel point is determined, the first preset contour line passing through the preset pixel point can be determined. Specifically, since each pixel point in the target elastic image corresponds to a specific value of the elastic physical quantity, the specific value of the elastic physical quantity corresponding to the preset pixel point can be compared with the specific value of the elastic physical quantity of each pixel point in the ROI region, and then the first preset contour line in the ROI region can be determined.

[0124] (1.3) drawing the contour line in the region of interest based on the drawing parameters and the first preset contour line.

[0125] ​After the first preset contour lines for the medical equipment are drawn within the ROI region, the remaining contour lines can be drawn within the ROI region using the drawing parameters. For example... Figure 4 As shown, Figure 4 In this context, ds is used to represent the difference between adjacent contour lines.

[0126] As another optional implementation of this embodiment, S223 above includes the following steps:

[0127] (2.1) Obtain the second preset contour line within the region of interest.

[0128] Using the same principle as the preset pixels described above, a second preset contour line can be determined within the region of interest.

[0129] (2.2) Based on the drawing parameters and the second preset contour lines, draw contour lines in the region of interest.

[0130] Using the second preset contour line and drawing parameters, the medical device can draw the remaining contour lines within the region of interest.

[0131] in, Figure 4 The diagram shows contour lines drawn within the ROI, which can be closed curves, such as... Figure 4 As shown by L1, L3, and Ln; it can also be a non-closed curve, such as... Figure 4 As shown in L2.

[0132] S23. Analyze the target elasticity image based on the drawn contour lines.

[0133] Please see details Figure 2 S13 of the illustrated embodiment will not be described again here.

[0134] The elastic image analysis method provided in this embodiment first determines the region of interest in the target elastic image, and then draws contour lines within the region of interest. This reduces the amount of data processing and ensures that the drawn contour lines better meet the user's needs.

[0135] As an optional implementation of this embodiment, before analyzing the target elasticity image, the drawn contour lines can be processed, for example, deleted, added, expanded, or contracted. Taking expansion or contraction as an example, before S23 above, the following steps are also included:

[0136] (1) Obtain the first target contour line determined in the drawn contour lines.

[0137] The first target contour can be selected by the user from all the contours, or selected by the medical device from all the contours based on the specific value of the elastic physical quantity determined by the user, and the like.

[0138] As shown in Figure 5 , if L4 is selected as the first target contour, the elastic physical quantity corresponding to the first target contour is A1, and the first target contour is expanded or shrunk.

[0139] (2) Determine the parameter for expanding or shrinking the first target contour.

[0140] The parameter for expanding or shrinking includes the number of times and the step length.

[0141] For example, the number of times is N, and the step length is Δs.

[0142] (3) Based on the parameter for expanding or shrinking, the first target contour is expanded or shrunk.

[0143] The medical device can realize one-time shrinking of L4 to obtain the contour L5, or one-time expansion of L4 to obtain the contour L3, by using the parameter for expanding or shrinking.

[0144] Of course, when it is necessary to delete the contour, the contour to be deleted can be selected for deletion. Through the deletion operation, some curves (for example, L1) or other regions (for example, L6 or L7) can be deleted to reduce unnecessary visual interference.

[0145] The addition of the contour can be completed by manually marking a point, for example, p1, in the ROI region, so as to add the contour (for example, L2) passing through the point, so as to further meet the selection requirement of the region of interest.

[0146] In the embodiment, an elastic image analysis method is provided, which can be used in the medical device described above, Figure 6 The flowchart of the elastic image analysis method according to the embodiment of the present application is shown in Figure 6 , and the flowchart includes the following steps:

[0147] S31, obtaining a target elastic image.

[0148] For details, please refer to S21 of the embodiment shown in Figure 3 , which will not be repeated here.

[0149] S32, drawing a contour based on the target elastic image.

[0150] For details, please refer to S22 of the embodiment shown in Figure 3 , which will not be repeated here.

[0151] S33, analyzing the target elasticity image according to the drawn contour lines.

[0152] The medical apparatus analyzes the target elasticity image by using the area formed by the adjacent contour lines, calculating the average value of the elasticity physical quantity in the formed area, or the area of the formed area. By using the ratio of different areas, or the change of the average elasticity physical quantity of the area over time, etc., the pathological condition of the related tissue can be more clearly reflected, and more effective auxiliary diagnosis information can be provided for the clinician. Specifically, the above S33 includes the following steps:

[0153] S331, obtaining a plurality of second target contour lines determined in the drawn contour lines.

[0154] For example, please refer to L1, L2, …, LN in Figure 7 , Figure 7 respectively represent different contour lines, and S1, S2, S3, …, SN respectively represent the areas formed between different contour lines, and also represent the average value of the elasticity physical quantity in the area. Among them, S1 is the area formed between contour lines L1 and L2, and the average elasticity physical quantity in the area is S1.

[0155] The determination of the plurality of second target contour lines can be determined by the user in all contour lines, for example, selecting L1, L2 and L3 as the second target contour lines.

[0156] S332, using the plurality of second target contour lines to determine a plurality of closed areas formed by the plurality of second target contour lines.

[0157] Taking L1, L2 and L3 as the second target contour lines as an example, a closed area S1 is formed between L1 and L2, and a closed area S2 is formed between L2 and L3. Since the contour line can be a closed curve or an open curve, if the determined second target contour line is an open curve, it needs to be adjusted to a closed curve.

[0158] Specifically, the above S332 includes the following steps:

[0159] (1) determining whether there is an open contour line in the plurality of second target contour lines;

[0160] The medical apparatus can traverse the pixel points on each contour line in turn to determine whether it is a closed curve. When there is an open contour line in the plurality of second target contour lines, (2) is executed; otherwise, S333 is directly executed.

[0161] (2) obtaining a target pixel point determined on the open contour line, and connecting the target pixel point to adjust the open contour line to a closed curve.

[0162] For example, please refer to Figure 5 , Figure 5 If the contour L10 is an open curve, the target pixel points p2 and p3 can be determined on the L10, and the contour L10 can be adjusted to a closed curve through the line between p2 and p3.

[0163] The determination of the target pixel points can be artificial or other ways, and the determination method of the target pixel points is not limited herein.

[0164] S333, determining the analysis result of the plurality of closed regions based on the elastic physical quantity corresponding to the plurality of closed regions.

[0165] Please refer to Figure 7 , the medical device can calculate the ratio of S1 and S2, which can reflect the soft and hard relationship between the adjacent regions S1 and S2, and the same principle can also realize the comparison relationship between two non-adjacent closed regions; of course, the comparison relationship between three or more closed regions can also be realized, for example, the ratio of S5, S4 and S3, or the normalized ratio with one of them as a reference, which can represent the hardness change process between the three adjacent regions.

[0166] For example, the ratio of the average elastic physical quantity between the plurality of adjacent closed regions can also be calculated, which can effectively reflect the soft and hard relationship between the adjacent tissues, that is, it can represent the transition of the tissue space lesion. In addition, in the case of equal contour interval, the area ratio of different closed regions can also be calculated to reflect the soft and hard change speed of the tissue. For example, if the intervals between L1, L2 and L3 are equal, as shown in Figure 7 , the area of S1 is greater than that of S2, which means that the hardness change of S2 region is greater than that of S1 region.

[0167] As an optional implementation of the embodiment, the user can also select a reference region in the target elastic region, and compare and analyze the target elastic image with the reference region by using the drawn contour.

[0168] Specifically, the above S33 further includes:

[0169] (1) Obtain the reference region determined in the target elastic image.

[0170] For example, please refer to Figure 5 , in Figure 5The reference region determined in the embodiment is L8 or L9. The reference region L8 or L9 is taken as a comparison reference. For example, the user identifies the reference region L8 or L9 as a normal tissue region, and then the subsequent analysis of the target elasticity image can be performed by using the contour line and the reference region.

[0171] (2) The analysis of the target elasticity image by using the reference region and the contour line.

[0172] The analysis of the target elasticity image by using the reference region and the contour line can refer to the analysis in S33 of the embodiment shown in Figure 6 , which will not be described herein again.

[0173] The analysis method of the elasticity image provided in the embodiment can compare and analyze the tissue hardness and the change transition information between the region formed by the contour line and the reference region by determining the reference region in the target elasticity image and using the contour line.

[0174] An analysis method of an elasticity image is provided in the embodiment, which can be used in the medical device, Figure 8 The flowchart of the analysis method of the elasticity image according to the embodiment of the present application is shown in Figure 8 , which includes the following steps:

[0175] S41, obtaining a target elasticity image.

[0176] For details, please refer to S31 of the embodiment shown in Figure 6 , which will not be described herein again.

[0177] S42, drawing a contour line based on the target elasticity image.

[0178] For details, please refer to S32 of the embodiment shown in Figure 6 , which will not be described herein again.

[0179] S43, analyzing the target elasticity image according to the drawn contour line.

[0180] When the medical device analyzes the target elasticity image, the target gray image (i.e., the ultrasound B image) corresponding to the target elasticity image can be combined. Specifically, the above-mentioned S43 includes the following steps:

[0181] S431, obtaining a target gray image corresponding to the target elasticity image.

[0182] Please refer to Figure 1 , the signal preprocessing unit processes the ultrasound echo data and sends them into the B image processing unit and the elasticity physical quantity obtaining unit for processing, so as to obtain the target gray image and the target elasticity image respectively.

[0183] S432, draw the contour line corresponding to the contour line on the target elasticity image on the target grayscale image.

[0184] Since the pixels on the target grayscale image and the target elasticity image are one-to-one corresponding, the medical device can draw the contour line corresponding to the contour line on the target elasticity image on the target grayscale image by using the position of the pixel.

[0185] As shown in FIG. 4, the display interface of the medical device is divided into three parts, which are the ultrasound B-mode imaging area, the elasticity imaging area and the display screen. Among them, the ultrasound B-mode imaging area is used to display the target grayscale image, the elasticity imaging area is used to display the target elasticity image, and the display screen is used to display the analysis result. Figure 9

[0186] In the elasticity imaging area, different contour lines and corresponding areas can be marked in different display forms, such as different colors, different lines or thicknesses, etc., so as to visually distinguish the differences between them. In addition, while the elasticity imaging area is displayed, the contour line of the corresponding position is also given in the ultrasound B-mode imaging area, so that the user can more clearly see the corresponding area in the target grayscale image.

[0187] S433, based on the contour line on the target elasticity image and the contour line on the target grayscale image, analyzing the target elasticity image.

[0188] The medical device can analyze the target elasticity image in combination with the target grayscale image. Specifically, the above S433 includes the following steps:

[0189] (1) superimposed display the target elasticity image and the target grayscale image.

[0190] The medical device can superimposed display the target elasticity image and the target grayscale image, for example, the target elasticity image is displayed above the target grayscale image, or the target elasticity image is displayed below the target grayscale image, etc.

[0191] Since the pixels on the target elasticity image and the target grayscale image are one-to-one corresponding, the target elasticity image and the target grayscale image can be superimposed displayed by using the positional relationship of the pixels.

[0192] (2) adjusting the transparency of the target elasticity image, and / or the transparency of the target grayscale image, to analyze the target elasticity image.

[0193] ​The medical device can display the target elasticity image and the target grayscale image by adjusting the transparency of the target elasticity image and / or the target grayscale image (for example, adjusting the transparency of the target elasticity image, or the transparency of the target grayscale image; or adjusting the transparency of the target elasticity image and the target grayscale image simultaneously), so that the target elasticity image and the target grayscale image can be displayed in a matching manner by adjusting the corresponding transparency, and the target elasticity image can be analyzed better.

[0194] The analysis method of the elasticity image provided in the embodiment can analyze the target elasticity image by comparing and quantifying the results, and can intuitively analyze the target elasticity image.

[0195] The analysis method of the elasticity image provided in the embodiment can realize the selection of the region of interest based on the elasticity data, and the selection of the region in the embodiment is mainly realized by extracting the contour line of the elasticity data to realize the automatic or semi-automatic division of the region. The contour line can accurately realize the calibration of the elasticity physical quantity in the elasticity data, and the region selected by the contour line can accurately enclose the lesion region with similar elasticity physical quantity. By comparing and analyzing the average elasticity physical quantity of two or more regions, the hardness of different regions and the transition information thereof can be quantitatively analyzed; in the result display module, the contour line information of the elasticity data is also given in the ultrasound B image, and the lesion range of the tissue, the distribution and transition information of the different hardness of the tissue can be further understood by combining the ultrasound B image and the contour line.

[0196] As an optional implementation manner of the embodiment, as shown in Figure 10 The target elasticity image includes the following steps:

[0197] S51, acquiring a plurality of image data.

[0198] The plurality of image data can be the plurality of image data obtained by the medical device directly from the ultrasonic transducer and after signal preprocessing; or can be the plurality of image data obtained by the medical device from other electronic devices after signal preprocessing of the collected ultrasonic image data, and the like. The source of the plurality of image data is not limited herein.

[0199] In the embodiment, the target elasticity image includes the following steps: Figure 1As shown, the signal acquisition unit collects the ultrasonic echo signal at a certain sampling frequency, so the image data acquired by the medical device is continuous image data. After acquiring the image data, the medical device stores it in a preset space. When multiple frames of image data are stored in the preset space, subsequent frame pair construction is performed. The image data in the preset space is stored in time sequence. The number of multiple frames of image data stored in the preset space can be set according to actual conditions, and is not limited herein.

[0200] S52, starting from the first frame of image data of the multiple frames of image data, sequentially form multiple sets of frame pair image data with adjacent preset number of image data.

[0201] Each set of frame pair image data includes a preset number of frame pair image data.

[0202] The preset space of the medical device stores multiple frames of image data, and the medical device stores new image data in the preset space after acquiring the new image data to ensure that the multiple frames of image data stored in the preset space are real-time collected image data.

[0203] For example, the medical device can extract the first frame of image data and the adjacent preset number of image data from the preset space, and then use the first frame of image data and each adjacent image data to form a set of frame pair image data. As the medical device continuously acquires new image data, the image data in the preset space is updated, and then the medical device can form multiple sets of frame pair image data using the continuously updated preset space.

[0204] The frame pair image data is composed of first image data and second image data, wherein the first image data is the first frame of image data in the multiple frames of image data, and the first image data can be regarded as image data before compression, and the second image data can be regarded as image data after compression.

[0205] The preset number k is related to the compression period, that is, the preset number can be set as the maximum interval between the data before compression and the data after compression. The size of k is related to the frequency of user pressing and the frame rate of the medical device. When the user presses too fast or the frame rate of the medical device is too high, the preset number k also needs to be set larger, and vice versa. For example, before actually performing elastography, the pressing frequency of the user is detected, and the detected pressing frequency and the frame rate of the medical device are used to query the preset number from a preset data table to obtain the preset number corresponding to the pressing frequency of the user and the frame rate of the medical device. The data table is a preset correspondence between the preset number and the pressing frequency of the user and the frame rate of the medical device.

[0206] This step will be described in detail below.

[0207] S53, screening the multiple sets of frame pair image data to obtain target frame pair image data.

[0208] After obtaining the multiple sets of frame pair image data, the medical device screens the multiple sets of frame pair image data to obtain target frame pair image data. The target frame pair image data can be frame pair image data in the current multiple sets of frame pair image data, or target frame pair image data obtained by screening the multiple sets of frame pair image data last time, and the like.

[0209] Each frame pair image data is composed of two image data, one of which represents image data before compression, and the other represents image data after compression. Since the frequency and intensity of the user's pressing are unstable, the relative displacement between the image data before compression and the image data after compression can be considered to be within a certain threshold range. Therefore, the distance between the two image data in each frame pair image data in the multiple sets of frame pair image data can be calculated to obtain the relative displacement. Then, it is determined whether the calculated relative displacement is within the threshold range. When the calculated relative displacement is within the threshold range, it is determined that the frame pair image data is target frame pair image data. Of course, other screening methods can also be used, which will be described in detail below.

[0210] S54, performing elastic calculation on the target frame pair image data to obtain elastic calculation results, and storing the elastic calculation results.

[0211] After obtaining the target frame pair image data, the medical device performs elastic calculation thereon to obtain multiple elastic calculation results, and stores the elastic calculation results. The elastic calculation method can be selected according to actual conditions, which is not limited herein.

[0212] S55, forming a target elastic image based on the stored elastic calculation results.

[0213] After obtaining the elastic calculation results, the medical device can calculate the average of the stored elastic calculation results to form a target elastic image, or can perform weighted summation on the stored elastic calculation results to form the target elastic image. However, the protection scope of the present application is not limited to the above two methods, and other methods can also be used, which is not limited herein.

[0214] It should be noted that the medical device is cyclically executed S12-S15, wherein each execution of S14 increases an elastic calculation result in the storage interval, and the elastic image is formed by using the stored elastic calculation result. For example, after the first execution of S14, there is one elastic calculation result in the storage interval, and then the medical device forms the elastic image by using the elastic calculation result; after the second execution of S14, there are two elastic calculation results in the storage interval, and then the medical device forms the elastic image by using the two elastic calculation results, and so on. When the obtained elastic calculation results fill the storage interval, the next obtained elastic calculation result will replace the earliest obtained elastic calculation result to update the storage interval. The size of the storage interval can be set according to the actual situation (which will be described in detail below) to increase the stability of the image output.

[0215] By constructing the frame pair of the obtained multi-frame image data, rich selectivity is provided for the subsequent screening of the frame pair image data, and the dependence on the operation method is reduced to a certain extent. In addition, the multi-group frame pair image data formed before the elastic calculation is screened to avoid the calculation of redundant image data, thereby improving the output efficiency of the elastic image.

[0216] As another optional implementation of the embodiment, as shown in Figure 11 The method for obtaining the target elastic image includes the following steps:

[0217] S61, obtaining multi-frame image data.

[0218] For details, please refer to S51 of the embodiment shown in Figure 10 , which will not be repeated here.

[0219] S62, starting from the first frame of image data of the multi-frame image data, sequentially forming a plurality of groups of frame pair image data with adjacent image data of a preset number.

[0220] Each group of frame pair image data includes a preset number of frame pair image data.

[0221] After the medical device obtains the image data, it is sequentially stored in the preset space; and after obtaining a new frame of image data, the preset space is updated to construct a plurality of groups of frame pair image data. Specifically, the above S62 includes the following steps:

[0222] S621, storing the multi-frame image data in the preset space in time sequence.

[0223] For example, M frames of image data can be stored in the preset space, and the medical device sequentially stores the acquired image data in the preset space in time sequence. The length of the preset space can be set according to actual conditions, and is not limited herein.

[0224] S622, starting from the first frame of image data in the preset space, sequentially form a group of frame pair image data with the adjacent preset number of image data.

[0225] M frames of image data are stored in the preset space, and the preset number is k. Please refer to Figure 12 , Figure 12 The principle of frame pair construction is shown. Assuming that the multi-frame image data is M frames, F(i) is the i-th frame of image data, k is the preset number, F(i) is set as the image data before compression, and the subsequent k consecutive frames are set as the image data after compression, that is, a group of frame pair image data is constructed, and the group of frame pair image data has k frame pair image data.

[0226] As shown in Figure 12 , F(i) is the first frame of image data in the preset space, F(i) and F(i+1) form the first pair of frame pair image data,..., F(i) and F(i+k-1) form the (k-1) pair of frame pair image data, and F(i) and F(i+k) form the k pair of frame pair image data; the above first pair of frame pair image data to the k pair of frame pair image data are called a group of frame pair image data.

[0227] S623, acquire a new frame of image data.

[0228] S624, store the new frame of image data into the preset space, and delete the first frame of image data to update the preset space to form a plurality of groups of frame pair image data.

[0229] By analogy, each time a frame of image data is updated, the newly obtained frame of image data is stored in the preset space, and the first frame of image data in the preset space is deleted to obtain an updated preset space. At this time, the medical device sets the next frame of image data of the first frame of image data in S622 as the data before compression, and the subsequent k frames of image data as the image data after compression, and can also construct a group of frame pair image data. The size of k is related to the frequency of user pressing and the frame rate of the system. When the user presses too fast or the system frame rate is too high, the continuous frame number k also needs to be set larger, and vice versa.

[0230] Since the maximum interval between the image data before compression and the image data after compression is k, it can be considered that the first M-k frames of image data in the preset space are image data before compression, and then:

[0231] When the 1st frame image data is taken as the image data before compression, the 1st group of frame pair image data is formed, and the group of frame pair image data has k pairs of frame pair image data;

[0232] When the 2nd frame image data is taken as the image data before compression, the 2nd group of frame pair image data is formed, and the group of frame pair image data has k pairs of frame pair image data;

[0233] …

[0234] When the (M-k-1)th frame image data is taken as the image data before compression, the (M-k)th group of frame pair image data is formed, and the group of frame pair image data has k pairs of frame pair image data.

[0235] Therefore, when the data amount of the data storage space reaches M frames, a total of (M-k)*k groups of frame pairs can be constructed, that is, (M-k) frames are taken as the data before compression.

[0236] It should be noted that the medical device can start the frame pair construction process after storing M frame image data in the preset space. Since the maximum interval between the image data before compression and the image data after compression is k, the medical device can also start the frame pair construction process after storing K+1 frame image data in the preset space. In the following description, the frame pair construction process is described in detail after the preset space is full of M frame image data.

[0237] Specifically, taking M=7 and k=3 as an example, please refer to Table 1.

[0238] Table 1: Frame pair image data construction

[0239] Frame 1 Frame 2 Frame 3 Frame 4 Frame 5 Frame 6 Frame 7 F(1) F(2) F(3) F(4) F(5) F(6) F(7) F(2) F(3) F(4) F(5) F(6) F(7) F(8) F(3) F(4) F(5) F(6) F(7) F(8) F(9) F(4) F(5) F(6) F(7) F(8) F(9) F(10) F(5) F(6) F(7) F(8) F(9) F(10) F(11)

[0240] As shown in Table 1, the first row in Table 1 is used to represent the frame number of the image data stored in the preset space in time sequence. The image data represented by the first row is F(1)-F(7) in turn. After the medical device obtains F(8), F(1) is deleted and F(8) is stored in the 7th frame of the preset space. At this time, F(2) is the first frame image data in the preset space, and the same is true for the subsequent frames. As described above, the first M-k frame image data in the preset space is the image data before compression, so F(1)-F(4) can be taken as the image data before compression, and the frame pair construction process is performed.

[0241] Please refer to Table 1, and the constructed frame pair image data is shown in Table 2:

[0242] Table 2: Frame pair image data construction

[0243] Number of groups Image data before compression Frame pair image data 1 F(1) F(1) and F(2), F(1) and F(3), F(1) and F(4) 2 F(2) F(2) and F(3), F(2) and F(4), F(2) and F(5) 3 F(3) F(3) and F(4), F(3) and F(5), F(3) and F(6) 4 F(4) F(4) and F(5), F(4) and F(6), F(4) and F(7)

[0244] Therefore, as shown in Table 2, the M frames of image data in the preset space can form M-K groups of frame pair image data, each group of frame pair image data has K pairs of frame pair image data, and therefore, the M frames of image data can form (M-k)*k pairs of frame pair image data in total. Taking M=7 and k=3 as an example, 7 frames of image data can form 12 pairs of frame pair image data in total.

[0245] Please refer to Table 1 again. After the medical device obtains F(11), the next construction of multiple groups of frame pair image data can use F(2)-F(5) as the image data before compression. Since F(2)-F(4) has formed frame pair image data as the image data before compression in the current construction of frame pair image data, only F(5) needs to be processed in the next construction of frame pair image data, and therefore, the next construction of frame pair image data only needs to process k pairs of frame pair image data, and (M-k)*k pairs of frame pair image data can also be formed.

[0246] In the embodiment, the medical device sets multiple frames of image data as the image data before compression in a limited preset space (M frames), reduces the risk of error of the first frame of data, and only needs to calculate the evaluation index of k pairs of data each time, but (M-k)*k pairs of frame pair can be provided for screening. The effectiveness of the frame pair is taken into account, the real-time processing of the algorithm is ensured, and the image output rate of the elastography is greatly improved.

[0247] S63, screening the multiple groups of frame pair image data to obtain target frame pair image data.

[0248] For details, please refer to S53 of the embodiment shown in Figure 10 S53 of the embodiment shown in the figure will not be repeated here.

[0249] S64, performing elastic calculation on the target frame pair image data to obtain an elastic calculation result, and storing the elastic calculation result.

[0250] For details, please refer to S54 of the embodiment shown in Figure 10 S54 of the embodiment shown in the figure will not be repeated here.

[0251] S65, forming a target elastic image based on the stored elastic calculation result.

[0252] For details, please refer to S55 of the embodiment shown in Figure 10 S55 of the embodiment shown in the figure will not be repeated here.

[0253] The obtained multi-frame image data is sequentially stored in the preset space and frame pair construction is performed, the newly obtained image data frame is sequentially stored in the preset space, and the first frame image data in the preset space is replaced, so that the image data in the preset space is used to construct multi-group frame pair image data, thereby providing rich frame pair image data for subsequent screening.

[0254] As another optional implementation of the embodiment, as shown in Figure 13 The target elasticity image includes the following steps:

[0255] S71, obtaining multi-frame image data.

[0256] For details, please refer to S61 of the embodiment shown in Figure 11 The details are not repeated here.

[0257] S72, starting from the first frame image data of the multi-frame image data, sequentially forming multi-group frame pair image data with the adjacent preset number of image data.

[0258] Each group of frame pair image data includes a preset number of frame pair image data.

[0259] For details, please refer to S62 of the embodiment shown in Figure 11 The details are not repeated here.

[0260] S73, screening the multi-group frame pair image data to obtain target frame pair image data.

[0261] After the medical device forms the multi-group frame pair image data in S72, it screens it.

[0262] Specifically, the above-mentioned S73 includes the following steps:

[0263] S731, using the first image data and the second image data of each frame pair image data in the multi-group frame pair image data to calculate the displacement data matrix corresponding to each frame pair image data.

[0264] The medical device screens the multi-group frame pair image data, each frame pair image data has k pairs of frame pair image data, each pair of frame pair image data is composed of first image data and second image data, wherein the first image data is the image data before compression as described above, and the second image data is the image data after compression as described above. The medical device uses the first image data and the second image data to calculate the M*N displacement data matrix corresponding to the frame pair image data. Wherein M*N is the size of the collected image data.

[0265] For example, the first image data is represented as: Z1=I1+iQ1, and the second image data is represented as: Z2=I2+iQ2;

[0266] The operation of conjugate multiplication on the first image data and the second image data is:

[0267] Z MxN =Z1*conj(Z2);

[0268] In the formula, conj is a conjugate operation, Z MxN is a complex matrix of M rows and N columns, containing phase information related to displacement information. The displacement information can be quickly calculated by extracting the phase information, which can be expressed as:

[0269]

[0270] In the formula, Phase(Z MxN ) is a phase information extraction operation on the complex matrix Z MxN ; c is the speed of sound 1540 m / s; f0 is the center frequency of the transmitted ultrasound wave; Disp MxN is a displacement data matrix.

[0271] S732, according to the displacement data matrix corresponding to each frame pair of image data, screening the plurality of frame pairs of image data to obtain target frame pair of image data.

[0272] As shown above, each frame pair of image data has obtained the corresponding displacement data matrix Disp MxN in S731. Therefore, for the plurality of frame pairs of image data, the medical device can use the k displacement data matrices corresponding to the k frame pairs of image data to screen the frame pairs of image data.

[0273] Specifically, the above S732 can include the following steps:

[0274] (1) Extracting a first fitting parameter of the displacement curve along the depth direction from the displacement data matrix, and / or extracting a second fitting parameter of the displacement curve along the horizontal direction from the displacement data matrix.

[0275] The first fitting parameter includes a first fitting slope and a first fitting degree, and the second fitting parameter includes a second fitting degree.

[0276] The medical device can screen each frame pair of image data using only the first fitting parameter of the displacement curve along the depth direction, or using only the second fitting parameter of the displacement curve along the horizontal direction, or combining the displacement curve along the depth direction with the displacement curve along the horizontal direction to screen each frame pair of image data.

[0277] Specifically, when the medical device needs to screen each set of frame pair image data by using the first fitting parameter of the displacement curve in the depth direction, the medical device averages each row of data of the displacement data matrix corresponding to each set of frame pair image data to obtain a displacement curve of the frame pair image data in the depth direction, as shown in Figure 14a ; and then fits the displacement curve in the depth direction to obtain a first fitting slope k axial and a first fitting degree R axial . The medical device can perform linear fitting on the displacement curve in the depth direction to obtain the first fitting slope k axial and the first fitting degree R axial .

[0278] When the medical device needs to screen the frame pair image data by using the second fitting parameter of the displacement curve in the horizontal direction, the medical device averages each column of data of the displacement data matrix corresponding to each set of frame pair image data to obtain a displacement curve of the frame pair image data in the horizontal direction, as shown in Figure 14b ; and then fits the displacement curve in the horizontal direction to obtain a second fitting degree R lateral . The medical device can perform quadratic fitting or multiple fitting on the displacement curve in the horizontal direction to obtain the second fitting degree R lateral .

[0279] Ideally, when a pressing force is applied to the tissue, a deformation of the tissue occurs, that is, a relative displacement change occurs, and the displacement information in the depth direction should be a monotonically increasing curve. Linear fitting is performed on the curve, and the slope obtained by fitting reflects the degree of the pressing force applied to the tissue to some extent. The greater the pressure, the greater the displacement change, and the obtained slope also relatively increases. Conversely, the higher the fitting degree, the better the extracted displacement signal in the depth direction. Conversely, the higher the fitting degree, the better the data continuity, and the more accurate the displacement information calculation result.

[0280] (2) Screening a plurality of sets of frame pair image data based on the first fitting parameter and / or the second fitting parameter to obtain target frame pair image data.

[0281] After obtaining the first fitting parameter and / or the second fitting parameter corresponding to each set of frame pair image data in the plurality of sets of frame pair image data, the medical device screens the frame pair image data by using the first fitting parameter and / or the second fitting parameter corresponding to each set of frame pair image data to obtain target frame pair image data.

[0282] Specifically, as shown in Figure 15 the steps include:

[0283] (2.1) In the multiple sets of frame pair image data, it is judged whether there is frame pair image data satisfying a preset condition. The preset condition is that the first fitting slope is between a preset slope range, and the first fitting degree is greater than or equal to a first fitting threshold, and the second fitting degree is greater than or equal to a second fitting threshold.

[0284] When there is frame pair image data satisfying the preset condition, S2.2 is executed; otherwise, S2.3 is executed.

[0285] Elastography relies on the user's operation, and the pressing force cannot be too large or too small, so the first fitting slope k axial should be within a reasonable range. If the system presets the maximum slope as k max , and the minimum slope as k min , then k axial should satisfy: k min ≤ k axial ≤ k max . The first fitting degree R axial characterizes the quality of the extracted displacement signal along the depth direction. If the system presets the first fitting threshold along the depth direction as R axial_min , then R axial should satisfy: R axial ≥ R axial_min . The second fitting degree R lateral characterizes the quality of the extracted displacement signal along the horizontal direction. If the system presets the second fitting threshold along the horizontal direction as R lateral_min , then R lateral should satisfy: R lateral ≥ R lateral_min .

[0286] Therefore, the fitting parameters corresponding to the frame pair image data satisfying the preset condition described above can be represented as:

[0287]

[0288] (2.2) From the frame pair image data satisfying the preset condition, the frame pair image data corresponding to the first fitting slope closest to the target threshold is selected to obtain the target frame pair image data.

[0289] When there is at least one pair of frame pair image data satisfying the preset condition in a certain set of frame pair image data, a pair of frame pair image data whose first fitting slope is closest to the target threshold k best is selected from them as the target frame pair image data.

[0290] (2.3) determining whether the number of frames not satisfying the preset condition exceeds a preset value.

[0291] If none of the groups of frame pair image data satisfy the preset condition, a counter count is used to count the number of groups of frame pair image data not satisfying the preset condition in the whole process of forming the elastographic image. A preset value Q is set in the medical device.

[0292] When the number of groups of frame pair image data not satisfying the preset condition does not exceed the preset value, i.e., count≤Q, (2.4) is performed; otherwise, (2.5) is performed.

[0293] (2.4) extracts the target frame pair image data of the last time as the current target frame pair image data.

[0294] If none of the current groups of frame pair image data satisfy the preset condition, the medical device extracts the target frame pair image data of the last time as the current target frame pair image data.

[0295] (2.5) outputs an empty elastographic result, or does not display.

[0296] If the value of the counter count exceeds the preset value in the whole process of elastography, it indicates that the current elastography fails, and an empty elastographic result can be output, or not displayed.

[0297] S74, performs elastographic calculation on the target frame pair image data to obtain an elastographic calculation result, and stores the elastographic calculation result.

[0298] For details, please refer to Figure 11 S64 of the embodiment shown, which will not be described here.

[0299] S75, forms a target elastographic image based on the stored elastographic calculation result.

[0300] The medical device performs composite processing on the stored elastographic result to obtain a final output display elastographic image. Specifically, if there are multiple stored elastographic results, the medical device performs weighted summation on the multiple elastographic calculation results to obtain a target elastographic image. As shown in Figure 16 Assuming that the multiple selected elastographic calculation results are represented as E(i-n+1), E(i-n+2), …, E(n), and the corresponding coefficients are a(1), a(2), …, a(n), then the i-th frame composite result out(i) is:

[0301] out(i) = a(1) * E(i-k+1) + a(2) * E(i-k+2) + … + a(k) * E(i)

[0302] Wherein, i represents the number of the current frame, out is the result of the elastic composition, n can be specified by the system adjustment, which is used to determine the range of the composition, the allocation of the weighting coefficient a can be the average weighting, the function of the distance variable or the function based on the evaluation score, etc. The setting of the weighting system is not limited to the above setting.

[0303] In the embodiment, the medical device forms the elastic image by using the existing elastic calculation results in the storage interval, instead of forming the elastic image after the storage interval is full, which can ensure the real-time of the elastic imaging and increase the stability of the image output. In addition, when setting the size of the storage interval, the update rate of the elastic image also needs to be considered, because the elastic image is formed by using all the elastic calculation results after the storage interval is full. If the size of the storage interval is too small, the elastic image will be updated frequently, which will show the phenomenon of flickering. If the size of the storage interval is too large, the elastic imaging will have a large delay because of the large number of elastic calculation results.

[0304] By calculating the displacement data matrix for each frame pair image data in multiple frame pair image data, the multiple frame pair image data is filtered by using the calculated displacement data matrix, that is, a part of the frame pair image data is filtered by using the displacement data matrix before the elastic calculation, so as to reduce the subsequent elastic calculation amount and improve the output rate of the elastic image. The elastic calculation results obtained after the calculation are compounded, which avoids the obvious jump of the continuous elastic image when the pressing has a large difference, and ensures the stable output of the target elastic image.

[0305] In the embodiment, the multiple frame image data obtained is first frame pair constructed, which can construct multiple combinations of frame pair image data in limited storage space, provides rich selectivity for the subsequent filtering of the frame pair image data, and reduces the dependence on the operation method to a certain extent. In addition, the application also provides an effective filtering criterion, which can filter out a suitable pair of frame pair image data from the multiple frame image data with less calculation amount, which improves the real-time of the clinical operation. The frame pair image data filtered is subjected to high-precision elastic calculation, which improves the accuracy of the elastic imaging. Finally, the multiple high-precision elastic calculation results are subjected to compound processing, which further ensures the stability of the elastic image output. The elastic imaging method in the embodiment reduces the dependence on the operation method and ensures the real-time acquisition of high-quality elastic results, and improves the output rate of the target elastic imaging.

[0306] In this embodiment, an elastic image analysis device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the description of which has been made above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0307] The present embodiment provides an elastic image analysis device, as shown in Figure 17 , comprising:

[0308] an elastic image acquisition module 81, configured to acquire a target elastic image;

[0309] a contour drawing module 82, configured to draw a contour based on the target elastic image;

[0310] an analysis module 83, configured to analyze the target elastic image according to the drawn contour.

[0311] The elastic image analysis device in the present embodiment is presented in the form of functional units, where the units refer to ASIC circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0312] Further description of the functions of the above-mentioned modules is the same as the corresponding embodiments described above, and will not be repeated here.

[0313] The present embodiment also provides a medical device having the above-mentioned Figure 17 elastic image analysis device.

[0314] Please refer to Figure 18 , Figure 18 is a structural schematic diagram of a medical device provided by an optional embodiment of the present application, as shown in Figure 18As shown, the medical device can include at least one processor 91, such as a CPU (Central Processing Unit), at least one communication interface 93, a memory 94, at least one communication bus 92. Wherein the communication bus 92 is used to realize the connection communication between the components. Wherein the communication interface 93 can include a display, a keyboard, and the optional communication interface 93 can also include a standard wired interface, a wireless interface. The memory 94 can be a high-speed RAM memory (Random Access Memory), or a non-volatile memory, such as at least one disk memory. The memory 94 can also be at least one storage device located away from the aforementioned processor 91. Wherein the processor 91 can be combined with Figure 17 The described device, the memory 94 stores an application program, and the processor 91 calls the program code stored in the memory 94 for executing any of the above method steps.

[0315] Wherein the communication bus 92 can be a PCI (peripheral component interconnect) bus or an EISA (extended industry standard architecture) bus, etc. The communication bus 92 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 18 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0316] Wherein the memory 94 can include volatile memory (English: volatile memory), such as RAM (random-access memory); the memory can also include non-volatile memory (English: non-volatile memory), such as flash memory, hard disk (English: hard disk drive, abbreviated: HDD) or solid-state disk (English: solid-state drive, abbreviated: SSD); the memory 94 can also include a combination of the above types of memory.

[0317] Wherein the processor 91 can be a CPU (central processing unit), a network processor (English: network processor, abbreviated: NP), or a combination of CPU and NP.

[0318] The processor 91 can further include a hardware chip. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0319] Optionally, the memory 94 is further configured to store program instructions. The processor 91 can invoke the program instructions to implement the method for analyzing an elastic image as described in embodiments 1 to 8, or to implement the method for obtaining a target elastic image as described in embodiments 9 to 13. Figure 2 、 3 Figure 10 、 11

[0320] The embodiments of the present application further provide a non-transitory computer storage medium, which stores computer executable instructions. The computer executable instructions can execute the method for analyzing an elastic image in any of the above method embodiments. The storage medium can be a disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory, a Hard Disk Drive (HDD) or a Solid-State Drive (SSD), etc. The storage medium can also include a combination of the above types of storage.

[0321] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the appended claims.​​

Claims

1. An analysis method of an elastic image, characterized by, The method comprises the following steps: acquiring a target elasticity image; drawing contour lines based on the target elasticity image, the contour lines being used to represent curves of an elasticity physical quantity with the same value in the target elasticity image, the contour lines being drawn based on preset pixel points in a region of interest in the target elasticity image, the preset pixel points being determined through an interactive manner; analyzing the target elasticity image according to the drawn contour lines.

2. The method of claim 1, wherein, The step of drawing contour lines based on the target elasticity image comprises the following steps: determining a region of interest in the target elasticity image; acquiring drawing parameters of the contour lines; wherein the drawing parameters comprise the number of the contour lines and the difference of the elasticity physical quantity between adjacent contour lines; drawing the contour lines in the region of interest based on the drawing parameters.

3. The method of claim 2, wherein, The step of drawing the contour lines in the region of interest based on the drawing parameters comprises the following steps: acquiring preset pixel points in the region of interest; determining a first preset contour line by using the preset pixel points; drawing the contour lines in the region of interest based on the drawing parameters and the first preset contour line.

4. The method of claim 2, wherein, The step of drawing the contour lines in the region of interest based on the drawing parameters comprises the following steps: acquiring preset pixel points in the region of interest; determining a first preset contour line by using the preset pixel points; acquiring a second preset contour line in the region of interest; drawing the contour lines in the region of interest based on the drawing parameters, the first preset contour line and the second preset contour line.

5. The method of claim 1, wherein, The method further comprises the following steps before the step of analyzing the target elasticity image according to the drawn contour lines: acquiring a first target contour line determined in the drawn contour lines; determining a parameter of outward expansion or inward contraction of the first target contour line; wherein the parameter of outward expansion or inward contraction comprises a number of times and a step length; outwardly expanding or inwardly contracting the first target contour line based on the parameter of outward expansion or inward contraction.

6. The method of claim 1, wherein, The step of analyzing the target elasticity image according to the drawn contour lines comprises the following steps: acquiring a plurality of second target contour lines determined in the drawn contour lines; determining a plurality of closed regions formed by the plurality of second target contour lines by using the plurality of second target contour lines; determining analysis results of the plurality of closed regions based on the elasticity physical quantity corresponding to the plurality of closed regions.

7. The method of claim 6, wherein, The step of determining the plurality of closed regions formed by the plurality of second target contour lines by using the plurality of second target contour lines comprises the following steps: judging whether there is an open contour line in the plurality of second target contour lines; when there is an open contour line in the plurality of second target contour lines, acquiring a target pixel point determined on the open contour line and connecting the target pixel point to adjust the open contour line to a closed curve.

8. The method of claim 1 or 6, wherein, The step of analyzing the target elasticity image according to the drawn contour lines comprises the following steps: acquiring a reference region determined in the target elasticity image; analyzing the target elasticity image by using the reference region and the drawn contour lines.

9. The method of claim 1, wherein, The analysis of the target elasticity image according to the drawn contour line further includes: obtaining a target grayscale image corresponding to the target elasticity image; drawing a contour line corresponding to the contour line on the target elasticity image on the target grayscale image; analyzing the target elasticity image based on the contour line on the target elasticity image and the contour line on the target grayscale image.

10. The method of claim 9, wherein, The analysis of the target elasticity image based on the contour line on the target elasticity image and the contour line on the target grayscale image includes: superimposed display of the target elasticity image and the target grayscale image; adjusting the transparency of the target elasticity image and / or the transparency of the target grayscale image to analyze the target elasticity image.

11. The method of claim 1, wherein, The obtaining of the target elasticity image includes: obtaining a plurality of image data; starting from the first image data of the plurality of image data, sequentially forming a plurality of sets of frame pair image data with adjacent preset number of image data; wherein each set of frame pair image data includes the preset number of frame pair image data; screening the plurality of sets of frame pair image data to obtain target frame pair image data; performing elasticity calculation on the target frame pair image data to obtain elasticity calculation results, and storing the elasticity calculation results; forming the target elasticity image based on the stored elasticity calculation results.

12. A medical device, characterized by It includes: a memory and a processor, which are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the analysis method of the elasticity image in any one of claims 1-11.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the computer to perform the analysis method of the elasticity image in any one of claims 1-11.

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