Processing method for improving contrast ratio of blood vessel ultrasonic image and image processing system

Abstract

The invention discloses a processing method and an image processing system for improving contrast of a vascular ultrasound image, and the processing method comprises the steps: adjusting an original mapping curve, and obtaining a target mapping curve, the original mapping curve being used for reflecting the relation between the intensity of an ultrasound signal in a blood vessel and the gray scale of an original vascular ultrasound image, the change rate of the target mapping curve in the gray level overlapping area is greater than that of the original mapping curve in the gray level overlapping area, the gray level in the gray level overlapping area corresponds to various image targets at the same time, and the various image targets are obtained by identifying the original vascular ultrasound image; the original blood vessel ultrasonic image is obtained through the original mapping curve and the ultrasonic signal; and processing the ultrasonic signal based on the target mapping curve to obtain a target blood vessel ultrasonic image. Therefore, the contrast of different image targets can be improved.

Description

[0001] This application is a divisional application of a patent application with an application date of 2021-11-23, an application number of 2021113966250, and an invention name of a processing method and image processing system for enhancing the contrast of vascular ultrasound images. Technical Field

[0002] The present disclosure generally relates to general image data processing or generation, and more particularly to a processing method and image processing system for improving the contrast of vascular ultrasound images. Background Art

[0003] The intravascular ultrasound system can effectively help doctors observe the characteristics of lesions inside blood vessels. However, due to the complex internal tissue composition of diseased blood vessels and the large signal attenuation and low signal-to-noise ratio of the high-frequency ultrasound system, the identification of blood, tissue, fibrous plaques, calcified plaques, stents and other tissues is not obvious, which increases the difficulty of diagnosis.

[0004] Generally speaking, image contrast enhancement is usually achieved through grayscale transformation in the image domain. However, current image data processing methods for enhancing image contrast are difficult to directly apply to vascular ultrasound images. First, intravascular ultrasound images usually have a low signal-to-noise ratio, and the grayscale values ​​of the components in the blood vessels are not very different (such as blood and far-field tissue, tissue and fibrous plaque, etc.), making it difficult for grayscale transformation to effectively distinguish the various tissue components and maximize the use of the dynamic range of the image. Secondly, traditional grayscale transformation is based on the adjustment of image visual contrast, which has not been combined with the clinical application of intravascular ultrasound images and cannot meet the needs of intravascular ultrasound images (such as reducing the impact of blood flow interference, enhancing the brightness of stents, and weakening system noise). Summary of the invention

[0005] The present disclosure is made in view of the above-mentioned prior art conditions, and its purpose is to provide a data processing method and an image processing system that can enhance the contrast of vascular ultrasound images.

[0006] To this end, a first aspect of the present disclosure provides a processing method for enhancing the contrast of a vascular ultrasound image, comprising: obtaining an ultrasound signal in a blood vessel, obtaining an original vascular ultrasound image through an original mapping curve and the ultrasound signal, the original mapping curve being used to reflect the relationship between the intensity of the ultrasound signal and the grayscale of the original vascular ultrasound image, identifying the original vascular ultrasound image to obtain multiple image targets, counting multiple target grayscale ranges matching the multiple image targets, each image target in the multiple image targets being matched one-to-one with each target grayscale range in the multiple target grayscale ranges, the target grayscale range being used to reflect the range of grayscale values ​​of the image target in the original vascular ultrasound image, obtaining a grayscale overall area consisting of a grayscale overlap area and a grayscale non-overlap area based on the multiple target grayscale ranges, adjusting the ultrasound signal so that the range of the grayscale overlap area is less than a first preset value and the grayscale overall area is greater than the first preset range, adjusting the original mapping curve to obtain a target mapping curve, the change rate of the target mapping curve in the grayscale overlap area is greater than the change rate of the original mapping curve in the grayscale overlap area, and processing the ultrasound signal based on the target mapping curve to obtain a target vascular ultrasound image.

[0007] In this case, a vascular ultrasound image can be preliminarily obtained, and a variety of image targets can be identified by converting electrical signals into images. Statistical methods can be used to obtain the relationship between different image targets and image grayscale and signal intensity. Since the grayscale in the grayscale overlapping area corresponds to multiple image targets at the same time, increasing the change rate of the target mapping curve in the overlapping area can increase the contrast between different image targets, thereby improving the clarity of the vascular ultrasound image.

[0008] In addition, in the processing method involved in the present disclosure, optionally, the ultrasound signal is obtained through an intravascular ultrasound system, and the intravascular ultrasound system obtains an ultrasound signal matching the vascular ultrasound image through time gain compensation, and obtains background noise, the maximum intensity value of the ultrasound signal and the original mapping curve based on the ultrasound signal.

[0009] In addition, in the processing method involved in the present disclosure, optionally, the ultrasound signal is obtained by an intravascular ultrasound system, and when the ultrasound signal is obtained, the background noise and the maximum intensity value of the ultrasound signal are obtained. In this case, the original mapping curve can be obtained based on the background noise and / or the maximum intensity value. In this case, the generated original mapping curve can be matched with the imaging signal, that is, the original mapping curve can be adapted to various environments.

[0010] In addition, in the processing method involved in the present disclosure, optionally, the gain coefficient of the time gain compensation is adjusted so that the range of the grayscale overlap area is less than the first preset value and the grayscale overall area is greater than the first preset range. In this case, the grayscale overall area can have a larger range, so that the available grayscale range can be fully used. Since the larger the grayscale overall area, the more likely there is a clear image target in the vascular ultrasound image, thereby expanding the grayscale overall area can improve the clarity of the vascular ultrasound image.

[0011] In addition, in the processing method involved in the present disclosure, optionally, a target intensity range is obtained based on the target grayscale range, the target intensity range is used to reflect the range of the signal intensity of the image target in the ultrasonic signal, and an intensity overall area composed of an intensity overlap area and an intensity non-overlap area is obtained based on the target intensity range, and a gain coefficient of time gain compensation is adjusted so that the range of the intensity overlap area is less than a second preset value and the intensity overall area is greater than a second preset range. In this case, the gain coefficient can be adjusted based on the signal intensity in the ultrasonic signal.

[0012] In addition, in the processing method involved in the present disclosure, optionally, the change rate of the target mapping curve in the grayscale non-overlapping area is smaller than the change rate of the original mapping curve in the grayscale non-overlapping area. In this case, since the small change rate can make the same or similar image targets in the grayscale non-overlapping area maintain similar grayscale, it is easier to identify the shape of the image target.

[0013] In addition, in the processing method involved in the present disclosure, optionally, the histogram information includes the maximum grayscale value, the minimum grayscale value and the average grayscale value of the various image targets. In this case, the target grayscale range can be directly obtained based on the maximum grayscale value, the minimum grayscale value and the average grayscale value.

[0014] In addition, in the processing method involved in the present disclosure, optionally, multiple groups of the ultrasound signals are processed based on the original mapping curve to obtain multiple original vascular ultrasound images, and histogram information matching the grayscale of multiple image targets in the multiple original vascular ultrasound images is counted and multiple target grayscale ranges are obtained. In this case, the histogram information matching the grayscale of multiple image targets in the multiple original vascular ultrasound images can be counted and multiple target grayscale ranges can be obtained, so that the features of multiple vascular ultrasound images can be used at the same time, thereby improving the versatility of the processing method.

[0015] In addition, in the processing method of the present disclosure, optionally, the multiple image targets include blood, blood vessel wall, stent, or diseased tissue. In this case, the blood, blood vessel wall, stent, or diseased tissue in the ultrasonic vascular image can be identified, and then the pathological condition in the blood vessel can be identified.

[0016] In addition, in the processing method involved in the present disclosure, optionally, the intensity range of the ultrasonic signal is -100 dB to 0 dB. In this case, the intensity range of the ultrasonic signal can be fully utilized, and the original mapping curve can be applied to most ultrasonic transducers.

[0017] A second aspect of the present disclosure provides an image processing system for enhancing the contrast of a vascular ultrasound image, and uses the processing method involved in the first aspect of the present disclosure to obtain a target vascular ultrasound image.

[0018] According to the present disclosure, a processing method and an image processing system for enhancing the contrast of a vascular ultrasound image can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Embodiments of the present disclosure will now be explained in further detail, by way of example only, with reference to the accompanying drawings, in which:

[0020] Figure 1 is a schematic diagram showing the application of the intravascular ultrasound system involved in the example of the present disclosure.

[0021] Figure 2 Schematic diagram showing an enlarged image of an imaging device placed in a blood vessel according to an example of the present disclosure.

[0022] Figure 3 is a schematic diagram showing an ultrasound image of a blood vessel involved in an example of the present disclosure.

[0023] Figure 4 The figure is a flow chart showing a data processing method for enhancing the contrast of a vascular ultrasound image according to the present disclosure.

[0024] Figure 5 is a schematic diagram showing an original mapping curve involved in the present disclosure.

[0025] Figure 6 Schematic diagram showing the grayscale overlapping area and the grayscale non-overlapping area involved in the present disclosure.

[0026] Figure 7 is a schematic diagram showing a partial area of ​​an original blood vessel ultrasound image involved in the present disclosure.

[0027] Figure 8 is a schematic diagram showing a target mapping curve involved in the present disclosure.

[0028] Fig. 9 FIG. 4 is a schematic diagram showing a partial area of ​​an ultrasound image of a target blood vessel involved in the present disclosure. DETAILED DESCRIPTION

[0029] Hereinafter, the preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same symbols are assigned to the same components, and repeated descriptions are omitted. In addition, the accompanying drawings are only schematic diagrams, and the ratio of the dimensions of the components or the shapes of the components may be different from the actual ones.

[0030] It should be noted that the terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0031] The present disclosure also relates to a processing system for enhancing the contrast of a vascular ultrasound image, which can process the ultrasound image to improve the contrast of the ultrasound image.

[0032] The present disclosure relates to an ultrasonic image processing method, which is applicable to ultrasonic images obtained by time gain compensation and can be used to improve the contrast of ultrasonic images.

[0033] The present disclosure also relates to a processing method for enhancing the contrast of a vascular ultrasound image, which can be used to improve the contrast of a vascular ultrasound image.

[0034] Hereinafter, the processing system and processing method involved in the present disclosure will be described by taking an intravascular ultrasound system and an intravascular ultrasound image as examples.

[0035] Figure 1 is a schematic diagram showing the application of the intravascular ultrasound system involved in the example of the present disclosure. Figure 2 Schematic diagram showing an enlarged image of an imaging device placed in a blood vessel according to an example of the present disclosure.

[0036] In some examples, the intravascular ultrasound system 1 may include: an imaging device 10, which is placed in a blood vessel and transmits imaging signals to the blood vessel wall while rotating to obtain a vascular ultrasound image in the blood vessel; a transmission shaft 20, which is connected to the imaging device 10 and extends along the blood vessel to the outside of the body; an outer tube 30, which extends along the blood vessel to the outside of the body and has an inner cavity for accommodating the imaging device 10 and the transmission shaft 20, and the imaging device 10 can rotate in the inner cavity of the outer tube 30; and a motor 40, which is located outside the body and can be connected to the transmission shaft 20, and the motor 40 can actuate the imaging device 10 via the transmission shaft 20 (see Figure 1 and Figure 2 ).

[0037] In some examples, the intravascular ultrasound system 1 also includes a processor 50 (see Figure 1 ), which is used to process the vascular ultrasound image. In some examples, the intravascular ultrasound system 1 also includes a display 60 (see Figure 1 ), which is used to display a vascular ultrasound image (e.g., an original ultrasound image, a target ultrasound image, or other images obtained based on the target ultrasound image). The intravascular ultrasound system 1 as described in the present disclosure can acquire a vascular ultrasound image in a blood vessel, process the vascular ultrasound image, and display the vascular ultrasound image (e.g., an original ultrasound image, a target ultrasound image, or other images obtained based on the target ultrasound image).

[0038] In some examples, as described above, the intravascular ultrasound system 1 may include: a processor 50 for processing an image acquired by rotation. In some examples, the intravascular ultrasound system 1 may include: a processor 50 for processing a vascular ultrasound image in a blood vessel acquired by the imaging device 10. In some examples, the intravascular ultrasound system 1 may also include: a display 60 for displaying a vascular ultrasound image (e.g., an original ultrasound image, a target ultrasound image, or other images obtained based on the target ultrasound image).

[0039] In some examples, the imaging device 10 can be introduced into a blood vessel and can transmit imaging signals to the blood vessel wall, for example, transmitting imaging signals to the blood vessel wall substantially along the blood vessel radial direction D (see Figure 2 ). In some examples, the imaging device 10 may be an ultrasonic transducer, and the imaging signal may be an ultrasonic signal, such as an ultrasonic pulse. In some examples, the ultrasonic transducer may transmit an ultrasonic pulse to the vessel wall along the vessel radial direction D, and receive an ultrasonic echo with vessel information. In other embodiments, the imaging signal may also be an optical signal, an electromagnetic wave signal, etc.

[0040] In some examples, the motor 40 can actuate the imaging device 10 via the drive shaft 20, for example, within the lumen of the outer tube 30 (see Figure 2In some examples, the motor 40 can drive the imaging device 10 to rotate via the transmission shaft 20, for example, along the R direction substantially orthogonal to the axial direction of the blood vessel (see Figure 2 In some examples, the motor 40 can drive the imaging device 10 to move along the extension direction of the blood vessel via the transmission shaft 20, for example, along the L direction toward the outside of the body (see Figure 2 ).

[0041] Figure 3 is a schematic diagram showing an ultrasound image of a blood vessel involved in an example of the present disclosure.

[0042] In some examples, an ultrasound image of a blood vessel in the blood vessel can be acquired based on the imaging signal. In some examples, the ultrasound image of a blood vessel in the blood vessel acquired based on the imaging device 10 can be a cross-sectional image substantially along the radial direction D of the blood vessel (see Figure 3 In some examples, the imaging device 10 may continuously transmit imaging signals to the blood vessel wall to acquire a continuous plurality of frames of blood vessel ultrasound images.

[0043] In some examples, the processor 50 can be used to execute the processing method for enhancing the contrast of vascular ultrasound images involved in the present disclosure. In some examples, the processing method for enhancing the contrast of vascular ultrasound images involved in the present disclosure can also be referred to as a processing method.

[0044] The present disclosure relates to an image processing system for enhancing the contrast of a vascular ultrasound image, which can obtain a target vascular ultrasound image using the processing method described herein. In some examples, the processor 50 can sequentially perform front-end image processing, signal domain contrast enhancement, back-end image processing, and image domain signal enhancement. In some examples, the front-end image processing may include converting an ultrasound signal into an original vascular ultrasound image and denoising the original vascular ultrasound image. In some examples, the signal domain contrast enhancement may include filtering the ultrasound signal, adjusting the original mapping curve to obtain a target mapping curve, and obtaining a target vascular ultrasound image. In this case, the ultrasound signal can be processed in the image domain and the signal domain, and a target vascular ultrasound image with a higher contrast can be obtained.

[0045] In some examples, a processing method for enhancing the contrast of a blood vessel ultrasound image may include: obtaining an ultrasound signal in the blood vessel, and obtaining an original blood vessel ultrasound image using an original mapping curve and the ultrasound signal.

[0046] In some examples, the processing method for enhancing the contrast of a vascular ultrasound image may include: obtaining an ultrasound signal matching the vascular ultrasound image through time gain compensation, obtaining an original mapping curve based on the ultrasound signal, the original mapping curve is used to reflect the relationship between the intensity of the ultrasound signal and the grayscale of the original vascular ultrasound image, and processing the ultrasound signal based on the original mapping curve to obtain the original vascular ultrasound image. In this case, a vascular ultrasound image can be preliminarily obtained, and a variety of image targets can be identified by converting electrical signals into images.

[0047] In some examples, the original vascular ultrasound image can be identified to obtain multiple image targets. In this case, different image targets in the original vascular ultrasound image can be distinguished, and then information of different image targets can be obtained.

[0048] In some examples, multiple target grayscale ranges matching multiple image targets can be counted. Specifically, histogram information matching the grayscale of multiple image targets can be counted and multiple target grayscale ranges can be obtained. Each image target in the multiple image targets is matched one by one with each target grayscale range in the multiple target grayscale ranges. The target grayscale range is used to reflect the range of grayscale values ​​of the image target in the original vascular ultrasound image. Based on the multiple target grayscale ranges, the grayscale overall area consisting of grayscale overlapping areas and grayscale non-overlapping areas is obtained. In this case, the relationship between different image targets and image grayscale and signal intensity can be obtained using statistical methods, and the grayscale range and signal intensity range of different image targets can be obtained.

[0049] In some examples, the ultrasound signal can be adjusted so that the range of the grayscale overlap area is less than the first preset value and the grayscale overall area is greater than the first preset range. In some examples, the gain coefficient of the time gain compensation can be adjusted so that the range of the grayscale overlap area is less than the first preset value and the grayscale overall area is greater than the first preset range. In this case, the contrast between different image targets can be improved, and the grayscale range and intensity range of the vascular ultrasound image can be expanded as much as possible.

[0050] In some examples, the original mapping curve can be adjusted to obtain a target mapping curve, the change rate of the target mapping curve in the grayscale overlap region is greater than the change rate of the original mapping curve in the grayscale overlap region, and the ultrasound signal is processed based on the target mapping curve to obtain the target blood vessel ultrasound image. In this case, since the grayscale in the grayscale overlap region corresponds to multiple image targets at the same time, increasing the change rate of the target mapping curve in the overlap region can improve the contrast between different image targets, thereby improving the clarity of the blood vessel ultrasound image.

[0051] Figure 4The figure is a flow chart showing a data processing method for enhancing the contrast of a vascular ultrasound image according to the present disclosure. Figure 5 is a schematic diagram showing an original mapping curve involved in the present disclosure. Figure 6 Schematic diagram showing the grayscale overlapping area and the grayscale non-overlapping area involved in the present disclosure. Figure 7 is a schematic diagram showing a partial image of an original blood vessel ultrasound image involved in the present disclosure. Figure 8 is a schematic diagram showing a target mapping curve involved in the present disclosure. Fig. 9 FIG. 4 is a schematic diagram showing a partial image of an ultrasound image of a target blood vessel involved in the present disclosure.

[0052] In some examples, such as Figure 4 As shown, the processing method may also include: obtaining an ultrasonic signal (step S100), obtaining an original mapping curve and obtaining an original vascular ultrasonic image (step S200), identifying an image target (step S300), adjusting a gain coefficient of time gain compensation (step S400), obtaining a target mapping curve and obtaining a target vascular ultrasonic image (step S500).

[0053] In some examples, in step S100, an ultrasound signal may be obtained. In some examples, the ultrasound signal may be obtained by an intravascular ultrasound system 1. In some examples, the intravascular ultrasound system 1 obtains an ultrasound signal that matches a vascular ultrasound image through time gain compensation.

[0054] In some examples, in step S200 , an original mapping curve and an original vascular ultrasound image may be obtained.

[0055] In some examples, an imaging signal (e.g., an ultrasound signal) can be obtained by the intravascular ultrasound system 1. When the imaging signal (e.g., an ultrasound signal) is obtained, the maximum intensity value of the background noise and the imaging signal (e.g., an ultrasound signal) and the original mapping curve are obtained. Specifically, the maximum intensity value of the background noise and the imaging signal (e.g., an ultrasound signal) can be obtained by the imaging signal, and the original mapping curve can be obtained based on the background noise and / or the maximum intensity value. In this case, the generated original mapping curve can be matched with the imaging signal, that is, the original mapping curve can be adapted to various environments.

[0056] In some examples, the background noise may be a signal acquired while the imaging device 10 is idling (eg, rotating outside the body).

[0057] In some examples, such as Figure 5As shown, the original mapping curve can be used to reflect the relationship between the intensity of the ultrasound signal and the grayscale of the original vascular ultrasound image. In some examples, the grayscale value of the original vascular ultrasound image ranges from 0 to 255. In some examples, in the original vascular ultrasound image, the grayscale value and the intensity of the ultrasound signal can be linearly, nonlinearly, or segmentedly related.

[0058] In some examples, the point (-80.77, 5) in the original mapping curve can be represented as follows: an ultrasonic signal with a signal strength of -80.77 dB can be converted into a pixel point with a grayscale value of 5.

[0059] In some examples, the original mapping curve may be obtained based on a maximum intensity value of the imaging signal.

[0060] In some examples, the original mapping curve may be an S-shaped curve. For example, the formula of the original mapping curve may be a Logistic function, a Sigmoid function, or a piecewise linear function.

[0061] In some examples, the original ultrasound image may be obtained using the original mapping curve.

[0062] In some examples, multiple groups of ultrasound signals can be processed based on the original mapping curve to obtain multiple original vascular ultrasound images. In this case, histogram information matching the grayscale of multiple image targets in the multiple original vascular ultrasound images can be counted and multiple target grayscale ranges can be obtained, so that the features of the multiple vascular ultrasound images can be used at the same time, thereby improving the versatility of the processing method.

[0063] In some examples, the intensity range of the ultrasonic signal of the original mapping curve is -100dB to 0dB. In some examples, the intensity range of the ultrasonic signal of the original mapping curve may also be the range of the receiving field of the ultrasonic transducer. In this case, the intensity range of the ultrasonic signal can be fully utilized, and the original mapping curve can be applied to most ultrasonic transducers.

[0064] In some examples, in step S300, an image target may be identified.

[0065] In some examples, the various image targets may include blood, blood vessel walls, stents, or diseased tissues. In some examples, the diseased tissues may include thrombi, plaques, aneurysms, etc. In this case, the blood, blood vessel walls, stents, or diseased tissues in the ultrasound blood vessel image can be identified, and the diseased conditions in the blood vessel can be identified.

[0066] In some examples, an image target may correspond to a tissue or object in a blood vessel (e.g., blood, blood vessel wall, stent, or diseased tissue as described above), and different image targets may be located at different depths (or arranged in sequence in the depth direction). In some examples, the depth direction may be the direction of ultrasound propagation. In other words, the vascular ultrasound image may be divided into multiple depths according to the propagation time of ultrasound. In some examples, due to the irregular shapes and sizes of image targets, different image targets may span multiple depths, and different image targets may also span the same depth.

[0067] In some examples, multiple regions may be divided according to image targets. In this case, due to the irregular shapes and sizes of image targets, the mapping curve can be adjusted more in line with the actual situation compared to dividing the regions according to depth, thereby improving the contrast of different image targets.

[0068] In some examples, blood may also be referred to as a first image target, a blood vessel wall may also be referred to as a second image target, a stent may also be referred to as a third image target, and so on.

[0069] In some examples, the image objects may be identified manually, that is, different image objects may be manually outlined or framed, which can improve the accuracy of identification.

[0070] In some examples, image targets can be identified by machines, that is, by building a recognition model, training the recognition model using a database, and then using the recognition model to outline or select different image targets. In this case, the recognition efficiency can be improved for a large number of vascular ultrasound images.

[0071] In some examples, in step S400 , a gain coefficient of time gain compensation may be adjusted.

[0072] In some examples, before adjusting the gain coefficient of the time gain compensation, histogram information matching the grayscale of multiple image targets can be counted and multiple target grayscale ranges can be obtained. In some examples, the target grayscale range can be the grayscale range of the image target, and one image target corresponds to one target grayscale range.

[0073] In some examples, each image target in the plurality of image targets is matched one by one with each target grayscale range in the plurality of target grayscale ranges, and the target grayscale range is used to reflect the range of grayscale values ​​of the image target in the original vascular ultrasound image. For example, blood may correspond to a first target grayscale range, and the first target grayscale range is 10 to 60. In other words, it can be determined through the histogram information that the grayscale value range of blood in the original vascular ultrasound image is between 10 and 60. In this case, the target grayscale range of various image targets can be determined.

[0074] In some examples, the histogram information matched with the grayscale of various image targets may include the maximum grayscale value, the minimum grayscale value and the average grayscale value of the various image targets. In this case, the target grayscale range can be directly obtained based on the maximum grayscale value, the minimum grayscale value and the average grayscale value. For example, the target grayscale range can be the range between the maximum grayscale value and the minimum grayscale value. However, the present disclosure is not limited thereto, and the target grayscale range can be calculated based on the maximum grayscale value, the minimum grayscale value and the average grayscale value. For example, the target grayscale range can be slightly larger or slightly smaller than the range between the maximum grayscale value and the minimum grayscale value. For example, the target grayscale range can be obtained by calculating the average grayscale value.

[0075] In some examples, as described above, histogram information matching the grayscale of multiple image targets in multiple original vascular ultrasound images can be counted and multiple target grayscale ranges can be obtained. At this time, the histogram information can include the maximum grayscale value in the multiple original vascular ultrasound images, the minimum grayscale value in the multiple original vascular ultrasound images, and / or the average grayscale value in the multiple original vascular ultrasound images, and the histogram information can also include the average value of the maximum grayscale value of the image target in the multiple original vascular ultrasound images and the average value of the minimum grayscale value of the image target in the multiple original vascular ultrasound images. In some examples, the target grayscale range can be between the average value of the maximum grayscale value of the image target in the multiple original vascular ultrasound images and the average value of the minimum grayscale value of the image target in the multiple original vascular ultrasound images. In this case, the target grayscale range can be obtained based on the multiple original vascular ultrasound images.

[0076] In some examples, such as Figure 6As shown, a grayscale overall area consisting of grayscale overlapping areas (including Cg1 and Cg2) and grayscale non-overlapping areas (such as area NCg) can be obtained based on multiple target grayscale ranges (including G1, G2, and G3). Specifically, target grayscale ranges (including G1, G2, and G3) matching different image targets can have overlapping areas, and the overlapping areas of target grayscale ranges (including G1, G2, and G3) matching different image targets can be called grayscale overlapping areas (including Cg1 and Cg2). In the target grayscale range (including G1, G2, and G3) of an image target, if there is at least one area that does not overlap with the target grayscale ranges (including G1, G2, and G3) of other image targets, then the area can be called a non-overlapping area. For example, if the target grayscale range G1 of the blood vessel wall is 100 to 140, the target grayscale range G3 of the stent is 150 to 200, and the target grayscale range G2 of the lesion tissue is 130 to 160, then 140 to 150 in the target grayscale range G2 corresponding to the lesion tissue can be called the grayscale non-overlapping area NCg of the lesion tissue, and 130 to 140 (Cg1) and 150 to 146 (Cg2) in the target grayscale range G2 corresponding to the lesion tissue can be called the grayscale overlapping area (including Cg1 and Cg2) of the lesion tissue. In this case, the grayscale overlapping area (including Cg1 and Cg2) between different image targets can be determined, and the smaller the range of the grayscale overlapping area (including Cg1 and Cg2), the higher the contrast between different image targets in the vascular ultrasound image, and then the gain coefficient of the time gain compensation can be adjusted based on the grayscale overlapping area (including Cg1 and Cg2) to improve the contrast between different image targets in the vascular ultrasound image.

[0077] In some examples, the grayscale overall region Wg may be the total range of target grayscale ranges (including G1, G2, and G3) matched to each image target. For example, when the image target is composed of a blood vessel wall, a diseased tissue, and a stent, the target grayscale range G1 of the blood vessel wall is 100 to 140, the target grayscale range G3 of the stent is 150 to 200, and the target grayscale range G2 of the diseased tissue is 130 to 160, then the grayscale overall region Wg may be 100 to 160. In this case, the grayscale ranges of multiple image targets can be determined, and then the proportion of the grayscale ranges of multiple image targets in the available grayscale range (0 to 255) can be determined, and then the gain coefficient of the time gain compensation can be adjusted based on the grayscale overall region Wg to adjust the range of the grayscale overall region Wg.

[0078] In some examples, the gain coefficient of the time gain compensation can be adjusted so that the range of the grayscale overlap area (including Cg1 and Cg2) is less than the first preset value. In some examples, the first preset value can be 10 to 50, for example, the first preset value can be 10, 11, 12, 13, 15, 20, 25, 30, 40, 50... However, the present disclosure is not limited to this. In some examples, the first preset value can also be greater than 50. In this case, the gain coefficient can be adjusted to make the range of the grayscale overlap area (including Cg1 and Cg2) the first preset value, thereby reducing the range of the grayscale overlap area (including Cg1 and Cg2), better distinguishing different image targets in the image, and thus improving the contrast of the vascular ultrasound image.

[0079] In some examples, the gain coefficient of the time gain compensation can be adjusted so that the grayscale overall area Wg is greater than the first preset range. In some examples, the first preset range can be less than the available grayscale range (i.e., 0 to 255). In some examples, the first preset range can be defined by the area between two points, for example, the first preset range can be 10 to 245, 20 to 240, 30 to 230... In some examples, the first preset range can also be defined by a ratio, for example, the first preset range can be a continuous area or a discontinuous area with a proportion greater than 90% in the available grayscale range (0 to 255), the first preset range can be a continuous area or a discontinuous area with a proportion greater than 80% in the available grayscale range (0 to 255), the first preset range can be a continuous area or a discontinuous area with a proportion greater than 70% in the available grayscale range (0 to 255), and the first preset range can be a continuous area or a discontinuous area with a proportion greater than 60% in the available grayscale range (0 to 255). In this case, the overall grayscale area Wg can have a larger range, so that the available grayscale range can be fully utilized. Since the larger the overall grayscale area Wg is, the more likely there is a clear image target in the vascular ultrasound image, thereby expanding the overall grayscale area Wg can improve the clarity of the vascular ultrasound image.

[0080] In some examples, as described above, the gain coefficient can be adjusted so that the range of the grayscale overlap area (including Cg1 and Cg2) is less than the first preset value and the grayscale overall area Wg is greater than the first preset range. Specifically, if the range of the grayscale overlap area (including Cg1 and Cg2) between the vascular wall and the lesion tissue is large, the gain coefficient of the corresponding position can be adjusted, for example, the gain coefficient near the vascular wall position is increased and the gain coefficient near the lesion tissue position is reduced to change the size of the ultrasound signal corresponding to the vascular wall and the lesion tissue, thereby reducing the grayscale overlap area (including Cg1 and Cg2) between the vascular wall and the lesion tissue. In some examples, medical staff can also adjust the gain coefficient according to test experience to obtain a suitable vascular ultrasound image.

[0081] In some examples, a target intensity range (including S1, S2, and S3) can also be obtained based on the target grayscale range, and the target intensity range (including S1, S2, and S3) is used to reflect the range of signal intensity of the image target in the ultrasonic signal. Based on the target intensity range (including S1, S2, and S3), a gray intensity overall area Ws composed of an intensity overlap area (including Cs1 and Cs2) and an intensity non-overlap area NCs is obtained, and the gain coefficient of the time gain compensation is adjusted so that the range of the intensity overlap area (including Cs1 and Cs2) is less than the second preset value and the intensity overall area Ws is greater than the second preset range. In this case, the gain coefficient can be adjusted based on the signal intensity in the ultrasonic signal.

[0082] In some examples, such as Figure 6 As shown, the intensity overall area Ws composed of intensity overlapping areas (including Cs1 and Cs2) and intensity non-overlapping areas NCs can be obtained based on multiple target intensity ranges (including S1, S2, and S3). Specifically, the target intensity ranges (including S1, S2, and S3) matching different image targets can have overlapping areas, and the overlapping areas of the target intensity ranges (including S1, S2, and S3) matching different image targets can be called intensity overlapping areas (including Cs1 and Cs2). In the target intensity range (including S1, S2, and S3) of an image target, if there is at least one area that does not overlap with the target intensity ranges (including S1, S2, and S3) of other image targets, then the area can be called a non-overlapping area. For example, if the target intensity range S1 of the blood vessel wall is -55dB to -50dB, the target intensity range S3 of the stent is -45dB to -35dB, and the target intensity range S2 of the lesion tissue is -52dB to -38dB, then -50dB to -45dB in the target intensity range S2 corresponding to the lesion tissue can be called the intensity non-overlapping area NCs of the lesion tissue, and -52dB to -50dB and -45dB to -38dB in the target intensity range S2 corresponding to the lesion tissue can be called the intensity overlapping area (including Cs1 and Cs2) of the lesion tissue. In this case, the intensity overlapping area (including Cs1 and Cs2) between different image targets can be determined, and the smaller the range of the intensity overlapping area (including Cs1 and Cs2), the higher the contrast between different image targets in the vascular ultrasound image, and then the gain coefficient of the time gain compensation can be adjusted based on the intensity overlapping area (including Cs1 and Cs2) to improve the contrast between different image targets in the vascular ultrasound image.

[0083] In some examples, the intensity overall region Ws may be the total range of target intensity ranges (including S1, S2, and S3) that match each image target. For example, when the image target is composed of a blood vessel wall, a diseased tissue, and a stent, the target intensity range S1 of the blood vessel wall is -55dB to -50dB, the target intensity range S2 of the stent is -45dB to -35dB, and the target intensity range S2 of the diseased tissue is -52dB to -38dB, then the intensity overall region Ws may be -55dB to -38dB. In this case, the intensity ranges of multiple image targets can be determined, and then the proportion of the intensity ranges of multiple image targets in the available intensity range (the available intensity range may be the receiving domain of the ultrasonic transducer, for example, the available intensity range may be -100dB to 0dB) can be determined, and then the gain coefficient of the time gain compensation can be adjusted based on the intensity overall region Ws to the range of the intensity overall region Ws.

[0084] In some examples, the gain coefficient of the time gain compensation can be adjusted so that the range of the intensity overlap area (including Cs1 and Cs2) is less than the second preset value. In some examples, the second preset value can be 1dB to 10dB, for example, the second preset value can be 1dB, 1.1dB, 1.2dB, 1.5dB, 2dB, 5dB, 7dB, 8dB, 9dB, 10dB... However, the present disclosure is not limited thereto. In some examples, the second preset value can also be greater than 10dB or less than 1dB. In this case, the gain coefficient can be adjusted so that the range of the intensity overlap area (including Cs1 and Cs2) is less than the second preset value, thereby reducing the range of the intensity overlap area (including Cs1 and Cs2), and better distinguishing different image targets in the image, thereby improving the contrast of the vascular ultrasound image.

[0085] In some examples, the gain coefficient of the time gain compensation can be adjusted so that the intensity overall area Ws is greater than the second preset range. In some examples, the second preset range can be less than the available intensity range (-100dB to 0dB). In some examples, the second preset range can be defined by the area between two points, for example, the second preset range can be -90dB to -10dB, -95dB to -5dB, -100dB to -0dB... In some examples, the second preset range can also be defined by a ratio, for example, the second preset range can be a continuous area or a discontinuous area with a proportion greater than 90% in the available intensity range (-100dB to 0dB), the second preset range can be a continuous area or a discontinuous area with a proportion greater than 80% in the available intensity range (-100dB to 0dB), the second preset range can be a continuous area or a discontinuous area with a proportion greater than 70% in the available intensity range (-100dB to 0dB), and the second preset range can be a continuous area or a discontinuous area with a proportion greater than 60% in the available intensity range (-100dB to 0dB). In this case, the intensity overall area Ws can have a larger range, so that the available intensity range can be fully used. Since the larger the intensity overall area Ws, the more likely there is a clear image target in the vascular ultrasound image, thereby expanding the intensity overall area Ws can improve the clarity of the vascular ultrasound image.

[0086] In some examples, after adjusting the gain coefficient, it can be determined whether the adjusted histogram information meets the preset requirements. If it meets the preset requirements, step S500 is performed. If it does not meet the preset requirements, the gain coefficient is readjusted and step S100 is performed. In this case, the gain coefficient that meets the preset requirements can be obtained through multiple cycles.

[0087] In some examples, the preset requirement may be that the range of the grayscale overlap region (including Cg1 and Cg2) is smaller than a first preset value and the grayscale overall region Wg is larger than the first preset range.

[0088] In some examples, the preset requirement may be that the range of the intensity overlap region (including Cs1 and Cs2) is smaller than a second preset value and the intensity overall region Ws is larger than the second preset range.

[0089] In some examples, the preset requirement may be that the range of the grayscale overlapping area (including Cg1 and Cg2) is less than a first preset value and the overall grayscale area Wg is greater than the first preset range, and the range of the intensity overlapping area (including Cs1 and Cs2) is less than a second preset value and the overall intensity area Ws is greater than the second preset range.

[0090] In some examples, in step S500 , a target mapping curve may be obtained and a target blood vessel ultrasound image may be obtained.

[0091] In some examples, such as Figure 8 As shown, the original mapping curve can be adjusted to obtain a target mapping curve, and the change rate of the target mapping curve in the grayscale overlap area is greater than the change rate of the original mapping curve in the grayscale overlap area.

[0092] In some examples, the rate of change may also be referred to as a slope, a degree of change, or a degree of fluctuation, etc. In this case, since a larger rate of change can more effectively distinguish different image objects in the grayscale overlap region, the contrast of different image objects can be improved.

[0093] In some examples, the target mapping curve has a greater change rate in the grayscale overlap region than in the grayscale non-overlap region. In this case, since the grayscale overlap region contains more complex image objects, the contrast of different image objects can be improved in the grayscale overlap region.

[0094] In some examples, such as Figure 8 As shown, the original mapping curve can be adjusted to obtain a target mapping curve, and the change rate of the target mapping curve in the grayscale non-overlapping area is smaller than the change rate of the original mapping curve in the grayscale non-overlapping area. In this case, the small change rate can make the same or similar image objects in the grayscale non-overlapping area maintain similar grayscales, thereby facilitating the recognition of the shape of the image object.

[0095] In some examples, such as Figure 7 and Fig. 9 As shown, the processing method described in the present disclosure can be used to enhance the contrast of vascular ultrasound images.

[0096] In some examples, the target blood vessel ultrasound image may be grayscale stretched to enhance image contrast. In this case, the image contrast can be further enhanced.

[0097] Although the present disclosure is specifically described above in conjunction with the accompanying drawings and embodiments, it is to be understood that the above description does not limit the present disclosure in any form. Those skilled in the art may modify and change the present disclosure as needed without departing from the essential spirit and scope of the present disclosure, and these modifications and changes all fall within the scope of the present disclosure.

Claims

1. A processing method for improving the contrast of a vascular ultrasound image, characterized in that: include: An original mapping curve is adjusted to obtain a target mapping curve, wherein the original mapping curve is used to reflect the relationship between the intensity of the ultrasonic signal in the blood vessel and the grayscale of the original blood vessel ultrasonic image, the change rate of the target mapping curve in the grayscale overlap area is greater than the change rate of the original mapping curve in the grayscale overlap area, and the grayscale in the grayscale overlap area corresponds to multiple image targets at the same time, and the multiple image targets are obtained by identifying the original blood vessel ultrasonic image, and the original blood vessel ultrasonic image is obtained by the original mapping curve and the ultrasonic signal; and the ultrasonic signal is processed based on the target mapping curve to obtain a target blood vessel ultrasonic image.

2. The processing method according to claim 1, characterized in that: The change rate of the target mapping curve in the grayscale non-overlapping area is smaller than the change rate of the original mapping curve in the grayscale non-overlapping area, and the grayscale in the grayscale non-overlapping area corresponds to an image target.

3. The processing method according to claim 2, characterized in that: The change rate of the target mapping curve in the grayscale overlap area is greater than the change rate of the target mapping curve in the grayscale non-overlap area.

4. The processing method according to claim 1, characterized in that: The ultrasonic signal is adjusted to meet at least one of the following requirements: The range of the grayscale overlap region is made smaller than a first preset value, and / or the grayscale overall region is made larger than a first preset range, wherein the grayscale overall region is composed of the grayscale overlap region and the grayscale non-overlap region, and the grayscale in the grayscale non-overlap region corresponds to an image target; Make the range of the intensity overlapping area smaller than a second preset value, and / or make the overall intensity area larger than a second preset range, wherein the overall intensity area composed of the intensity overlapping area and the intensity non-overlapping area is obtained based on multiple target intensity ranges matching the multiple image targets, and the target intensity range is used to reflect the range of signal intensity of the image target in the ultrasonic signal.

5. The processing method according to claim 4, characterized in that: The ultrasonic signal is adjusted by adjusting the gain coefficient of time gain compensation.

6. The processing method according to claim 4, characterized in that: A plurality of target grayscale ranges matching the plurality of image targets are counted, each image target in the plurality of image targets is matched one by one with each target grayscale range in the plurality of target grayscale ranges, the target grayscale ranges being used to reflect the range of grayscale values ​​of the image targets in the original vascular ultrasound image; and the grayscale overall area consisting of the grayscale overlapping area and the grayscale non-overlapping area is obtained based on the plurality of target grayscale ranges.

7. The processing method according to claim 6, characterized in that: Based on the original mapping curve, multiple groups of ultrasound signals are processed to obtain multiple original vascular ultrasound images, and histogram information matching the grayscale of the multiple image targets in the multiple original vascular ultrasound images is counted to obtain the multiple target grayscale ranges.

8. The processing method according to claim 1, characterized in that: The ultrasonic signal is obtained through an intravascular ultrasonic system. When the ultrasonic signal is obtained, background noise and a maximum intensity value of the ultrasonic signal are obtained, and the original mapping curve is obtained based on the background noise and / or the maximum intensity value.

9. The processing method according to claim 1, characterized in that: One of the image targets corresponds to a tissue or object within a blood vessel.

10. An image processing system for improving the contrast of a vascular ultrasound image, characterized in that: The target blood vessel ultrasonic image is obtained by using the processing method described in any one of claims 1 to 9.

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