Ultrasonic image processing method and device, electronic equipment and storage medium

Abstract

The application provides an ultrasonic image processing method, device, electronic equipment, storage medium and computer product. The ultrasonic image processing method comprises the following steps: acquiring an ultrasonic image containing at least one blood vessel region; performing image segmentation on the ultrasonic image for the blood vessel region in the ultrasonic image to obtain a first segmentation image, wherein the first segmentation image comprises a blood vessel region; determining a center pixel of each blood vessel region in the first segmentation image; correcting the blood vessel region in the first segmentation image based on the center pixel and the ultrasonic image to obtain a second segmentation image; and synthesizing the second segmentation image and the ultrasonic image to obtain a tissue attenuation image in which the blood vessel region is filtered out. The scheme can obtain a more accurate tissue attenuation image in which the blood vessel region is filtered out, which is helpful for medical staff to make accurate diagnosis.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically, to an ultrasonic image processing method, apparatus, electronic device, storage medium, and computer program product. Background Technology

[0002] With the development of technology, ultrasound imaging technology has become increasingly mature. It uses ultrasound beams to scan a target object and obtains images of the internal organs of the target object by receiving and processing the reflected signals. Ultrasound imaging is undoubtedly affected by internal organs and tissues such as blood vessels within the target object.

[0003] Ultrasound waves are affected by sound attenuation when propagating through tissues; that is, the energy of sound waves decreases as the propagation distance increases. This characteristic of ultrasound waves can be utilized for tissue attenuation imaging. Its basic principle is to use the different attenuation rates of ultrasound waves in different tissues, calculate the corresponding attenuation coefficients using the frequency method, and then display the attenuation coefficients of different tissue parts using color mapping. Currently, tissue attenuation imaging is used in many medical examinations and diagnoses, such as the quantitative measurement of non-alcoholic fatty liver disease. The human liver has abundant blood vessels. When ultrasound waves encounter the blood vessel walls, optical phenomena such as refraction and reflection occur, and when they encounter blood flow, Doppler frequency shift occurs. This can lead to the inability to clearly distinguish tissues and blood vessels in the final ultrasound image, affecting the accuracy of diagnosis.

[0004] In related technologies, the location of blood vessels is determined and filtered out based on ultrasound echo signals. However, due to the inherent characteristics of ultrasound waves, it is easy to filter out too many or too few blood vessel areas, making it difficult to accurately filter out various types of blood vessels. Summary of the Invention

[0005] The present invention was proposed in view of the above-mentioned problems.

[0006] According to a first aspect of the present invention, an ultrasonic image processing method is provided. The method includes:

[0007] Acquire ultrasound images containing at least one vascular region;

[0008] For the blood vessel region in the ultrasound image, the ultrasound image is segmented to obtain a first segmented image, the first segmented image including the blood vessel region;

[0009] Determine the center pixel of each of the blood vessel regions in the first segmented image;

[0010] Based on the center pixel and the ultrasound image, the blood vessel region in the first segmented image is corrected to obtain a second segmented image; and

[0011] The second segmented image and the ultrasound image are combined to obtain a tissue attenuation image with the vascular region filtered out.

[0012] For example, the step of correcting the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain the second segmented image includes:

[0013] For the center pixel of each blood vessel region in the first segmented image, the expansion operation is performed cyclically in the first segmented image based on the ultrasound image, using the center pixel as the initial reference pixel, until a new reference pixel cannot be determined.

[0014] Specifically, the expansion operation involves determining a new reference pixel among several first pixels based on the pixel value corresponding to the reference pixel position in the ultrasonic image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasonic image.

[0015] Based on the positions of all reference pixels, the corrected blood vessel region in the first segmented image is determined, wherein all reference pixels include the center pixel and the new reference pixel;

[0016] The second segmented image is determined based on the corrected blood vessel region in the first segmented image.

[0017] For example, determining the new reference pixel among a plurality of first pixels based on the pixel value corresponding to the reference pixel position in the ultrasonic image and the pixel values ​​corresponding to a plurality of first pixel positions adjacent to the reference pixel position in the ultrasonic image includes:

[0018] Determine the average value of the pixel values ​​corresponding to all the current reference pixels in the ultrasonic image;

[0019] Determine a first absolute value of the difference between the pixel value corresponding to each first pixel in the ultrasonic image and the average value;

[0020] The new reference pixel is selected from all the first pixels based on the first absolute value of the difference between the pixel value corresponding to each first pixel in the ultrasonic image and the average value.

[0021] For example, selecting the new reference pixel from all the first pixels based on the first absolute value of the difference between the pixel value of each of the first pixels and the average value includes:

[0022] The smallest first absolute value is determined from the first absolute values ​​determined for each first pixel, and the smallest first absolute value is compared with a first preset threshold to obtain a comparison result;

[0023] If the comparison result indicates that the smallest first absolute value is less than the first preset threshold, then the first pixel corresponding to the smallest first absolute value is determined as the new reference pixel.

[0024] For example, determining the second segmented image based on the corrected blood vessel region in the first segmented image includes:

[0025] The second segmented image is determined based on the position of pixels in the corrected blood vessel region in the first segmented image, wherein the blood vessel region in the second segmented image includes pixels corresponding to the pixel positions in the corrected blood vessel region in the first segmented image.

[0026] For example, determining the center pixel of each of the blood vessel regions in the first segmented image includes:

[0027] For each blood vessel region in the first segmented image, the center pixel in each blood vessel region is determined based on the average coordinates of the pixel positions within each blood vessel region.

[0028] For example, the step of segmenting the ultrasound image for the blood vessel region in the ultrasound image to obtain a first segmented image includes:

[0029] For the Nth row of pixels in the ultrasound image, determine the median pixel value of the Nth row of pixels, where N is a positive integer less than or equal to the number of rows in the ultrasound image;

[0030] Determine the second absolute value corresponding to each pixel value of the Nth row of pixels, and compare the second absolute value corresponding to each pixel value of the Nth row of pixels with the second preset threshold, wherein the second absolute value is the absolute value of the difference between each pixel value of the Nth row of pixels and the median;

[0031] The first segmented image is obtained based on the second pixel in the ultrasound image, wherein the second pixel is a pixel in the ultrasound image whose corresponding second absolute value is greater than the second preset threshold, and the pixel position of the blood vessel region in the first segmented image corresponds to the position of the second pixel in the ultrasound image.

[0032] For example, before determining the center pixel of each blood vessel region in the first segmented image, the method further includes:

[0033] Determine the area of ​​each blood vessel region in the first segmented image;

[0034] Delete the identified blood vessel regions whose area is smaller than the area threshold.

[0035] For example, before performing image segmentation on the ultrasound waves, the method further includes:

[0036] The ultrasonic image is denoised to obtain a denoised ultrasonic image.

[0037] For example, before performing image segmentation on the ultrasound waves, the method further includes:

[0038] The pixel values ​​corresponding to the pixels in the ultrasonic image are normalized to obtain a normalized ultrasonic image.

[0039] According to a second aspect of the present invention, an ultrasonic image processing apparatus is also provided, comprising:

[0040] Image receiving module, used to acquire ultrasound images containing at least one vascular region;

[0041] An image segmentation module is used to segment the ultrasound image for the blood vessel region in the ultrasound image to obtain a first segmented image, wherein the first segmented image includes the blood vessel region.

[0042] An image determination module is used to determine the center pixel of each blood vessel region in the first segmented image;

[0043] An image correction module is used to correct the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain a second segmented image; and

[0044] An image synthesis module is used to synthesize the second segmented image and the ultrasound image to obtain a tissue attenuation image with the vascular region filtered out.

[0045] According to a third aspect of the present invention, an electronic device is also provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, are used to perform the above-described ultrasonic image processing method.

[0046] According to a fourth aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which, when executed, are used to perform the above-described ultrasonic image processing method.

[0047] According to a fifth aspect of the present invention, a computer program product is also provided, comprising computer program instructions that, when executed, perform the above-described ultrasonic image processing method.

[0048] In the above technical solution, the ultrasound image containing the blood vessel region is segmented. The blood vessel region is then corrected based on the center pixel of the blood vessel region in the first segmented image to obtain a second segmented image. The second segmented image and the ultrasound image are then combined to obtain a more accurate tissue attenuation image that filters out the blood vessel region, which helps medical staff make accurate diagnoses.

[0049] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0050] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0051] Figure 1 A schematic flowchart of an ultrasonic image processing method according to an embodiment of the present invention is shown;

[0052] Figure 2 A schematic flowchart illustrating the acquisition of a first segmented image according to an embodiment of the present invention is shown;

[0053] Figure 3 A schematic flowchart illustrating the acquisition of a second segmented image according to an embodiment of the present invention is shown;

[0054] Figure 4 A schematic flowchart of an extended operation according to an embodiment of the present invention is shown;

[0055] Figure 5 A schematic flowchart illustrating the determination of a new reference pixel in a first pixel based on a first absolute value according to an embodiment of the present invention is shown.

[0056] Figure 6 A schematic flowchart illustrating the vascular region filtering according to an embodiment of the present invention is shown;

[0057] Figure 7A schematic flowchart of an ultrasonic image processing method according to another embodiment of the present invention is shown;

[0058] Figure 8 A schematic block diagram of an ultrasound host according to an embodiment of the present invention is shown;

[0059] Figure 9 A schematic block diagram of an ultrasonic image processing apparatus according to an embodiment of the present invention is shown;

[0060] Figure 10 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0062] To at least partially solve the above problems, the present invention provides an ultrasound image processing method, which performs image segmentation on an ultrasound image containing a vascular region, corrects the segmented image obtained after image segmentation, and synthesizes the corrected segmented image and the ultrasound image to obtain a more accurate tissue attenuation image that filters out the vascular region.

[0063] Figure 1 A schematic flowchart of an ultrasonic image processing method according to an embodiment of the present invention is shown. The method may include steps S1100 to S1900.

[0064] In step S1100, an ultrasound image containing at least one vascular region is acquired.

[0065] For example, an ultrasound image can be an RGB image or a grayscale image. An ultrasound image can be a still image or any frame from a dynamic video. An ultrasound image can be an image of any suitable size and resolution. An ultrasound image can be a raw image directly acquired by an ultrasound device, or an image obtained after preprocessing the raw image. This preprocessing can include any operation to improve the visual effect of the ultrasound image, increase its clarity, or highlight certain features in the image. Exemplarily, but not limitingly, the preprocessing can include operations such as digitization, geometric transformation, normalization, and filtering of the raw image. The ultrasound image can also be a synthesized image without affecting subsequent ultrasound image processing.

[0066] After imaging, each tissue in the target object forms a corresponding region in the ultrasound image, such as a blood vessel region, an organ tissue region (e.g., a liver tissue region), etc. Blood vessel regions are the areas that need to be filtered out in the ultrasound image; an ultrasound image may contain one or more blood vessel regions.

[0067] In step S1300, the ultrasound image is segmented for the blood vessel region in the ultrasound image to obtain a first segmented image, which includes the blood vessel region.

[0068] Ultrasonic images can be segmented using methods such as threshold-based segmentation, region-based segmentation, edge-based segmentation, and segmentation based on specific theories.

[0069] When segmenting ultrasound images to target vascular regions, the vascular and non-vascular regions can be treated as two distinct objects. This approach highlights the image information of the vascular regions, making them easier to distinguish. Therefore, in the first segmented image, the vascular and non-vascular regions are more easily differentiated than in the original ultrasound image. For example, in the first segmented image, areas composed of black pixels can represent non-vascular regions, and areas composed of white pixels can represent vascular regions. Black and white are merely illustrative examples; other colored pixels can also be used to represent either vascular or non-vascular regions.

[0070] The pixels in the first segmented image correspond to the pixel positions in the ultrasound image.

[0071] The first segmented image can be an image of the same size as the ultrasound image, meaning that the pixels in the first segmented image can correspond one-to-one with the pixels in the ultrasound image.

[0072] Without affecting the image processing results, the first segmented image can also be an image of a different size than the ultrasound image. When the size of the first segmented image is larger than that of the ultrasound image, each pixel in the ultrasound image can correspond to multiple pixels in the first segmented image; when the size of the first segmented image is smaller than that of the ultrasound image, each pixel in the first segmented image can correspond to multiple pixels in the ultrasound image.

[0073] Since image segmentation is the process of dividing a digital image into non-overlapping regions, when vascular and non-vascular regions can be clearly distinguished, the first segmented image can also be represented by a matrix of 0s and 1s, with each element corresponding to a pixel in the ultrasound image. For example, in the first segmented image represented by a matrix, the region composed of elements with a value of 0 can represent the vascular region in the first segmented image, and the region composed of elements with a value of 1 can represent the non-vascular region (organ / tissue region) in the first segmented image.

[0074] In step S1500, the center pixel of each blood vessel region in the first segmented image is determined.

[0075] In this step, the center pixel of each blood vessel region in the first segmented image is determined. In other words, each blood vessel region corresponds to one center pixel; when there are multiple blood vessel regions, multiple center pixels can be determined. It can be understood that each blood vessel region is a connected region. For the image segmentation step S120, there may be instances of inaccurate segmentation results. However, the inaccuracy mainly occurs at the boundaries between blood vessel regions and other regions. Since the center pixel of a blood vessel region is located far from the boundary of the blood vessel region, it usually belongs to the blood vessel region.

[0076] When determining the center pixel, its position can be determined to facilitate subsequent operations. The center pixel can be any pixel in the blood vessel region that is far from the boundary of the blood vessel region, or it can be determined by calculation based on the pixels in the blood vessel region. It can be determined based on the average coordinates of the pixel positions in each blood vessel region, or by setting weights for each pixel position and determining the center pixel based on those weights, or by determining the center pixel based on the median coordinates of all pixel positions, or by using the pixel located at the center of the blood vessel region as the center pixel.

[0077] Specifically, for each blood vessel region in the first segmented image, the center pixel in each blood vessel region can be determined based on the average coordinates of the pixel positions within that region. For each blood vessel region, a first average of the row coordinates of all pixels within that region can be calculated, and this first average can be used as the row coordinate value of the center pixel of that region; a second average of the column coordinates of all pixels within that region can be calculated, and this second average can be used as the column coordinate value of the center pixel of that region.

[0078] In the above technical solution, the center pixel of each blood vessel region is determined based on the average coordinates of the pixel positions within each blood vessel region. This ensures that the determined center pixel is located further away from the boundary of the blood vessel region, guaranteeing that it is the pixel specifically designed for imaging the blood vessel region, and thus ensuring the accuracy of the tissue attenuation image obtained based on this center pixel.

[0079] In step S1700, based on the center pixel and the ultrasound image, the blood vessel region in the first segmented image is corrected to obtain the second segmented image.

[0080] As mentioned earlier, the center pixel is the pixel that determines the blood vessel region, and the ultrasound image includes original image information. The blood vessel region in the first segmented image can be corrected based on these two factors. Correction can include expanding or shrinking the boundary of the blood vessel region in the first segmented image. For example, the pixels in non-blood vessel regions can be changed based on the center pixel of the blood vessel region and the ultrasound image, so that pixels in non-blood vessel regions are classified into blood vessel regions, thereby increasing the blood vessel region in the first segmented image. The pixels in non-blood vessel regions can also be changed by directly adjusting their values. For example, when the blood vessel region in the first segmented image is represented by white pixels and the non-blood vessel region by black pixels, the black pixels can be directly adjusted to white pixels to increase the blood vessel region represented by the white pixels. Similarly, the white pixels can be directly adjusted to black pixels to decrease the blood vessel region represented by the white pixels. Alternatively, an additional image can be determined based on the center pixel and the ultrasound image, and this image can be merged with the first segmented image to change the pixels in the non-blood vessel region. The additional image can contain corrected vascular regions. By merging this image with the first segmented image, the vascular regions in the original first segmented image can be replaced to correct the vascular regions in the first segmented image. At this time, the pixels of non-vascular regions in the first segmented image may also change. The additional image avoids information in the ultrasound image.

[0081] When making corrections, you can do so on a single pixel basis or on a multi-pixel basis.

[0082] When the first segmented image is represented by a matrix, with regions consisting of elements with a value of 1 representing non-vascular regions and regions consisting of elements with a value of 0 representing vascular regions, the vascular regions in the first segmented image can be enlarged by adjusting the values ​​of the elements representing non-vascular regions to 0. Conversely, the vascular regions in the first segmented image can also be reduced by adjusting the values ​​of the elements representing vascular regions to 1.

[0083] In step S1900, the second segmented image and the ultrasound image are combined to obtain a tissue attenuation image with the vascular region filtered out.

[0084] During the synthesis process, pixels corresponding to the vascular region in the second segmented image can be directly removed from the ultrasound image, or they can be replaced with a color that is easily distinguishable from non-vascular regions. For example, the color used to distinguish non-vascular regions could be the pixel color of the vascular region in the first segmented image, allowing the corresponding pixels in the ultrasound image to be directly replaced.

[0085] The second segmented image and the ultrasound image can be synthesized using any relevant image fusion algorithm.

[0086] In tissue attenuation images, only pixels from non-vascular regions can be retained, or vascular regions can be retained while removing pixel information corresponding to those vascular regions, so that the tissue attenuation image contains only valid pixels from non-vascular regions. This can improve the contrast between vascular and non-vascular regions in the tissue attenuation image.

[0087] In the above technical solution, the ultrasound image containing the blood vessel region is segmented. The blood vessel region is then corrected based on the center pixel of the blood vessel region in the first segmented image to obtain a second segmented image. The second segmented image and the ultrasound image are then combined to obtain a more accurate tissue attenuation image that filters out the blood vessel region, which helps medical staff make accurate diagnoses.

[0088] Figure 2 A schematic flowchart illustrating the process of obtaining a first segmented image according to an embodiment of the present invention is shown. Figure 2 As shown, the above step S1300 may include steps S1310 to S1330.

[0089] In step S1310, for the Nth row of pixels in the ultrasound image, the median value of the pixel values ​​in the Nth row is determined, where N is a positive integer less than or equal to the number of rows in the ultrasound image.

[0090] Pixel values ​​can be grayscale values, or RGB values, etc., without affecting the ultrasound image processing. For the Nth row of pixels in an ultrasound image, the median pixel value of that row can be determined. Specifically, the pixel values ​​of that row can be arranged from largest to smallest, and the pixel value in the middle of the arranged values ​​is the median pixel value. For example, when N is a positive integer equal to the number of rows in the ultrasound image, the median pixel value of each row of pixels can be determined separately.

[0091] In step S1320, the second absolute value corresponding to each pixel value of the Nth row of pixels is determined, and the second absolute value and the second preset threshold are compared respectively, wherein the second absolute value is the absolute value of the difference between each pixel value of the Nth row of pixels and the median value.

[0092] The second absolute value represents the difference between a pixel's value and a second preset threshold. The larger the second absolute value, the more significant the difference between the pixel's value and the median. It can be understood that in an ultrasound image, the blood vessel region occupies only a small portion; the majority of the image consists of regions outside of blood vessels. Therefore, for any row of pixels in an ultrasound image, the median pixel value can represent the pixel value of that other region, and the difference between the pixel value of a blood vessel region and this median is significant. In other words, the more significant the difference between a pixel value and the median, the more likely the corresponding pixel is a pixel value from a blood vessel region.

[0093] The second preset threshold can be a threshold determined before performing the above-mentioned ultrasonic image processing, or a threshold determined during the ultrasonic image processing. The second preset threshold can be a fixed threshold, or it can be adjusted according to the system or manually.

[0094] In step S1330, a first segmented image is obtained based on the second pixel in the ultrasound image, wherein the second pixel is a pixel in the ultrasound image whose corresponding second absolute value is greater than a second preset threshold, and the pixel position of the blood vessel region in the first segmented image corresponds to the position of the second pixel in the ultrasound image.

[0095] Since the pixel position of the blood vessel region in the first segmented image corresponds to the second pixel position in the ultrasound image, the position of the pixel corresponding to the second pixel in the first segmented image can be determined based on the position of the second pixel in the ultrasound image, thereby determining the blood vessel region in the first segmented image and obtaining the first segmented image.

[0096] In the above technical solution, taking advantage of the fact that blood vessel regions account for a relatively small proportion in ultrasound images, a second absolute value is determined based on the median pixel value of each row of pixels. The second absolute value is then compared with a second preset threshold, and a first segmented image is obtained based on the second pixel. This solution obtains a more accurate first segmented image with less computational cost.

[0097] Figure 3 A schematic flowchart illustrating the process of obtaining a second segmented image according to an embodiment of the present invention is shown. Figure 3 As shown, step S1700 may include steps S1710 to S1730.

[0098] In step S1710, for the center pixel of each blood vessel region in the first segmented image, the center pixel is used as the initial reference pixel. An expansion operation is performed cyclically in the first segmented image based on the ultrasound image until a new reference pixel cannot be determined. Specifically, in each expansion operation, a new reference pixel is determined from several first pixels based on the pixel value corresponding to the reference pixel position in the ultrasound image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasound image.

[0099] In step S1720, the corrected blood vessel region in the first segmented image is determined based on the positions of all reference pixels, wherein all reference pixels include the center pixel and the new reference pixel.

[0100] Understandably, since the first segmented image is a segmented image obtained by segmenting the ultrasound image, each pixel position in the first segmented image can correspond to a pixel value in the ultrasound image.

[0101] In the above steps, each blood vessel region in the first segmented image is corrected according to the corresponding pixel value of the adjacent pixel position in the ultrasound image to obtain the corrected blood vessel region of the first segmented image.

[0102] For each blood vessel region in the first segmented image, the following operations can be performed. First, the center pixel position of the blood vessel region is determined in the first ultrasound image, and this determined center pixel position is used as the initial position for the expansion operation. The pixel corresponding to this center pixel position is then determined in the ultrasound image. The expansion operation is used to determine a new reference pixel based on the current reference pixel, i.e., to expand the current blood vessel region in the first segmented image. This operation can be performed once or multiple times. If an expansion operation is performed but no new reference pixel is determined, the expansion operation for the current blood vessel region ends. Specifically, each time the expansion operation is performed, the pixel value corresponding to the current reference pixel position in the ultrasound image can be determined based on the current reference pixel position in the first segmented image, and the pixel values ​​corresponding to several first pixels adjacent to the reference pixel in the ultrasound image can also be determined. The pixel value corresponding to the reference pixel position in the ultrasound image and the pixel values ​​corresponding to several first pixels adjacent to the reference pixel in the ultrasound image can be subtracted or divided to determine the difference between the pixel value corresponding to the reference pixel position in the ultrasound image and the pixel values ​​corresponding to several first pixels adjacent to the reference pixel in the ultrasound image. When the difference is small, it indicates that the reference pixel corresponding to that difference and several first pixels at that reference pixel position all belong to the blood vessel region. In other words, during the above expansion operation, when the difference between the pixel value of the determined reference pixel and the pixel value of the first pixel is small, the first pixel can be determined as the new reference pixel for the blood vessel region. When the difference between the two pixel values ​​is large, it indicates that the first pixel does not belong to the blood vessel region, and therefore, it cannot be used as the new reference pixel for the blood vessel region. If no corresponding new reference pixel is found for all current reference pixels, the expansion operation for the blood vessel region ends.

[0103] For example, after all expansion operations are completed, the pixel corresponding to the reference pixel in the first segmented image is determined as the pixel within the corrected vascular region, thereby determining the pixel within the corrected vascular region in the first segmented image. It is understood that the corrected vascular region in the first segmented image may differ from the vascular region in the first segmented image before the expansion operations. In other words, based on all reference pixels of the ultrasound image, the corrected vascular region in the first segmented image can be determined, thereby enabling the correction of the first segmented image.

[0104] Alternatively, after completing all expansion operations, the blood vessel region in the second segmentation image can be determined based on both the pixels of the blood vessel region in the first segmentation image and the reference pixels determined after the expansion operations. In this alternative example, the pixels in the first segmentation image corresponding to the aforementioned two positions can be used as pixels in the corrected blood vessel region.

[0105] In step S1730, a second segmented image is determined based on the corrected blood vessel region in the first segmented image.

[0106] Understandably, after all the expansion operations are completed, the non-vascular regions in the first segmented image are also corrected while the vascular regions in the first segmented image are corrected, so the corrected non-vascular regions in the first segmented image can be obtained.

[0107] After identifying the corrected vascular regions in the first segmented image, the pixels of these corrected vascular regions can be used as the pixels of the vascular regions in the second segmented image, and the pixels of the corrected non-vascular regions in the first segmented image can be used as the pixels of the non-vascular regions in the second segmented image. This allows the determination of the second segmented image. The vascular regions in the second segmented image correspond one-to-one with the corrected vascular regions in the first segmented image.

[0108] For example, the first segmented image after all expansion operations can also be processed, and then the processed first segmented image can be used as the second segmented image. Processing operations can include noise reduction, filtering, pixel transformation, and other operations to improve the accuracy of blood vessel regions in the first segmented image.

[0109] In this alternative example, step S1730 may include: determining a second segmented image based on the position of pixels in the corrected blood vessel region in the first segmented image, wherein the blood vessel region in the second segmented image includes pixels corresponding to the pixel positions in the corrected blood vessel region in the first segmented image.

[0110] During the above steps, all reference pixels in the first segmented image can be identified as pixels in the corrected vascular region. Then, the pixels in the corrected vascular region of the first segmented image are used as pixels in the vascular region of the second segmented image. It can be understood that pixels that originally belonged to the vascular region can remain unchanged; pixels that originally did not belong to the vascular region can have their pixel values ​​changed to make them belong to the vascular region. In the example where the pixel value of the vascular region pixels in the first segmented image is 0 and the pixel value of the non-vascular region pixels is 1, their pixel values ​​can be set to 0, so that the pixel values ​​at the corrected vascular region pixel positions in the first segmented image are all set to 0. At this point, the modified first segmented image can be used as the second segmented image, where the vascular region not only includes the pixels with pixel values ​​set to 0, but also retains the original vascular region from the first segmented image.

[0111] In the alternative example described above, pixels from all corrected vascular regions in the first segmented image are combined to determine the pixels of the vascular region in the second segmented image. By adding vascular regions based on the expansion operation while preserving the vascular regions before the expansion operation, the determined vascular regions are more accurate, thus ensuring the accuracy of the second segmented image.

[0112] In the above technical solution, a reference pixel is determined, and an expansion operation is cyclically performed on the first segmented image based on the ultrasound image. A new reference pixel is determined among the first pixels adjacent to the reference pixel. After the cyclic expansion operation is completed, the corrected blood vessel region in the first segmented image is determined based on the determined reference pixels, thereby obtaining the second segmented image. By expanding the blood vessel region pixel by pixel, the corrected blood vessel region in the first segmented image can be determined more accurately, thus allowing for the determination of a more accurate second segmented image based on the corrected blood vessel region in the first segmented image.

[0113] Figure 4 A schematic flowchart of an expansion operation according to an embodiment of the present invention is shown. It will be understood that the expansion operation is performed for each blood vessel region in the first segmented image. Figure 4 As shown, the above extended operation may include steps S1711 to S1713.

[0114] In step S1711, the average value of the pixel values ​​corresponding to all current reference pixels in the ultrasonic image is determined.

[0115] It can be understood that all the current reference pixels here are the blood vessel regions in the first segmented image that the current expansion operation targets. At the start of the expansion operation, all the current reference pixels include only one initial reference pixel, which is the center pixel of each blood vessel region in the first image. The number of current reference pixels can be increased as the expansion operation is performed iteratively.

[0116] In step S1712, a first absolute value is determined between the pixel value corresponding to each first pixel in the ultrasonic image and the average value.

[0117] The first pixel can be determined based on at least a portion of the current reference pixels. It is understood that at any given time, the current reference pixels constitute a connected component. The first pixel can be determined based on the edge pixels within this connected component. Specifically, for example, the first pixel can be determined based on the current first and last row of reference pixels, as well as the first and last reference pixels in each current row of reference pixels. Only pixels in the ultrasound image that are adjacent to these reference pixels are determined as the first pixel.

[0118] During the cyclic execution of the expansion operation in the ultrasound image, the first pixel can change as the expansion operation is performed. For each first pixel, the absolute value of the difference between it and the average value determined in step S1711 is determined, referred to as the first absolute value. The first absolute value represents the difference between the pixel value of the first pixel and the average value, that is, it represents the degree of difference between the first pixel and the current reference pixel. The larger the first absolute value, the more obvious the difference between the first pixel and the current reference pixel.

[0119] In step S1713, a new reference pixel is selected from all the first pixels based on the first absolute value.

[0120] When the first absolute value is small, it indicates that the first pixel is less different from the current reference pixel. For example, a third preset threshold can be preset. The first absolute value is directly compared with the third preset threshold. If the first absolute value is less than the third preset threshold, the first pixel can be considered similar to the current reference pixel and both are classified as pixels in the blood vessel region.

[0121] For example, after selecting a new reference pixel in the first pixel, the already determined reference pixels will change. At this time, the already determined reference pixels include the new reference pixel and the initial reference pixel. The above expansion operation can continue to be performed to determine a new reference pixel based on the already determined reference pixels and several first pixels adjacent to the new reference pixel, until all reference pixels are determined after all expansion operations are performed.

[0122] In the above technical solution, a new reference pixel is determined among all first pixels based on the absolute value of the difference between the pixel value of each first pixel and the average pixel value of the reference pixel. In this solution, the average value changes with the current reference pixel, thus taking into account the changes in pixel values ​​at different locations in the blood vessel region of the ultrasound image when determining the new reference pixel. This allows for a more accurate determination of the reference pixel representing the blood vessel region, and consequently, a more accurate determination of the second segmented image.

[0123] Figure 5 A schematic flowchart illustrating the process of determining a new reference pixel in a first pixel based on a first absolute value, according to an embodiment of the present invention, is shown. Figure 5 As shown, step S1713 may include steps S713a to S713b.

[0124] In step S713a, the smallest first absolute value is determined among the first absolute values, and the smallest first absolute value is compared with the first preset threshold to obtain the comparison result.

[0125] As described in step S1712, for each first pixel, the difference between the pixel value and the average value of the first pixel is calculated, and the first absolute value of the difference is calculated. For a blood vessel region, multiple first absolute values ​​can be determined. All the first absolute values ​​corresponding to the blood vessel region can be compared to determine the smallest first absolute value. Then, the smallest first absolute value is compared with a first preset threshold. The first preset threshold can be a threshold determined before ultrasound image processing or a threshold determined during ultrasound image processing. The first preset threshold can be a fixed threshold or can be adjusted according to the system or manually.

[0126] In step S713b, when the comparison result of step S713a indicates that the smallest first absolute value is less than the first preset threshold, the first pixel corresponding to the smallest first absolute value is determined as the new reference pixel.

[0127] It is understandable that the smallest first absolute value may correspond to one first pixel or multiple first pixels. When the smallest first absolute value corresponds to only one first pixel, this expansion operation will determine only one first pixel as the new reference pixel. When the smallest first absolute value corresponds to multiple first pixels, multiple first pixels can be determined as the new reference pixels simultaneously.

[0128] When the comparison result indicates that the smallest absolute value is not less than the first preset threshold, it means that the blood vessel region has been completely determined, and the expansion operation can be ended. At this point, the obtained reference pixels constitute the new blood vessel region.

[0129] In the above technical solution, the pixel whose pixel value is closest to the average value among the first pixels is first determined. Then, based on the difference between the two, it is determined whether this first pixel can be determined as the reference pixel. In this way, not only is the accuracy of the determined blood vessel region guaranteed, but the amount of computation is also small.

[0130] As mentioned above, in the ultrasound image processing method described above, steps S1500 and S1700 are both performed on each blood vessel region in the ultrasound image, thereby obtaining a second segmented image containing accurate blood vessel region information. It is understood that various types of noise may exist in the ultrasound image. This noise can hinder the identification and segmentation of blood vessel regions. In other words, this noise is not a blood vessel region, but it may be mistakenly segmented as a blood vessel region in step S1300. Therefore, before step S1500, the ultrasound image processing method described above may further include a step of filtering out blood vessel regions.

[0131] Figure 6 A schematic flowchart illustrating the filtration of vascular regions according to an embodiment of the present invention is shown. Figure 6As shown, steps S1410 to S1420 may be included before step S1500.

[0132] In step S1410, the area of ​​each blood vessel region in the first segmented image is determined.

[0133] The number of pixels in each blood vessel region can be determined, and this number can be used as the area of ​​each blood vessel region. Alternatively, the area of ​​each blood vessel region can be calculated based on the position coordinates of the pixels within that region.

[0134] In step S1420, blood vessel regions with an area smaller than the determined area threshold are deleted.

[0135] The area threshold can be a threshold determined before or during ultrasonic image processing. It can be a fixed threshold or adjustable by the system or manually. The area threshold can be the number of pixels or a numerical area value.

[0136] It is understandable that noise regions typically occupy a smaller area compared to actual blood vessel regions. In the above technical solution, deleting blood vessel regions with areas smaller than a threshold removes misidentified small noise regions. This not only avoids interference from noise regions in blood vessel region identification, leading to more accurate identification of blood vessel regions, but also reduces the computational load in subsequent steps.

[0137] For example, before performing image segmentation on the ultrasonic image in step S1300, the method further includes step S1210: denoising the ultrasonic image to obtain a denoised ultrasonic image. The denoising can be any image denoising method, such as mean filtering, wavelet denoising, median filtering, etc., to reduce noise in the ultrasonic image.

[0138] For example, median filtering can be used to reduce noise in ultrasound images. When performing median filtering on an ultrasound image, a median filter kernel can be determined, and then the ultrasound image can be filtered according to the median filter kernel to obtain the filtered ultrasound image. Median filtering can remove fine noise from the ultrasound image.

[0139] In the above technical solution, noise reduction of the ultrasound image can remove small noises in the ultrasound image, thereby reducing the interference of noise on the ultrasound image processing and more accurately determining the blood vessel area.

[0140] For example, before performing image segmentation on the ultrasonic image in step S1300, the method further includes step S1220: normalizing the pixel values ​​corresponding to the pixels in the ultrasonic image to obtain a normalized ultrasonic image. The maximum pixel value can be determined from the pixel values ​​of the pixels in the ultrasonic image, and the corresponding pixel value can be normalized according to the ratio of the pixel value of each pixel to the maximum pixel value to obtain a normalized ultrasonic image.

[0141] The normalization here can be any normalization method that meets the requirements, such as min-max normalization, range normalization, logarithmic normalization, etc.

[0142] In the above technical solution, normalizing the ultrasound image can speed up the system's processing of the ultrasound image and improve the efficiency of identifying blood vessel regions in the ultrasound image.

[0143] Alternatively, other numerical transformation methods besides normalization can be used to denoise ultrasound images, such as Z-score normalization.

[0144] For example, all of the above steps can be implemented using artificial intelligence models. The types and architectures of artificial intelligence models are not limited here.

[0145] Figure 7 A schematic flowchart of an ultrasonic image processing method according to another embodiment of the present invention is shown.

[0146] like Figure 7 As shown, an ultrasound image can be obtained through an ultrasound host. This ultrasound image can be represented by an M x N matrix, corresponding to the scanning range of acoustic attenuation imaging. The ultrasound image can be analyzed and processed to determine the vascular regions within it. First, the ultrasound image can be normalized and small noise can be removed using median filtering. For an ultrasound image, the maximum pixel value `max(Image)` can be found among all pixels in the image. Based on this maximum pixel value, the normalization result is output: `NormImage = Image / max(Image)`, where `Image` represents the pixel value. Median filtering is then applied to the normalized result `NormImage`. Assuming the median filter kernel is `MedCor`, the output of the median filter is represented as the matrix `MedImage`. It can be understood that `MedImage` represents the ultrasound image after median filtering.

[0147] Next, a threshold judgment can be performed on the pixel values ​​of all pixels in MedImage to obtain the first segmented image through image segmentation. This first segmented image can be represented by a matrix MaskSignal. The median of each row's elements in MedImage is taken, resulting in a vector Med (with a total of M elements). All elements in MaskSignal are initialized to 1, and the corresponding element is set to 0 based on the absolute value of the difference between the value of each element in each row and the median. When the absolute value of the difference between the value of an element in a row and the median is greater than a second preset threshold T, that element is set to 0. Further processing of MaskSignal can then be performed to transform it into a second segmented image, VasMask, capable of accurately locating blood vessel regions.

[0148] Small noise blocks within the MaskSignal can be filtered out. The algorithm iterates through all elements within the MaskSignal. When an element is 1 and its right or lower element is 0, the element with a value of 0 is used as the starting element. The algorithm recursively finds all connected elements with a value of 0 and records their coordinates. If the number of these elements is less than the area threshold MinArea, all of these elements are set to 1. If it is greater than MinArea, the coordinates of the center element of these elements are found and recorded as the center pixel of the blood vessel region in the first segmented image. The depth search function described above can be used to search for elements connected to the target location that meet the conditions and store their corresponding coordinates in the variable Points. The stored elements correspond to pixels in the blood vessel region. The number of elements stored in Points determines whether to filter out the corresponding region. If the region is not filtered out, the center pixel of the region is calculated and its position coordinates are stored in the matrix PointsForExpend as input data for subsequent neighborhood mean blood vessel subtraction operations.

[0149] After the above steps, we obtain the result MaskSignal after filtering out small-scale noise points, and the central element set matrix PointsForExpend. Subsequently, using these two sets of inputs, combined with the median-filtered result MedImage, we calculate the second segmented image VasMask. Specifically, we can determine the pixels within the blood vessel region of the ultrasound image based on the first segmented image, and then determine the second segmented image based on the pixels within the blood vessel region and their adjacent pixels.

[0150] Assuming the coordinates of a certain position stored in PointsForExpend are (x, y), store these coordinates in the set matrix VasPoints used to record the position coordinates of the reference pixel. For (x, y), take its four nearest neighbors (x-1, y), (x+1, y), (x, y-1), and (x, y+1), and store them in the set matrix VasPointsBackup. Set a mean threshold MeanMin (first preset threshold). Calculate the average grayscale value VasPointsGray of the corresponding pixel (reference pixel) in MedImage for all coordinates in VasPoints. For all coordinates in the set matrix VasPointsBackup, obtain the grayscale value of the corresponding pixel (first pixel) in MedImage, and find the value whose grayscale value is closest to VasPointsGray and its corresponding coordinates. If the absolute value of the difference between this value and VasPointsGray (the first absolute difference) is less than MeanMin, then the corresponding coordinates are stored in the matrix VasPoints as a new reference pixel. This process is repeated until no new reference pixels are generated. Finally, for the input coordinates (x, y), the expanded coordinate result VasPoints can be obtained. The expanded coordinate result VasPoints corresponds to the pixel position of the blood vessel region in the second segmented image.

[0151] Repeat the above operation for all coordinates in PointsForExpend. That is, after performing the expansion operation in a loop, you can get the set of coordinates of all pixel positions in the blood vessel region. You can set all the pixel values ​​of the corresponding positions in MaskSignal to zero, which is the output VasMask.

[0152] By setting the median filter kernel MedCor, the second preset threshold T, the area threshold MinArea, and the first preset threshold MeanMin to appropriate default values, a VasMask for blood vessel removal can be obtained through the above ultrasound image processing method. By superimposing the ultrasound image with VasMask, a tissue attenuation image with blood vessels filtered out can be obtained.

[0153] Before outputting the tissue attenuation image using the aforementioned ultrasound image processing method, it can be determined whether to use an adaptive algorithm. If the adaptive algorithm is chosen, the user-defined attenuation value is first obtained, and relevant parameters are set according to the range of that value. Then, the aforementioned ultrasound image processing method is executed using the set parameters to filter out blood vessels from the ultrasound image and output the tissue attenuation image after removing the vascular regions. The attenuation value range can be divided into three levels. Table 1 shows the relationship between the attenuation value and the parameter settings.

[0154] Table 1

[0155]

[0156] In Table 1, "3×3", "5×5", and "7×7" represent the sizes of different median filter kernels. T1, T2, and T3 represent the second preset thresholds corresponding to different attenuation ranges, MinArea1, MinArea2, and MinArea3 represent the area thresholds corresponding to different attenuation ranges, and MeanMin1, MeanMin2, and MeanMin3 represent the first preset thresholds corresponding to different attenuation ranges. When it is determined that the adaptive algorithm is not used, the tissue attenuation image after filtering out blood vessels can be directly output.

[0157] Figure 8 A schematic block diagram of an ultrasound host according to an embodiment of the present invention is shown. Figure 8 As shown, the ultrasound host can acquire ultrasound images containing at least one vascular region, and obtain a tissue attenuation image with the vascular region filtered out according to the aforementioned ultrasound image processing method. The ultrasound host may include an image display area that can display the ultrasound image containing at least one vascular region. The ultrasound host may be equipped with an input device, such as a button, mouse, keyboard, or touchscreen. The user can use the input device to activate the aforementioned ultrasound image processing method and input an attenuation value. Figure 8 The touchscreen shown can display options for triggering the aforementioned ultrasonic image processing procedure, such as... Figure 8 The "Adaptive Vascular Filtration" option is shown. Figure 8 The "Adaptive Vascular Filtering" option shown can also be other control components displayed on the touchscreen to prompt the user to activate the aforementioned ultrasound image processing method. The ultrasound host may also include hardware that, when operated by the user, can activate the aforementioned ultrasound image processing method. This hardware structure can be a control component such as a button or knob, or it can be the aforementioned input device.

[0158] Figure 9 A schematic block diagram of an ultrasonic image processing apparatus 800 according to an embodiment of the present invention is shown. Figure 9 As shown, the ultrasonic image processing device 800 includes an image receiving module 810, an image segmentation module 830, an image determination module 850, an image correction module 870, and an image synthesis module 890.

[0159] Image receiving module 810 is used to acquire ultrasound images containing at least one vascular region.

[0160] The image segmentation module 830 is used to segment the ultrasound image for the blood vessel region in the ultrasound image to obtain a first segmented image, the first segmented image including the blood vessel region.

[0161] Image determination module 850 is used to determine the center pixel of each blood vessel region in the first segmented image.

[0162] Image correction module 870 is used to correct the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain a second segmented image.

[0163] The image synthesis module 890 is used to synthesize the second segmented image and the ultrasound image to obtain a tissue attenuation image with the vascular region filtered out.

[0164] For example, the image correction module 870 includes a first correction submodule, a second correction submodule, and a third correction submodule. The first correction submodule is used to perform an expansion operation cyclically in the first segmented image based on the ultrasound image, using the center pixel of each blood vessel region in the first segmented image as an initial reference pixel, until no new reference pixel can be determined. Specifically, the expansion operation involves determining a new reference pixel among several first pixels based on the pixel value corresponding to the reference pixel position in the ultrasound image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasound image. The second correction submodule is used to determine the corrected blood vessel region in the first segmented image based on the positions of all reference pixels, where all reference pixels include the center pixel and the new reference pixel. The third correction submodule is used to determine the second segmented image based on the corrected blood vessel region in the first segmented image.

[0165] For example, the image determination module 850 includes a mean determination submodule, a difference determination submodule, and a first determination submodule. The mean determination submodule is used to determine the average value of the pixel values ​​corresponding to all current reference pixels in the ultrasonic image. The difference determination submodule is used to determine a first absolute value of the difference between the pixel value corresponding to each first pixel in the ultrasonic image and the average value. The first determination submodule is used to select a new reference pixel from all first pixels based on the first absolute value.

[0166] For example, the image determination module 850 may further include a first numerical comparison submodule and a second determination submodule. The numerical comparison submodule is used to determine the smallest first absolute value among the first absolute values, and compare the smallest first absolute value with a first preset threshold to obtain a comparison result. The second determination submodule is used to determine the first pixel corresponding to the smallest first absolute value as a new reference pixel if the comparison result indicates that the smallest first absolute value is less than the first preset threshold.

[0167] For example, the image correction module 870 may further include a fourth correction submodule. The fourth correction submodule is used to determine a second segmented image based on the position of pixels in the corrected blood vessel region in the first segmented image, wherein the blood vessel region in the second segmented image includes pixels corresponding to the pixel positions in the corrected blood vessel region in the first segmented image.

[0168] For example, the image correction module 870 may further include a center pixel determination submodule. The center pixel determination submodule is used to determine the center pixel of each blood vessel region in the first segmented image based on the average coordinates of the pixel positions in each blood vessel region.

[0169] For example, the image segmentation module 830 includes a median determination submodule, a second numerical comparison submodule, and an image segmentation submodule. The median determination submodule is used to determine the median value of the pixels in the Nth row of the ultrasound image, where N is a positive integer less than or equal to the number of rows in the ultrasound image. The second numerical comparison submodule is used to determine the second absolute value corresponding to each pixel value in the Nth row, and compare the second absolute value with a second preset threshold, where the second absolute value is the absolute value of the difference between each pixel value in the Nth row and the median. The image segmentation submodule is used to obtain a first segmented image based on second pixels in the ultrasound image, where the second pixel is a pixel in the ultrasound image whose corresponding second absolute value is greater than the second preset threshold, and the pixel position of the blood vessel region in the first segmented image corresponds to the position of the second pixel in the ultrasound image.

[0170] For example, the ultrasound image processing apparatus 800 may further include an area determination module. The area determination module may include an area determination submodule and an area comparison submodule. Before performing image segmentation on the ultrasound, the area determination submodule is used to determine the area of ​​each blood vessel region in the first segmented image. The area comparison submodule is used to delete blood vessel regions whose determined area is less than an area threshold.

[0171] For example, the ultrasonic image processing apparatus 800 may also include a noise reduction module. Before image segmentation of the ultrasonic waves, the noise reduction module is used to reduce the noise in the ultrasonic image to obtain a noise-reduced ultrasonic image.

[0172] For example, the ultrasonic image processing apparatus 800 may further include a normalization module. Before performing image segmentation on the ultrasonic wave, the normalization module is used to normalize the pixel values ​​corresponding to the pixels in the ultrasonic image to obtain a normalized ultrasonic image.

[0173] According to another aspect of the present invention, an electronic device is also provided. Figure 10 A schematic block diagram of an electronic device 900 according to an embodiment of the present invention is shown. Figure 10 As shown, the electronic device 900 includes a processor 910 and a memory 920, wherein the memory 920 stores computer program instructions, which are executed by the processor 910 to perform the ultrasonic image processing method described above.

[0174] Furthermore, according to another aspect of the present invention, a storage medium is provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, the computer or processor performs corresponding steps of the ultrasonic image processing method described above in the embodiments of the present invention, and is used to implement corresponding modules in the ultrasonic image processing apparatus described above in the embodiments of the present invention. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0175] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions, which, when executed, are used to perform the above-described ultrasonic image processing method.

[0176] Those skilled in the art can understand the specific implementation and beneficial effects of the above-described ultrasonic image processing device, electronic device, storage medium, and computer program product by reading the detailed description of the ultrasonic image processing method above, and for the sake of brevity, they will not be described in detail here.

[0177] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0178] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0180] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0181] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, the method of the invention should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0182] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0183] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0184] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the ultrasonic image processing apparatus according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0185] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0186] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An ultrasonic image processing method, characterized in that, The method includes: Acquire ultrasound images containing at least one vascular region; For the blood vessel region in the ultrasound image, the ultrasound image is segmented to obtain a first segmented image, the first segmented image including the blood vessel region; Determine the center pixel of each of the blood vessel regions in the first segmented image; Based on the center pixel and the ultrasound image, the blood vessel region in the first segmented image is corrected to obtain a second segmented image; and The second segmented image and the ultrasound image are combined to obtain a tissue attenuation image with the vascular region filtered out; The step of correcting the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain the second segmented image includes: For the center pixel of each blood vessel region in the first segmented image, the expansion operation is performed cyclically in the first segmented image based on the ultrasound image, using the center pixel as the initial reference pixel, until a new reference pixel cannot be determined. Specifically, the expansion operation involves determining a new reference pixel among several first pixels based on the difference between the pixel value corresponding to the reference pixel position in the ultrasonic image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasonic image. Based on the positions of all reference pixels, the corrected blood vessel region in the first segmented image is determined, wherein all reference pixels include the center pixel and the new reference pixel; The second segmented image is determined based on the corrected blood vessel region in the first segmented image.

2. The method according to claim 1, characterized in that, The step of determining the new reference pixel from among several first pixels based on the difference between the pixel value corresponding to the reference pixel position in the ultrasonic image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasonic image includes: Determine the average value of the pixel values ​​corresponding to all the current reference pixels in the ultrasonic image; Determine a first absolute value of the difference between the pixel value corresponding to each first pixel in the ultrasonic image and the average value; Based on the first absolute value, the new reference pixel is selected from all the first pixels.

3. The method according to claim 2, characterized in that, The step of selecting the new reference pixel from all the first pixels based on the first absolute value includes: Determine the smallest first absolute value among the first absolute values, and compare the smallest first absolute value with a first preset threshold to obtain a comparison result; If the comparison result indicates that the smallest first absolute value is less than the first preset threshold, then the first pixel corresponding to the smallest first absolute value is determined as the new reference pixel.

4. The method according to claim 1, characterized in that, Determining the second segmented image based on the corrected blood vessel region in the first segmented image includes: The second segmented image is determined based on the position of pixels in the corrected blood vessel region in the first segmented image, wherein the blood vessel region in the second segmented image includes pixels corresponding to the pixel positions in the corrected blood vessel region in the first segmented image.

5. The method according to claim 1, characterized in that, Determining the center pixel of each blood vessel region in the first segmented image includes: For each blood vessel region in the first segmented image, the center pixel in each blood vessel region is determined based on the average coordinates of the pixel positions within each blood vessel region.

6. The method according to any one of claims 1 to 5, characterized in that, The step of segmenting the ultrasound image for the vascular region in the ultrasound image to obtain a first segmented image includes: For the Nth row of pixels in the ultrasonic image, determine the median pixel value of the Nth row of pixels, where N is a positive integer less than or equal to the number of rows in the ultrasonic image; Determine the second absolute value corresponding to each pixel value of the Nth row of pixels, and compare the second absolute value with the second preset threshold, wherein the second absolute value is the absolute value of the difference between each pixel value of the Nth row of pixels and the median value; The first segmented image is obtained based on the second pixel in the ultrasound image, wherein the second pixel is a pixel in the ultrasound image whose corresponding second absolute value is greater than the second preset threshold, and the pixel position of the blood vessel region in the first segmented image corresponds to the position of the second pixel in the ultrasound image.

7. The method according to any one of claims 1 to 5, characterized in that, Before determining the center pixel of each blood vessel region in the first segmented image, the method further includes: Determine the area of ​​each blood vessel region in the first segmented image; Delete the identified blood vessel regions whose area is smaller than the area threshold.

8. The method according to any one of claims 1 to 5, characterized in that, Before performing image segmentation on the ultrasound waves, the method further includes: The ultrasonic image is denoised to obtain a denoised ultrasonic image.

9. The method according to any one of claims 1 to 5, characterized in that, Before performing image segmentation on the ultrasound waves, the method further includes: The pixel values ​​corresponding to the pixels in the ultrasonic image are normalized to obtain a normalized ultrasonic image.

10. An ultrasonic image processing device, characterized in that, include: Image receiving module, used to acquire ultrasound images containing at least one vascular region; An image segmentation module is used to segment the ultrasound image for the blood vessel region in the ultrasound image to obtain a first segmented image, wherein the first segmented image includes the blood vessel region. An image determination module is used to determine the center pixel of each blood vessel region in the first segmented image; An image correction module is used to correct the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain a second segmented image. as well as An image synthesis module is used to synthesize the second segmented image and the ultrasound image to obtain a tissue attenuation image with the blood vessel region filtered out. The step of correcting the blood vessel region in the first segmented image based on the center pixel and the ultrasound image to obtain the second segmented image includes: For the center pixel of each blood vessel region in the first segmented image, the expansion operation is performed cyclically in the first segmented image based on the ultrasound image, using the center pixel as the initial reference pixel, until a new reference pixel cannot be determined. Specifically, the expansion operation involves determining a new reference pixel among several first pixels based on the difference between the pixel value corresponding to the reference pixel position in the ultrasonic image and the pixel values ​​corresponding to several first pixel positions adjacent to the reference pixel position in the ultrasonic image. Based on the positions of all reference pixels, the corrected blood vessel region in the first segmented image is determined, wherein all reference pixels include the center pixel and the new reference pixel; The second segmented image is determined based on the corrected blood vessel region in the first segmented image.

11. An electronic device comprising a processor and a memory, characterized in that, The memory stores computer program instructions, which, when executed by the processor, are used to perform the ultrasonic image processing method as described in any one of claims 1 to 9.

12. A storage medium on which program instructions are stored, characterized in that, The program instructions, when executed, are used to perform the ultrasonic image processing method as described in any one of claims 1 to 9.

13. A computer program product comprising computer program instructions, characterized in that, The computer program instructions, when executed, are used to perform the ultrasonic image processing method as described in any one of claims 1 to 9.

Images