Blood vessel image enhancement and segmentation method and system of puncture robot

Abstract

The invention belongs to the medical field, and provides a blood vessel image enhancement and segmentation method and system for a puncture robot, and the method comprises the steps: obtaining a plurality of continuous original ultrasonic echo signals; mapping the plurality of original ultrasonic echo signals into a grayscale image sequence; acquiring a first distance sequence of the needle point of the puncture needle on the grayscale image and a probe and a corresponding needle point grayscale value sequence; fitting the first distance sequence and the needle point gray value sequence into a first curve; adjusting blood vessel detection thresholds at different positions on the gray level image according to the first curve; and determining a blood vessel on the grayscale image according to the adjusted blood vessel detection threshold, enhancing the brightness of the determined blood vessel, and segmenting the blood vessel.

Description

Technical Field

[0001] The present invention belongs to the medical field, and in particular relates to a blood vessel image enhancement and segmentation method and system for a puncture robot. Background Art

[0002] In the existing technology, puncture robots often rely on conventional human vision-friendly ultrasound images for operation guidance. However, these ultrasound images are processed in a complex way, mainly to enhance the doctor's visual experience, making the images clearer, higher in contrast, and easier to understand and interpret. Such images are processed through a series of enhancements, such as denoising, smoothing, and contrast adjustment, to help doctors quickly identify tissue boundaries, blood vessels, and other anatomical structures.

[0003] Although these processings are helpful to doctors, for puncture robots, traditional ultrasound images contain many unnecessary changes, which may even affect the accuracy of the robotic system. The puncture robot relies on accurate image data to locate the needle tip, navigate the path, and detect the target tissue in real time. However, during conventional ultrasound image processing, some details may be overly simplified or smoothed, resulting in information that is irrelevant to the robot operation being enhanced, while key signals (such as the weak echo characteristics of tiny blood vessels) are suppressed or blurred.

[0004] For example, noise removal and dynamic range compression in ultrasound images usually weaken the grayscale changes at different depths in the image, making it impossible for the robot to accurately perceive the true reflection intensity between layers. Such changes are beneficial to human vision, but may increase the misjudgment of target tissues for puncture robots. In addition, image smoothing operations will blur the edges and weaken the fine recognition of key structures such as needle tips and blood vessels, thus affecting the accurate positioning and path planning of the robot system.

[0005] Therefore, although conventional ultrasound images optimize the doctor's visual experience, they contain redundant information and processed features that the robot does not need, which makes the robot easily misled or loses the perception of key details when performing puncture operations based on these images, thus affecting the accuracy and safety of the operation. The existing technology still has room for improvement in this regard, that is, how to provide the puncture robot with more accurate ultrasound image data that retains the original details of the signal, so as to improve its autonomous decision-making and operation capabilities. Summary of the invention

[0006] In order to solve the problems in the prior art, the present invention provides a blood vessel image enhancement and segmentation method for a puncture robot, the method comprising the following steps:

[0007] Acquire a plurality of continuous original ultrasonic echo signals;

[0008] Mapping the plurality of original ultrasonic echo signals into a grayscale image sequence;

[0009] Acquire a first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding needle tip grayscale value sequence;

[0010] Fitting the first distance sequence and the needle tip gray value sequence into a first curve;

[0011] adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve;

[0012] The blood vessels on the grayscale image are determined according to the adjusted blood vessel detection threshold, the brightness of the determined blood vessels is enhanced, and the blood vessels are segmented.

[0013] Another aspect of the present invention is to provide a blood vessel image enhancement and segmentation system for a puncture robot, comprising the following modules:

[0014] An acquisition module, used for acquiring a plurality of continuous original ultrasonic echo signals;

[0015] A mapping module, used for mapping the plurality of original ultrasonic echo signals into a grayscale image sequence;

[0016] A calculation module, used for obtaining a first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding needle tip grayscale value sequence;

[0017] A fitting module, used for fitting the first distance sequence and the needle tip gray value sequence into a first curve;

[0018] an adjustment module, used for adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve;

[0019] The processing module is used to determine the blood vessels on the grayscale image according to the adjusted blood vessel detection threshold, enhance the brightness of the determined blood vessels, and segment the blood vessels.

[0020] The blood vessel image enhancement and segmentation method of the puncture robot proposed in the present invention significantly improves the recognizability of blood vessels and surrounding tissues by processing the original ultrasonic echo signal and generating a grayscale image. Compared with the traditional method, this method directly maps the original ultrasonic signal, avoiding excessive image reconstruction and processing steps, thereby retaining the subtle differences in the signal and enhancing the recognition ability of complex tissues. This is particularly critical for the precise positioning of the puncture robot in medical operations.

[0021] By fitting the distance sequence and gray value sequence of the needle tip position, generating the first curve and dynamically adjusting the blood vessel detection threshold, this method can maintain a fixed ratio of blood vessel grayscale to needle tip grayscale at different depths, thereby improving the consistency and accuracy of blood vessel detection. At the same time, by setting upper and lower thresholds, interference signals in non-vascular areas are eliminated, accurate blood vessel segmentation is achieved, and the visualization of blood vessels is further optimized through brightness enhancement and morphological processing. This method provides more accurate image input for the puncture robot and improves the safety and reliability of medical operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of the method of the present invention;

[0024] Figure 2 It is a schematic diagram of the first distance. DETAILED DESCRIPTION

[0025] Below, the invention is preferably described in conjunction with the accompanying drawings and specific implementation methods.

[0026] This embodiment solves the above problem through the following steps:

[0027] In one embodiment, reference Figure 1 The present invention provides a blood vessel image enhancement and segmentation method of a puncture robot. The puncture robot in the present invention refers to a robot device used for medical operations, which can perform puncture operations under the operation of a doctor or automatic control. The robot achieves accurate puncture of the target area by accurately controlling the movement and position of the puncture needle to assist in medical operations.

[0028] First, a plurality of continuous original ultrasonic echo signals are acquired.

[0029] Ultrasonic waves are continuously emitted through an ultrasonic probe, and echo signals in the target tissue are received through an ultrasonic receiving device. The ultrasonic echo signals are signals reflected from different depths and positions in the target tissue or region, and the signals contain information such as the density and structure inside the tissue. These echo signals are collected in a continuous form by a receiving device, which ensures the temporal and spatial consistency of the ultrasonic signals and can provide accurate tissue information for subsequent image processing steps. In the present invention, the continuous refers to maintaining the coherence of the ultrasonic signal and the continuity of the data at a sufficiently high sampling rate within a certain time period to ensure that the dynamic changes in the target tissue can be fully described. The multiple original ultrasonic echo signals refer to the initial echo data without any processing or transformation, which are used to construct a grayscale image sequence.

[0030] The plurality of original ultrasound echo signals are mapped into a grayscale image sequence.

[0031] Through signal processing and image conversion algorithms, the received continuous ultrasonic echo signals are mapped to corresponding gray levels according to their intensity to generate a two-dimensional grayscale image sequence. The process includes the following steps:

[0032] First, the original ultrasonic echo signal obtained by the ultrasonic probe is preprocessed to extract its reflection intensity information. Then, according to the preset mapping function, the amplitude or energy value of each signal is converted into a grayscale value, where a higher signal intensity corresponds to a brighter grayscale level and a lower signal intensity corresponds to a darker grayscale level. This mapping rule is used to accurately reflect the density, boundary and internal structure information of the target tissue on the grayscale image.

[0033] Assuming that the reflection intensity of a certain ultrasound echo signal acquired at a specific moment is 250 units, according to the set mapping rule, the reflection intensity is mapped to a grayscale value of 200 (within the grayscale value range of 0-255). Therefore, the corresponding pixel point in the grayscale image will be displayed as a brighter point in the grayscale level. The adjacent ultrasound echo signal intensity is 50 units, and the corresponding grayscale value after mapping is 20, then the corresponding pixel point will be displayed as a darker area. Through this continuous signal processing, the grayscale images generated frame by frame will constitute an image sequence, forming a complete two-dimensional image representation of the target tissue.

[0034] The process of directly mapping multiple raw ultrasound echo signals into grayscale images simplifies the complex operation of processing echo signals into human-friendly visual images in the traditional imaging process. This direct mapping directly converts the signal intensity into grayscale values ​​in proportion by omitting the complex reconstruction and rendering process of the signal, and immediately generates an image that can be analyzed and processed by the algorithm. Specifically, in this method, the intensity data of the ultrasound echo signal is not subjected to conventional processing such as interpolation, noise reduction or morphological operations after acquisition, but the signal value is directly mapped to the pixel value in the grayscale image. At the same time, direct mapping retains the subtle differences in the ultrasound echo signal, ensuring that these detailed information, such as slight changes in signal intensity, can be used in the subsequent analysis process, enhancing the ability to recognize complex tissue structures.

[0035] A first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding grayscale value sequence of the needle tip are obtained.

[0036] Since the ultrasound image is fan-shaped and the probe is located at the vertex of the image, the pixel points in the grayscale image are distributed in a fan-shaped manner from the vertex of the probe outward. The puncture needle usually appears in a linear structure in the grayscale image, and the needle tip is its frontmost part.

[0037] In order to identify the needle tip position, an image processing algorithm suitable for sector images, such as polar coordinate transformation, can be used to transform the curve structure of the sector image into a linear structure suitable for detection.

[0038] The first distance sequence is a radial distance sequence of the pointer tip relative to the ultrasound probe (i.e., the vertex of the image). In a sector image, each frame of the image can be represented as a point in a polar coordinate system, where the probe is located at the origin. Figure 2 As shown in FIG. 1 , the position of the needle tip can be determined by calculating the radial distance r of its polar coordinates, where r is the distance between the needle tip at the probe vertex and the needle tip. In each frame of the image, the radial distance r is calculated. i (in pixels or millimeters), thus forming a radial distance sequence of the needle tip changing with time or image frame number. This radial distance sequence is used to record the depth change of the puncture needle tip during the entire puncture process.

[0039] Assume that in a certain frame of image, the polar coordinates of the needle tip are (r, θ), where r represents the radial distance and θ represents the angle. In the present invention, the change of the radial distance is focused on, and the change of the angle is ignored.

[0040] The needle tip gray value sequence refers to the gray value sequence corresponding to the position of the needle tip in each frame of gray image. In an ultrasound system, the reflection intensity of a signal is inversely proportional to the square of the distance. Therefore, during the puncture process, as the puncture goes deeper, the gray value of the needle tip on the image will continue to change.

[0041] In the sector grayscale image, the needle tip is located at a specific position (r,θ) in polar coordinates. By obtaining the grayscale value g of this point i , that is, the grayscale intensity value of the pixel where the needle tip is located, can form a needle tip grayscale value sequence corresponding to each frame of the image. The needle tip grayscale value reflects the reflection intensity of the ultrasonic echo signal in the area where the needle tip is located. The needle tip grayscale value sequence in multiple consecutive frames of images is used to record the ultrasonic reflection intensity of the needle tip over time.

[0042] In the ultrasound sector image, the radial distance sequence of the needle tip and the gray value sequence of the needle tip are interrelated, recording the distance of the needle tip relative to the probe and its gray value changes during the puncture process. As the needle tip penetrates into different tissue layers, the radial distance r and the gray value g will change synchronously. This correspondence can provide accurate data support for subsequent image processing algorithms, especially for adjusting the threshold of blood vessel detection.

[0043] Since the material of the puncture needle is usually metal, it can generate significant ultrasonic reflection signals, so the echo signal of the puncture needle is relatively strong in the ultrasonic image, and the needle body and needle tip are usually shown as brighter areas in the grayscale image. This strong signal reflection makes the puncture needle easier to identify in the image, and the converted grayscale image is more referenceable. The present invention will use the needle tip grayscale as a reference for subsequent operations.

[0044] Furthermore, the present invention uses Hough transform to identify the needle tip. Hough transform is a common method for detecting straight lines or curves in an image and is suitable for identifying objects that appear as straight lines in an image. When identifying a puncture needle, Hough transform can identify the straight line structure of the needle body, and the needle tip can be determined by finding the endpoint of the straight line.

[0045] The first distance sequence and the needle tip gray value sequence are fitted into a first curve.

[0046] A first distance sequence of the needle tip relative to the probe identified by the ultrasound image is obtained, and the sequence represents the pixel distance of the needle tip in each image frame. At the same time, a needle tip gray value sequence corresponding to the first distance sequence is obtained, and the gray value sequence represents the reflection intensity of the needle tip in each frame of the gray image, reflecting the echo signal intensity at the position of the needle tip.

[0047] Next, the two sequences are fitted with a fitting algorithm to generate a first curve reflecting the relationship between the needle tip position and the grayscale value. The fitting algorithm can select linear fitting, polynomial fitting, exponential fitting or other suitable data fitting methods according to actual needs, and ensure that the fitting curve can accurately describe the change of the needle tip distance from the probe and the trend of grayscale value change during the puncture process in an optimal fitting manner.

[0048] Specifically, each value r in the first distance sequencei The value g in the corresponding needle tip gray value sequence i One-to-one correspondence, as the coordinate points on the horizontal axis and the vertical axis, respectively, through the data fitting algorithm, a curve representing the relationship between the needle tip distance and the gray value is obtained. The first curve can intuitively reflect the change law of the needle tip reflection signal intensity as the needle tip position (distance from the probe) changes during the puncture process.

[0049] The generated first curve can be used to further adjust the threshold setting in image processing, thereby dynamically optimizing the blood vessel detection and enhancement process according to the depth and reflection intensity of the needle tip.

[0050] Example: If the first distance sequence of the needle tip is [r1,r2,...,r n ]The sequence of the tip grayscale value is [g1,g2,...,g n ] then (r1,g1),(r2,g2),...,(r n ,g n ) as the fitting point, the first curve g(r) generated by the selected fitting algorithm can be expressed as:

[0051] g(r)=f(r);

[0052] Among them, f(r) is the fitted function, which describes the relationship between the needle tip distance and the gray value.

[0053] The blood vessel detection thresholds at different positions on the grayscale image are adjusted according to the first curve.

[0054] A first curve is obtained by fitting the first distance sequence and the needle tip gray value sequence, and the curve reflects the gray value change of the needle tip at different depths. In order to ensure that the gray value of the blood vessel and the gray value of the needle tip maintain a constant proportional relationship in the entire image, the change trend of the needle tip gray value with depth in the first curve is analyzed, and the dynamic threshold of blood vessel detection is set based on this.

[0055] The specific steps are as follows:

[0056] Calculate the blood vessel detection threshold ratio: Set a fixed ratio k between the blood vessel grayscale value and the needle tip grayscale value, that is:

[0057]

[0058] Among them, g vessel is the gray value of blood vessels, g needle is the gray value of the needle tip, and k is a fixed proportional constant. Dynamically calculate the gray value of the needle tip at different positions: According to the first curve, the gray value g of the needle tip needle (r) is the function of the tip changing with depth r. For different depths r, the tip gray value g at that position is calculated by the first curve.needle (r).

[0059] Determine the blood vessel detection threshold: According to the gray value of the needle tip g needle (r) and the set proportional constant k, calculate the blood vessel detection threshold T(r) at the corresponding depth position. The blood vessel detection threshold T(r) should satisfy the following relationship:

[0060] T(r)=k·g needle (r);

[0061] This formula ensures that at different depths r, the detection threshold of the blood vessel is always in a fixed proportion to the grayscale value of the needle tip.

[0062] Dynamically adjust the blood vessel detection threshold at each position: In each frame of the grayscale image, according to the distance of the needle tip (i.e., the pixel position in the first distance sequence), the grayscale value of the needle tip corresponding to the position is calculated by the first curve, and then the above-mentioned ratio formula is applied to dynamically determine the blood vessel detection threshold at the depth. This ensures that the grayscale value ratio of the blood vessel to the needle tip remains constant at each depth, which helps to maintain consistency in image processing.

[0063] Apply the adjusted blood vessel detection threshold: Use the calculated threshold T(r) to detect and enhance blood vessels at various locations in the grayscale image. Since the blood vessel detection threshold is based on the reflection intensity of the needle tip and a fixed proportional relationship, it can ensure that the contrast between the blood vessel and the needle tip remains consistent at different depths, thereby improving the reliability of detection.

[0064] Through the above method, the blood vessel detection threshold is adjusted based on the first curve to ensure that the ratio of blood vessel grayscale to needle tip grayscale is fixed, so that blood vessels at different depths in the entire image can be consistently detected, avoiding the threshold deviation problem caused by the change of reflection signal with depth.

[0065] For example:

[0066] Assume that at a certain depth r1, the tip gray value is g needle (r1) = 200, proportional constant k = 0.8, then the blood vessel detection threshold T(r1) is:

[0067] T(r1)=0.8×200=160;

[0068] Similarly, at another depth r2, if the tip gray value is g needle (r2) = 150, then the corresponding blood vessel detection threshold T(r2) is:

[0069] T(r2)=0.8×150=120;

[0070] This method ensures that at different depths, the blood vessel detection threshold and the needle tip grayscale value maintain a fixed proportional relationship, thereby improving the consistency and accuracy of blood vessel detection.

[0071] The blood vessels on the grayscale image are determined according to the adjusted blood vessel detection threshold, the brightness of the determined blood vessels is enhanced, and the blood vessels are segmented.

[0072] In each frame of grayscale image, a dynamic blood vessel detection threshold adjusted according to the first curve is applied, where the threshold has an upper and lower limit T min (r) and T max (r) to ensure that only vascular structures that meet the grayscale range are detected. The upper and lower thresholds represent the lowest and highest grayscale values ​​of the vascular reflection signal, respectively, and are defined as:

[0073] T min (r)≤g(x,y)≤T max (r);

[0074] Among them, g(x,y) is the gray value of the pixel in the image, T min (r) and T max (r) are the dynamic minimum and maximum vessel detection thresholds associated with the distance r, respectively.

[0075] For each pixel g(x,y), only when its gray value is between the threshold range T min (r) and T max (r), it is determined to be a vascular region. The specific judgment conditions are:

[0076] if T min (r)≤g(x,y)≤T max (r), then (x, y)∈vascular region;

[0077] This step can more accurately exclude interference signals and non-vascular areas by defining upper and lower limits.

[0078] After the blood vessel areas are detected, the grayscale values ​​of these areas are enhanced. The purpose of enhancement is to increase the contrast between the blood vessels and the background, making the blood vessels more prominent in the image. The enhancement method can be linear adjustment or nonlinear adjustment, and the specific calculation is:

[0079] g enhanced (x,y)=α·g vessel (x,y);

[0080] Among them, α is the enhancement coefficient, g enhanced (x, y) is the enhanced gray value, g vessel (x, y) is the original grayscale value, which ensures that the grayscale value of the enhanced vascular area is significantly improved while maintaining the overall grayscale balance of the image.

[0081] Segment the vascular region based on the upper and lower thresholds of the vascular region and the enhanced brightness. Use morphological processing or a connectivity-based segmentation algorithm to extract the complete vascular structure. Specific operations include:

[0082] Morphological processing: Through operations such as dilation and erosion, the edges of blood vessels are optimized and isolated noise is eliminated.

[0083] Region growing: Based on the grayscale value range of the vascular region, it gradually expands from the seed point to ensure that the entire vascular contour is segmented.

[0084] The segmented blood vessel region can be output in the form of a binary image or an enhanced grayscale image for robot image recognition.

[0085] On the other hand, the present invention also provides a blood vessel image enhancement and segmentation system for a puncture robot, comprising the following modules:

[0086] An acquisition module, used for acquiring a plurality of continuous original ultrasonic echo signals;

[0087] A mapping module, used for mapping the plurality of original ultrasonic echo signals into a grayscale image sequence;

[0088] A calculation module, used for obtaining a first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding needle tip grayscale value sequence;

[0089] A fitting module, used for fitting the first distance sequence and the needle tip gray value sequence into a first curve;

[0090] an adjustment module, used for adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve;

[0091] The processing module is used to determine the blood vessels on the grayscale image according to the adjusted blood vessel detection threshold, enhance the brightness of the determined blood vessels, and segment the blood vessels.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0093] The prior art mentioned in the above background technology section and specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters.

Claims

1. A blood vessel image enhancement and segmentation method for a puncture robot, characterized in that: The method comprises the following steps: Acquire a plurality of continuous original ultrasonic echo signals; Mapping the plurality of original ultrasonic echo signals into a grayscale image sequence; Acquire a first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding needle tip grayscale value sequence; Fitting the first distance sequence and the needle tip gray value sequence into a first curve; adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve; The blood vessels on the grayscale image are determined according to the adjusted blood vessel detection threshold, the brightness of the determined blood vessels is enhanced, and the blood vessels are segmented.

2. The blood vessel image enhancement and segmentation method of the puncture robot according to claim 1, characterized in that: Mapping the plurality of original ultrasonic echo signals into a grayscale image sequence comprises: According to a preset mapping function, the amplitude or energy value of each signal is converted into a grayscale value, wherein a higher signal intensity corresponds to a brighter grayscale level and a lower signal intensity corresponds to a darker grayscale level.

3. The blood vessel image enhancement and segmentation method of the puncture robot according to claim 1, characterized in that Use Hough transform to identify the needle tip.

4. The blood vessel image enhancement and segmentation method of the puncture robot according to claim 1, characterized in that: Adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve includes: Set a fixed ratio k between the grayscale value of the blood vessel and the grayscale value of the needle tip, that is: Among them, g vesse l is the gray value of blood vessels, g nee dl e is the gray value of the needle tip, k is a fixed proportional constant; For different depths r, the tip gray value g at that position is calculated by the first curve needle (r); According to the gray value of the needle tip g needle (r) and the set proportional constant k, calculate the blood vessel detection threshold at the corresponding depth position: T(r)=k·g needle (r)。 5. The blood vessel image enhancement and segmentation method of the puncture robot according to claim 1, characterized in that: Determining the blood vessels on the grayscale image according to the adjusted blood vessel detection threshold includes: In each frame of grayscale image, the dynamic vessel detection threshold adjusted according to the first curve is applied, where the vessel detection threshold is the dynamic minimum and maximum vessel detection threshold T associated with the distance r. min (r) and T max (r), For each pixel (x, y), only when its gray value g(x, y) is between the threshold range T min (r) and T max (r), it is determined to be a vascular area.

6. A blood vessel image enhancement and segmentation system for a puncture robot, characterized in that: The system includes the following modules: An acquisition module, used for acquiring a plurality of continuous original ultrasonic echo signals; A mapping module, used for mapping the plurality of original ultrasonic echo signals into a grayscale image sequence; A calculation module, used for obtaining a first distance sequence between the puncture needle tip and the probe on the grayscale image and a corresponding needle tip grayscale value sequence; A fitting module, used for fitting the first distance sequence and the needle tip gray value sequence into a first curve; an adjustment module, used for adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve; The processing module is used to determine the blood vessels on the grayscale image according to the adjusted blood vessel detection threshold, enhance the brightness of the determined blood vessels, and segment the blood vessels.

7. The blood vessel image enhancement and segmentation system of the puncture robot according to claim 6, characterized in that: Mapping the plurality of original ultrasonic echo signals into a grayscale image sequence comprises: According to a preset mapping function, the amplitude or energy value of each signal is converted into a grayscale value, wherein a higher signal intensity corresponds to a brighter grayscale level and a lower signal intensity corresponds to a darker grayscale level.

8. The blood vessel image enhancement and segmentation system of the puncture robot according to claim 6, characterized in that Use Hough transform to identify the needle tip.

9. The blood vessel image enhancement and segmentation system of the puncture robot according to claim 6, characterized in that: Adjusting the blood vessel detection thresholds at different positions on the grayscale image according to the first curve includes: Set a fixed ratio k between the grayscale value of the blood vessel and the grayscale value of the needle tip, that is: Among them, g vesse l is the gray value of blood vessels, g nee dl e is the gray value of the needle tip, k is a fixed proportional constant; For different depths r, the tip gray value g at that position is calculated by the first curve needle (r); According to the gray value of the needle tip g needle (r) and the set proportional constant k, calculate the blood vessel detection threshold at the corresponding depth position: T(r)=k·g needle (r)。 10. The blood vessel image enhancement and segmentation system of the puncture robot according to claim 6, characterized in that: Determining the blood vessels on the grayscale image according to the adjusted blood vessel detection threshold includes: In each frame of grayscale image, the dynamic vessel detection threshold adjusted according to the first curve is applied, where the vessel detection threshold is the dynamic minimum and maximum vessel detection threshold T associated with the distance r. min (r) and T max (r) For each pixel (x, y), only when its gray value g(x, y) is between the threshold range T min (r) and T max (r), it is determined to be a vascular area.

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