CN109272485B - Method and device for repairing three-dimensional model of blood vessel and electronic equipment - Google Patents
Method and device for repairing three-dimensional model of blood vessel and electronic equipment Download PDFInfo
- Publication number
- CN109272485B CN109272485B CN201810893094.8A CN201810893094A CN109272485B CN 109272485 B CN109272485 B CN 109272485B CN 201810893094 A CN201810893094 A CN 201810893094A CN 109272485 B CN109272485 B CN 109272485B
- Authority
- CN
- China
- Prior art keywords
- point
- blood vessel
- gray
- pixel point
- images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 210000004204 blood vessel Anatomy 0.000 title claims abstract description 263
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000005381 potential energy Methods 0.000 claims abstract description 86
- 230000000694 effects Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 8
- 230000008439 repair process Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 11
- 210000003445 biliary tract Anatomy 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 206010004637 Bile duct stone Diseases 0.000 description 2
- 208000031481 Pathologic Constriction Diseases 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 208000037804 stenosis Diseases 0.000 description 2
- 230000036262 stenosis Effects 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 230000001131 transforming effect Effects 0.000 description 2
- 208000015163 Biliary Tract disease Diseases 0.000 description 1
- 238000002583 angiography Methods 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 210000000013 bile duct Anatomy 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 208000003167 cholangitis Diseases 0.000 description 1
- 210000003459 common hepatic duct Anatomy 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 210000003228 intrahepatic bile duct Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000002945 steepest descent method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000019553 vascular disease Diseases 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The embodiment of the invention discloses a method, a device and electronic equipment for repairing a three-dimensional model of a blood vessel, wherein the method comprises the following steps: acquiring position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model and M CT images corresponding to the blood vessel disconnected region; obtaining a gray contrast enhancement coefficient of each pixel point in M CT images based on the historical gray information of the blood vessel of the three-dimensional blood vessel model; determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images; and determining the shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path. And then, the blood vessel three-dimensional model of the blood vessel disconnected region is automatically repaired without manual participation, so that the repairing efficiency is improved, and the repairing precision is high.
Description
Technical Field
The embodiment of the invention relates to the technical field of image processing, in particular to a method and a device for repairing a three-dimensional model of a blood vessel and electronic equipment.
Background
The blood vessels of the human body are complicated and numerous in fine branch and minor node. The original CT image often has many noises due to imaging devices, manual operations, etc., and the gray scale of the blood vessels on the image is not uniform, so that there are often disconnected parts during the blood vessel extraction, and at this time, the blood vessels in the disconnected region need to be repaired.
In the prior art, a blood vessel in an area of disconnection is repaired manually, specifically, a disconnection position on a three-dimensional model is mapped to a two-dimensional image to obtain a plurality of two-dimensional images. And then, manually drawing and repairing the blood vessel of the disconnected region on each two-dimensional image, and finally, superposing the repaired two-dimensional images to obtain a modified three-dimensional model of the blood vessel.
As can be seen from the above, the prior art has the disadvantages of heavy repair task, high manpower resource consumption and long repair time.
Disclosure of Invention
The embodiment of the invention provides a method and a device for repairing a three-dimensional model of a blood vessel and electronic equipment.
In a first aspect, an embodiment of the present invention provides a method for repairing a three-dimensional model of a blood vessel, including:
acquiring position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model and M CT images corresponding to the blood vessel disconnected region;
based on the historical gray information of the blood vessel of the three-dimensional blood vessel model, correcting the gray value of each pixel point in the M CT images to obtain the gray contrast enhancement coefficient of each pixel point in the M CT images;
determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images;
and determining a shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
In a possible implementation manner of the first aspect, the modifying the gray scale value of each pixel point in the M CT images based on the historical gray scale information of the blood vessel of the three-dimensional blood vessel model to obtain the gray scale contrast enhancement coefficient of each pixel point in the M CT images includes:
acquiring a gray level histogram of historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shifting a gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value;
and correcting the first pixel points with the gray values lower than the target gray average value in the M CT images to obtain the gray contrast enhancement coefficient of each first pixel point in the M CT images.
In another possible implementation manner of the first aspect, the modifying first pixel points in the M CT images, whose gray values are lower than the target gray average value, to obtain a gray contrast enhancement coefficient of each first pixel point in the M CT images includes:
and taking the square of the ratio of the gray value of the first pixel point to the average value of the target gray values as a gray contrast enhancement coefficient of the first pixel point.
In another possible implementation manner of the first aspect, the determining potential energy of each pixel point in the M CT images according to the gray scale contrast enhancement coefficient of each pixel point in the M CT images includes:
aiming at each pixel point in M CT images, determining an included angle between a connecting line of the pixel point and the starting point and a connecting line of the starting point and the end point according to the position information of the starting point and the end point;
and determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value.
In another possible implementation manner of the first aspect, the determining, according to the position information of the start point and the end point, an included angle between a connection line between the pixel point and the start point and a connection line between the start point and the end point includes:
according to the formula
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
In another possible implementation manner of the first aspect, the determining the potential energy of the pixel point according to the included angle, the gray scale contrast enhancement coefficient of the pixel point, the gray scale value of the pixel point, and the target gray scale average value includes:
according to the formula
Determining potential energy P (x) of the pixel point x;
wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants.
In another possible implementation manner of the first aspect, the determining the shortest path between the starting point and the ending point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the ending point includes:
determining a minimum activity graph between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and processing the minimum activity graph based on the steepest descent direction, and determining the shortest path between the starting point and the end point.
In another possible implementation manner of the first aspect, the repairing the three-dimensional model of the blood vessel disconnected region according to the shortest path includes:
obtaining the blood vessel radius corresponding to each pixel point on the shortest path, and obtaining the blood vessel section surrounded by the blood vessel radius corresponding to each pixel point;
and connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel in the disconnected region of the blood vessel.
In a second aspect, the present embodiment provides a repairing apparatus for a three-dimensional model of a blood vessel, including:
the acquisition module is used for acquiring the position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model and M CT images corresponding to the blood vessel disconnected region;
an enhancement coefficient obtaining module, configured to modify a gray scale value of each pixel in the M CT images based on historical gray scale information of the blood vessel of the three-dimensional blood vessel model, so as to obtain a gray scale contrast enhancement coefficient of each pixel in the M CT images;
the potential energy determining module is used for determining the potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images;
the shortest path determining module is used for determining the shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and the repairing module is used for repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
In a possible implementation manner of the second aspect, the enhancement coefficient obtaining module includes:
the average gray level obtaining unit is used for obtaining a gray level histogram of the historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shifting the gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value;
and the enhancement coefficient acquisition unit is used for correcting the first pixel points with the gray values lower than the target gray average value in the M CT images to acquire the gray contrast enhancement coefficient of each first pixel point in the M CT images.
In another possible implementation manner of the second aspect, the enhancement coefficient obtaining unit is specifically configured to use a square of a ratio of the gray value of the first pixel to the target gray average value as the gray contrast enhancement coefficient of the first pixel.
In another possible implementation manner of the second aspect, the potential energy determination module includes:
an included angle obtaining unit, configured to determine, for each pixel point in the M CT images, an included angle between a connection line between the pixel point and the start point and a connection line between the start point and the end point according to the position information of the start point and the end point;
and the potential energy determining unit is used for determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value.
In another possible implementation manner of the second aspect, the included angle obtaining unit is specifically configured to,
according to the formula
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
In another possible implementation manner of the second aspect, the potential energy determination unit is specifically configured to,
according to the formula
Determining potential energy P (x) of the pixel point x;
wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants.
In another possible implementation manner of the second aspect, the shortest path determining module includes:
the minimum activity graph determining unit is used for determining a minimum activity graph between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and the shortest path determining unit is used for processing the minimum activity graph based on the steepest descent direction and determining the shortest path between the starting point and the end point.
In another possible implementation manner of the second aspect, the shortest path determining unit is specifically configured to obtain a blood vessel radius corresponding to each pixel point on the shortest path, and a blood vessel section surrounded by the blood vessel radius corresponding to each pixel point; and connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel in the disconnected region of the blood vessel.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a memory for storing a computer program;
a processor for executing the computer program to implement the method for repairing a three-dimensional model of a blood vessel according to any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium, in which a computer program is stored, and the computer program, when executed, implements the method for repairing a three-dimensional model of a blood vessel according to any one of the first aspect.
According to the method, the device and the electronic equipment for repairing the three-dimensional model of the blood vessel, provided by the embodiment of the invention, the position information of the starting point and the end point of the disconnected region of the blood vessel in the three-dimensional blood vessel model and M CT images corresponding to the disconnected region of the blood vessel are obtained; based on the historical gray information of the blood vessel of the three-dimensional blood vessel model, correcting the gray value of each pixel point in the M CT images to obtain the gray contrast enhancement coefficient of each pixel point in the M CT images; determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images; and determining a shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path. According to the method, the three-dimensional model of the blood vessel in the blood vessel disconnection area is automatically repaired without manual participation, so that manpower is liberated, and the repairing efficiency is improved. Meanwhile, the gray contrast enhancement coefficient of each pixel point in the M CT images corresponding to the blood vessel disconnected region is determined, and the potential energy of each pixel point is corrected by using the gray contrast enhancement coefficient, so that the shortest path between the starting point and the end point obtained based on the corrected potential energy is more accurate, and the accurate repair of the blood vessel three-dimensional model can be realized based on the accurate shortest path.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a blood vessel disconnected region in a three-dimensional model of a blood vessel according to an embodiment;
FIG. 3 is a schematic diagram of a start point and an end point of a blood vessel disconnected region in the three-dimensional model of a blood vessel according to the embodiment;
FIG. 4 is a schematic diagram of a repaired three-dimensional model of a blood vessel according to an embodiment;
FIG. 5 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to a second embodiment of the present invention;
fig. 6 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to a third embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a repairing apparatus for a three-dimensional model of a blood vessel according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a repairing apparatus for a three-dimensional model of a blood vessel according to a second embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a repairing apparatus for a three-dimensional model of a blood vessel according to a third embodiment of the present invention;
FIG. 10 is a schematic structural diagram of a repairing apparatus for a three-dimensional model of a blood vessel according to a fourth embodiment of the present invention;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Vascular diseases are becoming one of the most important diseases worldwide because of their high mortality, high disability rate and high medical risk, and becoming the first killer to endanger human health. For example, biliary tract diseases such as bile duct stones and biliary tract stenosis are common diseases in biliary tract surgery, and due to intrahepatic bile duct stones, cholangitis and biliary tract obstruction, the biliary tract structure may be distorted, deformed, expanded and narrowed, which may also cause changes in other hepatic duct systems. The clear structures of blood vessels and bile ducts in medical images have very important significance for the auxiliary diagnosis, treatment and surgical planning of diseases.
The blood vessels of the human body are arteries and veins, which are densely and hemp-interwoven into a net, and the blood vessels of an adult are more than 1000 hundred million in size and small in size. When the vein-phase or artery-phase blood vessels are automatically generated by using the region segmentation, the vein-phase or artery-phase blood vessels often have disconnected places when the blood vessels are thin or the characteristics of the image blood vessels are not obvious due to Digital Imaging and Communications in Medicine (DICOM). At this time, the prior art uses a manual drawing mode to manually correct the blood vessel. However, the manual repair of blood vessels is cumbersome, consumes human resources, and takes a long time to repair.
In order to solve the above technical problem, in the embodiment of the present application, a gray contrast enhancement coefficient of a pixel point in M CT images corresponding to a blood vessel disconnected region is obtained, potential energy of each pixel point in the M CT images is improved by using the gray contrast enhancement coefficient, a shortest path between a start point and an end point of the blood vessel disconnected region is determined based on the improved potential energy, and a blood vessel three-dimensional model can be repaired according to the shortest path. The whole repairing process is automatically carried out, does not need the participation of manpower, liberates the manpower, and has simple repairing process, high repairing speed and high repairing precision.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention. As shown in fig. 1, the method of this embodiment may include:
s101, obtaining position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model, and M CT images corresponding to the blood vessel disconnected region.
The execution subject of the embodiment is an electronic device having a function of repairing a three-dimensional model of a blood vessel, and specifically is a processor in the electronic device.
Currently, some examples of electronic devices are: a mobile phone (mobile phone), a tablet computer, a notebook computer, a palm top computer, a Mobile Internet Device (MID), a wearable device, a Virtual Reality (VR) device, an Augmented Reality (AR) device, a wireless terminal in industrial control (industrial control), a wireless terminal in self driving (self driving), a wireless terminal in remote surgery (remote medical supply), a wireless terminal in smart grid (smart grid), a wireless terminal in transportation safety (smart security), a wireless terminal in city (smart city), a wireless terminal in home (smart home), and the like.
The embodiment does not limit the specific process of obtaining the three-dimensional blood vessel model, for example, the electronic device may obtain the three-dimensional blood vessel model from a database. Optionally, the electronic device of this embodiment has a scanning module, and can perform scanning (for example, scanning a patient) to obtain a three-dimensional blood vessel model.
The three-dimensional blood vessel model obtained in this example has a blood vessel disconnected region (region in a circle in fig. 2).
At this time, the present embodiment needs to acquire coordinates of the start point and the end point of the blood vessel disconnected region in fig. 2, that is, coordinates of the start point a and the end point b as shown in fig. 3.
As shown in fig. 2, due to a scanning device, etc., when a blood vessel is extracted from a CT image, incomplete blood vessel extraction occurs, and a blood vessel disconnected region (a region in a dotted circle in fig. 2) occurs.
Before automatic repair of a three-dimensional model of a blood vessel is carried out, two three-dimensional points (namely a starting point and an end point) at the connection part of a blood vessel break are obtained, and coordinates of the two points on a two-dimensional screen are obtained.
The process of acquiring the starting point a and the end point b comprises the following steps:
and clicking the disconnected position of the three-dimensional model of the blood vessel by the mouse to obtain the two-dimensional screen coordinate of the mouse click position. And then loading a three-dimensional model of the blood vessel to be repaired, and acquiring the three-dimensional coordinates of all the vertexes. And transforming the vertex into a model view matrix coordinate system through the model view matrix, transforming the vertex in the model view matrix into a projection matrix, and calculating the 2D screen coordinate of the current 3D vertex through the position and the size of the viewport. And circularly traversing the screen coordinates of the vertex and the screen coordinates acquired by the mouse, taking the point columns which are equal within a certain threshold value as interest points, selecting the point with the minimum depth value, returning the corresponding three-dimensional vertex coordinates, namely the points acquired by the mouse, and storing the three-dimensional vertex coordinates. Through the above steps, the coordinates of the start point a and the end point b can be obtained.
Optionally, the coordinates of the starting point a and the ending point b may also be determined according to other existing methods, which is not limited in this embodiment.
After the starting point and the end point of the blood vessel disconnected region are obtained according to the method, M CT images corresponding to the blood vessel disconnected region are obtained, wherein M is a positive integer.
For example, the blood vessel disconnection region is divided into M along the vertical direction of the blood vessel disconnection region, a CT image of the cross section of each blood vessel disconnection region is obtained, and M CT images are obtained.
The size of the M in this embodiment is not limited, and is determined specifically according to actual needs.
S102, based on the historical gray level information of the blood vessel of the three-dimensional blood vessel model, correcting the gray level value of each pixel point in the M CT images to obtain the gray level contrast enhancement coefficient of each pixel point in the M CT images.
The blood vessel historical grayscale information of the three-dimensional blood vessel model in this embodiment may be grayscale information of N historical CT images of the three-dimensional blood vessel model, where the grayscale information of the N historical CT images may be directly read from other devices, or the grayscale information of each CT image may be obtained by processing the N historical CT images.
In the above-described manner, the three-dimensional blood vessel model corresponding to the blood vessel history gray scale information does not have the blood vessel disconnected region in S101, so that the gray scale information of the blood vessel disconnected region in S101 can be obtained from the gray scale information of the N history CT images of the three-dimensional blood vessel model.
And in the step S101, the gray scale information of the three-dimensional blood vessel model changes due to the presence of the blood vessel disconnected region, and at this time, the gray scale value of each pixel point in the M CT images can be corrected by using the blood vessel historical gray scale information of the three-dimensional blood vessel model, so as to obtain the gray scale contrast enhancement coefficient of each pixel point in the M CT images.
In one example, a target gray average value of historical gray information of a blood vessel of a three-dimensional blood vessel model is obtained, and the gray value of each pixel point in M CT images is corrected by using the target gray average value, so that a gray contrast enhancement coefficient of each pixel point in the M CT images is obtained.
In another example, a pixel point 2 corresponding to each pixel point 1 in M CT images in the blood vessel historical gray information of the three-dimensional blood vessel model is obtained, and the gray value of the pixel point 1 in the M CT images is corrected by using the gray value of the pixel point 2 in the blood vessel historical gray information, so as to obtain the gray contrast enhancement coefficient of each pixel point 1 in the M CT images.
In this embodiment, the specific process of obtaining the gray contrast enhancement coefficient of each pixel point in the M CT images by correcting the gray value of each pixel point in the M CT images based on the historical gray information of the blood vessel based on the three-dimensional blood vessel model is not limited.
S103, determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images.
Specifically, according to the obtained gray contrast enhancement coefficient of each pixel point in the M CT images, the potential energy corresponding to each pixel point in the M CT images is corrected by using the gray contrast enhancement coefficient of each pixel point in the M CT images, and each corrected potential energy is divided into potential energy parts serving as the potential energy of each pixel point in the M CT images.
In an example, taking a pixel point x in M CT images as an example, according to the above steps, the gray contrast increasing coefficient of the pixel point x is obtained as g (p (x)), where p (x) is the gray value of the pixel point x.
At this time, according to the existing method, the potential energy P0(x) before the correction of the pixel point x is determined, then, g (P (x)) is used to correct the potential energy P0(x), so as to obtain the potential energy of the corrected pixel point x, and the potential energy is used as the final potential energy P (x) of the pixel point x. For example, the product or weighted product of g (P (x)) and P0(x) is taken as P (x). Optionally, any parameter in P0(x) may be modified by g (P (x)), so as to obtain P (x).
In another example, the gray value p (x) of the pixel point x is corrected by using g (p (x)), and the final potential energy p (x) of the pixel point x is determined according to the corrected gray value p (x).
Optionally, the following method may also be used to obtain the final potential energy p (x) of the determined pixel point x.
The method can obtain the potential energy of each pixel point in M CT images.
S104, determining a shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
Specifically, potential energy of each pixel point in the M CT images is obtained according to the steps, and thus the shortest path between the starting point and the end point is found according to the potential energy of each pixel point in the M CT images, the starting point and the end point.
For example, traversing each pixel point in the field of the starting point a by taking the starting point a as a middle point, traversing each pixel point c in the field of the starting point a, then traversing each pixel point in the field of the c point by taking the c point as a middle point, obtaining a pixel point d with the minimum potential energy in the field, repeating the steps until the position of the end point b is found, connecting the pixel points with the minimum potential energy between the points a and b, and obtaining the shortest path between the starting point a and the end point b.
Optionally, the shortest path between the starting point a and the ending point b may also be obtained by using other shortest path search algorithms (e.g., ant colony algorithm, genetic algorithm, etc.).
In a possible implementation manner of this embodiment, the determining, in the step S104, a shortest path between the starting point and the ending point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the ending point may further include:
s104a, determining a minimum activity graph between the starting point and the ending point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the ending point.
S104b, processing the minimum activity graph based on the steepest descent method, and determining the shortest path between the starting point and the end point.
Specifically, the starting point x is obtained according to the formula (1)startAnd the minimum acquisition graph between the pixel points x
Wherein,
as a starting point xstartThe smallest figure obtained between the two and the pixel point x, and ^ jΩP (C (s)) is an energy minimization model, s is an arc length parameter on the curve C,
represents xstartAll paths to pixel point x.
Taking the formula (2) and the formula (3) as boundary conditions of the formula (1), and carrying out the iteration until the end point x is foundendSo as to obtain the minimum activity diagram of the starting point and the end point
Then, the minimum activity graph is aligned based on the steepest descent direction
And processing to determine the shortest path between the starting point and the end point.
For example, according to equation (4), the shortest path between the starting point and the ending point can be obtained by a backtracking method.
Wherein, C (0) ═ xend
According to the embodiment, the minimum activity graph between the starting point and the end point is determined according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, the minimum activity graph is processed based on the steepest descent direction, the shortest path between the starting point and the end point is accurately determined, and the accuracy of the blood vessel repair based on the shortest path is further improved.
And obtaining the shortest path between the starting point and the end point according to the method, and then repairing the blood vessel three-dimensional model of the blood vessel disconnected region based on the shortest path.
For example, the radius of the blood vessel in the blood vessel disconnected region can be obtained according to the blood vessel historical gray scale information of the three-dimensional blood vessel model, and the shortest path is taken as a central line, so that the repair of the blood vessel three-dimensional model in the blood vessel disconnected region can be completed.
Optionally, the radius of the blood vessel at the starting point and the ending point may also be obtained, the radius of the blood vessel at the starting point is taken as the scanning starting point, the radius of the blood vessel at the ending point is taken as the scanning ending point, and the shortest path is taken as the central line, so that the blood vessel three-dimensional model of the blood vessel disconnected region can be obtained through scanning.
In a possible implementation manner of this embodiment, the repairing the three-dimensional model of the blood vessel in the blood vessel disconnected region according to the shortest path in S104 may include:
s104c, obtaining the blood vessel radius corresponding to each pixel point on the shortest path, and obtaining the blood vessel section surrounded by the blood vessel radius corresponding to each pixel point.
S104d, connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel disconnected region.
Specifically, according to the image corresponding to the blood vessel disconnected region, the blood vessel radius corresponding to each pixel point on the shortest path is obtained, for example, the blood vessel radius corresponding to each pixel point on the shortest path is obtained through a ray-casting algorithm. At this time, taking a pixel point k on the shortest path as an example, taking the pixel point k as a circle center, and taking a blood vessel radius corresponding to the pixel point k as a radius, a blood vessel section can be generated. If the shortest path includes Q pixel points, Q vessel sections can be obtained.
And superposing the Q blood vessel sections, and generating a blood vessel three-dimensional surface patch by using a sliding cube method, thereby obtaining a blood vessel three-dimensional model of the blood vessel disconnected region as shown in fig. 4.
According to the method for repairing the three-dimensional model of the blood vessel, provided by the embodiment of the invention, the position information of the starting point and the end point of the disconnected region of the blood vessel in the three-dimensional blood vessel model and M CT images corresponding to the disconnected region of the blood vessel are obtained; based on the historical gray information of the blood vessel of the three-dimensional blood vessel model, correcting the gray value of each pixel point in the M CT images to obtain the gray contrast enhancement coefficient of each pixel point in the M CT images; determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images; and determining a shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path. According to the method, the three-dimensional model of the blood vessel in the blood vessel disconnection area is automatically repaired without manual participation, so that manpower is liberated, and the repairing efficiency is improved. Meanwhile, the gray contrast enhancement coefficient of each pixel point in the M CT images corresponding to the blood vessel disconnected region is determined, and the potential energy of each pixel point is corrected by using the gray contrast enhancement coefficient, so that the shortest path between the starting point and the end point obtained based on the corrected potential energy is more accurate, and the accurate repair of the blood vessel three-dimensional model can be realized based on the accurate shortest path.
Fig. 5 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to a second embodiment of the present invention. On the basis of the above embodiment, the present embodiment relates to a specific process of correcting the gray scale value of each pixel point in the M CT images based on the historical gray scale information of the blood vessel of the three-dimensional blood vessel model to obtain the gray scale contrast enhancement coefficient of each pixel point in the M CT images. As shown in fig. 5, the S102 may include:
s201, obtaining a gray level histogram of historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shifting a gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value.
Specifically, the blood vessel gray values of the N historical CT images of the three-dimensional blood vessel model are obtained as the blood vessel historical gray information of the three-dimensional blood vessel model. The grey values of the blood vessels of the N historical CT images form a grey histogram with the interval of [0,255], the abscissa represents the size of the grey value, and the ordinate represents the number of pixel points.
According to the gray level histogram, a curve (close to gaussian distribution) is fitted, and the gray level mean value of the gray level histogram can be obtained according to the curve. Because the gray-scale values of the pixels in the disconnected region of the blood vessel are lower than those of the extracted part due to the problems of angiography and stenosis, the average value of the curve (i.e., the gray-scale average value of the gray-scale histogram) is shifted to the left by a preset pixel value, for example, by 10 pixel values, based on the above assumption, and the target gray-scale average value is obtained.
S202, correcting the first pixel points with the gray values lower than the target gray average value in the M CT images to obtain the gray contrast enhancement coefficient of each first pixel point in the M CT images.
And dividing each pixel point in the M CT images into a first pixel point and a second pixel point according to the target gray average value, specifically, marking the pixel points with the gray values smaller than the target gray average value as the first pixel points, and marking the pixel points with the gray values larger than or equal to the target gray average value as the second pixel points.
The gray value of the second pixel point is larger, which indicates that the second pixel point is a background or a known blood vessel, and therefore, the second pixel point is not corrected, that is, the gray contrast enhancement coefficient of the second pixel point is 1.
And if the gray value of the first pixel point is smaller, the probability that the first pixel point is a pixel point in the blood vessel disconnected region is larger, so that the first pixel point is corrected according to the target gray average value to obtain the gray contrast enhancement coefficient of the first pixel point.
In this embodiment, the gray contrast enhancement coefficient of the first pixel point is determined, and the first pixel point with the gray level smaller than the average gray level is suppressed, so that the enhancement of the blood vessel is realized, and the energy value corresponding to the first pixel point is lower when the minimum path is searched, so that the first pixel point is preferentially traversed in the backtracking process.
Optionally, in this embodiment, a ratio of the gray value of the first pixel point to the target gray average value may be used as the gray contrast enhancement coefficient of the first pixel point.
Optionally, the difference between the gray value of the first pixel point and the target gray average value is used as the gray contrast enhancement coefficient of the first pixel point.
Optionally, other modes may also be used, and the gray value of the first pixel point is corrected according to the target gray average value to obtain the gray contrast enhancement coefficient of the first pixel point.
In a possible implementation manner of this embodiment, the step S202 may include:
s202a, taking the square of the ratio of the gray value of the first pixel point to the target gray average value as the gray contrast enhancement coefficient of the first pixel point.
For example,
wherein, g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, and mean _ grey is a target gray average value.
The gray contrast enhancement coefficient of each pixel point in the M CT images can be obtained according to the formula (5).
According to the method for repairing the blood vessel three-dimensional model, provided by the embodiment of the invention, the gray level histogram of the historical gray level information of the blood vessel of the three-dimensional blood vessel model is obtained, the gray level mean value of the gray level histogram is shifted to the left by a preset pixel value, a target gray level mean value is obtained, the first pixel points of which the gray level values are lower than the target gray level mean value in the M CT images are corrected, the gray level contrast enhancement coefficients of the first pixel points in the M CT images are obtained, and the accurate determination of the gray level contrast enhancement coefficients of the pixel points in the M CT images is further realized.
Fig. 6 is a flowchart of a method for repairing a three-dimensional model of a blood vessel according to a third embodiment of the present invention. On the basis of the above embodiment, the specific process of determining the potential energy of each pixel point in the M CT images according to the gray scale contrast enhancement coefficient of each pixel point in the M CT images according to the embodiment is described. As shown in fig. 6, the S103 may include:
s301, aiming at each pixel point in the M CT images, according to the position information of the starting point and the end point, determining an included angle between a connecting line of the pixel point and the starting point and a connecting line of the starting point and the end point.
In this embodiment, an included angle between each pixel point in the M CT images and a connection line T formed by the starting point and the ending point is determined, and the potential energy of each pixel point is determined according to the included angle, the gray scale contrast enhancement coefficient of the pixel point, the gray scale value of the pixel point, and the like.
For convenience of explanation, a pixel point x in M CT images is taken as an example for explanation, and other pixel points are referred to.
Firstly, a connecting line T between a starting point and an end point is obtained, then, a connecting line L between a pixel point X and the starting point is obtained, and an included angle between the T and the L can be obtained by utilizing a geometrical relation according to coordinates of the starting point, the end point and the X point.
In a possible implementation manner of this embodiment, the step S301 may include:
according to the formula
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
Specifically, a starting point x is obtainedstartTo the end point xendVector of (2)
And a starting point xstartDistance x-x to pixel point xstart. Then, the pixel point x and the starting point x can be obtained according to the above formula (6)startAnd the angle theta between the connecting line of the starting point and the end point.
With reference to the above description, each pixel point and the starting point x in M CT images can be obtainedstartAnd the angle between the connecting line of the starting point and the end point.
S302, determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value.
In this step, the pixel point x is taken as an example for explanation, and other pixel points are referred to.
According to the steps, an included angle between the pixel point x and a connecting line of the starting point and the end point can be obtained, and then according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the obtained target gray average value, the potential energy of the pixel point x can be determined.
In an example, the gray scale contrast enhancement coefficient of the pixel point x is used to correct the gray scale value of the pixel point x (for example, the gray scale contrast enhancement coefficient of the pixel point x is multiplied by the gray scale value of the pixel point x), and the corrected gray scale value, the included angle, the target gray scale average value, and the like are correspondingly calculated to obtain the potential energy of the pixel point.
In another example, the potential energy of a pixel point can be determined by equation (7),
in a possible implementation manner of this embodiment, the step S302 may include:
determining the potential energy P (x) of the pixel point x according to the formula (8)
Wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants. For example, λ is 1000 and β is 10.
The method for repairing the three-dimensional model of the blood vessel provided by the embodiment of the invention is characterized in that aiming at each pixel point in M CT images, according to the position information of the starting point and the end point, an included angle between a connecting line of the pixel point and the starting point and a connecting line of the starting point and the end point is determined; and determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value, and further realizing accurate determination of the potential energy of each pixel point, so that the repair of the three-dimensional model of the blood vessel based on the potential energy of the pixel point is more accurate.
Fig. 7 is a schematic structural diagram of a repair apparatus for a three-dimensional model of a blood vessel according to an embodiment of the present invention. As shown in fig. 7, the repair apparatus 100 for a three-dimensional model of a blood vessel of the present embodiment may include:
an obtaining module 110, configured to obtain position information of a start point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model, and M CT images corresponding to the blood vessel disconnected region;
an enhancement coefficient obtaining module 120, configured to modify a gray scale value of each pixel in the M CT images based on historical gray scale information of the blood vessel of the three-dimensional blood vessel model, so as to obtain a gray scale contrast enhancement coefficient of each pixel in the M CT images;
a potential energy determining module 130, configured to determine potential energy of each pixel point in the M CT images according to a gray contrast enhancement coefficient of each pixel point in the M CT images;
a shortest path determining module 140, configured to determine a shortest path between the starting point and the ending point according to potential energy of each pixel point in the M CT images and position information of the starting point and the ending point;
and the repairing module 150 is used for repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
The device for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention may be used to implement the technical solution of the above-described method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 8 is a schematic structural diagram of a repair device for a three-dimensional model of a blood vessel according to a second embodiment of the present invention. As shown in fig. 8, the enhancement coefficient obtaining module 120 further includes:
an average gray level obtaining unit 121, configured to obtain a gray level histogram of historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shift a gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value;
and the enhancement coefficient obtaining unit 122 is configured to correct the first pixel points in the M CT images, where the gray values of the first pixel points are lower than the target gray average value, to obtain a gray contrast enhancement coefficient of each first pixel point in the M CT images.
In a possible implementation manner of this embodiment, the enhancement coefficient obtaining unit 122 is specifically configured to use a square of a ratio of the gray value of the first pixel to the target gray average value as the gray contrast enhancement coefficient of the first pixel.
The device for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention may be used to implement the technical solution of the above-described method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 9 is a schematic structural diagram of a repair device for a three-dimensional model of a blood vessel according to a third embodiment of the present invention. As shown in fig. 9, the potential energy determination module 130 further includes:
an included angle obtaining unit 131, configured to determine, for each pixel point in the M CT images, an included angle between a connection line between the pixel point and the start point and a connection line between the start point and the end point according to the position information of the start point and the end point;
the potential energy determining unit 132 is configured to determine the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point, and the target gray average value.
In a possible implementation manner of this embodiment, the included angle obtaining unit 131 is specifically configured to,
according to the formula
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
In another possible implementation manner of this embodiment, the potential energy determination unit 132 is specifically configured to,
according to the formula
Determining potential energy P (x) of the pixel point x;
wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants.
The device for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention may be used to implement the technical solution of the above-described method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 10 is a schematic structural diagram of a repair apparatus for a three-dimensional model of a blood vessel according to a fourth embodiment of the present invention. As shown in fig. 10, the shortest path determining module 140 includes:
a minimum activity graph determining unit 141, configured to determine a minimum activity graph between the starting point and the ending point according to potential energy of each pixel point in the M CT images and position information of the starting point and the ending point;
a shortest path determining unit 142, configured to process the minimum activity graph based on a steepest descent direction, and determine a shortest path between the starting point and the end point.
In a possible implementation manner of this embodiment, the shortest path determining unit 142 is specifically configured to obtain a blood vessel radius corresponding to each pixel point on the shortest path, and a blood vessel section surrounded by the blood vessel radius corresponding to each pixel point; and connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel in the disconnected region of the blood vessel.
The device for repairing a three-dimensional model of a blood vessel according to an embodiment of the present invention may be used to implement the technical solution of the above-described method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 11, an electronic device 300 according to the embodiment includes:
a memory 310 for storing a computer program;
the processor 320 is configured to execute the computer program to implement the method for repairing a three-dimensional model of a blood vessel, which has similar implementation principles and technical effects, and will not be described herein again.
Further, when at least a part of the functions of the method for repairing a three-dimensional model of a blood vessel according to the embodiments of the present invention are implemented by software, the embodiments of the present invention further provide a computer storage medium for storing computer software instructions for repairing the three-dimensional model of the blood vessel, which, when executed on a computer, enable the computer to perform various possible methods for repairing a three-dimensional model of a blood vessel according to the embodiments of the present invention. The processes or functions described in accordance with the embodiments of the present invention may be generated in whole or in part when the computer-executable instructions are loaded and executed on a computer. The computer instructions may be stored on a computer storage medium or transmitted from one computer storage medium to another via wireless (e.g., cellular, infrared, short-range wireless, microwave, etc.) to another website site, computer, server, or data center. The computer storage media may be any available media that can be accessed by a computer or a data storage device, such as a server, data center, etc., that incorporates one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), among others.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (18)
1. A method for repairing a three-dimensional model of a blood vessel, comprising:
acquiring position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model and M CT images corresponding to the blood vessel disconnected region;
based on the historical gray information of the blood vessel of the three-dimensional blood vessel model, correcting the gray value of each pixel point in the M CT images to obtain the gray contrast enhancement coefficient of each pixel point in the M CT images;
determining potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images;
and determining a shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point, and repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
2. The method according to claim 1, wherein the modifying the gray scale value of each pixel point in the M CT images based on the historical gray scale information of the blood vessel of the three-dimensional blood vessel model to obtain the gray scale contrast enhancement coefficient of each pixel point in the M CT images comprises:
acquiring a gray level histogram of historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shifting a gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value;
and correcting the first pixel points with the gray values lower than the target gray average value in the M CT images to obtain the gray contrast enhancement coefficient of each first pixel point in the M CT images.
3. The method according to claim 2, wherein the correcting the first pixel points in the M CT images with gray values lower than the target gray average value to obtain the gray contrast enhancement coefficient of each first pixel point in the M CT images comprises:
and taking the square of the ratio of the gray value of the first pixel point to the average value of the target gray values as a gray contrast enhancement coefficient of the first pixel point.
4. The method of claim 2, wherein determining potential energy of each pixel point in the M CT images according to the gray scale contrast enhancement coefficient of each pixel point in the M CT images comprises:
aiming at each pixel point in M CT images, determining an included angle between a connecting line of the pixel point and the starting point and a connecting line of the starting point and the end point according to the position information of the starting point and the end point;
and determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value.
5. The method according to claim 4, wherein determining an angle between a line connecting the pixel point and the start point and a line connecting the start point and the end point according to the position information of the start point and the end point comprises:
according toFormula (II)
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
6. The method of claim 5, wherein determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point, and the target gray average value comprises:
according to the formula
Determining potential energy P (x) of the pixel point x;
wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants.
7. The method according to any one of claims 1-6, wherein determining the shortest path between the starting point and the ending point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the ending point comprises:
determining a minimum activity graph between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and processing the minimum activity graph based on the steepest descent direction, and determining the shortest path between the starting point and the end point.
8. The method of claim 7, wherein said repairing a three-dimensional model of a vessel of said vessel disconnected region based on said shortest path comprises:
obtaining the blood vessel radius corresponding to each pixel point on the shortest path, and generating a blood vessel section surrounded by the blood vessel radius corresponding to each pixel point;
and connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel in the disconnected region of the blood vessel.
9. A device for repairing a three-dimensional model of a blood vessel, comprising:
the acquisition module is used for acquiring the position information of a starting point and an end point of a blood vessel disconnected region in a three-dimensional blood vessel model and M CT images corresponding to the blood vessel disconnected region;
an enhancement coefficient obtaining module, configured to modify a gray scale value of each pixel in the M CT images based on historical gray scale information of the blood vessel of the three-dimensional blood vessel model, so as to obtain a gray scale contrast enhancement coefficient of each pixel in the M CT images;
the potential energy determining module is used for determining the potential energy of each pixel point in the M CT images according to the gray contrast enhancement coefficient of each pixel point in the M CT images;
the shortest path determining module is used for determining the shortest path between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and the repairing module is used for repairing the blood vessel three-dimensional model of the blood vessel disconnected region according to the shortest path.
10. The apparatus of claim 9, wherein the enhancement factor obtaining module comprises:
the average gray level obtaining unit is used for obtaining a gray level histogram of the historical gray level information of the blood vessel of the three-dimensional blood vessel model, and shifting the gray level average value of the gray level histogram to the left by a preset pixel value to obtain a target gray level average value;
and the enhancement coefficient acquisition unit is used for correcting the first pixel points with the gray values lower than the target gray average value in the M CT images to acquire the gray contrast enhancement coefficient of each first pixel point in the M CT images.
11. The apparatus according to claim 10, wherein the enhancement factor obtaining unit is specifically configured to use a square of a ratio of the gray scale value of the first pixel to the target gray scale average value as the gray scale contrast enhancement factor of the first pixel.
12. The apparatus of claim 10, wherein the potential energy determination module comprises:
an included angle obtaining unit, configured to determine, for each pixel point in the M CT images, an included angle between a connection line between the pixel point and the start point and a connection line between the start point and the end point according to the position information of the start point and the end point;
and the potential energy determining unit is used for determining the potential energy of the pixel point according to the included angle, the gray contrast enhancement coefficient of the pixel point, the gray value of the pixel point and the target gray average value.
13. The device according to claim 12, wherein the angle acquisition unit is in particular adapted to,
according to the formula
Determining the pixel point x and the starting point xstartAnd an angle theta between the connecting line of (a) and the connecting line of the starting point and the end point;
wherein, the
Said xendIs the end point.
14. The device according to claim 13, characterized in that the potential energy determination unit, in particular for,
according to the formula
Determining potential energy P (x) of the pixel point x;
wherein g (p (x)) is a gray contrast enhancement coefficient of a pixel point x, p (x) is a gray value of the pixel point x, mean _ grey is the target gray average value, and λ and β are both positive constants.
15. The apparatus of any of claims 9-14, wherein the shortest path determining module comprises:
the minimum activity graph determining unit is used for determining a minimum activity graph between the starting point and the end point according to the potential energy of each pixel point in the M CT images and the position information of the starting point and the end point;
and the shortest path determining unit is used for processing the minimum activity graph based on the steepest descent direction and determining the shortest path between the starting point and the end point.
16. The apparatus according to claim 15, wherein the shortest path determining unit is specifically configured to obtain a vessel radius corresponding to each pixel point on the shortest path, and a vessel cross section surrounded by the vessel radii corresponding to each pixel point; and connecting the sections of the blood vessels to obtain a three-dimensional model of the blood vessel in the disconnected region of the blood vessel.
17. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program for implementing a method of repairing a three-dimensional model of a blood vessel according to any one of claims 1-8.
18. A computer storage medium, characterized in that the storage medium has stored therein a computer program which, when executed, implements a method of repairing a three-dimensional model of a blood vessel according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810893094.8A CN109272485B (en) | 2018-08-07 | 2018-08-07 | Method and device for repairing three-dimensional model of blood vessel and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810893094.8A CN109272485B (en) | 2018-08-07 | 2018-08-07 | Method and device for repairing three-dimensional model of blood vessel and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109272485A CN109272485A (en) | 2019-01-25 |
| CN109272485B true CN109272485B (en) | 2020-12-01 |
Family
ID=65153451
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810893094.8A Active CN109272485B (en) | 2018-08-07 | 2018-08-07 | Method and device for repairing three-dimensional model of blood vessel and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109272485B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114332202B (en) * | 2021-12-30 | 2023-11-24 | 北京阅影科技有限公司 | Vascular model processing method and device and computer readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923713A (en) * | 2010-08-04 | 2010-12-22 | 中国科学院自动化研究所 | Method for extracting central line of coronary artery vessel |
| CN104282009A (en) * | 2013-07-02 | 2015-01-14 | 上海联影医疗科技有限公司 | Coronary artery extraction method |
| CN108133478A (en) * | 2018-01-11 | 2018-06-08 | 苏州润心医疗器械有限公司 | A kind of method for extracting central line of coronary artery vessel |
| CN108335284A (en) * | 2018-01-09 | 2018-07-27 | 北京理工大学 | A kind of coronary artery vessel centerline matching process and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7860290B2 (en) * | 2006-04-21 | 2010-12-28 | Siemens Medical Solutions Usa, Inc. | Three-dimensional (3D) modeling of coronary arteries |
| US8073227B2 (en) * | 2008-05-09 | 2011-12-06 | Siemens Aktiengesellschaft | System and method for geometric modeling of tubular structures |
| US10102633B2 (en) * | 2015-11-30 | 2018-10-16 | Hyland Switzerland Sarl | System and methods of segmenting vessels from medical imaging data |
-
2018
- 2018-08-07 CN CN201810893094.8A patent/CN109272485B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101923713A (en) * | 2010-08-04 | 2010-12-22 | 中国科学院自动化研究所 | Method for extracting central line of coronary artery vessel |
| CN104282009A (en) * | 2013-07-02 | 2015-01-14 | 上海联影医疗科技有限公司 | Coronary artery extraction method |
| CN108335284A (en) * | 2018-01-09 | 2018-07-27 | 北京理工大学 | A kind of coronary artery vessel centerline matching process and system |
| CN108133478A (en) * | 2018-01-11 | 2018-06-08 | 苏州润心医疗器械有限公司 | A kind of method for extracting central line of coronary artery vessel |
Non-Patent Citations (4)
| Title |
|---|
| Identification and reconnection of interrupted vessels in retinal vessel segmentation;Vinaysk S.Joshi 等;《2011 IEEE International Symposium on Biomedical Imaging：From Nano to Macro》;20110609;第1416-1420页 * |
| Semi-automatic 3D segmentation of carotid lumen in contrast-enhanced computed tomography angiography images;Hamidreza Hemmati 等;《Physica Medica》;20151001;第1098-1104页 * |
| 基于最小路径的血管图像分割算法;陈鉴;《中国优秀硕士学位论文全文数据库医药卫生科技辑》;20170315;第E062-41页 * |
| 血管内OCT图像序列中血管三维重建方法的研究;陈艳花;《中国优秀硕士学位论文全文数据库医药卫生科技辑》;20170315;第E062-27页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109272485A (en) | 2019-01-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108805871B (en) | Blood vessel image processing method and device, computer equipment and storage medium | |
| CN109118542B (en) | Calibration method, device, equipment and storage medium between laser radar and camera | |
| CN113160189B (en) | Blood vessel center line extraction method, device, equipment and storage medium | |
| JP7187680B2 (en) | LINE STRUCTURE EXTRACTION APPARATUS AND METHOD, PROGRAM AND TRAINED MODEL | |
| CN110246216B (en) | Spine model generation method, spine model generation system and terminal | |
| CN117115150B (en) | Method, computing device and medium for determining branch vessels | |
| CN110070529A (en) | A kind of Endovascular image division method, system and electronic equipment | |
| CN111724413B (en) | Blood vessel tracking method, coronary image segmentation method, medium and electronic equipment | |
| CN115731232A (en) | Blood vessel image processing method and device, readable storage medium and electronic equipment | |
| CN115456947A (en) | Deep learning-based acetabulum injury repair method | |
| CN109272485B (en) | Method and device for repairing three-dimensional model of blood vessel and electronic equipment | |
| US11790537B2 (en) | Tracking device, endoscope system, and tracking method | |
| CN115147360B (en) | Plaque segmentation method and device, electronic equipment and readable storage medium | |
| CN115690143B (en) | Image segmentation method, device, electronic equipment and storage medium | |
| CN115131301B (en) | Method and system for intelligently identifying osteoarthritis based on deep learning | |
| CN115482261A (en) | Blood vessel registration method, device, electronic equipment and storage medium | |
| CN114550203B (en) | Method for determining three-dimensional coordinates of joint key points and related equipment thereof | |
| JP2022546303A (en) | Segmenting tubular features | |
| CN114565646A (en) | Image registration method, device, electronic device and readable storage medium | |
| JP2022059493A (en) | Model generation method, model generation device, image processing method, and image processing device | |
| Sabrowsky-Hirsch et al. | A content-driven architecture for medical image segmentation | |
| CN117726746B (en) | Three-dimensional human body reconstruction method, device, equipment, storage medium and program product | |
| CN112270643B (en) | Three-dimensional imaging data stitching method and device, electronic equipment and storage medium | |
| CN118095108B (en) | Method, system and equipment for reconstructing three-dimensional model in bored pile | |
| CN117942161B (en) | Single puncture needle power confirmation method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |