CN111862062A - Method, device and equipment for optimizing center line - Google Patents
Method, device and equipment for optimizing center line Download PDFInfo
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Abstract
The embodiment of the specification discloses a method, a device and equipment for center line optimization, and belongs to the technical field of medical images and computers. The method comprises the following steps: acquiring the central line data of the parent artery of the craniocerebral image data to be processed; screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data; performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point; and smoothing the second central line data to realize optimization of the central line data of the parent artery.
Description
Technical Field
The present disclosure relates to the field of medical imaging and computer technologies, and in particular, to a method, an apparatus, and a device for centerline optimization.
Background
Intracranial aneurysm, also called cerebral hemangioma, is mostly abnormal bulging on the wall of intracranial arterial vessel, and is the first cause of subarachnoid hemorrhage, and in cerebrovascular accidents, it is second to cerebral thrombosis and hypertensive cerebral hemorrhage, and is located in the third place. Intracranial aneurysms are classified into non-ruptured aneurysms and ruptured aneurysms, wherein most of the intracranial aneurysms are non-ruptured aneurysms, but once ruptured, spontaneous subarachnoid space bleeding is triggered to become ruptured aneurysms, the lethal disability rate of which exceeds 50 percent, and the life of a patient is seriously threatened.
The parent artery is a section of artery blood vessel containing aneurysm in cranial aneurysm, the parent artery central line has important significance for model selection of a stent to be intervened in a blood flow guiding simulation device, woven mesh display after the stent is released and the like, and the accuracy of the parent artery central line determines the model selection of the stent to be intervened, the woven mesh display accuracy after the stent is released and calculation of a series of parameters based on the parent artery central line.
However, the study on the centerline of the parent artery never considers the abnormal points on the centerline of the parent artery, and the optimization of the centerline of the parent artery is not studied.
Disclosure of Invention
The embodiment of the specification provides a method, a device and equipment for center line optimization, which are used for solving the following technical problems: at present, people do not consider abnormal points on the centerline of the parent artery for the research of the centerline of the parent artery and do not research the optimization of the centerline of the parent artery, thereby influencing the accuracy of calculation of a series of parameters based on the centerline of the parent artery and the accuracy of model selection of a stent to be intervened and the display of a woven mesh after the stent is released.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
the method for optimizing the center line provided by the embodiment of the specification comprises the following steps:
acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data;
performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
and smoothing the second central line data to realize optimization of the central line data of the parent artery.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data further includes:
homogenizing the second center line data to obtain third center line data;
and diluting the third center line data to obtain fourth center line data, and taking the fourth center line data as new second center line data so as to smoothly process the new second center line data and realize optimization of the center line data of the parent artery.
Further, the screening abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the central line data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the central line as the center of a circle and the point from the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data specifically includes:
and carrying out interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain the second centerline data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
and homogenizing every two points on the second central line by a fixed numerical step size to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
and diluting the third centerline data by taking N as a unit to obtain the fourth centerline data, wherein the fourth centerline data can be used as a scale.
Further, the method further comprises:
and acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
The present specification further provides a centerline optimization apparatus, including:
the acquisition module is used for acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
the screening module screens abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data, and the first central line data are corrected by the steps of: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
the correction module is used for carrying out interpolation processing on the first centerline data and correcting the first centerline data to obtain second centerline data;
and the smoothing module is used for smoothing the second central line data to realize optimization of the central line data of the parent artery.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data further includes:
the homogenization module is used for carrying out homogenization treatment on the second center line data to obtain third center line data;
and the dilution module is used for diluting the third center line data to obtain fourth center line data, and taking the fourth center line data as new second center line data so as to carry out smoothing treatment on the new second center line data and realize optimization of the center line data of the parent artery.
Further, the screening abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the central line data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the central line as the center of a circle and the point from the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data specifically includes:
and carrying out interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain the second centerline data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
and homogenizing every two points on the second central line by a fixed numerical step size to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
and diluting the third centerline data by taking N as a unit to obtain the fourth centerline data, wherein the fourth centerline data can be used as a scale.
Further, the apparatus further comprises:
and the radius correction module is used for acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
An embodiment of the present specification further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data;
performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
and smoothing the second central line data to realize optimization of the central line data of the parent artery.
Acquiring center line data of a tumor-laden artery of craniocerebral image data to be processed; screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data; performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point; and smoothing the second central line data to realize optimization of the central line data of the parent artery, so that the accuracy of calculation of a series of parameters based on the central line of the parent artery or the accuracy of model selection of the stent to be intervened and the accuracy of display of the woven mesh after the stent is released can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification 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, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a schematic diagram of a centerline optimization method provided in an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of another centerline optimization method provided in an embodiment of the present disclosure;
FIG. 3 is a block diagram of centerline optimization provided by embodiments of the present disclosure;
fig. 4 is a schematic diagram of a centerline optimization device provided in an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present disclosure, shall fall within the scope of protection of the present application.
Fig. 1 is a schematic diagram of a centerline optimization method provided in an embodiment of the present disclosure, where the optimization method includes:
step S101: and acquiring the central line data of the parent artery of the craniocerebral image data to be processed.
In the embodiment of the present specification, the craniocerebral image data to be processed is any one of CTA (CT angiography), MRA (magnetic resonance angiography), DSA (digital subtraction angiography); the craniocerebral image data to be processed can be two-dimensional image data or three-dimensional image data; the craniocerebral image data to be processed needs to be converted into a DICOM format so as to be convenient for subsequent processing.
In the embodiment of the specification, the obtaining of the centerline data of the parent artery adopts the following method: and extracting blood vessel data from the image data to be processed by a threshold segmentation method, performing surface reconstruction on the extracted blood vessel data, further segmenting the aneurysm, and acquiring centerline data of the aneurysm carrying the aneurysm.
Step S103: and screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data.
In the embodiment of the present specification, the method for screening the first centerline data is as follows: and screening abnormal central line data from the central line data of the parent artery as first central line data based on the variance. In a specific implementation process, screening out first centerline data from acquired centerline data of a parent artery, specifically comprising: traversing each point on the centerline data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the centerline as the center of a circle and the point of the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value. In a specific implementation, the preset value may be 0.5. It should be noted in particular that the advancement direction is the direction along the direction of the parent artery, i.e. from the proximal release point to the distal release point.
Step S105: performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: and correcting the position of the abnormal point in the first central line data and the radius corresponding to the abnormal point.
In an embodiment of this specification, the obtaining of the second centerline data specifically includes: and performing interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain second centerline data. In the specific implementation process, the positions of the abnormal points in the first centerline data and the radii corresponding to the abnormal points need to be corrected. The radius corresponding to the abnormal point is obtained by interpolation of the radius or diameter of the normal point before and after the abnormal point, and the correction of the position of the abnormal point is obtained by interpolation of the position of the normal point before and after the abnormal point.
Step S107: and smoothing the second central line data to realize optimization of the central line data of the parent artery.
In the embodiments of the present disclosure, the smoothing process may use a Sinc smoothing function, or may use other smoothing processes, and the specific manner of the smoothing process does not limit the present disclosure.
By adopting the optimization method for the central line of the parent artery, provided by the embodiment of the specification, abnormal points on the central line of the parent artery can be eliminated, and the accuracy of calculation of a series of parameters based on the central line of the parent artery or the accuracy of type selection of a stent to be intervened and display of a woven mesh after the stent is released is improved.
Fig. 2 is a schematic diagram of another centerline optimization method provided in an embodiment of the present disclosure, where the optimization method includes:
step S201: and screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data.
Step S203: and performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data.
Step S205: and carrying out homogenization treatment on the second center line data to obtain third center line data.
The second centerline data obtained through the calibration process in the foregoing step further needs to be subjected to a homogenization process. Specifically, the data of the third center line is obtained by homogenizing every two points on the second center line in steps of fixed values. In practice, the step size for the fixed value is preferably 0.01.
Step S207: and diluting the third center line data to obtain fourth center line data, and taking the fourth center line data as new second center line data so as to smoothly process the new second center line data and realize optimization of the center line data of the parent artery.
Step S209: and smoothing the new second center line data to realize optimization of the center line data of the parent artery.
By adopting the optimization method of the central line of the parent artery, provided by the embodiment of the specification, abnormal points on the central line of the parent artery can be eliminated, and the accuracy of calculation of a series of parameters based on the central line of the parent artery or the accuracy of type selection of a stent to be intervened and display of a woven mesh after the stent is released is improved; and the normalized and diluted central line data can be used as a data scale for subsequent research.
The optimized central line data of the parent artery is further used for correcting the radius/diameter of the parent artery.
Step S211: and acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
In the embodiment of the present specification, the slope of the change in radius is (radius of the current point-radius of the previous point)/radius of the previous point.
In the embodiment of the present specification, the corrected radius is obtained based on the loop judgment. Specifically, the loop ends when the slope of the radius change is >0.1, and the corrected radius is (radius of the current point + radius of the previous point)/2.
In one embodiment of the present disclosure, the radius of each point on the centerline is defined as the selected point P on the centerline and its corresponding maximum radius P1, and the perpendicular line perpendicular to the vector P1P is defined as the diameter of each point on the centerline.
To further understand the centerline optimization method provided by the embodiments of the present specification, the present specification further provides a frame diagram of centerline optimization, as shown in fig. 3. Fig. 3 is a frame diagram of centerline optimization provided in an embodiment of the present specification, where the frame diagram includes:
in an embodiment of the present specification, abnormal points are screened from the central line data of the parent artery, the abnormal points are repaired, the central line data is further homogenized, diluted, and finally smoothed, so as to optimize the central line data of the parent artery. The optimized center line data of the parent artery can be further used for radius correction of the parent artery to obtain a corrected radius.
In one embodiment of the present disclosure, anomaly points are screened from the centerline data of the parent artery, repaired, and finally smoothed to optimize the centerline data of the parent artery.
The foregoing describes a centerline optimization method, and accordingly, the present specification further provides a centerline optimization apparatus, as shown in fig. 4. Fig. 4 is a schematic diagram of a centerline optimization apparatus provided in an embodiment of the present disclosure, where the apparatus includes:
the acquiring module 401 acquires the central line data of the parent artery of the craniocerebral image data to be processed;
the screening module 403 is configured to screen abnormal points in the parent artery centerline data to obtain first centerline data, where the first centerline data is abnormal centerline data, and the correcting the first centerline data includes: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
a correction module 405, configured to perform interpolation processing on the first centerline data, and correct the first centerline data to obtain second centerline data;
and the smoothing module 407 is used for smoothing the second centerline data to optimize the centerline data of the parent artery.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data further includes:
the homogenizing module 409 is used for homogenizing the second center line data to obtain third center line data;
and the dilution module 411 is configured to dilute the third centerline data to obtain fourth centerline data, and use the fourth centerline data as new second centerline data, so as to smooth the new second centerline data and optimize the centerline data of the parent artery.
Further, the screening abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the central line data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the central line as the center of a circle and the point from the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value.
Further, the interpolating the first centerline data, and correcting the first centerline data to obtain second centerline data specifically includes:
and carrying out interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain the second centerline data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
and homogenizing every two points on the second central line by a fixed numerical step size to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
and diluting the third centerline data by taking N as a unit to obtain the fourth centerline data, wherein the fourth centerline data can be used as a scale.
Further, the apparatus further comprises:
and the radius correction module 413 is used for acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
An embodiment of the present specification further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data;
performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
and smoothing the second central line data to realize optimization of the central line data of the parent artery.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the nonvolatile computer storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the partial description of the embodiments of the method.
The apparatus, the electronic device, the nonvolatile computer storage medium and the method provided in the embodiments of the present description correspond to each other, and therefore, the apparatus, the electronic device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
As will be appreciated by one skilled in the art, the present specification embodiments may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (15)
1. A method of centerline optimization, the method comprising:
acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data;
performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data, wherein the correction of the first centerline data comprises: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
and smoothing the second central line data to realize optimization of the central line data of the parent artery.
2. The method of claim 1, wherein the interpolating the first centerline data to correct the first centerline data to obtain second centerline data further comprises:
homogenizing the second center line data to obtain third center line data;
and diluting the third center line data to obtain fourth center line data, and taking the fourth center line data as new second center line data so as to smoothly process the new second center line data and realize optimization of the center line data of the parent artery.
3. The method of claim 1, wherein the screening for outliers in the parent artery centerline data to obtain first centerline data, comprises:
traversing each point on the central line data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the central line as the center of a circle and the point from the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value.
4. The method according to claim 1, wherein the interpolating the first centerline data and correcting the first centerline data to obtain second centerline data specifically includes:
and carrying out interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain the second centerline data.
5. The method of claim 2, wherein the homogenizing the second centerline data to obtain third centerline data, specifically comprises:
and homogenizing every two points on the second central line by a fixed numerical step size to obtain the third central line data.
6. The method of claim 2, wherein the diluting the third centerline data to obtain fourth centerline data comprises:
and diluting the third centerline data by taking N as a unit to obtain the fourth centerline data, wherein the fourth centerline data can be used as a scale.
7. The method of claim 1, wherein the method further comprises:
and acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
8. A centerline optimizing apparatus, the apparatus comprising:
the acquisition module is used for acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
the screening module screens abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data, and the first central line data are corrected by the steps of: correcting the position of an abnormal point in the first central line data and the radius corresponding to the abnormal point;
the correction module is used for carrying out interpolation processing on the first centerline data and correcting the first centerline data to obtain second centerline data;
and the smoothing module is used for smoothing the second central line data to realize optimization of the central line data of the parent artery.
9. The apparatus of claim 8, wherein the interpolating the first centerline data to correct the first centerline data to obtain second centerline data further comprises:
the homogenization module is used for carrying out homogenization treatment on the second center line data to obtain third center line data;
and the dilution module is used for diluting the third center line data to obtain fourth center line data, and taking the fourth center line data as new second center line data so as to carry out smoothing treatment on the new second center line data and realize optimization of the center line data of the parent artery.
10. The apparatus of claim 8, wherein the screening for outliers in the parent artery centerline data to obtain first centerline data comprises:
traversing each point on the central line data of the parent artery, calculating a tangent plane of the blood vessel by taking the advancing direction of the current point as a normal vector, calculating the variance of the radius by taking the point of the central line as the center of a circle and the point from the tangent plane as the radius, and considering the current point as an abnormal point when the variance is greater than a preset value.
11. The apparatus according to claim 8, wherein the interpolating the first centerline data and correcting the first centerline data to obtain second centerline data specifically includes:
and carrying out interpolation processing on normal points before and after the abnormal point in the first centerline data along the advancing direction, so as to correct the first centerline data and obtain the second centerline data.
12. The apparatus of claim 9, wherein the homogenizing the second centerline data to obtain third centerline data comprises:
and homogenizing every two points on the second central line by a fixed numerical step size to obtain the third central line data.
13. The apparatus of claim 9, wherein the diluting the third centerline data to obtain fourth centerline data comprises:
and diluting the third centerline data by taking N as a unit to obtain the fourth centerline data, wherein the fourth centerline data can be used as a scale.
14. The apparatus of claim 8, wherein the apparatus further comprises:
and the radius correction module is used for acquiring the slope of radius change based on the optimized central line data of the parent artery and the central line data of the parent artery, and correcting the radius of the parent artery to obtain the corrected radius.
15. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
acquiring the central line data of the parent artery of the craniocerebral image data to be processed;
screening abnormal points in the parent artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data;
performing interpolation processing on the first centerline data, and correcting the first centerline data to obtain second centerline data;
and smoothing the second central line data to realize optimization of the central line data of the parent artery.
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