CN109345585B

CN109345585B - Method and system for measuring morphological parameters of intracranial aneurysm image

Info

Publication number: CN109345585B
Application number: CN201811260353.XA
Authority: CN
Inventors: 王文智; 冯雪; 宋凌; 杨光明; 秦岚
Original assignee: Union Strong Beijing Technology Co ltd
Current assignee: Union Strong Beijing Technology Co ltd
Priority date: 2018-10-26
Filing date: 2018-10-26
Publication date: 2021-11-30
Anticipated expiration: 2038-10-26
Also published as: CN109345585A; WO2020083374A1

Abstract

The embodiment of the specification provides a method and a system for measuring morphological parameters of an intracranial aneurysm image. The embodiment of the specification solves the problems that the morphological parameter measurement of the intracranial aneurysm image cannot realize full-automatic measurement and the measurement consistency is difficult to guarantee through the measurement of the morphological parameter of the intracranial aneurysm image. The measuring method comprises the following steps: segmenting an intracranial tumor-bearing blood vessel image from MRA three-dimensional DICOM data; segmenting an intracranial aneurysm image; morphological parameters of images of intracranial aneurysms were measured. The method and the system for measuring the morphological parameters of the intracranial aneurysm image, provided by the embodiment of the specification, can realize automation of intracranial aneurysm image measurement, quickly measure the morphological parameters of the intracranial aneurysm image, and ensure consistency of measurement results of the morphological parameters of the aneurysm image.

Description

Method and system for measuring morphological parameters of intracranial aneurysm image

Technical Field

The present disclosure relates to the field of medical imaging, and in particular, to a method and a system for measuring morphological parameters of an intracranial aneurysm image.

Background

Intracranial aneurysms are a neoplastic protrusion of the arterial wall caused by local abnormal dilation of the intracranial arterial lumen, a common vascular disease. Intracranial unbroken aneurysms are reported to have a prevalence of up to 7% in adults in our country, and post-rupture subarachnoid hemorrhage can lead to severe disability or death. The data of the national statistical office in 2014 show that the acute cerebrovascular disease is the second leading cause of death in the population of China. Aneurysmal subarachnoid hemorrhage is the most common acute cerebrovascular disease after cerebral arterial thrombosis and hypertensive cerebral hemorrhage, the death rate is up to 64 percent, about 15 percent of patients die before hospital, and the treatment levels in different economic development level areas are greatly different, so the subarachnoid hemorrhage becomes one of the most common reasons causing death of residents in China. Therefore, the timely and effective screening and prevention work of the unbroken aneurysm can greatly reduce the risk of future disease of the aneurysm carrier.

In the prior art, the measurement of an intracranial aneurysm image basically depends on experienced personnel, manual measurement is carried out by utilizing a computer, the measurement speed is low, the randomness of the measurement result is high, the accuracy is not ideal, and only simple parameters such as line segment distance can be measured by the method; for complex parameters such as volume or angle, manual measurement is very inconvenient, and accuracy is difficult to guarantee. The improvement of aneurysm measurement, mainly simulation modeling or the improvement of traditional manual measurement mode, can't realize the measurement of full-automatic mode of aneurysm morphological parameter, and its uniformity is difficult to guarantee.

Therefore, there is a need for an automated method of morphological parameter measurement of images of intracranial aneurysms that can quickly measure morphological parameters of intracranial aneurysms.

Disclosure of Invention

The embodiment of the specification provides a method and a system for measuring morphological parameters of an intracranial aneurysm image, which are used for solving the following technical problems: the method can quickly measure the morphological parameters of the intracranial aneurysm image and ensure the consistency of the measurement results of the morphological parameters of the aneurysm.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

the method for measuring the morphological parameters of the intracranial aneurysm image provided by the embodiment of the specification comprises the following steps:

segmenting an intracranial tumor-bearing blood vessel image from MRA three-dimensional DICOM data;

segmenting an intracranial aneurysm image on the intracranial aneurysm vessel image;

measuring morphological parameters of the intracranial aneurysm image.

Furthermore, a gray scale range is selected from the three-dimensional DICOM data of the MRA, and an intracranial tumor-carrying blood vessel image is segmented.

Further, on the intracranial parent artery blood vessel image, calculating the central line and the radius of the parent artery blood vessel, and segmenting the intracranial aneurysm image.

And further, generating an aneurysm neck by using the intracranial aneurysm image obtained by segmentation, and measuring morphological parameters of the intracranial aneurysm image.

The embodiment of the specification provides a system for measuring morphological parameters of an intracranial aneurysm image, which comprises the following units:

the input interface is used for inputting the three-dimensional DICOM data of the MRA;

the processing workstation is used for measuring morphological parameters of the intracranial aneurysm image;

an output unit: and outputting the measurement result of the morphological parameters of the intracranial aneurysm image.

Further, segmenting an intracranial tumor-bearing blood vessel image from the three-dimensional DICOM data of the MRA;

measuring morphological parameters of the intracranial aneurysm image.

The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects: the embodiment of the specification is based on MRA three-dimensional DICOM data, so that the automatic measurement of the morphological parameters of the intracranial aneurysm image is realized, the morphological parameters of the intracranial aneurysm image can be rapidly measured, and the consistency of the morphological parameter measurement results of the intracranial aneurysm image is ensured.

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 flowchart of a method for measuring morphological parameters of an intracranial aneurysm image provided by the present specification;

fig. 2 is a flow chart of a segmentation process of an intracranial tumor-bearing blood vessel image provided in the present specification;

fig. 3 is a flowchart of a method for determining a gray scale range of an MRA image provided in the present specification;

fig. 4 is a flow chart of a segmentation of an intracranial aneurysm image provided by the present specification;

FIG. 5 is a schematic diagram illustrating determination of a minimum rectangle for two points in a two-dimensional space provided herein;

FIG. 6 is a schematic diagram of three points determining a minimum rectangle in a two-dimensional space provided herein;

FIG. 7 is a flow chart of a surface reconstruction of an intracranial aneurysm image provided by the present description;

FIG. 8 is a schematic diagram illustrating the definition of morphological parameters of an aneurysm provided by the present specification;

FIG. 9 is a schematic view of the measurement of morphological parameters of an aneurysm provided by the present specification;

fig. 10 is a schematic view of a system for measuring morphological parameters of an intracranial aneurysm provided by the present specification.

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 flowchart of a method for measuring morphological parameters of an intracranial aneurysm image provided in this specification. The method comprises the following steps:

step S101: from the MRA three-dimensional DICOM data, images of intracranial tumor-bearing vessels are segmented.

The morphological parameter measurement of the intracranial aneurysm image is usually carried out by adopting the three-dimensional DICOM data of MRA of the intracranial aneurysm. MRA (Magnetic Resonance Angiography) is a technique for visualizing blood vessels in X-ray sequence pictures. The basic principle is based on saturation effect, inflow enhancement effect and flow dephasing effect. MRA is a technique in which a pre-saturation zone is placed at the tip of a 3D slab to saturate the venous blood flow and the arterial blood flow in the opposite direction enters the 3D slab, thereby generating MR signals because it is not saturated. During scanning, a thicker volume is divided into a plurality of thin layers for excitation, the thickness of the excited volume is reduced to reduce the inflow saturation effect, the scanning volume range can be ensured, thin layer images of a plurality of layers of adjacent layers are obtained, the images are clear, the fine structure of blood vessels is well displayed, and the spatial resolution is improved. MRA, because of its high quality imaging characteristics, is also being used gradually for the diagnosis of intracranial arterial vasculopathy.

DICOM is a network communication interface standard for digital medical images. The DICOM standard is a protocol commonly followed by a group of DICOM-compatible devices, by which various syntax, semantic commands and related information may be exchanged between systems. The main objective of the protocol is to ensure that the diagnosis information, treatment information, medical images and other various related data of the patient can be communicated between different devices and systems.

The MRA image is stored as a DICOM format file. The DICOM-formatted file includes necessary information of a general image such as image size, height, width, number of bytes per pixel, and also stores a large amount of medical information such as patient name, age, hospital name, imaging time, examination site, and the like in data elements of the data set. DICOM data is stored in order, with the first byte in the array representing the pixel in the upper left corner of the image and the last byte representing the pixel in the lower right corner of the image.

Due to the limitation of the irradiation position of the device, the MRA effect of the intracranial aneurysm can only be two-dimensional, and the two-dimensional image can only acquire the morphological parameter indexes of the basic intracranial aneurysm image: size, aspect ratio, angle of inclination of the aneurysm, etc., do not enable measurement of morphological parameters of complex intracranial aneurysm images, such as the volume of the aneurysm. The measurement of three-dimensional morphological parameters is more meaningful for the research of morphological parameters of intracranial aneurysm images. Therefore, in order to measure the morphological parameters of the intracranial aneurysm image, the MRA three-dimensional DICOM data needs to be further processed, and the intracranial aneurysm vessel image is segmented first.

Fig. 2 is a flowchart of a segmentation process of an intracranial tumor-bearing blood vessel image provided in this specification, and the specific process includes:

step S201: a gray scale range is selected from DICOM data of the MRA image.

The determination of the gray scale range is a precondition for the subsequent segmentation of the tumor-bearing blood vessel image. The embodiment of the specification realizes the determination of the gray scale range by adopting a region growing mode.

Fig. 3 is a flowchart of a method for determining a gray scale range of an MRA image according to an embodiment of the present disclosure. The method specifically comprises the following steps:

step S301: and determining the upper limit and the lower limit of the preliminary gray scale range of the MRA image, and determining a seed point.

Extracting the maximum value and the minimum value of the pixel value of the whole MRA image, traversing the pixel value of the whole MRA image, recording the pixel point with the pixel value as the maximum value, and taking the point as a seed point of a primary region growing method; meanwhile, calculating the average value of all pixel values with the pixel values smaller than the maximum value, and taking the average value as the lower limit 1 of the preliminary gray scale range and the maximum value of the pixel as the upper limit 1 of the preliminary gray scale. In this specification, a seed point may be the starting point for growth, the seed point being located on a tumor-bearing vessel.

Step S302: the MRA image is initially segmented by an initial region growing method.

Based on the gray scale range and the seed point determined in step S301, the MRA image is preliminarily segmented by a preliminary region growing method, so as to obtain an image including tumor-bearing vessel pixels.

Step S303: the lower limit of the gray scale range of the MRA image is preliminarily determined.

Taking the MRA image segmented in the step S302 as a template, extracting pixels at the same position in the original image, and calculating the mean value of the pixels of the original image; meanwhile, taking the segmented MRA image as a template, extracting pixels at the same position in the background image, and calculating the average value of the pixels of the background image; the average of the mean of the pixels of the original image and the mean of the pixels of the background image is taken as the lower limit 2 of the preliminary gray scale range.

Step S304: and preliminarily dividing the MRA image by taking the lower limit 2 of the preliminary gray scale range and the upper limit 1 of the preliminary gray scale range as the gray scale range and taking the seed points as seed points through a region growing method.

The change in the gray scale range affects the MRA image obtained by the segmentation. Therefore, after the above-described gradation range is changed, the MRA image division needs to be performed again.

Step S305: and determining the lower limit of the preliminary gray scale range and judging.

And repeating the method for determining the lower limit of the preliminary gray scale range in the step S303, wherein the lower limit of the preliminary gray scale range obtained in the nth cycle is 2-n. Comparing the lower limit 2-n of the preliminary gray scale range with the lower limit 2- (n-1) of the preliminary gray scale range, and if the absolute value of the difference value of the two is less than 5 and n is more than or equal to 3, ending the cycle; if the absolute value of the difference between the two is greater than or equal to 5, or n is less than 3, the process proceeds to step S304.

Step S306: the gray scale range of the MRA image is determined.

The lower limit 2-n of the preliminary gradation range obtained in step S305 is set as the lower limit of the gradation range, and the upper limit 1 of the preliminary gradation range obtained in step S301 is set as the upper limit of the gradation range, that is, the point at which the pixel is the largest is set as the upper limit of the gradation range.

Step S202: and (4) segmenting the intracranial tumor-carrying blood vessel image by using an interval binary method.

And (4) segmenting the intracranial tumor-bearing blood vessel image by using the gray scale range determined in the step (S306). If the gray value of the pixel value in the MRA image is within the gray range, the pixel value is reserved, otherwise, the pixel value is set to be 0, and therefore the intracranial tumor-bearing blood vessel image of the MRA image is segmented.

Step S102: an intracranial aneurysm image is segmented.

The image of the intracranial tumor-bearing blood vessel segmented in the step S101 needs to be further segmented, so that the segmentation of the image of the aneurysm on the intracranial tumor-bearing blood vessel is realized, and the segmented intracranial aneurysm image is obtained.

Fig. 4 is a flow chart of a segmentation of an intracranial aneurysm image provided by the present specification. The method comprises the following steps:

step S401: and selecting seed points and positioning points from the images of the intracranial tumor-bearing blood vessels to be segmented.

And selecting seed points and positioning points by using the obtained intracranial tumor-bearing blood vessel image. The seed points and the positioning points are both space coordinates, so that the starting points of growth are defined as the seed points and the points selected on the tumor-carrying blood vessels are defined as the positioning points for the convenience of distinguishing. The seed points can be selected from the surface of the aneurysm body and the interior of the aneurysm body. And the location points are selected above the parent vessel intersecting the aneurysm. Because the intracranial aneurysm includes a conventional lateral aneurysm and a bifurcation aneurysm, different positioning point selection methods are adopted for selecting the positioning points according to the type of the intracranial aneurysm. For the conventional side edge tumor, two points are required to be provided at the upstream and the downstream of the tumor-carrying blood vessel, and the two points are generally selected within the range of 5-10mm away from the aneurysm; for bifurcation hemangioma, a positioning point is required to be given at the upstream of a tumor-carrying blood vessel, and a positioning point is respectively given at each branch at the downstream, so that the total number of the three positioning points is only required. The upstream locating point is a locating point 1, the downstream locating point is a locating point 2, and for the bifurcated blood vessel, the downstream locating point comprises two locating points. The anchor site may be placed on the surface of the parent vessel or within the parent vessel without distinction.

Step S402: and performing image expansion by using the seed points and the positioning points to generate an expanded aneurysm image.

And intercepting the local three-dimensional image, namely determining a minimum cuboid according to the coordinates of the seed points and the coordinates of the positioning points, performing horizontal and longitudinal pixel incremental extension to enable the extended minimal cuboid to comprise all intracranial aneurysm images, and intercepting the local three-dimensional image by using a cuboid region determined after extension. Although the local three-dimensional image includes the entire intracranial aneurysm image, the local three-dimensional image is not favorable for the segmentation of the subsequent aneurysm image due to the influence of noise, and therefore further image expansion is required. And with the seed points as starting points, after the local images are expanded for 16 times, the generated aneurysm image can be used for cutting a subsequent aneurysm image.

Fig. 5 is a schematic diagram of determining a minimum rectangle at two points in a two-dimensional space according to an embodiment of the present disclosure. And determining the minimum cuboid in the three-dimensional space according to the two positioning points and the seed point by a similar method.

Fig. 6 is a schematic diagram of determining a minimum rectangle by three points in a two-dimensional space according to an embodiment of the present disclosure. In a three-dimensional space, according to a similar method, a minimum cuboid is determined according to three positioning points and seed points.

Step S403: and calculating the central line and the radius of the tumor-carrying blood vessel by using the seed points and the positioning points.

Extraction of the central line of the tumor-carrying blood vessel and the segmentation of intracranial aneurysm are important. And calculating an inscribed circle in the tumor-bearing blood vessel cavity point by point along the tumor-bearing blood vessel from the tumor-bearing blood vessel cavity contained in the intercepted partial image, taking a connecting line of the centers of the maximum inscribed circles as the center line of the tumor-bearing blood vessel between the positioning points, and taking the radius of the maximum inscribed circle as the radius of the tumor-bearing blood vessel at the point.

Step S404: and reconstructing the tumor-carrying blood vessel based on the central line and the radius of the tumor-carrying blood vessel.

In order to effectively segment an aneurysm, reconstruction of a tumor-laden blood vessel is required. Based on the center line and the radius of the tumor-carrying blood vessel, the tumor-carrying blood vessel surface is generated point by point along the center line by taking the point on the center line as the spherical center and the radius at the point as the radius, and the tumor-carrying blood vessel reconstruction is carried out.

Step S405: and cutting the obtained complete aneurysm image by using the aneurysm-carrying blood vessel to obtain a segmented aneurysm image.

And reconstructing a tumor-carrying blood vessel by using the central line, cutting the obtained complete aneurysm image by using the reconstructed tumor-carrying blood vessel, realizing the segmentation of the tumor-carrying blood vessel and the aneurysm, and obtaining a segmented aneurysm image.

Step S406: and performing region growing on the seed points and the segmented aneurysm image to obtain the segmented aneurysm image.

Since the segmented aneurysm image has interference such as unclear boundaries, the segmented aneurysm image needs to be further processed to remove interference factors so as to obtain a clean and complete aneurysm image. The obtained segmented aneurysm image is a binary image, so that a region growing method is adopted, the seed points are taken as growing points, the connected aneurysm image is obtained, and finally the completely segmented aneurysm image is obtained.

Step S103: measurement of morphological parameters of intracranial aneurysm images.

The segmented complete intracranial aneurysm image obtained in step S102 may be subjected to measurement of morphological parameters. For the convenience of illustration, the above-mentioned segmented intracranial aneurysm image first needs to be subjected to surface reconstruction of the intracranial aneurysm image.

Fig. 7 is a flowchart of surface reconstruction of an intracranial aneurysm image, provided by the present specification, the flowchart including:

step 701: surface reconstruction of intracranial aneurysm images.

The three-dimensional surface reconstruction is realized by adopting an MC (marching cubes) algorithm in the specification. The MC algorithm has the basic idea that small cubes with regular hierarchical shapes in a three-dimensional data space are processed one by one, eight vertexes of the small cubes are composed of four pixel points on adjacent layers, the small cubes are classified to be intersected with an isosurface, intersection points of the isosurface and the edges of the small cubes are calculated by adopting an interpolation method, and finally the points are connected in a certain mode to form an approximate representation of the isosurface according to the relative positions of the isosurface and the intersection points. The 3D surface reconstruction is achieved programmatically using the 3D Visualization tool Visualization Toolokki (VTK).

Step 702: and carrying out smoothing processing by utilizing a windowed Sinc function.

The three-dimensional surface reconstructed by the MC algorithm has the situations of poor surface seam processing, inaccurate data and the like, so that smoothing processing is required.

Step 703: surface data of images of intracranial aneurysms are obtained.

And performing three-dimensional reconstruction and smoothing treatment based on the MC algorithm to obtain surface data of the intracranial aneurysm image.

Intracranial aneurysm morphological parameters include aneurysm neck, aneurysm diameter, aneurysm height, aneurysm width, aneurysm injection angle, aneurysm volume. Wherein, the key parameter of the morphological parameter of the intracranial aneurysm is the calculation of the neck center of the aneurysm.

Fig. 8 is a schematic diagram illustrating the definition of morphological parameters of an aneurysm according to an embodiment of the present invention. The method specifically comprises the following steps:

d (aneurysm major diameter): the size of the aneurysm is the maximum distance from one point at the top of the aneurysm to the midpoint of the neck of the aneurysm;

h (aneurysm height): the maximum vertical distance from one point at the top of the aneurysm to the neck connecting line of the aneurysm;

w (aneurysm width): a maximum distance perpendicular to the aneurysm major axis;

IA (inflow angle): the included angle between the major diameter of the aneurysm and the central axis of the parent artery;

PV (parent artery diameter):

side wall portion: PV ═ (D1+ D2)/2;

a crotch part: PV ═ D1+ D2+ D3)/3, (Di ═ Dia + Dib)/2(i ═ 1,2,3) in the embodiments of the present specification, on the basis of manual selection of seed points and two positioning points, automatic measurement is realized.

Fig. 9 is a schematic diagram illustrating measurement of morphological parameters of an aneurysm according to an embodiment of the present disclosure. Specifically, the idea of aneurysm parameter measurement is as follows:

in the visualization tool VTK, the aneurysm surface and the blood vessel surface intersect with each other, and the aneurysm surface and the blood vessel surface need to be truly intersected, cannot be included, and cannot be overlapped, so that the aneurysm surface data needs to be amplified to be truly intersected to obtain an intersection line. When the data is amplified, the whole aneurysm surface data is amplified according to three coordinate directions. The amplified surface of the aneurysm is intersected with the surface of the original blood vessel, and the intersection is the neck of the aneurysm.

Calculating the space geometric center of the point set according to the aneurysm neck point set, taking the geometric center as the aneurysm neck center, wherein the center may not be on the aneurysm, and calculating the minimum distance between the center and the aneurysm boundary, which is recorded as dmin; then calculating the average distance value from the tumor neck to the center of the tumor neck, and taking the average distance value as the radius of the tumor neck, wherein the diameter of the tumor neck is 2 times of the radius of the tumor neck.

And (4) calculating a tumor neck normal vector, confirming a central point of the shortest path along the central line of the blood vessel carrying the tumor artery, and taking a connecting line of the central point of the path and the central point of the tumor neck and the direction pointing to the aneurysm as the tumor neck normal vector.

The projection of the connecting line of the point on the aneurysm and the center point of the aneurysm on the normal vector of the aneurysm neck is taken, the maximum value of the projection is taken as the height calculation value of the aneurysm, and if the center point of the aneurysm neck is in the aneurysm, the height calculation value is taken as the height of the aneurysm; if the neck center point is outside the aneurysm, the height calculation minus dmin described above is taken as the aneurysm height.

Calculating the diameter of the aneurysm, namely finding out the maximum value of a connecting line between the aneurysm and the central point of the neck of the aneurysm as a diameter calculation value of the aneurysm, wherein if the central point of the neck of the aneurysm is in the aneurysm, the diameter calculation value is the diameter of the aneurysm; if the neck center is outside the aneurysm, the diameter calculation minus dmin is taken as the aneurysm diameter.

The calculation of the width of the aneurysm requires that the distance between any two connecting lines perpendicular to the diameter direction of the aneurysm on an aneurysm image is calculated first, and the maximum value of the distance is taken as the width of the aneurysm.

The calculation of the aneurysm volume requires counting the number of pixel points in the aneurysm image, and then taking the product of the number of pixel points and voxels as the aneurysm volume.

Calculating the aneurysm incidence angle, namely calculating a point on a central line corresponding to an upstream positioning point of the central line of the parent artery vessel, wherein the included angle between a connecting line of the point and the central point of the path and the diameter of the aneurysm is the aneurysm incidence angle.

Fig. 10 is a measurement system of morphological parameters of an intracranial aneurysm image provided by the present specification. The system comprises:

an input interface: input of three-dimensional DICOM data for MRA;

a processing workstation: realizing the measurement of morphological parameters of the intracranial aneurysm;

an output unit: and outputting the result of the morphological parameters of the intracranial aneurysm.

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 Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (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 magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that 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

1. A method for measuring morphological parameters of an intracranial aneurysm image, comprising the steps of:

segmenting an intracranial tumor-bearing vessel image from MRA three-dimensional DICOM data, comprising: determining an initial gray level upper limit 1 and an initial gray level lower limit 1 of the MRA, determining seed points, and initially dividing the MRA through initial region growth by using the initial gray level upper limit 1, the initial gray level lower limit 1 and the seed points; the MRA is segmented again by a region growing method by determining the lower limit 2 of the initial recovery range, the upper limit 1 of the initial gray scale range and the seed points again, and the lower limit of the initial gray scale range obtained in the nth cycle is 2-n; comparing the lower limit 2-n of the preliminary gray scale range with the lower limit 2- (n-1) of the preliminary gray scale range, if the absolute value of the difference value of the two is less than 5 and n is more than or equal to 3, ending the cycle, taking the lower limit 2-n of the preliminary gray scale range as the lower limit of the gray scale range and the upper limit 1 of the preliminary gray scale range as the upper limit of a recovery range, and segmenting the intracranial tumor-bearing blood vessel image by using an interval binary method;

segmenting an intracranial aneurysm image on the intracranial aneurysm vessel image, comprising: selecting seed points of the intracranial tumor-carrying blood vessel image and positioning points of the intracranial tumor-carrying blood vessel image on the intracranial tumor-carrying blood vessel image; performing image expansion based on the seed points of the intracranial tumor-bearing blood vessel image and the positioning points of the intracranial tumor-bearing blood vessel image to generate an expanded aneurysm image; calculating the central line and the radius of a tumor-carrying artery on the expanded aneurysm image based on the seed point of the intracranial tumor-carrying blood vessel image and the positioning point of the intracranial tumor-carrying blood vessel image, and segmenting the intracranial aneurysm image;

generating an aneurysm neck based on the segmented intracranial aneurysm image;

taking the geometric center of the aneurysm neck as the center of the aneurysm neck of the aneurysm;

measuring morphological parameters of the intracranial aneurysm image based on the center of the neck of the aneurysm, specifically comprising:

calculating the average distance value from the tumor neck to the center of the tumor neck, and taking the average distance value as the radius of the tumor neck, wherein the diameter of the tumor neck is 2 times of the radius of the tumor neck;

determining the central point of the shortest path along the central line of the parent artery blood vessel, wherein the connecting line of the central point of the path and the central point of the parent neck and the direction pointing to the aneurysm serve as a parent neck normal vector;

the projection of the connecting line of the point on the aneurysm and the center point of the aneurysm on the normal vector of the aneurysm neck is taken, the maximum value of the projection is taken as the height calculation value of the aneurysm, and if the center point of the aneurysm neck is in the aneurysm, the height calculation value is taken as the height of the aneurysm; if the center point of the neck of the aneurysm is outside the aneurysm, the result of subtracting dmin from the calculated height is taken as the height of the aneurysm, wherein dmin is the minimum distance from the center point of the neck of the aneurysm to the boundary of the aneurysm;

finding out the maximum value of a connecting line between the aneurysm and the central point of the aneurysm neck as a diameter calculation value of the aneurysm, wherein if the central point of the aneurysm neck is in the aneurysm, the diameter calculation value is the diameter of the aneurysm; if the neck center is outside the aneurysm, the diameter calculation minus the dmin is taken as the aneurysm diameter;

calculating the distance between any two connecting lines perpendicular to the diameter direction of the aneurysm on the aneurysm image, and taking the maximum value of the distance as the width of the aneurysm;

and calculating a point on the central line corresponding to the upstream positioning point of the central line of the parent artery vessel, wherein the included angle between the connecting line of the point and the central point of the path and the diameter of the aneurysm is the incident angle of the aneurysm.

2. A system for measuring morphological parameters of images of intracranial aneurysms, comprising the following units:

a processing workstation for segmenting an intracranial tumor-bearing vessel image from the MRA three-dimensional DICOM data, comprising: determining an initial gray level upper limit 1 and an initial gray level lower limit 1 of the MRA, determining seed points, and initially dividing the MRA through initial region growth by using the initial gray level upper limit 1, the initial gray level lower limit 1 and the seed points; the MRA is segmented again by a region growing method by determining the lower limit 2 of the initial recovery range, the upper limit 1 of the initial gray scale range and the seed points again, and the lower limit of the initial gray scale range obtained in the nth cycle is 2-n; comparing the lower limit 2-n of the preliminary gray scale range with the lower limit 2- (n-1) of the preliminary gray scale range, if the absolute value of the difference value of the two is less than 5 and n is more than or equal to 3, ending the cycle, taking the lower limit 2-n of the preliminary gray scale range as the lower limit of the gray scale range and the upper limit 1 of the preliminary gray scale range as the upper limit of a recovery range, and segmenting the intracranial tumor-bearing blood vessel image by using an interval binary method;

generating an aneurysm neck based on the segmented intracranial aneurysm image;

measuring morphological parameters of the intracranial aneurysm image based on a neck center of the aneurysm;

an output unit: outputting the measurement result of the morphological parameters of the intracranial aneurysm image, which specifically comprises the following steps: