CN112967236B

CN112967236B - Image registration method, device, computer equipment and storage medium

Info

Publication number: CN112967236B
Application number: CN202110189069.3A
Authority: CN
Inventors: 高菲菲; 薛忠; 董昢; 曹晓欢
Original assignee: Lianying Intelligent Medical Technology Beijing Co ltd; Shanghai United Imaging Intelligent Healthcare Co Ltd
Current assignee: Lianying Intelligent Medical Technology Beijing Co ltd; Shanghai United Imaging Intelligent Healthcare Co Ltd
Priority date: 2018-12-29
Filing date: 2018-12-29
Publication date: 2024-02-27
Anticipated expiration: 2038-12-29
Also published as: CN109754396A; CN109754396B; CN112967236A

Abstract

The application relates to a registration method, a registration device, computer equipment and a storage medium of images. The method comprises the following steps: acquiring a reference image and a floating image to be registered; acquiring a reference anatomical marker point set to be registered corresponding to a reference image and a floating anatomical marker point set to be registered corresponding to a floating image; determining a mark point intersection according to a matching result of names of all mark points in the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered; determining an initial reference anatomical landmark set and an initial floating anatomical landmark set from the reference anatomical landmark set to be registered and the floating anatomical landmark set to be registered respectively according to the landmark intersection; at least one stage of image registration is performed on the reference image and the floating image based on the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and the anatomical landmark point-based registration model. The method greatly improves the application range of image registration.

Description

Image registration method, device, computer equipment and storage medium

The patent application of the invention is a divisional application of Chinese patent application with the application date of 2018, 12, 29, 201811637721.8 and the name of image registration method, device, computer equipment and storage medium.

Technical Field

The present disclosure relates to the field of image processing technologies, and in particular, to a method and apparatus for registering images, a computer device, and a storage medium.

Background

The image registration can realize matching and overlapping of two or more images acquired at different times, different imaging devices or under different conditions, for example, the images such as an electronic computed tomography (Computed Tomography, CT) image, a positron emission tomography (Positron Emission Computed Tomography, PET) image and the like can be matched and overlapped, so that the information of the CT image and the information of the PET image which participate in registration are displayed on the same image, a better auxiliary effect is provided for clinical medical diagnosis, and the image registration method is a key technology in the field of image processing.

In the conventional technology, if a region of interest (Region Of Interest, ROI) is an irregular region, the irregular region in the image to be registered is extracted, and registration is performed based on the irregular region; and if the ROI is a key point, extracting the key point in the image to be registered, and registering based on the key point.

However, in the conventional technology, when image registration is performed, the image to be registered can only be registered based on single semantic information such as an irregular area or key points, so that the application range of the conventional registration method is low.

Disclosure of Invention

Based on this, it is necessary to provide an image registration method, apparatus, computer device and storage medium aiming at the problem that the application range of the conventional registration method is low because the conventional technology can only register the images to be registered based on single semantic information such as irregular areas or key points.

In a first aspect, embodiments of the present application provide an image registration method, which may include:

acquiring a reference image and a floating image to be registered;

extracting semantic information from the reference image and the floating image to obtain a marked reference image and a marked floating image which comprise the semantic information;

determining target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to the semantic information;

and carrying out image registration on the reference image and the floating image according to the semantic information and the target image registration model.

In one embodiment, the semantic information includes: at least one of a segmented region and an anatomical landmark point of the floating image and at least one of a segmented region and an anatomical landmark point of the reference image; the preset image registration model comprises a segmentation-based image registration model and an anatomical marker point-based registration model.

In one embodiment, when the target image registration model is the anatomical landmark point-based registration model, the image registering the reference image and the floating image according to the semantic information and the target image registration model includes:

acquiring a to-be-registered reference anatomical marker point set of the marker reference image and a to-be-registered floating anatomical marker point set of the marker floating image;

and carrying out image registration on the reference image and the floating image according to the reference anatomical mark point set to be registered, the floating anatomical mark point set to be registered and the registration model based on anatomical mark points.

In one embodiment, the image registering the reference image and the floating image according to the reference anatomical landmark point set to be registered, the floating anatomical landmark point set to be registered, and the anatomical landmark point-based registration model includes:

determining a mark point intersection according to a matching result of the names of all mark points in the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered;

determining an initial reference anatomical landmark set and an initial floating anatomical landmark set from the reference anatomical landmark set to be registered and the floating anatomical landmark set to be registered, respectively, according to the landmark intersection;

Image registration is performed on the reference image and the floating image according to the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and the anatomical landmark point-based registration model.

In one embodiment, when the target image registration model is the segmentation-based image registration model, the performing image registration on the reference image and the floating image according to the semantic information and the target image registration model includes:

obtaining a segmentation reference image corresponding to the marking reference image and a segmentation floating image corresponding to the floating image;

and carrying out image registration on the reference image and the floating image according to the segmentation reference image, the segmentation floating image and the segmentation-based image registration model.

In one embodiment, the method further comprises:

acquiring a registration result after image registration of the reference image and the floating image;

and carrying out image integration on the registration result according to the registration result and a preset image integration model.

In one embodiment, after said image registering the reference image and the floating image, the method further comprises:

Acquiring the target transformation matrix;

determining a similarity measurement value between the downsampled reference image and a transformed floating image corresponding to the downsampled floating image according to the target transformation matrix, the downsampled reference image obtained by downsampling the reference image and the downsampled floating image obtained by downsampling the floating image;

performing at least one of translation operation, rotation operation, tilting operation and scaling operation on the target transformation matrix, and extracting initial parameters corresponding to the target transformation matrix;

and determining a target parameter according to the similarity measurement value, the initial parameter and a preset gradient descent method.

In a second aspect, embodiments of the present application provide an image registration apparatus, which may include:

the first acquisition module is used for acquiring a reference image and a floating image to be registered;

the first extraction module is used for extracting semantic information of the reference image and the floating image to obtain a marked reference image and a marked floating image which comprise the semantic information;

the first determining module is used for determining target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to the semantic information;

And the registration module is used for registering the reference image and the floating image according to the semantic information and the target image registration model.

In a third aspect, embodiments of the present application provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program as follows:

acquiring a reference image and a floating image to be registered;

In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

acquiring a reference image and a floating image to be registered;

In the image registration method, the image registration device, the computer equipment and the readable storage medium provided by the embodiment, the computer equipment can acquire the reference image and the floating image to be registered; extracting semantic information from the reference image and the floating image to obtain a marked reference image and a marked floating image which comprise the semantic information; determining target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to semantic information; and finally, carrying out image registration on the marked reference image and the marked floating image according to the semantic information and the target image registration model. In this embodiment, the computer device may extract the semantic information of the reference image and the floating image first, so that according to different semantic information, different target image registration models are used to register the reference image and the floating image, so as to complete registration of the reference image and the floating image including multiple semantic information, thereby solving the limitation that in the prior art, only the reference image and the floating image can be registered based on a single semantic information, and greatly improving the application range of image registration.

Drawings

FIG. 1 is a schematic diagram of an internal structure of a computer device according to one embodiment;

FIG. 2 is a flow chart of an image registration method according to an embodiment;

FIG. 3 is a flowchart of another embodiment of an image registration method;

FIG. 4 is a flowchart of an image registration method according to another embodiment;

FIG. 5 is a flowchart of an image registration method according to another embodiment;

FIG. 6 is a flowchart of an image registration method according to another embodiment;

FIG. 7 is a schematic diagram of an image registration apparatus according to an embodiment;

fig. 8 is a schematic structural view of an image registration apparatus according to another embodiment;

fig. 9 is a schematic structural view of an image registration apparatus according to still another embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

The image registration method provided by the embodiment of the application can be applied to the computer equipment shown in fig. 1. The computer device comprises a processor, a memory, and a computer program stored in the memory, wherein the processor is connected through a system bus, and when executing the computer program, the processor can execute the steps of the method embodiments described below. Optionally, the computer device may further comprise a network interface, a display screen and an input means. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, which stores an operating system and a computer program, an internal memory. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. Optionally, the computer device may be a server, a personal computer (personal computer, PC), a personal digital assistant, other terminal devices, such as a tablet (portable android device, PAD), a mobile phone, etc., and a cloud or remote server, which is not limited to the specific form of the computer device in the embodiments of the present application.

It should be noted that, in the image registration method provided in the embodiment of the present application, the execution body may be an image registration apparatus, and the image registration apparatus may be implemented as part or all of a computer device in a manner of software, hardware, or a combination of software and hardware. In the following method embodiments, the execution subject is a computer device.

Fig. 2 is a flowchart of an image registration method according to an embodiment. The present embodiment relates to a process in which a computer device determines an image registration model from semantic information extracted from a reference image and a floating image, and performs image registration on the reference image and the floating image. As shown in fig. 2, the method may include:

s202, acquiring a reference image and a floating image to be registered.

Specifically, the reference image and the floating image may be images of the same modality or images of different modalities, for example, the reference image and the floating image may be CT images, or one may be CT images, and the other may be PET images. Alternatively, the computer device may register two or more images obtained, such as one of the images as a reference image and the other as a floating image, and map the floating image to the reference image to achieve alignment of the reference image with the floating image under the anatomical structure. Alternatively, the reference image and the floating image may be images of the same individual, or images of different individuals, or images containing the same anatomical structure, or images containing a part of the same anatomical structure. Alternatively, the reference image and the floating image may be two-dimensional images or three-dimensional images, which is not particularly limited in this embodiment.

S204, extracting semantic information of the reference image and the floating image to obtain a marked reference image and a marked floating image which comprise the semantic information.

Specifically, after the computer device acquires the input reference image and the floating image, semantic information in the reference image and the floating image can be extracted according to a preset trained neural network model, for example, if a region corresponding to a lung is detected, the computer device can segment the region corresponding to the lung, so that the semantic information corresponding to the lung is extracted: if the bone is detected, marking the position corresponding to the bone by using a marking point, so as to mention semantic information corresponding to the bone: anatomically marked points. After the computer equipment utilizes the preset neural network model to extract language information of the reference image and the floating image, the marked reference image and the marked floating image containing the extracted semantic information can be obtained.

S206, determining target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to the semantic information.

Specifically, the image registration model is a model for registering a marker reference image and a marker floating image obtained after extracting semantic information, such as a corresponding algorithm model of a surface matching algorithm, a mutual information method, a standard orthogonalization matrix method, a least square method and the like. For the marker reference image and the marker floating image containing different semantic information, the computer device may register the two with different registration models, i.e. the marker reference image and the marker floating image comprising the segmented region and the marker reference image and the marker floating image comprising the anatomical marker points may correspond to different image registration models.

Optionally, the semantic information includes: at least one of the segmented region of the floating image and the anatomical landmark point and at least one of the segmented region of the reference image and the anatomical landmark point. The semantic information may be anatomical mark points in the reference image and the floating image, or may be segmented regions in the reference image and the floating image. Further, the anatomical marker points may be geometric marker points, such as gray extremum or intersection points of linear structures, or anatomical marker points that are clearly visible in anatomical form and can be precisely positioned, such as key marker points or feature points of human tissues, organs or lesions; the segmented region may be a curve or curved surface corresponding to the reference image and the floating image, such as a lung, liver or irregular region.

Alternatively, the preset image registration model may include a registration model based on segmentation and a registration model based on anatomical landmark points. Further, the image registration model based on segmentation is an image registration model which can carry out image registration on the marked reference image and the marked floating image comprising the segmentation area, such as an algorithm model corresponding to a surface matching algorithm, a mutual information method, a gray mean square error method and the like; the registration model based on the anatomical marker points is a registration model which can carry out image registration on the marker reference image and the marker floating image comprising the anatomical marker points, such as an algorithm model corresponding to a singular value decomposition algorithm, an iterative closest point method, a standard orthogonalization matrix method and the like.

And S208, carrying out image registration on the reference image and the floating image according to the semantic information and the target image registration model.

Specifically, according to the difference of semantic information, the computer device may select a corresponding target image registration model, and perform image registration on the reference image and the floating image. Optionally, the reference image or the floating image may include a segmented region and anatomical points at the same time, and at this time, the computer device may register the reference image and the anatomical points in the floating image using a target image registration model corresponding to the anatomical points, and register the segmented region in the reference image and the floating image using a target image registration model corresponding to the segmented region; the reference image and the segmented region in the floating image may be registered by using the target image registration model corresponding to the segmented region, and then the reference image and the anatomical point in the floating image may be registered by using the target image registration model corresponding to the anatomical point, or the reference image and the anatomical point in the floating image may be registered by using the target image registration model corresponding to the anatomical point, and the segmented region in the reference image and the segmented region in the floating image may be registered by using the target image registration model corresponding to the segmented region.

Optionally, the computer device may also incorporate a graphics processor (Graphics Processing Unit, GPU) processing portion of the operations supporting the parallel computing architecture (Compute Unified Device Architecture, CUDA) to further speed up the above-described registration algorithm for registering the reference image and the floating image, while ensuring that the relevant operation processing for image registration continues using the CPU therein.

According to the image registration method provided by the embodiment, the computer equipment can acquire the reference image and the floating image to be registered; extracting semantic information from the reference image and the floating image to obtain a marked reference image and a marked floating image which comprise the semantic information; determining target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to semantic information; and finally, carrying out image registration on the marked reference image and the marked floating image according to the semantic information and the target image registration model. In this embodiment, the computer device may extract the semantic information of the reference image and the floating image first, so that according to different semantic information, different target image registration models are used to register the reference image and the floating image, so as to complete registration of the reference image and the floating image including multiple semantic information, thereby solving the limitation that in the prior art, only the reference image and the floating image can be registered based on a single semantic information, and greatly improving the application range of image registration.

Fig. 3 is a flowchart of an image registration method according to another embodiment. This embodiment relates to a process in which the computer device registers the reference image and the floating image based on the anatomical landmark point-based registration model and the semantic information when the target image registration model is the anatomical landmark point-based registration model described above. Based on the above embodiment, optionally, the step S208 may include:

s302, acquiring a to-be-registered reference anatomical marker point set of the marker reference image and a to-be-registered floating anatomical marker point set of the marker floating image.

Specifically, the reference anatomical landmark point set to be registered and the floating anatomical landmark point set to be registered are sets of coordinate information of each anatomical landmark point. Alternatively, the anatomical landmark points may be manually pre-marked landmark points.

S304, performing image registration on the reference image and the floating image according to the reference anatomical mark point set to be registered, the floating anatomical mark point set to be registered and the registration model based on anatomical mark points.

Specifically, the registration model based on the anatomical marker points may be any one of algorithm models corresponding to singular value decomposition algorithm, iterative nearest point algorithm, standard orthogonalization matrix method and other methods. The computer device may perform image registration on the reference image and the floating image according to the acquired set of reference anatomical landmark points to be registered, the set of floating anatomical landmark points to be registered, and a preset anatomical landmark point-based registration model.

Optionally, the step S304 may specifically include: determining a mark point intersection according to the matching result of the names of the mark points in the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered; determining an initial reference anatomical landmark set and an initial floating anatomical landmark set from the reference anatomical landmark set to be registered and the floating anatomical landmark set to be registered, respectively, according to the landmark intersection; and performing image registration on the reference image and the floating image according to the initial reference anatomical marker point set, the initial floating anatomical marker point set and the registration model based on anatomical marker points.

Wherein each anatomical landmark point has a unique name, and anatomical landmark points with the same name for the reference anatomical landmark point set to be registered and the anatomical landmark point in the floating anatomical landmark point set to be registered form a landmark point intersection of the reference anatomical landmark point set to be registered and the floating anatomical landmark point set to be registered. Alternatively, the computer device may also take as the intersection of the anatomical landmark points of the same anatomical landmark point number in the reference anatomical landmark point set to be registered and the floating anatomical landmark point set to be registered. After determining the marker point intersection, the computer device may use a point set corresponding to the marker point intersection in the reference anatomical marker point set to be registered as an initial reference anatomical marker point set, and select a point set corresponding to the marker point intersection in the floating anatomical marker point set to be registered as an initial floating anatomical marker point set, so that the initial reference anatomical marker point set and the initial floating anatomical marker point set may be input into a preset registration model based on anatomical marker points, and alignment of the reference image and the floating image under the same anatomical structure is achieved.

In the step S304, the computer device may perform image registration on the reference image and the floating image according to an initial set of reference anatomical landmark points and an initial set of floating anatomical landmark points selected from the set of reference anatomical landmark points to be registered and the set of floating anatomical landmark points to be registered, and using a registration model based on anatomical landmark points. Optionally, the process of performing image registration on the reference image and the floating image by using the registration model based on the anatomical marker points may be divided into three stages of registration processes, where each stage may obtain a corresponding registration result, and the three stages of registration processes are as follows:

the registration process of the first stage can be seen from S3042 to S3046:

s3042, determining a first registration result according to the initial reference anatomical marker point set, the initial floating anatomical marker point set and the registration model based on anatomical marker points; the first registration result includes a first set of registration result points and a first transformation matrix.

Specifically, after the computer device inputs the initial reference anatomical marker point set and the initial floating anatomical marker point set into a preset registration model based on anatomical marker points, a first registration result point set and a first transformation matrix after spatial transformation of the floating anatomical marker point set to be registered can be obtained. The first registration result point set and the first transformation matrix form a first registration result.

S3044, determining a first floating anatomical landmark point set corresponding to the first spatial distance in a preset ratio according to the first spatial distance set and the preset ratio; and the first spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the first registration result point set is recorded in the first spatial distance set.

Specifically, after the first registration result point set is obtained, the computer device may calculate the first registration result point set according to the formula d1= ||p _f1 –P _re1 || ₂ Calculating a first spatial distance D1 between the reference anatomical marker point set to be registered and each corresponding marker point in the first registration result point set, wherein P _f1 For a point set consisting of marker points in a set of reference anatomical marker points to be registered and corresponding marker points in a first set of registration result points, P _re1 Is the first set of registration result points. Alternatively, the above-mentioned preset ratio may be (0, 1)]Any value within. Alternatively, the first floating anatomical marker point set corresponding to the first spatial distance in the preset ratio may be selected directly, or the distances in the first spatial distance may be sorted in ascending order, and then the first floating anatomical marker point set corresponding to the first spatial distance in the preset ratio is selected, where the smaller the first spatial distance between the reference anatomical marker point set to be registered and each corresponding marker point in the first registration result point set is, the higher the registration result accuracy is, so that the first spatial distance is And after ascending order of the distances from the first floating anatomical landmark point set, selecting a first floating anatomical landmark point set corresponding to the first space distance in a preset ratio, so that the accuracy of registration can be improved. The first set of floating anatomical landmark points is a set of points corresponding to a first spatial distance within a preset ratio selected from the set of floating anatomical landmark points to be registered.

And S3046, when the number of the marking points in the first floating anatomical marking point set is smaller than the preset number threshold value, taking the first transformation matrix as a target transformation matrix.

Specifically, the target transformation matrix is a matrix used for registering the marked reference image and the marked floating image, and the computer equipment can use the target transformation matrix to realize the registration of the marked reference image and the marked floating image. Alternatively, the computer device may compare the number of marker points in the first set of floating anatomical marker points with a preset number threshold, and determine whether to take the above-mentioned first transformation matrix as the target transformation matrix according to the comparison result. Alternatively, the preset number threshold may be 5. When the number of the marker points in the first floating anatomical marker point set is smaller than the preset number threshold, the first transformation matrix is taken as the target transformation matrix, and S30422 is continuously executed.

When the number of marker points in the first floating anatomical marker point set is not less than the preset number threshold, a second stage registration process is required.

The registration process of the second stage can be seen from S3048 to S30416:

s3048, acquiring a first reference anatomical landmark point set corresponding to the first floating anatomical landmark point set in the reference anatomical landmark point set to be registered.

In this step, the first reference anatomical landmark set is a point set formed by the points corresponding to the points whose names or numbers are the same as those of the marks in the first floating anatomical landmark set.

S30410, determining a second transformation matrix from the first set of reference anatomical landmark points, the first set of floating anatomical landmark points and the anatomical landmark-based registration model.

Specifically, as with the method for determining the first transformation matrix described above, the computer device may input the first set of reference anatomical landmark points and the first set of floating anatomical landmark points into a preset anatomical landmark-based registration model to obtain the second transformation matrix.

And S30512, determining a second registration result point set according to the second transformation matrix and the floating anatomical marker point set to be registered.

In this step, the computer device may perform spatial transformation on the floating anatomical landmark set to be registered by using the second transformation matrix according to the product of the obtained second transformation matrix and the floating anatomical landmark set to be registered, and obtain the second registration result point set by combining interpolation methods such as neighbor interpolation, bilinear interpolation, or tri-linear interpolation.

S30414, determining a second floating anatomical landmark set corresponding to a second spatial distance smaller than a preset distance threshold according to the second spatial distance set and the preset distance threshold; and the second spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the second registration result point set is recorded in the second spatial distance set.

In this step, after the second registration result point set is obtained, the computer device may calculate the second registration result point set according to formula d2= ||p _f –P _re2 || ₂ Calculating a second spatial distance D2 between the reference anatomical marker point set to be registered and each corresponding marker point in the second registration result point set, wherein P _f2 For the point set corresponding to each marker point in the reference anatomical marker point set to be registered and the second registration result point set, P _re2 Is the second set of registration result points. Optionally, the preset distance threshold may be set as required, for example, the distance threshold may be determined according to an actual distance between the reference anatomical landmark set to be registered and each corresponding landmark in the second registration result point set, where the actual distance is acceptable to the user. The second floating anatomical landmark point set is obtained from the floating anatomical structure to be registered Marking a point set corresponding to a second spatial distance within a preset distance threshold selected in the point set.

And S30416, when the number of the marking points in the second floating anatomical marking point set is smaller than the preset threshold number, taking the second transformation matrix as the target transformation matrix.

In this step, the computer device may compare the number of marker points in the second set of floating anatomical marker points with a preset number threshold, and determine whether to use the second transformation matrix as the target transformation matrix according to the comparison result. When the number of the marker points in the second floating anatomical marker point set is smaller than the preset number threshold, the second transformation matrix is taken as the target transformation matrix, and S30422 is continuously executed.

When the number of marker points in the second set of floating anatomical marker points is not less than the preset number threshold, a third stage registration process is required.

The registration process of the third stage can be seen from S30118 to S30420:

s30118, acquiring a second reference anatomical landmark point set corresponding to the second floating anatomical landmark point set in the reference anatomical landmark point set to be registered.

In this step, the second reference anatomical landmark set is a point set corresponding to a landmark having the same name or number as the landmark in the second floating anatomical landmark set selected from the reference anatomical landmark set to be registered.

S30420, determining a third transformation matrix according to the second reference anatomical marker point set, the second floating anatomical marker point set and the registration model based on anatomical marker points, and taking the third transformation matrix as the target transformation matrix.

In this step, as in the method for determining the first transformation matrix and the second transformation matrix described above, the computer device may input the second set of reference anatomical landmark points and the second set of floating anatomical landmark points into a preset registration model based on anatomical landmark points, thereby obtaining a third transformation matrix, and after obtaining the third transformation matrix, the computer device may directly use the third transformation matrix as the target transformation matrix.

And S30422, carrying out image registration on the reference image and the floating image according to the target transformation matrix.

Specifically, the computer device may map the marked floating image under the marked reference image space according to the product of the matrix formed by the coordinate position of each pixel point of the floating image and the target transformation matrix, and combine interpolation methods such as neighbor interpolation, bilinear interpolation, or trilinear interpolation, so as to achieve alignment of the marked reference image and the marked floating image under the anatomical structure, thereby completing image registration of the marked reference image and the marked floating image.

Alternatively, the above-mentioned preset ratio and preset distance threshold may be adjusted according to the following manner: adding noise to each mark point in the reference image and the floating image to be registered, registering the reference image and the floating image to be registered by utilizing the three-stage registration mode to obtain a new target transformation matrix, registering the reference image and the floating image by utilizing the new target transformation matrix, calculating a similarity metric value between the reference image and the floating image after registration by utilizing a preset similarity metric model according to an obtained registration result, comparing the similarity metric value with a preset similarity metric threshold value, and adjusting at least one of the preset ratio and the preset distance threshold value until the finally obtained similarity metric value is larger than the preset similarity metric threshold value if the similarity metric value is smaller than the preset similarity metric threshold value, so that the preset ratio and the preset distance threshold value are adjusted to be proper values, and the registration accuracy of the image registered by utilizing the adjusted preset ratio and the preset threshold value algorithm model is higher. It should be noted that the mean, variance and number of the added noise may be set randomly.

According to the image registration method provided by the embodiment, the computer equipment can acquire the to-be-registered reference anatomical mark point set of the marked reference image and the to-be-registered floating anatomical mark point set of the marked floating image; according to the reference anatomical mark point set to be registered, the floating anatomical mark point set to be registered and the registration model based on anatomical mark points, the marked reference image and the marked floating image are subjected to image registration in three stages, and each stage utilizes the mark points in a certain condition such as a preset ratio or a preset distance threshold value to perform image registration instead of performing image registration by using all the mark points, so that the calculated amount is greatly reduced, and the registration speed is improved; in addition, the set of the marker points in each stage is different, so that the influence of the false detection on the registration accuracy of part of anatomical marker points can be reduced, and the marker points in each stage are marker points which are screened and determined according to a preset ratio or a preset distance threshold value and the like and can improve the registration accuracy, so that the image registration accuracy can be improved by the method of carrying out registration in stages provided by the embodiment.

When the target image registration model is a segmentation-based image registration model, the computer device may perform image registration on the marker reference image and the marker floating image using an image registration method provided by a further embodiment shown in fig. 4. The embodiment relates to a realization process that the computer equipment performs image registration on the marked reference image and the marked floating image according to the extracted segmentation areas and the corresponding segmentation-based image registration model. Based on the above embodiment, optionally, another implementation manner of S208 may include:

S402, obtaining a segmentation reference image corresponding to the marking reference image and a segmentation floating image corresponding to the floating image.

Specifically, the segmented reference image and the segmented floating image may be images corresponding to the reference image and the floating image to be registered after semantic information extraction according to the preset trained neural network model. Optionally, the computer device may perform segmentation of any region of the reference image and the floating image to be registered by using the preset trained neural network model, so as to obtain a segmented reference image and a segmented floating image.

S404, carrying out image registration on the reference image and the floating image according to the segmentation reference image, the segmentation floating image and the segmentation-based image registration model.

Specifically, the image registration model based on segmentation may be any one of algorithm models corresponding to registration methods such as a surface matching algorithm, a mutual information method, a gray-scale mean square error method and the like. The computer equipment can determine a target segmentation transformation matrix according to the acquired segmentation reference image, the segmentation floating image and the segmentation-based image registration model, so that the floating image to be registered is mapped to the space coordinates of the reference image according to the target segmentation transformation matrix to finish registration of the reference image and the floating image.

According to the image registration method provided by the embodiment, the computer equipment can acquire the segmentation reference image corresponding to the mark reference image and the segmentation floating image corresponding to the floating image; and image registering the reference image and the floating image according to the segmented reference image, the segmented floating image and the segmented image-based registration model. In this embodiment, the computer device may directly perform image registration on the reference image and the floating image by using a preset image registration model based on segmentation according to the segmented reference image and the segmented floating image obtained after extracting the semantic information, so that the implementation manner is simpler.

Fig. 5 is a diagram of an image registration method according to another embodiment. The present embodiment relates to a registration result obtained by registering a reference image and a floating image according to the above embodiment by a computer device, and a process of performing image integration on the registration result by using a preset image integration model. On the basis of the above embodiment, optionally, the above method may further include:

s502, acquiring a registration result after image registration of the reference image and the floating image.

In this step, the registration result is a registered reference image and floating image obtained by performing image registration on the reference image and floating image.

S504, performing image integration on the registration result according to the registration result and a preset image integration model.

In this step, the preset image integration model may be any one of tri-linear interpolation, B-spline interpolation, and the like. Image integration may be the use of an algorithm to organically combine two or more registered images from different imaging devices or acquired at different times. The computer device may integrate the reference image and the floating image in the registration result by using a preset image integration model, so as to obtain a distorted image in which the floating image and the reference image are integrated together in the reference image space.

According to the image registration method provided by the embodiment, the computer equipment can acquire a registration result after image registration of the reference image and the floating image; therefore, the registration result is subjected to image integration according to the registration result and a preset image integration model, so that the reference image and the floating image are integrated into one image, the advantages of the images are complementarily and organically combined, a new image with more abundant information is obtained, and a doctor is better assisted in judging the condition of a patient by using the integrated image.

Fig. 6 is a flowchart of an image registration method according to another embodiment. The present embodiment relates to a target matrix obtained by a computer device according to the above embodiment, and an image obtained by downsampling a reference image and a floating image, and adjusting a similarity metric value by using a gradient descent method to determine a realization process of a target parameter. On the basis of the above embodiment, optionally, the above method may further include:

s602, acquiring the target transformation matrix.

S604, determining a similarity measurement value between the downsampled reference image and the transformed floating image corresponding to the downsampled floating image according to the target transformation matrix, the downsampled reference image obtained by downsampling the reference image and the downsampled floating image obtained by downsampling the floating image.

Specifically, the computer device may downsample the reference image and the floating image to obtain a downsampled reference image and a downsampled floating image, alternatively, may perform a downsampling operation on the reference image and the floating image to obtain a downsampled reference image and a downsampled floating image, spatially transform the downsampled floating image by using the target transformation matrix to obtain a transformed floating image, and further determine a similarity metric value between the transformed floating image and the downsampled reference image by using a calculation model of a preset similarity metric value, such as an algorithm model corresponding to a mutual information method, a gray-scale mean square error method, and the like.

S606, performing at least one operation of translation operation, rotation operation, tilting operation and scaling operation on the target transformation matrix, and extracting initial parameters corresponding to the target transformation matrix.

Specifically, if the reference image and the floating image are three-dimensional images, the corresponding target transformation matrix may be a matrix of 4*4, and the computer device may perform a translation operation, a rotation operation, a tilting operation, and a scaling operation on the target transformation matrix, decompose the target transformation matrix into four matrices 4*4, such as a translation matrix, a rotation matrix, a tilting matrix, and a scaling matrix, and further obtain initial parameters corresponding to the 12 target transformation matrices according to translation distances, rotation angles, tilting angles, scaling scales, and the like of the four matrices 4*4 in the three-dimensional coordinate system. Similarly, if the reference image and the floating image are two-dimensional images, the computer device may obtain initial parameters corresponding to 8 target transformation matrices.

S608, determining a target parameter according to the similarity measurement value, the initial parameter and a preset gradient descent method.

Specifically, the computer device may adjust the initial parameter according to a preset gradient descent method, so that the similarity measurement value reaches an optimum value, and take an adjusted parameter corresponding to the optimum similarity measurement value as a target parameter. Alternatively, the computer device may determine a final transformation matrix corresponding to the target parameter according to the target parameter, and register the reference image and the floating image using the final transformation matrix.

Alternatively, the computer device may perform a plurality of downsampling operations on the reference image and the floating image, for example, three downsampling operations and obtain a corresponding downsampled reference image and downsampled floating image, respectively. Further, the downsampled reference image may include a first downsampled reference image corresponding to the first downsampling, a second downsampled reference image corresponding to the second downsampling, and a third downsampled reference image corresponding to the third downsampling, and similarly, the downsampled floating image may include a first downsampled floating image corresponding to the first downsampling, a second downsampled floating image corresponding to the second downsampling, and a third downsampled floating image corresponding to the third downsampling. At this time, the target parameters may be determined using the following method: the first step: the computer equipment can perform space transformation on the third downsampled floating image by utilizing the target transformation matrix, map the third downsampled floating image to a space coordinate system corresponding to the third downsampled reference image to obtain a transformed third floating image, and determine a first similarity measurement value between the transformed third floating image and the third downsampled reference image by utilizing a calculation model of a preset similarity measurement value; and a second step of: the computer device may adjust the initial parameters by using a preset gradient descent method to make the first similarity measurement value optimal, determine a new target transformation matrix according to the parameters corresponding to the optimal first similarity measurement value, and continuously perform the operations of the first step and the second step on the second downsampled floating image and the downsampled reference image by using the new target transformation matrix until the operations of the first step and the second step are performed on the initial reference image and the floating image, and use the parameters corresponding to the finally obtained optimal similarity measurement value as target parameters, so that the computer device may determine a final transformation matrix corresponding to the target parameters according to the target parameters, and register the reference image and the floating image by using the final transformation matrix.

Optionally, the computer device may perform image integration on the registration result obtained by performing image registration on the reference image and the floating image by using an image integration method corresponding to the embodiment shown in fig. 5, optimize the integration result obtained by performing image registration on the reference image and the floating image by using a registration result obtained by performing image registration on the reference image and the floating image by using a final transformation matrix provided by this embodiment, perform image optimization on the registration result obtained by performing image registration on the reference image and the floating image by using an image optimization method provided by this embodiment, and perform image integration on the registration result obtained by performing image registration on the reference image and the floating image by using an image integration method corresponding to the embodiment shown in fig. 5 by using a final transformation matrix provided by this embodiment.

According to the image registration method provided by the embodiment, the computer equipment can acquire the target transformation matrix, and determine a similarity measurement value between the downsampled reference image and the transformed floating image corresponding to the downsampled floating image according to the target transformation matrix, the downsampled reference image obtained by downsampling the reference image and the downsampled floating image obtained by downsampling the floating image; performing at least one of translation operation, rotation operation, tilting operation and scaling operation on the target transformation matrix, and extracting initial parameters corresponding to the target transformation matrix; and then, determining the target parameter according to the similarity measurement value, the initial parameter and a preset gradient descent method, wherein the target parameter is the parameter corresponding to the optimal similarity measurement value, so that the final transformation matrix determined according to the target parameter is also better, the final transformation matrix is utilized to register the floating image and the reference image, the accuracy of registering the floating image and the reference image is higher, and the accuracy of registering the images is further improved.

The following describes the process of the image registration method of the embodiment of the present application by way of a simple example. The method comprises the following steps:

s702, the computer device acquires a reference image and a floating image to be registered.

S704, extracting semantic information of the reference image and the floating image by the computer equipment to obtain a marked reference image and a marked floating image which comprise the semantic information; the semantic information includes: at least one of a segmented region and an anatomical landmark point of the floating image and at least one of a segmented region and an anatomical landmark point of the reference image.

S706, the computer equipment determines target image registration models corresponding to the mark reference image and the mark floating image respectively from preset image registration models according to the semantic information; the preset image registration model comprises a segmentation-based image registration model and an anatomical marker point-based registration model.

S708, the computer equipment judges whether the target image registration model is the registration model based on the anatomical marker points, if yes, the step S710 is continued, and if not, the step S740 is executed.

S710, the computer device acquires a set of reference anatomical landmark points to be registered of the landmark reference image and a set of floating anatomical landmark points to be registered of the landmark floating image.

S712, the computer equipment determines a mark point intersection set with the same names of mark points in the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered according to the matching result of the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered, selects the mark point intersection set in the reference anatomical mark point set to be registered as an initial reference anatomical mark point set, and selects the mark point intersection set in the floating anatomical mark point set to be registered as an initial floating anatomical mark point set.

The computer device determines the first registration result from the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and the anatomical landmark-based registration model S714.

S716, the computer equipment determines a first floating anatomical landmark point set corresponding to the first spatial distance in the preset ratio according to the first spatial distance set and the preset ratio; and the first spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the first registration result point set is recorded in the first spatial distance set.

S718, the computer device judges whether the number of the marking points in the first floating anatomical marking point set is smaller than the preset number threshold, if yes, the step S720 is continued, and if not, the step S722 is executed.

S720, the computer equipment takes the first transformation matrix as a target transformation matrix.

S722, the computer device obtains a first set of reference anatomical landmark points in the set of reference anatomical landmark points to be registered corresponding to the first set of floating anatomical landmark points.

S724, the computer device determines a second transformation matrix from the first set of reference anatomical landmark points, the first set of floating anatomical landmark points, and the anatomical landmark-based registration model.

S726, the computer device determines a second registration result point set according to the second transformation matrix and the floating anatomical landmark point set to be registered.

S728, the computer equipment determines a second floating anatomical landmark point set corresponding to a second spatial distance smaller than the preset distance threshold according to the second spatial distance set and the preset distance threshold; and the second spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the second registration result point set is recorded in the second spatial distance set.

And S730, the computer equipment judges whether the number of the marking points in the second floating anatomical marking point set is smaller than the preset threshold number, if yes, the step S732 is continuously executed, and if not, the step S734 is executed.

S732, the computer device takes the second transformation matrix as the target transformation matrix.

S734, the computer device obtains a second set of reference anatomical landmark points of the set of reference anatomical landmark points to be registered corresponding to the second set of floating anatomical landmark points.

S736, the computer device determines a third transformation matrix from the second set of reference anatomical landmark points, the second set of floating anatomical landmark points and the anatomical landmark point-based registration model, and takes the third transformation matrix as the target transformation matrix.

S738, the computer equipment performs image registration on the reference image and the floating image according to the target transformation matrix; after execution of S738, execution continues with S744.

S740, the computer device acquires the split reference image corresponding to the marker reference image and the split floating image corresponding to the floating image.

S742, the computer device image registers the reference image and the floating image according to the segmented reference image, the segmented floating image, and the segmentation-based image registration model.

S744, the computer device obtains a registration result after performing image registration on the reference image and the floating image.

And S746, the computer equipment integrates the registration result according to the registration result and a preset image integration model.

S748, the computer device obtains the target transformation matrix.

And S750, the computer equipment determines a similarity measurement value between the downsampled reference image and the transformed floating image corresponding to the downsampled floating image according to the target transformation matrix, the downsampled reference image obtained by downsampling the reference image and the downsampled floating image obtained by downsampling the floating image.

S752, the computer equipment performs at least one operation of translation operation, rotation operation, tilting operation and scaling operation on the target transformation matrix, and extracts initial parameters corresponding to the target transformation matrix.

S754, the computer equipment determines a target parameter according to the similarity measurement value, the initial parameter and a preset gradient descent method.

The working principle and technical effects of the image registration method provided in this embodiment are as described in the above embodiments, and are not described herein.

It should be understood that, although the steps in the flowcharts of fig. 2 to 6 are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur in sequence, but may be performed alternately or alternately with at least a portion of the other steps or sub-steps of other steps.

Fig. 7 is a schematic structural diagram of an image registration apparatus according to an embodiment. As shown in fig. 7, the apparatus may include a first acquisition module 702, a first extraction module 704, a first determination module 706, and a registration module 708.

Specifically, a first acquiring module 702 is configured to acquire a reference image and a floating image to be registered;

a first extraction module 704, configured to extract semantic information from the reference image and the floating image, to obtain a marked reference image and a marked floating image that include the semantic information;

A first determining module 706, configured to determine, according to the semantic information, a target image registration model corresponding to the marker reference image and the marker floating image respectively from preset image registration models;

a registration module 708 is configured to perform image registration on the reference image and the floating image according to the semantic information and the target image registration model.

Optionally, the semantic information includes: at least one of a segmented region and an anatomical landmark point of the floating image and at least one of a segmented region and an anatomical landmark point of the reference image; the preset image registration model comprises a segmentation-based image registration model and an anatomical marker point-based registration model.

The image registration apparatus provided in this embodiment may perform the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.

In another embodiment, the image registration apparatus provided in the embodiment shown in fig. 7, when the target image registration model is the registration model based on anatomical landmark points, the registration module 708 may optionally include a first acquisition unit and a first registration unit.

Specifically, a first acquisition unit is used for acquiring a to-be-registered reference anatomical marker point set of the marker reference image and a to-be-registered floating anatomical marker point set of the marker floating image;

and the first registration unit is used for carrying out image registration on the reference image and the floating image according to the reference anatomical mark point set to be registered, the floating anatomical mark point set to be registered and the registration model based on anatomical mark points.

In the image registration apparatus provided in still another embodiment, the first registration unit may include a first determination subunit, a second determination subunit, and a registration subunit, which are optional on the basis of the above embodiments.

Specifically, a first determining subunit is configured to determine a marker point intersection according to a matching result of names of each marker point in the reference anatomical marker point set to be registered and the floating anatomical marker point set to be registered;

a second determining subunit configured to determine an initial reference anatomical landmark set and an initial floating anatomical landmark set from the reference anatomical landmark set to be registered and the floating anatomical landmark set to be registered, respectively, according to the landmark intersection set;

A registration subunit for image registering the reference image and the floating image according to the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and the anatomical landmark-based registration model.

In the image registration apparatus structure provided in still another embodiment, optionally, the registration module 708 may further include a second acquisition unit and a second registration unit.

A second obtaining unit, configured to obtain a split reference image corresponding to the marker reference image and a split floating image corresponding to the floating image;

and a second registration unit for performing image registration on the reference image and the floating image according to the segmented reference image, the segmented floating image and the segmentation-based image registration model.

Fig. 8 is a schematic structural view of an image registration apparatus according to still another embodiment. The apparatus may further include a second acquisition module 710 and an integration module 712.

A second obtaining module 710, configured to obtain a registration result after performing image registration on the reference image and the floating image;

and the integration module 712 is configured to integrate the image of the registration result according to the registration result and a preset image integration model.

Fig. 9 is a schematic structural view of an image registration apparatus according to still another embodiment. The apparatus may further include a third acquisition module 714, a second determination module 716, a second extraction module 718, and a third determination module 720.

A third obtaining module 714 is configured to obtain the target transformation matrix.

A second determining module 716, configured to determine a similarity metric value between the downsampled reference image and the transformed floating image corresponding to the downsampled floating image according to the target transformation matrix, a downsampled reference image obtained by downsampling the reference image, and a downsampled floating image obtained by downsampling the floating image;

A second extracting module 718, configured to perform at least one of a translation operation, a rotation operation, a tilting operation, and a scaling operation on the target transformation matrix, and extract initial parameters corresponding to the target transformation matrix;

a third determining module 720, configured to determine a target parameter according to the similarity measurement value, the initial parameter and a preset gradient descent method.

In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:

acquiring a reference image and a floating image to be registered;

The computer device provided in the foregoing embodiments has similar implementation principles and technical effects to those of the foregoing method embodiments, and will not be described herein in detail.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring a reference image and a floating image to be registered;

The computer readable storage medium provided in the above embodiment has similar principle and technical effects to those of the above method embodiment, and will not be described herein.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims

1. A method of registration of images, the method comprising:

acquiring a reference image and a floating image to be registered;

acquiring a to-be-registered reference anatomical marker point set corresponding to the reference image and a to-be-registered floating anatomical marker point set corresponding to the floating image;

Determining a point set corresponding to the mark point intersection in the reference anatomical mark point set to be registered as an initial reference anatomical mark point set, and determining a point set corresponding to the mark point intersection in the floating anatomical mark point set to be registered as an initial floating anatomical mark point set;

performing at least one stage of image registration of the reference image and the floating image based on the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and an anatomical landmark point-based registration model; the registration model based on the anatomical mark points is a registration model for performing image registration on a mark reference image and a mark floating image which comprise the anatomical mark points; the marked reference image and the marked floating image are obtained by extracting semantic information of the reference image and the floating image.

2. The method of claim 1, wherein said performing at least one stage of image registration of the reference image and the floating image based on the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and an anatomical landmark-based registration model comprises:

Performing image registration according to the initial reference anatomical marker point set, the initial floating anatomical marker point set and the registration model based on anatomical marker points to obtain a first registration result; the first registration result comprises a first registration result point set and a first transformation matrix;

determining a first set of spatial distances according to the set of reference anatomical landmark points to be registered and the first set of registration result points; the first spatial distance set records a first spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the first registration result point set;

determining a first floating anatomical landmark set corresponding to a first spatial distance in a preset ratio according to the first spatial distance set and the preset ratio;

if the number of the marking points in the first floating anatomical marking point set is smaller than a preset number threshold, taking the first transformation matrix as a target transformation matrix;

and carrying out image registration on the reference image and the floating image according to the target transformation matrix.

3. The method of claim 2, wherein determining a first set of floating anatomical landmark points corresponding to a first spatial distance within a preset ratio from the first set of spatial distances and the preset ratio comprises:

Ranking each of the first spatial distances in the first set of spatial distances;

and selecting the first space distances in the preset ratio according to the sequence from small to large, and determining a first floating anatomical landmark point set corresponding to the selected first space distances.

4. The method of claim 2, wherein after said determining a first set of floating anatomical landmark points corresponding to a first spatial distance within the preset ratio, the method further comprises:

if the number of the marking points in the first floating anatomical marking point set is not smaller than a preset number threshold, a first reference anatomical marking point set corresponding to the first floating anatomical marking point set in the reference anatomical marking point set to be registered is obtained;

determining a second transformation matrix from the first set of reference anatomical landmark points, the first set of floating anatomical landmark points, and the anatomical landmark-based registration model;

determining a second registration result point set and a second spatial distance set according to the second transformation matrix and the floating anatomical marker point set to be registered; a second spatial distance between the reference anatomical mark point set to be registered and each corresponding mark point in the second registration result point set is recorded in the second spatial distance set;

Determining a second floating anatomical landmark set corresponding to a second spatial distance smaller than a preset distance threshold according to the second spatial distance set and the preset distance threshold;

and if the number of the marking points in the second floating anatomical marking point set is smaller than the preset number threshold value, taking the second transformation matrix as the target transformation matrix.

5. The method of claim 4, wherein after determining a second set of floating anatomical landmark points corresponding to a second spatial distance less than the preset distance threshold, the method further comprises:

if the number of the marking points in the second floating anatomical marking point set is not smaller than the preset number threshold, a second reference anatomical marking point set corresponding to the second floating anatomical marking point set in the reference anatomical marking point set to be registered is obtained;

determining a third transformation matrix from the second set of reference anatomical landmark points, the second set of floating anatomical landmark points and the anatomical landmark-based registration model, and taking the third transformation matrix as the target transformation matrix.

6. The method according to any of claims 2-5, wherein after said image registration of said reference image and said floating image according to said target transformation matrix, said method further comprises:

Acquiring the target transformation matrix;

performing downsampling operation on the reference image according to the target transformation matrix to obtain a downsampled reference image, performing downsampling operation on the floating image to obtain a downsampled floating image, and determining a similarity measurement value between the downsampled reference image and the transformed floating image corresponding to the downsampled floating image;

7. The method of claim 6, wherein after the obtaining the target transformation matrix, the method further comprises:

performing multiple downsampling operations on the reference image to obtain multiple downsampled reference images, and performing multiple downsampling operations on the floating image to obtain multiple downsampled floating images;

performing spatial transformation on the ith downsampled floating image by using the target transformation matrix to obtain a transformed ith floating image; the ith downsampled floating image is the last downsampled floating image;

Determining an ith similarity metric between the transformed ith floating image and an ith downsampled reference image; the ith downsampled reference image is the last downsampled reference image;

adjusting the initial parameters by using the preset gradient descent method to enable the ith similarity measurement value to reach the optimal value, and determining a new target transformation matrix according to the parameters corresponding to the optimal ith similarity measurement value;

and calculating an ith-1 similarity measurement value between the ith-1 transformed ith floating image and the ith-1 downsampled reference image by using the new target transformation moment until the similarity measurement value between the original reference image and the floating image is calculated, and taking a parameter corresponding to the optimal similarity measurement value as the target parameter.

8. An apparatus for registration of images, the apparatus comprising:

the image acquisition module is used for acquiring a reference image and a floating image to be registered;

the point set acquisition module is used for acquiring a to-be-registered reference anatomical marker point set corresponding to the reference image and a to-be-registered floating anatomical marker point set corresponding to the floating image;

The intersection determining module is used for determining a mark point intersection according to a matching result of the names of all mark points in the reference anatomical mark point set to be registered and the floating anatomical mark point set to be registered;

a marker point set determining module, configured to determine a point set corresponding to the marker point intersection in the reference anatomical marker point set to be registered as an initial reference anatomical marker point set, and determine a point set corresponding to the marker point intersection in the floating anatomical marker point set to be registered as an initial floating anatomical marker point set;

a registration module for performing at least one stage of image registration of the reference image and the floating image based on the initial set of reference anatomical landmark points, the initial set of floating anatomical landmark points, and an anatomical landmark point-based registration model; the registration model based on the anatomical mark points is a registration model for performing image registration on a mark reference image and a mark floating image which comprise the anatomical mark points; the marked reference image and the marked floating image are obtained by extracting semantic information of the reference image and the floating image.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.