CN112102295A - DR image registration method, device, terminal and computer-readable storage medium - Google Patents
DR image registration method, device, terminal and computer-readable storage medium Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/10072—Tomographic images
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Abstract
The invention is applicable to the field of image processing, and provides a DR image registration method, a DR image registration device, a DR image registration terminal and a computer-readable storage medium. The method comprises the following steps: respectively filtering a first DR image and a second DR image to be registered to obtain two sharpened DR images; respectively carrying out gradient calculation on the two sharpened DR images to obtain two gradient images; determining an overlapping area of the two gradient images, selecting a search image block from the foreground of the overlapping area of the first gradient image, and selecting a reference image block from the foreground of the overlapping area of the second gradient image; and determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point. Based on the registration point, a better spliced image can be obtained, and the clinical diagnosis requirement of the spliced image is met.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a DR image registration method, a DR image registration device, a DR image registration terminal and a computer-readable storage medium.
Background
Dr (digital radiography) apparatus, i.e. direct digital radiography systems, are becoming increasingly popular with hospital radiology due to their important value in clinical assisted diagnosis. The automatic splicing function is to register and splice a plurality of DR sub-images with overlapped areas into a large-format DR image, so that a doctor can observe or measure the complete pathological change degree of the spine or the lower limbs in one image.
The most key step in the automatic splicing function is the registration of two adjacent DR sub-images, and the position relation of the images is difficult to accurately obtain in the automatic registration of the DR images by the existing registration methods such as a phase correlation method, a characteristic point matching method and the like. The phase correlation method depends on the accuracy of the overlapping region, however, the accuracy of the overlapping region calculated most of the time is not high, and therefore the accuracy of the registration point obtained by the method is not high; although the accuracy of the obtained registration points is high based on the feature point matching method, the time is long, and DR images can be distorted to influence diagnosis. Therefore, DR images processed by the methods cannot meet the requirement of clinical auxiliary diagnosis.
Disclosure of Invention
The technical problem to be solved by the invention is how to make DR image registration meet the clinical diagnosis requirement of the spliced image.
In order to solve the above technical problem, in a first aspect, an embodiment of the present invention provides a DR image registration method, including the following steps:
respectively filtering the first DR image and the second DR image to be registered to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image;
respectively carrying out gradient calculation on the first sharpened DR image and the second sharpened DR image to obtain a first gradient image and a second gradient image;
determining an overlapping area of the first gradient image and the second gradient image, selecting a search image block from the foreground of the overlapping area of the first gradient image, and selecting a reference image block from the foreground of the overlapping area of the second gradient image;
and determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point.
In a second aspect, an embodiment of the present invention further provides a DR image registration apparatus, including:
the filtering module is used for respectively filtering the first DR image and the second DR image to be registered to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image;
the gradient calculation module is used for respectively carrying out gradient calculation on the first sharpened DR image and the second sharpened DR image to obtain a first gradient image and a second gradient image;
an overlap region determining module, configured to determine an overlap region of the first gradient image and the second gradient image;
the image block determining module is used for selecting a search image block from the foreground of the overlapping area of the first gradient image and selecting a reference image block from the foreground of the overlapping area of the second gradient image;
and the matching module is used for determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point.
In a third aspect, an embodiment of the present invention further provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the image processing method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the image processing method according to the first aspect.
In the embodiment provided by the invention, firstly, two input DR images to be registered are filtered to obtain a sharpened image, then, the gradient is calculated, finally, the normalization product gray matching is carried out on the overlapping area of the two gradient images, and the coordinate of the position with the maximum matching similarity is taken as the coordinate of the registration point. Based on the registration point, a better spliced image can be obtained, and the clinical diagnosis requirement of the spliced image is met.
Drawings
Fig. 1 is a flowchart of an implementation of a DR image registration method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of determining an overlap region in two gradient images provided by a first embodiment of the present invention;
FIG. 3 is an image of a human body collected by a flat panel detector according to a first embodiment of the present invention;
FIG. 4 is a diagram illustrating the effect of the DR image registration method shown in FIG. 1 on the human body image shown in FIG. 3;
fig. 5 is a schematic structural diagram of a DR image registration apparatus according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a terminal according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a DR image registration method according to a first embodiment of the present invention, where the image registration method is applied to a DR medical device product, and is disposed in an image processing module of the DR medical device product in a form of software, and may be applied to other image registration fields. The DR image registration method comprises the following steps:
step S101, filtering the first DR image and the second DR image to be registered respectively to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image.
The purpose of the DR image filtering processing is to sharpen the image, enhance the edge, outline or feature of some linear target elements and the part of gray level jump of the image, make the image clear, and improve the contrast between the image edge and the surrounding pixels.
The filtering process has various modes, and in this embodiment, a gaussian high-pass filtering process is adopted. For example, the first and second DR images are labeled img1 and img2, respectively, and the gaussian high-pass filtering process is performed on img1 and img2, respectively, using the matrices shown below:
the sharpened first and second sharpened DR images are gaussian-filtered images, respectively labeled as gaus _ img1 and gaus _ img 2.
And S102, respectively carrying out gradient calculation on the first sharpened DR image and the second sharpened DR image to obtain a first gradient image and a second gradient image.
The purpose of the image gradient calculation is to deepen the contour line of the target object in the image, and the gradient calculation needs to be performed in the horizontal direction and the vertical direction, so that the contour line of the target object in the horizontal direction and the contour line in the vertical direction are deepened respectively.
In this embodiment, a horizontal convolution kernel matrix is adopted for both the first sharpened DR image gaus _ img1 and the second sharpened DR image gaus _ img2 in the horizontal direction:
performing convolution, and adopting a vertical convolution kernel matrix in the vertical direction:
the convolution is performed to obtain the first gradient image grad _ img1 and the second gradient image grad _ img 2. Of course, other ways of calculating the image gradient are also possible.
Step S103, determining an overlapping area of the first gradient image and the second gradient image, selecting a search image block in the foreground of the overlapping area of the first gradient image, and selecting a reference image block in the foreground of the overlapping area of the second gradient image.
It is assumed that the sizes of the first and second gradient images grad _ img1 and grad _ img2 are W × H matrices, and the overlapping region size of the first and second gradient images grad _ img1 and grad _ img2 is W × H, as shown in fig. 2.
In the overlapping area in the first gradient image grad _ img1, an image block with size mxn (M is not more than W, N is not more than h) is taken as a search image block, and in the overlapping area in the second gradient image grad _ img2, an image block with size mxn (M is less than M, N is less than N) is taken as a reference image block T, and the selection needs to be paid attention to the selection in the foreground of the overlapping area and cannot be performed in the background of the overlapping area. In this embodiment, the size of the search image block is larger than that of the reference image block because several matching blocks need to be selected from the search image block to match with the reference image block, and the size of the selected matching block needs to be equal to that of the reference image block.
And step S104, determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point.
Specifically, the search image block and the reference image block may be normalized product-correlated gray scale matched according to the following formula:
And then, taking the coordinate of the position corresponding to the matching block with the maximum matching similarity in the searched image blocks as the coordinate of the registration point.
In the first embodiment, firstly, two input DR images to be registered are subjected to filtering processing to obtain a sharpened image, then, gradients are calculated, finally, normalization product gray level matching is performed on an overlapping area of the two gradient images, and coordinates of a position with the maximum matching similarity are taken as coordinates of a registration point. Based on the registration point, a better spliced image can be obtained, and the clinical diagnosis requirement of the spliced image is met. The effect of this embodiment is verified by using the human body image acquired by the flat panel detector shown in fig. 3, and it can be seen that the upper and lower DR images in fig. 3 contain overlapping regions, and fig. 4 is a processing effect diagram of the DR image registration method provided by this embodiment for the image shown in fig. 3. The method is helpful for diagnosis of doctors and has better clinical application value.
Referring to fig. 5, a second embodiment of the present invention provides a DR image registration apparatus, which may be a terminal, or a software module or a hardware module in the terminal, and may implement the DR image registration method in the first embodiment. The DR image registration apparatus includes:
the filtering module 51 is configured to perform filtering processing on the first DR image and the second DR image to be registered, respectively, to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image. The purpose of the DR image filtering processing is to sharpen the image, enhance the edge, outline or feature of some linear target elements and the part of gray level jump of the image, make the image clear, and improve the contrast between the image edge and the surrounding pixels.
A gradient calculating module 52, configured to perform gradient calculation on the first sharpened DR image and the second sharpened DR image respectively to obtain a first gradient image and a second gradient image. The purpose of the image gradient calculation is to deepen the contour line of the target object in the image, and the gradient calculation needs to be performed in the horizontal direction and the vertical direction, so that the contour line of the target object in the horizontal direction and the contour line in the vertical direction are deepened respectively.
An overlap region determining module 53, configured to determine an overlap region of the first gradient image and the second gradient image;
an image block determining module 54, configured to select a search image block in the foreground of the overlapping region of the first gradient image, and select a reference image block in the foreground of the overlapping region of the second gradient image.
And the matching module 55 is configured to determine matching blocks with the same size as the reference image block one by one with each position in the search image block as a center, perform normalization product correlation gray scale matching on each matching block and the reference image block, and use the coordinate of the position corresponding to the matching block with the largest matching similarity as the coordinate of the registration point.
Further, the filtering module 51 specifically performs gaussian high-pass filtering processing on the first DR image and the second DR image to be registered by using the following matrixes:
the gradient calculation module 52 is specifically configured to apply a horizontal convolution kernel matrix to both the first sharpened DR image and the second sharpened DR image in the horizontal direction:
performing convolution, and adopting a vertical convolution kernel matrix in the vertical direction:
and performing convolution. The matching module 55 performs normalization product correlation gray scale matching on each matching block and the reference image block according to the following formula:
In the second embodiment, firstly, two input DR images to be registered are filtered to obtain a sharpened image, then gradients are calculated, finally, normalization product gray matching is performed on an overlapping area of the two gradient images, and the coordinate of the position with the maximum matching similarity is taken as the coordinate of the registration point. Based on the registration point, a better spliced image can be obtained, and the clinical diagnosis requirement of the spliced image is met.
Referring to fig. 6, the third embodiment of the present invention further provides a terminal 6, which includes a memory 601, a processor 602 and a computer program stored in the memory 601 and executable on the processor 602, wherein the processor 602, when executing the computer program, implements the steps of the DR image registration method as in the first embodiment described above.
The memory 601 may include read only memory and random access memory, and may also include non-volatile random access memory.
The Processor 602 may be a Central Processing Unit (CPU), or other general purpose Processor such as a microprocessor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like.
The fourth embodiment of the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the image processing method in the foregoing first embodiment.
The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form.
The computer-readable storage medium may include: any entity or device capable of carrying the above-described computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer readable Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc.
In view of the above description of the image processing method, the image processing apparatus, the terminal and the computer readable storage medium provided by the present invention, those skilled in the art will recognize that changes may be made in the embodiments and applications of the invention in light of the above description, and therefore the disclosure of the present invention should not be interpreted as limiting the scope of the invention.
Claims (10)
1. A DR image registration method comprising the steps of:
respectively filtering the first DR image and the second DR image to be registered to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image;
respectively carrying out gradient calculation on the first sharpened DR image and the second sharpened DR image to obtain a first gradient image and a second gradient image;
determining an overlapping area of the first gradient image and the second gradient image, selecting a search image block from the foreground of the overlapping area of the first gradient image, and selecting a reference image block from the foreground of the overlapping area of the second gradient image;
and determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point.
2. The DR image registration method of claim 1 wherein the separately filtering the first and second DR images to be registered comprises:
using the followingThe matrix of (a) respectively performs Gaussian high-pass filtering processing on a first DR image and a second DR image to be registered:
3. the DR image registration method of claim 1 wherein the separately gradient computing the first and second sharpened DR images comprises:
adopting a horizontal convolution kernel matrix in the horizontal direction for both the first and second sharpened DR images:
performing convolution, and adopting a vertical convolution kernel matrix in the vertical direction:
and performing convolution.
4. The DR image registration method of claim 1 wherein the size of the search image block is larger than the size of the reference image block.
5. The DR image registration method of claim 1 wherein the normalizing product-dependent gray scale matching of each matching block to the reference image block comprises:
and carrying out normalization product correlation gray level matching on each matching block and the reference image block according to the following formula:
wherein, S is a matching block taken at a position (i, j) in the search image block, the size is m × n, T is a reference image block, (x, y) is coordinates, and R is the obtained similarity.
6. A DR image registration apparatus, comprising:
the filtering module is used for respectively filtering the first DR image and the second DR image to be registered to obtain a sharpened first sharpened DR image and a sharpened second sharpened DR image;
the gradient calculation module is used for respectively carrying out gradient calculation on the first sharpened DR image and the second sharpened DR image to obtain a first gradient image and a second gradient image;
an overlap region determining module, configured to determine an overlap region of the first gradient image and the second gradient image;
the image block determining module is used for selecting a search image block from the foreground of the overlapping area of the first gradient image and selecting a reference image block from the foreground of the overlapping area of the second gradient image;
and the matching module is used for determining matching blocks with the same size as the reference image block by taking each position in the search image block as a center one by one, performing normalization product correlation gray scale matching on each matching block and the reference image block, and taking the coordinate of the position corresponding to the matching block with the maximum matching similarity as the coordinate of the registration point.
7. The DR image registration apparatus of claim 6 wherein the filtering module performs gaussian high-pass filtering on the first DR image and the second DR image to be registered respectively by using a matrix as follows:
the gradient calculation module is specifically configured to apply a horizontal convolution kernel matrix to both the first and second sharpened DR images in a horizontal direction:
performing convolution, and adopting a vertical convolution kernel matrix in the vertical direction:
and performing convolution.
8. The DR image registration apparatus of claim 6 wherein the matching module performs normalized product correlation gray scale matching for each matching block and the reference image block according to the following formula:
wherein, S is a matching block taken at a position (i, j) in the search image block, the size is m × n, T is a reference image block, (x, y) is coordinates, and R is the obtained similarity.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the image processing method according to any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the image processing method according to any one of claims 1 to 5.
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