CN115100066A - Image reconstruction method and device - Google Patents
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- 238000012545 processing Methods 0.000 claims description 16
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- 210000001519 tissue Anatomy 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 7
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- 238000003759 clinical diagnosis Methods 0.000 description 3
- 238000002059 diagnostic imaging Methods 0.000 description 3
- 230000003902 lesion Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- 230000002308 calcification Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
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- 238000011161 development Methods 0.000 description 1
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- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
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- 210000005075 mammary gland Anatomy 0.000 description 1
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/10116—X-ray image
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Abstract
The application relates to an image reconstruction method and system, comprising the following steps: acquiring at least two first images; determining a target area in each first image; obtaining a segmentation area according to at least two target areas; dividing each first image according to the divided areas to obtain a second image corresponding to each first image; and reconstructing the second image to obtain a third image. According to the invention, the image target area is independently segmented and reconstructed, and the segmented target area is firstly expanded and then reconstructed, so that each pixel value on the projection image is guaranteed to be real and effective, the integrity of a subsequent reconstructed object can be guaranteed, unnecessary pixel points of a non-reconstructed target can be reduced as much as possible, and the reconstruction speed is accelerated.
Description
The patent application of the invention is a divisional application of Chinese patent application with the application date of 2016, 01, 29 and the application number of 2016100666844, named as "image reconstruction method and device".
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image reconstruction method and apparatus.
Background
The image reconstruction technology is a method for obtaining an image of a cross section of an object after computer processing according to a group of projection data of the cross section of the object, and is widely applied to various fields such as medical imaging, industrial nondestructive testing and the like, particularly in the field of medical imaging, the image reconstruction technology is widely applied and also becomes an important basis for doctors to carry out disease diagnosis.
In the field of X-ray imaging, although image reconstruction techniques use high-performance graphics cards or other computationally intensive coprocessors for reconstruction, the processing speed is still not ideal, and with the development of flat panel detector technology, the requirement of high resolution imaging is more and more urgent, such as the confirmation of information of a tiny lesion in a breast image corresponding to a small calcification point requires a high resolution image. High resolution (the resolution of the existing breast tomographic image can reach 2816 × 3584) imaging can increase the storage amount required for processing the image, and the requirement on the processing efficiency of a display card is higher and higher.
In the prior art, the whole projection image is generally input into a processing system for reconstruction, or the whole projection image is divided into a background area and a target area for reconstruction and then fused, the two methods need a large amount of calculated data, which results in that the reconstruction processing time is prolonged, and the data storage amount is increased, so that the whole reconstruction process has low efficiency, the whole diagnosis efficiency is influenced, the patient receiving capacity of a hospital is influenced, the patient shooting period is prolonged, and the patient may miss the optimal treatment time.
Therefore, how to improve the image reconstruction efficiency, shorten the examination period of the patient, and reduce the load of hardware becomes one of the problems to be solved at present.
Disclosure of Invention
In view of the above, there is a need to provide a method for effectively reducing image reconstruction time and image storage amount, so as to improve image reconstruction efficiency and enable a patient to obtain more timely diagnosis and treatment.
In order to achieve the object, the present invention provides a method for determining a reconstruction region, including:
acquiring at least two first images;
determining a target area in each first image;
and obtaining a reconstruction region according to the target region.
Optionally, the determining the target region in each first image includes:
calculating the mean value of the gray values of the first image;
comparing the gray values of all pixel points in the first image with the mean value of the gray values;
and taking the pixel point area with the gray value of the pixel point larger than the mean value of the gray values as a target area.
Optionally, the method for determining the reconstruction region,
and taking the maximum edge as the edge of the target area in the pixel point area of the first image with the gray value larger than the mean value of the gray values.
Optionally, the target region edge is obtained by a seed filling method.
Optionally, the reconstruction region is a union of the target regions of the at least two first images.
Optionally, when the width of the first image is an X axis and the height of the first image is a Y axis, the divided region is a rectangular region formed by two opposite points a (X1, Y1) and B (X2, Y2), where X1, Y1, X2, and Y2 are values corresponding to maximum abscissa of pixel points in all target regions of the first image; the corresponding value when the vertical coordinate of the pixel point is minimum; the corresponding value when the horizontal coordinate of the pixel point is minimum; the value corresponding to the maximum vertical coordinate of the pixel point.
Optionally, when the width of the first image is an X axis and the height is a Y axis, the divided region is a rectangular region formed by two opposite points a (X1, Y1) and B (X2, Y2), where:
x1, which is a value corresponding to the maximum abscissa of the pixel point in the target area pixel points of all the first images, Y1 is 0, X2 is 0, and Y2 is a ordinate value corresponding to the height of the first image;
or,
x1 is an abscissa value corresponding to the width of the first image, Y1 is 0, X2 is 0, and Y2 is a value corresponding to the maximum ordinate of a pixel point among all the pixel points of the target area of the first image.
Optionally, one of edge detection, threshold segmentation, region segmentation, or histogram segmentation methods is used to determine the target region.
Optionally, the first image is a breast projection image.
The invention also provides an image reconstruction method, which comprises the following steps:
acquiring at least two first images;
determining a target area in each first image;
obtaining a coordinate template according to the target area;
and reconstructing according to the coordinate template to obtain a third image.
The present invention also provides another image reconstruction method, including:
acquiring at least two first images;
determining a target area in each first image;
obtaining a segmentation area according to at least two target areas;
dividing each first image according to the divided areas to obtain a second image corresponding to each first image; and
and reconstructing the second image to obtain a third image.
Optionally, the image reconstruction method further includes:
the third image is augmented according to the size of the first image.
Optionally, the expansion is performed by using a single gray value having a gray difference with the edge of the target area.
The present invention also provides an image reconstruction apparatus comprising:
the acquisition unit is used for acquiring at least two first images;
a target area determination unit for determining a target area in each of the first images;
a divided region obtaining unit for obtaining a divided region according to at least two target regions;
the segmentation unit is used for segmenting each first image according to the segmentation region to obtain a second image corresponding to the first image;
and the reconstruction unit is used for reconstructing the second image to obtain a third image.
Optionally, the image reconstruction apparatus further includes:
and the expansion unit is used for expanding the third image according to the size of the first image.
Compared with the prior art, the invention only reconstructs the segmentation region in the projection image, improves the reconstruction speed by reducing the calculation amount and saves the storage space of the equipment.
Furthermore, in order to ensure the integrity of the reconstructed image, the invention adopts a method of taking intersection from the determined segmentation areas or a method of maximum frame so as to ensure that each effective pixel point of the projected image cannot be omitted.
In addition, the invention also adopts a method of reconstructing the template, reduces the reconstruction calculation amount, and also achieves the aims of improving the reconstruction speed and saving the storage space.
The reconstruction method can reduce about half of the storage amount and one third of the reconstruction time, better improves the reconstruction efficiency of the display card, further improves the diagnosis speed of a hospital and reduces the waiting time of patients.
Drawings
FIG. 1 is a schematic view of a projection image acquired by a breast imaging device;
FIG. 2 is a schematic diagram of a projection image binarization flow in the embodiment of the invention;
FIG. 3 is a schematic flow chart of a seed filling method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a process for merging target regions according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of rectangular partition according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures and examples are described in detail below. It is to be understood that the described embodiments are merely illustrative of but not exhaustive of the possible embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without inventive effort, are within the scope of the invention according to these examples.
As known from the background art, the conventional image reconstruction method has the disadvantages of large calculated data amount, low reconstruction speed and high requirement on hardware. For medical images used for clinical diagnosis, nearly half of the image area is a null area that does not require reconstruction. As in breast imaging, other regions than breast tissue structures are inactive regions, which are not relevant for clinical diagnosis. If irrelevant areas are removed in the reconstruction process, only areas necessary for clinical diagnosis are reconstructed, the reconstruction speed can be increased, and the burden on hardware is reduced.
Fig. 1 is a prior art breast projection image. As shown in fig. 1, the part with higher gray scale value in the graph is a breast tissue part 10, and the part with lower gray scale value on the right side is a background part 20, i.e., an invalid region. In the case of this image, the invalid region is more than half of the entire reconstructed image, i.e. more than half of the calculation process in the reconstruction process is not clinically meaningful.
In the invention, the reconstruction is only carried out on the effective part of the human tissue in the acquired projection image, thereby reducing the calculation amount, accelerating the reconstruction speed and lightening the load of equipment hardware.
The image reconstruction method of the present invention will be described in detail below with reference to the drawings. In the present embodiment, a breast tomography system is taken as an example for illustration, but the invention is not limited thereto, and the method can be used in other computed tomography systems or medical imaging systems to reconstruct medical images.
The image reconstruction method of the present invention includes:
acquiring at least two first images;
determining a target area in each first image;
obtaining a segmentation area according to at least two target areas;
dividing each first image according to the divided areas to obtain a second image corresponding to each first image; and
and reconstructing the second image to obtain a third image.
The first image is a projection image in the breast tomography, the breast tomography system comprises a bulb tube emitting X-rays and a detector arranged at the opposite position of the bulb tube, the bulb tube and the detector rotate around the breast as the center and shoot, generally speaking, a projection image is shot every 1 degree within the range of +/-15 degrees with the vertical direction being 0 degree in the breast imaging process, and therefore 31 projection images with different projection angles can be obtained. In some other breast tomography systems, a projection image may be captured at an interval of 2 ° within ± 25 ° with the vertical direction being 0 °, which is not limited herein.
According to the image reconstruction method provided by the invention, a target region, namely a region of interest, is determined in a plurality of projection images, and in the embodiment, a binarization method is adopted for determining the target region. As shown in fig. 2, the binarization method specifically comprises the following steps: s11, calculating the mean value of the gray values of all the pixel points in the projected image; s12, comparing the gray values of all pixel points in the projected image with the mean value of the gray values, if the gray values are larger than the mean value of the gray values, marking the pixel point as 1, and if the gray values are smaller than the mean value of the gray values, marking the pixel point as 0; and S13, calculating the edge of the maximum target area according to the labeling result, wherein the area with the pixel point labeled as 1 is the image target area.
In the actual processing process, there may be pixel points with a gray value of 0 in the determined target region, and in order to ensure the integrity of the target region, the maximum edge of the pixel point region with a gray value of 1 is used as the edge of the target region when the target region is determined in the binarization result. In this embodiment, the edge of the target area is completed by a seed filling method, which specifically includes the following steps: as shown in fig. 3, in the projection image after binarization, S21, taking the pixel point labeled as 1 as an initial seed point (generally, the pixel point labeled as 1 at the upper left corner or the lower left corner is taken as the initial seed point); s22, searching pixel points in a3 multiplied by 3 neighborhood; s23, judging whether a pixel point marked as 0 exists; s24, if the judgment result in the S23 is 'yes', the pixel point is marked as a boundary point; s25, if the judgment result in S23 is NO, marking the point as an internal point; s26, continuously judging whether a pixel point which is marked as 1 and is not internally/externally marked exists in a3 x 3 neighborhood of the seed pixel point; s27, if the judgment result in S26 is 'yes', marking the point as 1 and setting the unmarked point as a seed, and returning to S22 to continue the circulation; if the result of determination at S26 is "no", the operation is terminated. All pixel points marked as 1 in the binarized projection image can be marked through the steps, the boundary points are found out, the adjacent boundary points are connected together to form the edge, and the largest edge is taken as the edge of the target area when a plurality of edges exist.
In the present embodiment, the determination method of the target region is a binarization method, but other segmentation methods such as edge detection, threshold segmentation, region segmentation, histogram segmentation, and the like may also be used in other embodiments, and are not limited herein.
After the target region is determined, a segmentation region is obtained according to the target region, and the segmentation region completely includes all target regions in all projection images in order to prevent possible lesion points on the edge of the breast tissue from being missed. Specifically, the segmented regions are a union set of target regions of all projection images.
As shown in fig. 4 (the shapes of the target regions in the figure are only schematically drawn for convenience of illustration of the manner in which the target regions are merged), wherein fig. a1, a2, a3 are schematic diagrams of the target regions in the three projection images, respectively; FIG. b is a schematic illustration of the three target regions when taken together; fig. c is a schematic diagram of the segmented regions finally generated from the union of the target regions. As shown in the figure, the segmentation region obtained according to the union set operation covers the target region of all the projection images, and the reconstructed image obtained by reconstructing the projection images according to the segmentation region contains the lesion information of all the breast tissues, so that the calculation amount in the reconstruction process is well reduced, and the data storage amount is also reduced.
In addition to the above method of merging the target regions to obtain the divided regions, in other embodiments, other methods may be used to obtain the divided regions. As shown in fig. 5, in another embodiment of the present invention, the pixel point of the target region is labeled as 1, and the coordinate position of the pixel point is also determined, so that a region S framed by a rectangle formed by two diagonal points of coordinates a (X1, Y1) and B (X2, Y2) is used as the divided region. Wherein, X1 of the coordinate a (X1, Y1) is a value corresponding to the maximum abscissa of a pixel point among the pixel points labeled as 1 after all the projection images are binarized, and Y1 is a value corresponding to the minimum ordinate of a pixel point among the pixel points labeled as 1 after all the projection images are binarized; x2 of the coordinate B (X2, Y2) is a value corresponding to the minimum abscissa of the pixel point among the pixel points labeled as 1 after all the projection images are binarized, and Y2 is a value corresponding to the maximum ordinate of the pixel point among the pixel points labeled as 1 after all the projection images are binarized. In breast imaging, X2 is set to zero in standing shooting, and Y1 is set to zero in lying shooting.
In another embodiment, to simplify the calculation step for obtaining the rectangular divided region in the above embodiments, taking the standing shooting as an example, the rectangular divided region only considers the value corresponding to the maximum abscissa of the pixel point in the pixel points, for the coordinates a (X1, Y1) and B (X2, Y2), X1 is the value corresponding to the maximum abscissa of the pixel point labeled 1 after all the projection images are binarized, Y1 and X2 are equal to zero, and Y2 is Ymax (Ymax is the ordinate value corresponding to the height of the projection images). The segmented regions obtained by this method may not be as accurate as the rectangular segmented regions obtained in the previous embodiment, but the calculation step for generating the segmented regions may be simplified.
In another embodiment, the target region is not limited to a rectangular frame as long as it completely covers the target tissue (the object to be examined), and may be any other shape that is set in advance and conforms to the contour of the target tissue.
Then, a segmentation image obtained after segmentation according to the segmentation region is input into a system for reconstruction. Further, for the convenience of subsequent processing, the size of the reconstructed image is reduced to be consistent with that before image segmentation. Specifically, the background region is a region unrelated to human tissue, the background region of the non-target region is filled with pixel points with fixed gray values, and a complete reconstructed image is obtained for reference diagnosis by a doctor.
It should be noted that, in the present embodiment, a parallel manner is adopted when the image segmentation step is performed, that is, after the nth projection image is acquired, the (n + 1) th projection image is continuously acquired, and meanwhile, the system determines the target area of the nth projection image, instead of determining the target area after all projection images are acquired, the data processing speed is effectively increased.
In other embodiments of the present invention, a method of reconstructing through a coordinate template may also be adopted. For example, a 0-1 coordinate template is adopted, i.e., the breast area is set to 1 and the background area is set to 0 in the coordinate template. The coordinate template can be obtained in the process of taking the union set according to the target area, the reconstruction is carried out through the coordinate template, and only the target area is reconstructed according to the coordinate template during the reconstruction. And judging whether the coordinates of the pixel points are in the mammary gland region (target region) or not during reconstruction, if so, reconstructing, and otherwise, ignoring.
Corresponding to the above image reconstruction method, an embodiment of the present invention further provides an image reconstruction apparatus, including:
the acquisition unit is used for acquiring at least two first images;
a target area determination unit for determining a target area in each of the first images;
a divided region obtaining unit for obtaining a divided region according to at least two target regions;
the segmentation unit is used for segmenting each first image according to the segmentation region to obtain a second image corresponding to the first image;
and the reconstruction unit is used for reconstructing the second image to obtain a third image.
In this embodiment, the image reconstruction apparatus further includes: and the expansion unit is used for expanding the third image according to the size of the first image so as to facilitate subsequent processing.
The specific implementation method of the image reconstruction apparatus may refer to implementation of an image reconstruction method, and details are not repeated here.
Compared with the prior art, the method for reconstructing the target area only effectively reduces the data processing amount in the subsequent reconstruction process, well lightens the burden on the display card and improves the overall processing efficiency. It is verified that about half of the storage amount and 1/3 reconstruction time can be reduced in the reconstruction system by adopting the image reconstruction method of the invention.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.
Claims (13)
1. A method for determining a reconstruction region, comprising:
acquiring at least two first images, wherein the first images comprise a target area and a background part;
determining a target area in each first image;
a reconstruction region is obtained from the target region and includes a union of the target regions of the at least two first images.
2. The method of claim 1, wherein determining the target region in each first image comprises:
calculating the mean value of the gray values of the first image;
comparing the gray values of all pixel points in the first image with the mean value of the gray values;
and taking the pixel point area with the gray value of the pixel point larger than the mean value of the gray values as a target area.
3. The method of claim 2, wherein the edges of the target area are obtained by a seed filling method.
4. The method according to claim 1, further comprising obtaining a partition from the at least two target regions, wherein the width of the first image is X-axis, and the height of the first image is Y-axis, the partition is a rectangular region formed by two opposite points a (X1, Y1) and B (X2, Y2), where X1, Y1, X2, and Y2 are values corresponding to maximum pixel abscissa among pixel points of the target regions of all the first images; the corresponding value when the vertical coordinate of the pixel point is minimum; the corresponding value when the horizontal coordinate of the pixel point is minimum; and the corresponding value when the vertical coordinate of the pixel point is maximum.
5. The method according to claim 1, further comprising obtaining a divided region from the at least two target regions, the divided region being a rectangular region formed by two opposite points a (X1, Y1) and B (X2, Y2) when the width of the first image is X-axis and the height is Y-axis, wherein:
x1, which is a value corresponding to the maximum abscissa of the pixel point in the target area pixel points of all the first images, Y1 is 0, X2 is 0, and Y2 is a ordinate value corresponding to the height of the first image;
or,
x1 is an abscissa value corresponding to the width of the first image, Y1 is 0, X2 is 0, and Y2 is a value corresponding to the maximum ordinate of a pixel point among all the pixel points of the target area of the first image.
6. The method of claim 1, wherein determining the target region employs one of edge detection, thresholding, region segmentation, or histogram segmentation methods.
7. The method of claim 1, wherein the first image is a breast projection image.
8. An image reconstruction method, comprising:
acquiring at least two first images, wherein the first images comprise a target area and a background part;
determining a target area in each first image;
obtaining a coordinate template according to a mode of taking a union set of the target areas of the at least two first images;
and reconstructing according to the coordinate template to obtain a third image.
9. An image reconstruction method, comprising:
acquiring at least two first images, wherein the first images comprise a target area and a background part;
determining a target area in each first image;
obtaining a segmentation region according to at least two target regions, wherein the segmentation region comprises a union set of the target regions of the at least two first images;
dividing each first image according to the divided areas to obtain a second image corresponding to each first image; and
and reconstructing the second image to obtain a third image.
10. The image reconstruction method according to claim 8 or 9, further comprising:
the third image is augmented according to the size of the first image.
11. The image reconstruction method according to claim 10,
the expansion is performed by using a single gray value having a gray difference with the edge of the target area.
12. An image reconstruction apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring at least two first images, and the first images comprise target areas and background parts;
a target area determination unit for determining a target area in each of the first images;
a segmentation region obtaining unit, configured to obtain a segmentation region according to at least two target regions, where the segmentation region includes a union of the target regions of the at least two first images;
the segmentation unit is used for segmenting each first image according to the segmentation region to obtain a second image corresponding to the first image;
and the reconstruction unit is used for reconstructing the second image to obtain a third image.
13. The image reconstruction apparatus according to claim 12, further comprising:
and the expansion unit is used for expanding the third image according to the size of the first image.
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