CN105761217A - Image reconstruction method and device - Google Patents
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- CN105761217A CN105761217A CN201610066684.4A CN201610066684A CN105761217A CN 105761217 A CN105761217 A CN 105761217A CN 201610066684 A CN201610066684 A CN 201610066684A CN 105761217 A CN105761217 A CN 105761217A
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- 238000000034 method Methods 0.000 title claims abstract description 66
- 230000011218 segmentation Effects 0.000 claims abstract description 33
- 210000000481 breast Anatomy 0.000 claims description 19
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 238000003708 edge detection Methods 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 description 13
- 210000001519 tissue Anatomy 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 4
- 238000005192 partition Methods 0.000 description 4
- 238000003325 tomography Methods 0.000 description 4
- 238000003759 clinical diagnosis Methods 0.000 description 3
- 238000002059 diagnostic imaging Methods 0.000 description 3
- 238000005516 engineering process 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
- 230000002035 prolonged effect Effects 0.000 description 2
- 230000002308 calcification Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 210000005075 mammary gland Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 238000011282 treatment Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
- G06T2207/10124—Digitally reconstructed radiograph [DRR]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30068—Mammography; Breast
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- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The present invention provides an image reconstruction method and device. The method comprises: collecting at least two first images; determining the target image in each first image; obtaining a segmentation area according to at least two target areas; performing segmentation of each first image according to the segmentation areas to obtain second images corresponding to the first images; and performing reconstruction of the second images to obtain third images. Image target areas are respectively segmented to perform reconstruction, and the segmented target areas are expanded and then are reconstructed, so that each pixel value on a projection image is real and effective, and therefore the integrity of an object to be further reconstructed may be ensured, unnecessary pixel points of non-reconstruction targets may be reduced as much as possible, and the reconstruction speed may be accelerated.
Description
Technical Field
The invention relates to the field of image processing, in particular to an image reconstruction method and device.
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 set of projection data of the cross section of the object, and is widely applied to various fields such as medical imaging and industrial nondestructive testing, particularly in the field of medical imaging, the image reconstruction technology is widely applied and becomes an important basis for doctors to diagnose diseases.
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 hardware load becomes one of the problems to be solved at present
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method which can effectively reduce the image reconstruction time and the image storage amount so as to improve the image reconstruction efficiency and enable a patient to obtain more timely diagnosis and treatment.
In order to achieve the above 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 value 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 is a Y axis, the segmentation region is two opposite points a (X)1,Y1) And B (X)2，Y2) A rectangular region of formation wherein X1、Y1、X2、Y2Respectively representing the values corresponding to the maximum abscissa of the pixel points in the target area 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; 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 segmentation region is two opposite points a (X)1,Y1) And B (X)2，Y2) A rectangular area of formation, wherein:
X1the value Y corresponding to the maximum abscissa of the pixel point in the target area of all the first images1Is 0, X2Is 0, Y2The vertical coordinate value corresponding to the first image height;
or,
X1is the abscissa value, Y, corresponding to the first image width1Is 0, X2Is 0, Y2And the value is the corresponding value when the vertical coordinate of the pixel point is the maximum in 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 adopting the 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 comprehensible, specific embodiments accompanied with 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 during the reconstruction is of no clinical diagnostic significance.
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 other breast tomography systems, a projection image may be captured at 2 ° intervals 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, takes the pixel point labeled as 1 as the initial seed point (generally, takes the pixel point labeled as 1 at the upper left corner or the lower left corner 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 area is labeled as 1, and the coordinate position of the pixel point is determined, so that the coordinate A (X) is used1,Y1) And B (X)2，Y2) And a rectangular framed area S formed by two diagonal points is the divided area. Wherein, the coordinate A (X)1,Y1) X of (2)1Is the value corresponding to the maximum abscissa of the pixel point among the pixel points marked as 1 after all the projected images are binarized, Y1The image is a corresponding value when the vertical coordinate of a pixel point in pixel points marked as 1 is minimum after all projected images are subjected to binarization; coordinate B (X)2，Y2) X of (2)2Is the value corresponding to the minimum abscissa of the pixel point among the pixel points marked as 1 after all the projected images are binarized, Y2And the value is the corresponding value when the vertical coordinate of the pixel point in the pixel points marked as 1 is maximum after all the projected images are subjected to binarization. In breast imaging, X is taken in a standing position2Set to zero, Y when shooting in lying mode1Is set to zero.
In another embodiment, to simplify the calculation step for obtaining the rectangular partition area in the above embodiments, taking stand-alone shooting as an example, the rectangular partition area only considers the corresponding rectangular partition area when the abscissa of the pixel point in the pixel point is the maximumValue, for coordinate A (X)1,Y1) And B (X)2，Y2) In particular, X1Is the value corresponding to the maximum abscissa of the pixel point among the pixel points marked as 1 after all the projected images are binarized, Y1、X2Then is equal to zero, Y2＝Ymax(YmaxThe ordinate value corresponding to the height of the projected image). 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 has been verified that about half the storage and 1/3 reconstruction time can be reduced in a reconstruction system using the image reconstruction method of the present 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.
Claims (15)
1. A method for determining a reconstruction region, comprising:
acquiring at least two first images;
determining a target area in each first image;
and obtaining a reconstruction region according to the target region.
2. The method for determining the reconstruction region according to claim 1, wherein the determining the target region in each of the first images 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 value as a target area.
3. The method of determining a reconstruction region according to claim 2,
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.
4. The method of determining a reconstruction region according to claim 3,
the target area edge is obtained by a seed filling method.
5. The method of determining a reconstruction region according to claim 2,
the reconstruction region is a union of the target regions of the at least two first images.
6. The method of determining a reconstruction region according to claim 2,
when the width of the first image is X-axis and the height is Y-axis, the divided area is two opposite points A (X)1,Y1) And B (X)2，Y2) A rectangular region of formation wherein X1、Y1、X2、Y2Respectively representing the values corresponding to the maximum abscissa of the pixel points in the target area 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; the value corresponding to the maximum vertical coordinate of the pixel point.
7. The method of determining a reconstruction region according to claim 2,
when the width of the first image is X-axis and the height is Y-axis, the divided area is two opposite points A (X)1,Y1) And B (X)2，Y2) A rectangular area of formation, wherein:
X1the maximum abscissa of the pixel point in the target area of all the first images is the corresponding value,
Y1is 0, X2Is 0, Y2The vertical coordinate value corresponding to the first image height;
or,
X1is the abscissa value, Y, corresponding to the first image width1Is 0, X2Is 0, Y2And the value is the corresponding value when the vertical coordinate of the pixel point is the maximum in all the pixel points of the target area of the first image.
8. The method for determining a reconstruction region according to claim 1,
and determining the target area by adopting one of edge detection, threshold segmentation, area segmentation or histogram segmentation methods.
9. The method for determining a reconstruction region according to claim 1,
the first image is a breast projection image.
10. An image reconstruction method, comprising:
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.
11. An image reconstruction method, comprising:
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.
12. The image reconstruction method according to claim 10 or 11, further comprising:
the third image is augmented according to the size of the first image.
13. The image reconstruction method as claimed in claim 12,
the expansion is performed by using a single gray value having a gray difference with the edge of the target area.
14. 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.
15. The image reconstruction device of claim 14, 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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| CN201610066684.4A CN105761217A (en) | 2016-01-29 | 2016-01-29 | Image reconstruction method and device |
| CN202210768228.XA CN115100066A (en) | 2016-01-29 | 2016-01-29 | Image reconstruction method and device |
| CN202210769695.4A CN115100067A (en) | 2016-01-29 | 2016-01-29 | Image reconstruction method and device |
| CN202210769189.5A CN115082348A (en) | 2016-01-29 | 2016-01-29 | Image reconstruction method and device |
| US15/317,382 US10140735B2 (en) | 2015-09-15 | 2016-09-14 | Image reconstruction system and method |
| CN201680053776.7A CN108352078B (en) | 2015-09-15 | 2016-09-14 | Image reconstruction system and method |
| PCT/CN2016/099061 WO2017045618A1 (en) | 2015-09-15 | 2016-09-14 | Image reconstruction system and method |
| GB1706273.8A GB2547360B (en) | 2015-09-15 | 2016-09-14 | Image reconstruction system and method |
| US15/460,187 US9697623B1 (en) | 2015-09-15 | 2017-03-15 | Image reconstruction system and method |
| US15/608,935 US9875558B2 (en) | 2015-09-15 | 2017-05-30 | Image reconstruction system and method |
| US16/199,025 US10586355B2 (en) | 2015-09-15 | 2018-11-23 | Image reconstruction system and method |
| US16/199,016 US10600214B2 (en) | 2015-09-15 | 2018-11-23 | Image reconstruction system and method |
| US16/826,717 US11335041B2 (en) | 2015-09-15 | 2020-03-23 | Image reconstruction system and method |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2017045618A1 (en) * | 2015-09-15 | 2017-03-23 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
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| CN107545551A (en) * | 2017-09-07 | 2018-01-05 | 广州华端科技有限公司 | The method for reconstructing and system of digital galactophore body layer composograph |
| CN109961471A (en) * | 2017-12-14 | 2019-07-02 | 北京猎户星空科技有限公司 | A kind of mask method, device and the electronic equipment of objects in images position |
| CN111242877A (en) * | 2019-12-31 | 2020-06-05 | 北京深睿博联科技有限责任公司 | Mammary X-ray image registration method and device |
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| CN104794744A (en) * | 2014-01-16 | 2015-07-22 | 上海联影医疗科技有限公司 | Multi-planar reconstruction imaging method for medical images |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2017045618A1 (en) * | 2015-09-15 | 2017-03-23 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| US9697623B1 (en) | 2015-09-15 | 2017-07-04 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| GB2547360A (en) * | 2015-09-15 | 2017-08-16 | Shanghai United Imaging Healthcare Co Ltd | Image reconstruction system and method |
| GB2547360B (en) * | 2015-09-15 | 2018-02-14 | Shanghai United Imaging Healthcare Co Ltd | Image reconstruction system and method |
| US10140735B2 (en) | 2015-09-15 | 2018-11-27 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| US10586355B2 (en) | 2015-09-15 | 2020-03-10 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| US10600214B2 (en) | 2015-09-15 | 2020-03-24 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| US11335041B2 (en) | 2015-09-15 | 2022-05-17 | Shanghai United Imaging Healthcare Co., Ltd. | Image reconstruction system and method |
| CN107545551A (en) * | 2017-09-07 | 2018-01-05 | 广州华端科技有限公司 | The method for reconstructing and system of digital galactophore body layer composograph |
| CN109961471A (en) * | 2017-12-14 | 2019-07-02 | 北京猎户星空科技有限公司 | A kind of mask method, device and the electronic equipment of objects in images position |
| CN112948371A (en) * | 2019-12-10 | 2021-06-11 | 广州极飞科技股份有限公司 | Data processing method, data processing device, storage medium and processor |
| CN111242877A (en) * | 2019-12-31 | 2020-06-05 | 北京深睿博联科技有限责任公司 | Mammary X-ray image registration method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115100066A (en) | 2022-09-23 |
| CN115100067A (en) | 2022-09-23 |
| CN115082348A (en) | 2022-09-20 |
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