CN112102926A - Image processing method, device, equipment and storage medium - Google Patents
Image processing method, device, equipment and storage medium Download PDFInfo
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- CN112102926A CN112102926A CN202010908511.9A CN202010908511A CN112102926A CN 112102926 A CN112102926 A CN 112102926A CN 202010908511 A CN202010908511 A CN 202010908511A CN 112102926 A CN112102926 A CN 112102926A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
Abstract
The embodiment of the application provides an image processing method, an image processing device, an image processing apparatus and a storage medium, wherein the image processing method comprises the following steps: acquiring an image to be processed, wherein the image to be processed comprises a first target, and the first target is provided with preset labeling information; preprocessing the image to be processed to obtain a preprocessed image; carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target; acquiring color information of the first area; calculating a color correction matrix according to the color information and the preset labeling information; and performing color correction on the image to be processed according to the color correction matrix. The method and the device for acquiring the images realize the improvement of the consistency of the colors of the images acquired in different image acquisition scenes.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image processing method, an image processing apparatus, an image processing device, and a storage medium.
Background
With the development of modern medical technology, symptom images of patients can be acquired through image acquisition equipment, and the images are used for recording and storing the disease course and performing online auxiliary diagnosis on diseases. However, in different image capturing scenes, due to differences in lighting conditions, capturing angles and capturing devices, colors of captured images may be affected, and diseases requiring judgment of disease conditions according to colors, such as neonatal jaundice, may result in the captured images not being able to accurately reflect the progress of disease conditions.
Disclosure of Invention
An object of the embodiments of the present application is to provide an image processing method, an image processing device, and an image processing apparatus, which are used to improve consistency of colors of images acquired in different image acquisition scenes.
A first aspect of an embodiment of the present application provides an image processing method, including: acquiring an image to be processed, wherein the image to be processed comprises a first target, and the first target is provided with preset labeling information; preprocessing the image to be processed to obtain a preprocessed image; carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target; acquiring color information of the first area; calculating a color correction matrix according to the color information and the preset labeling information; and performing color correction on the image to be processed according to the color correction matrix.
In an embodiment, the pre-processing the image to be processed includes: carrying out Gaussian filtering denoising on the image to be processed; and adjusting the image to be processed to a state meeting a preset standard through rotating and scaling processing.
In an embodiment, the performing contour detection on the preprocessed image to obtain a plurality of first regions corresponding to the first target includes: acquiring color parameters of each pixel point in the preprocessed image; according to the color parameters, carrying out binarization processing on the preprocessed image to obtain a black-and-white image; extracting a plurality of color block outlines from the black and white image; and screening the color block outline according to a preset condition to obtain a plurality of first areas.
In an embodiment, the screening the color block outlines according to a preset condition to obtain a plurality of first regions includes: calculating the area ratio of the color block outline to the preprocessed image; judging whether the area ratio is within a preset ratio range or not; and when the area ratio is within a preset ratio range, determining the color block outline as the first area.
In an embodiment, the preset labeling information includes a shape of the first target, and the step of screening the color block outline according to a preset condition to obtain a plurality of first regions includes: judging whether the shape of the color block outline is the same as that of the first target or not; determining the color-block outline as the first region when the shape of the color-block outline is the same as the shape of the first object.
In an embodiment, the obtaining the color information of the first region includes: determining a central pixel point of the first area; acquiring the RGB value of the pixel point within a preset range from the central pixel point; and calculating the average value of the RGB values to obtain the color information.
In one embodiment, the color information includes RGB values and/or HSV color model parameters.
A second aspect of the embodiments of the present application provides an image processing apparatus, including: the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring an image to be processed, the image to be processed comprises a first target, and the first target is provided with preset marking information; the preprocessing module is used for preprocessing the image to be processed to obtain a preprocessed image; the detection module is used for carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target; the second acquisition module is used for acquiring the color information of the first area; the calculation module is used for calculating a color correction matrix according to the color information and the preset labeling information; and the correction module is used for carrying out color correction on the image to be processed according to the color correction matrix.
A third aspect of embodiments of the present application provides an electronic device, including: a memory to store a computer program; a processor configured to perform the method of the first aspect of the embodiments of the present application and any of the embodiments of the present application.
A fourth aspect of embodiments of the present application provides a non-transitory electronic device-readable storage medium, including: a program which, when run by an electronic device, causes the electronic device to perform the method of the first aspect of an embodiment of the present application and any embodiment thereof.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic view of an application scenario of an image processing method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating an image processing method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a distribution of color blocks in a first target according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a distribution of color blocks in a first target according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application.
Reference numerals:
110-client, 120-server, 200-electronic device, 210-bus, 220-processor, 230-memory, 400-image processing device, 410-first acquisition module, 420-preprocessing module, 430-detection module, 440-second acquisition module, 450-calculation module, 460-correction module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
In the description of the present application, the terms "first," "second," and the like are used for distinguishing between descriptions and do not denote an order of magnitude, nor are they to be construed as indicating or implying relative importance.
In the description of the present application, the terms "mounted," "disposed," "provided," "connected," and "configured" are to be construed broadly unless expressly stated or limited otherwise. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be mechanically or electrically connected; either directly or indirectly through intervening media, or may be internal to two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In the description of the present application, like reference numerals and letters refer to like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.
Please refer to fig. 1, which is a schematic view of an application scenario of an image processing method according to an embodiment of the present application. The application scene comprises a client 110 and a server 120, wherein the client 110 can be an image acquisition device with a camera and is used for shooting pictures or videos of disease symptoms and standard color cards and sending the pictures or videos to the server 120, so that the server 120 can obtain the disease symptoms pictures.
As shown in fig. 2, which is a schematic structural diagram of an electronic device 200 according to an embodiment of the present application, the electronic device 200 may serve as a server 120, and the electronic device 200 includes at least one processor 220 and a memory 230, where one processor is taken as an example in fig. 2. The processors 220 and the memory 230 are connected by a bus 210, and the memory 230 stores instructions executable by the at least one processor 220, the instructions being executed by the at least one processor 220 to cause the at least one processor 220 to perform an image processing method as in the embodiments described below.
As shown in fig. 3, which is a flowchart illustrating an image processing method according to an embodiment of the present application, the method may be executed by the electronic device 200 shown in fig. 2 to achieve an effect of improving consistency of colors of images captured in different image capturing scenes. The method comprises the following steps:
step 310: and acquiring an image to be processed.
In the above steps, the image to be processed may be acquired by an image acquisition device such as a mobile phone, a camera, and the like, where the image to be processed includes a first target, the first target has preset annotation information, the preset annotation information may specifically include a shape, an area, a reference color, and the like of the first target, and the reference color may be a real color of the first target in reality, or an image color acquired by the first target in a certain image acquisition scene.
In one embodiment, the first target may be a standard color card, and the standard color card may include four color blocks of yellow, green, black and gray, and may also include a smaller or larger number of color blocks of other colors. The standard color chart can be placed beside the wound, and a picture including the standard color chart and the wound is taken as an image to be processed.
Step 320: and preprocessing the image to be processed to obtain a preprocessed image.
In the above step, the operation of preprocessing the image to be processed may include: carrying out Gaussian filtering denoising on an image to be processed; and adjusting the image to be processed to a state meeting a preset standard through rotating and zooming processing. In an embodiment, the preset criterion may include, but is not limited to, an image size threshold, an image direction, and the like, the image to be processed may be adjusted to the image direction in the preset criterion by rotation, and the image to be processed exceeding the image size threshold may be scaled equally.
Step 330: and carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target.
In the foregoing step, performing contour detection on the preprocessed image to obtain a plurality of first regions corresponding to the first target may include: acquiring a color parameter of each pixel point in the preprocessed image; according to the color parameters, carrying out binarization processing on the preprocessed image to obtain a black-and-white image; extracting a plurality of color block outlines from a black and white image; and screening the color block outlines according to preset conditions to obtain a plurality of first areas. In an embodiment, each first area corresponds to a color block in a standard color card.
In an embodiment, the color parameters of each pixel point in the preprocessed image in the HSV color model may be obtained according to a conversion relationship (Hue, Hue; Saturation; Value, brightness) between an RGB color model (Red, Red; Green, Green; Blue, Blue) and an HSV color model, where the conversion relationship between the RGB color model and the HSV color model is as follows:
R'＝R÷255；G'＝G÷255；B'＝B÷255
Cmax＝max(R',G',B')；Cmin＝min(R',G',B')；Δ＝Cmax-Cmin
V＝Cmax
in an embodiment, after the color parameter of each pixel point in the preprocessed image is obtained, whether the color parameter of each pixel point is within the color parameter range of a color block in a standard color card can be judged, and binarization processing and contour extraction are performed on the preprocessed image according to the judgment result. The color parameter range of each color block in the standard color card can be predetermined by acquiring standard color card images shot by different image acquisition devices under different illumination conditions.
In an embodiment, the screening the color block outline according to the preset condition may include: calculating the area ratio of the color block outline to the preprocessed image; judging whether the area ratio is within a preset ratio range or not; and when the area ratio is within the preset ratio range, determining the color block outline as a first area.
In an embodiment, the screening the color block outline according to the preset condition may include: judging whether the shape of the color block outline is the same as that of the first target or not; when the shape of the patch outline is the same as the shape of the first object, the patch outline is determined to be the first region. The shape of the first target includes, but is not limited to, a rectangle, a parallelogram, a triangle, and the like.
In an embodiment, the first target may be a standard color card including four color blocks, and the four color blocks may be arranged in a parallelogram, as shown in fig. 4 and 5, the four color block outlines of the parallelogram may be determined as the first area by traversing a center point of each color block outline and searching the center point. It is understood that the standard color block may also be composed of three, two, one, or more than four color blocks.
Step 340: color information of the first region is acquired.
In the above step, the obtaining of the color information of the first region may include: determining a central pixel point of the first area; acquiring the RGB value of a pixel point within a preset range from a central pixel point; and calculating the average value of the RGB values to obtain color information. In one embodiment, the color information may include, but is not limited to, RGB values and/or HSV color model parameters. The twenty-four color cards are commonly used in the field of image processing, so for the color cards with less colors, such as the four color cards, the parameter values of various color models, such as an RGB color model, an HSV color model and the like, can be used as color information to improve the accuracy of the color correction result of the image to be processed.
Step 350: and calculating a color correction matrix according to the color information and the preset marking information.
In the above step, a color correction matrix is calculated according to the color information and the reference color in the preset annotation information, where the color correction matrix may represent a mapping relationship between a color of the first target in the image to be processed and a real color of the first target in reality, or a mapping relationship between a color of the first target in the image to be processed and a color of the first target image acquired under a certain image acquisition scene. In one embodiment, the method for calculating the color correction matrix includes, but is not limited to, least squares, Van der Monte matrix fitting, partial least squares regression, polynomial regression, and the like.
Step 360: and performing color correction on the image to be processed according to the color correction matrix.
In the above steps, according to the color correction matrix obtained in step 350, the image to be processed is corrected to the color of the previous image, and the previous image may be the color of the first target in reality or the color acquired in a certain image acquisition scene, so as to ensure that the image to be processed is consistent with the previous image observation, thereby facilitating the judgment of observers, such as doctors, on various color changes, for example, in the monitoring process of burn patients, the color of wounds changed with time is unified, which can help the doctors to judge the state of illness.
As shown in fig. 6, which is a schematic structural diagram of an image processing apparatus 400 according to an embodiment of the present application, the apparatus can be applied to the electronic device 200 shown in fig. 1, and includes: a first acquisition module 410, a pre-processing module 420, a detection module 430, a second acquisition module 440, a calculation module 450, and a correction module 460. The principle relationship of the modules is as follows:
a first obtaining module 410, configured to obtain an image to be processed, where the image to be processed includes a first target, and the first target has preset annotation information;
the preprocessing module 420 is configured to preprocess an image to be processed to obtain a preprocessed image;
a detection module 430, configured to perform contour detection on the preprocessed image to obtain a plurality of first regions corresponding to the first target;
a second obtaining module 440, configured to obtain color information of the first region;
the calculating module 450 is configured to calculate a color correction matrix according to the color information and preset labeling information;
and a correcting module 460, configured to perform color correction on the image to be processed according to the color correction matrix.
In one embodiment, the preprocessing module 420 is configured to: carrying out Gaussian filtering denoising on an image to be processed; and adjusting the image to be processed to a state meeting a preset standard through rotating and zooming processing.
In one embodiment, the detection module 430 is configured to: acquiring a color parameter of each pixel point in the preprocessed image; according to the color parameters, carrying out binarization processing on the preprocessed image to obtain a black-and-white image; extracting a plurality of color block outlines from a black and white image; and screening the color block outlines according to preset conditions to obtain a plurality of first areas.
In one embodiment, the detection module 430 is configured to: calculating the area ratio of the color block outline to the preprocessed image; judging whether the area ratio is within a preset ratio range or not; and when the area ratio is within the preset ratio range, determining the color block outline as a first area.
In one embodiment, the detection module 430 is configured to: judging whether the shape of the color block outline is the same as that of the first target or not; when the shape of the patch outline is the same as the shape of the first object, the patch outline is determined to be the first region.
In one embodiment, the second obtaining module 440 is configured to: determining a central pixel point of the first area; acquiring the RGB value of a pixel point within a preset range from a central pixel point; and calculating the average value of the RGB values to obtain color information.
For a detailed description of the image processing apparatus 400, please refer to the description of the related method steps in the above embodiments.
An embodiment of the present invention further provides a storage medium readable by an electronic device, including: a program that, when run on an electronic device, causes the electronic device to perform all or part of the procedures of the methods in the above-described embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like. The storage medium may also comprise a combination of memories of the kind described above.
In the embodiments provided in the present application, the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The above description is only a preferred embodiment of the present application, and is only for the purpose of illustrating the technical solutions of the present application, and not for the purpose of limiting the present application. Any modification, equivalent replacement, improvement or the like, which would be obvious to one of ordinary skill in the art and would be within the spirit and principle of the present application, should be included within the scope of the present application.
Claims (10)
1. An image processing method, comprising:
acquiring an image to be processed, wherein the image to be processed comprises a first target, and the first target is provided with preset labeling information;
preprocessing the image to be processed to obtain a preprocessed image;
carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target;
acquiring color information of the first area;
calculating a color correction matrix according to the color information and the preset labeling information;
and performing color correction on the image to be processed according to the color correction matrix.
2. The image processing method according to claim 1, wherein the preprocessing the image to be processed comprises:
carrying out Gaussian filtering denoising on the image to be processed;
and adjusting the image to be processed to a state meeting a preset standard through rotating and scaling processing.
3. The image processing method according to claim 1, wherein the performing contour detection on the preprocessed image to obtain a plurality of first regions corresponding to the first target comprises:
acquiring color parameters of each pixel point in the preprocessed image;
according to the color parameters, carrying out binarization processing on the preprocessed image to obtain a black-and-white image;
extracting a plurality of color block outlines from the black and white image;
and screening the color block outline according to a preset condition to obtain a plurality of first areas.
4. The image processing method according to claim 3, wherein the screening the color block contours according to a preset condition to obtain a plurality of the first regions comprises:
calculating the area ratio of the color block outline to the preprocessed image;
judging whether the area ratio is within a preset ratio range or not;
and when the area ratio is within a preset ratio range, determining the color block outline as the first area.
5. The image processing method according to claim 3, wherein the preset labeling information includes a shape of the first target, and the step of filtering the color block outline according to a preset condition to obtain a plurality of the first regions includes:
judging whether the shape of the color block outline is the same as that of the first target or not;
determining the color-block outline as the first region when the shape of the color-block outline is the same as the shape of the first object.
6. The image processing method according to claim 1, wherein the acquiring color information of the first region includes:
determining a central pixel point of the first area;
acquiring the RGB value of the pixel point within a preset range from the central pixel point;
and calculating the average value of the RGB values to obtain the color information.
7. The image processing method according to claim 1, wherein the color information comprises RGB values and/or HSV color model parameters.
8. An image processing apparatus characterized by comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring an image to be processed, the image to be processed comprises a first target, and the first target is provided with preset marking information;
the preprocessing module is used for preprocessing the image to be processed to obtain a preprocessed image;
the detection module is used for carrying out contour detection on the preprocessed image to obtain a plurality of first areas corresponding to the first target;
the second acquisition module is used for acquiring the color information of the first area;
the calculation module is used for calculating a color correction matrix according to the color information and the preset labeling information;
and the correction module is used for carrying out color correction on the image to be processed according to the color correction matrix.
9. An electronic device, comprising:
a memory to store a computer program;
a processor to perform the method of any one of claims 1 to 7.
10. A non-transitory electronic device readable storage medium, comprising: program which, when run by an electronic device, causes the electronic device to perform the method of any one of claims 1 to 7.
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| CN202010908511.9A CN112102926A (en) | 2020-09-02 | 2020-09-02 | Image processing method, device, equipment and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113764077A (en) * | 2021-07-27 | 2021-12-07 | 上海思路迪生物医学科技有限公司 | Pathological image processing method and device, electronic equipment and storage medium |
| CN115018739A (en) * | 2022-08-08 | 2022-09-06 | 北京国安广传网络科技有限公司 | Portable family doctor intelligent workstation system based on digital processing |
| CN115994874A (en) * | 2023-03-22 | 2023-04-21 | 赛维森(广州)医疗科技服务有限公司 | Slide image processing method, slide image processing device, slide, computer device and storage medium |
| CN117041531A (en) * | 2023-09-04 | 2023-11-10 | 无锡维凯科技有限公司 | Mobile phone camera focusing detection method and system based on image quality evaluation |
| CN113764077B (en) * | 2021-07-27 | 2024-04-19 | 上海思路迪生物医学科技有限公司 | Pathological image processing method and device, electronic equipment and storage medium |
-
2020
- 2020-09-02 CN CN202010908511.9A patent/CN112102926A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113764077A (en) * | 2021-07-27 | 2021-12-07 | 上海思路迪生物医学科技有限公司 | Pathological image processing method and device, electronic equipment and storage medium |
| CN113764077B (en) * | 2021-07-27 | 2024-04-19 | 上海思路迪生物医学科技有限公司 | Pathological image processing method and device, electronic equipment and storage medium |
| CN115018739A (en) * | 2022-08-08 | 2022-09-06 | 北京国安广传网络科技有限公司 | Portable family doctor intelligent workstation system based on digital processing |
| CN115994874A (en) * | 2023-03-22 | 2023-04-21 | 赛维森(广州)医疗科技服务有限公司 | Slide image processing method, slide image processing device, slide, computer device and storage medium |
| CN117041531A (en) * | 2023-09-04 | 2023-11-10 | 无锡维凯科技有限公司 | Mobile phone camera focusing detection method and system based on image quality evaluation |
| CN117041531B (en) * | 2023-09-04 | 2024-03-15 | 无锡维凯科技有限公司 | Mobile phone camera focusing detection method and system based on image quality evaluation |
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