WO2021259394A2 - 一种图像处理方法及装置、电子设备和存储介质 - Google Patents

一种图像处理方法及装置、电子设备和存储介质 Download PDF

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WO2021259394A2
WO2021259394A2 PCT/CN2021/122320 CN2021122320W WO2021259394A2 WO 2021259394 A2 WO2021259394 A2 WO 2021259394A2 CN 2021122320 W CN2021122320 W CN 2021122320W WO 2021259394 A2 WO2021259394 A2 WO 2021259394A2
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image
area
blood vessel
heart
coronary blood
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PCT/CN2021/122320
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English (en)
French (fr)
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WO2021259394A3 (zh
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梁隆恺
吴振洲
刘盼
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北京安德医智科技有限公司
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Publication of WO2021259394A3 publication Critical patent/WO2021259394A3/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • 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
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • 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
    • G06T2207/30048—Heart; Cardiac
    • 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
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • the present disclosure relates to the field of computer technology, and in particular to an image processing method and device, electronic equipment, and storage medium.
  • Coronary heart disease is a kind of cardiovascular disease, and it is also recognized as one of the diseases with the highest mortality rate in the world.
  • Coronary artery (coronary artery) calcification is related to the onset of coronary heart disease. Therefore, accurate judgments of coronary artery calcification, such as accurate calculation of calcification score, will help predict the prevalence of coronary heart disease patients.
  • the calcification area on the coronary artery branches will be determined in the segmented heart image, and then the coronary artery calcification score will be calculated. Since CT images cannot show the trend of blood vessels, the calcification area on the coronary artery branches cannot be accurately obtained, and thus the accurate coronary artery calcification score cannot be obtained.
  • the present disclosure proposes a technical solution for image processing.
  • an image processing method including:
  • a target calcification area having the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image is determined.
  • the coronary vascular image is a computed tomography CTA image
  • the heart image is a computed tomography CT image
  • the CTA image and the CT image are for the same target object Obtained from image collection.
  • the image registration is performed on the coronary blood vessel image and the heart image to obtain the corresponding relationship of at least one image point with the same name in the coronary blood vessel image and the heart image, include:
  • Image registration is performed on the coronary blood vessel area and the heart area to obtain the corresponding relationship between the image points of the same name in the coronary blood vessel area and the heart area.
  • the image registration of the coronary blood vessel area and the heart area to obtain the corresponding relationship between the image points of the same name in the coronary blood vessel area and the heart area includes:
  • Image registration is performed on the first rectangular area and the second rectangular area to obtain the corresponding relationship between the image points of the same name in the first rectangular area and the second rectangular area.
  • the first calcification area includes:
  • the determining the target calcification area in the first calcification area that has the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image includes:
  • the area overlapping the coronary blood vessel area in the first calcification area is taken as a target calcification area.
  • the coronary blood vessel image is a coronary blood vessel image obtained after excluding the blood vessel with the largest blood vessel volume value
  • the image registration is performed on the coronary blood vessel image and the heart image
  • the method further includes: counting the volume value of each blood vessel in the coronary blood vessel image; in the coronary blood vessel image, removing the blood vessel with the largest blood vessel volume value.
  • an image processing device including:
  • the input unit is used to obtain coronary vascular images and heart images
  • a registration unit configured to perform image registration on the coronary blood vessel image and the heart image to obtain the correspondence between the coronary blood vessel image and at least one image point with the same name in the heart image;
  • a first area determining unit configured to determine at least one first calcification area on the heart image
  • the target area determination unit is configured to determine the target calcification area in the first calcification area that has the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image.
  • the coronary vascular image is a computed tomography CTA image
  • the heart image is a computed tomography CT image
  • the CTA image and the CT image are for the same target object Obtained from image collection.
  • the registration unit includes:
  • the feature extraction subunit is used to determine the coronary blood vessel area in the coronary blood vessel image and the heart area in the heart image.
  • the first registration subunit is used for image registration of the coronary blood vessel area and the heart area to obtain the corresponding relationship between the image points of the same name in the coronary blood vessel area and the heart area.
  • the first registration subunit includes:
  • a first area division subunit configured to determine a first rectangular area in the coronary blood vessel image, the first rectangular area including the smallest rectangular area including the coronary blood vessel area;
  • a second area division subunit configured to determine a second rectangular area in the heart image, the second rectangular area including the smallest rectangular area including the heart area;
  • the second registration subunit is used to perform image registration on the first rectangular area and the second rectangular area to obtain the correspondence between the image points of the same name in the first rectangular area and the second rectangular area.
  • the first calcification area includes: an area where a voxel with a CT value greater than 130 Hu is located on the heart image.
  • the target area determining unit includes:
  • the graphic superimposing unit is configured to superimpose the first calcification area on the target area on the coronary vascular map according to the corresponding relationship, where the target area is the area where the image point with the same name of the first calcification area is located ;
  • the target area determination subunit is configured to use an area overlapping the coronary blood vessel area in the first calcification area as a target calcification area.
  • the image processing device further includes:
  • a statistical unit configured to count the volume values of each blood vessel in the coronary blood vessel image
  • the element removal unit is configured to remove the blood vessel with the largest blood vessel volume value in the coronary blood vessel image.
  • an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the foregoing method.
  • a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the acquired coronary vascular images are registered to the heart image, so as to obtain the corresponding relationship of the image points with the same name in the two images; then, the first calcification area is determined on the heart image, and the first calcification area is determined on the heart image.
  • a target calcification area that has a corresponding relationship with the image points of the same name as the coronary blood vessel area in the coronary blood vessel image. Since the determined target calcification area is an area with the same image point correspondence relationship with the coronary blood vessel area, then the target calcification area is the calcification area on the coronary artery branch, therefore, the accuracy of determining the calcification area on the coronary artery branch is improved.
  • it can also accurately display the calcification area on the coronary artery branch.
  • Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure.
  • Fig. 2 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure.
  • Fig. 3 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • the image processing method can be executed by electronic equipment such as a terminal device or a server, and the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, or a cordless
  • UE user equipment
  • PDAs personal digital assistants
  • the method can be implemented by a processor invoking computer-readable instructions stored in a memory.
  • the method can be executed by a server.
  • Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure. As shown in Fig. 1, the image processing method includes:
  • step S11 the coronary blood vessel image and the heart image are acquired.
  • the coronary blood vessel information can be clearly displayed in the coronary blood vessel image, and the heart information and the density difference of each tissue can be clearly displayed in the heart image.
  • Coronary blood vessel images and heart images can be input by the user or received from an image scanning device.
  • the image scanning device may be a computer tomography (Computed Tomography, CT) device.
  • CT Computer tomography
  • a CT scan of the heart can be performed to obtain a coronary blood vessel image that can clearly show the coronary blood vessels.
  • the heart image can highlight the image of the heart itself, and can also include images of other tissues; similarly, the coronary blood vessel images can highlight the coronary blood vessels, and can also include images of other tissues.
  • step S12 image registration is performed on the coronary blood vessel image and the heart image to obtain the corresponding relationship of at least one image point with the same name in the coronary blood vessel image and the heart image.
  • the relative position between the patient and the image scanning device may change. Therefore, the position of the target object on the image will also change, resulting in the incomplete matching of the information on these images, and the information of these images cannot be merged and presented correctly. Therefore, image registration can be performed on these images first.
  • image registration two or more images of the same/different mode can be subjected to spatial transformation, structure matching, and pixel superposition to achieve image spatial correspondence.
  • image registration methods can be, for example, feature-based image registration methods. The basic idea of this method is: extract image features from two or more images; Accurate; to obtain the correspondence between the image points of the same name in two or more images.
  • the characteristic points that are common in the two images and express the heart or coronary blood vessels from the coronary blood vessel image and the heart image For example, feature points such as image points at the junction of the heart and blood vessels, and image points on the outline of the heart. Then, the feature extraction algorithm is used to realize the image point extraction with the same name.
  • the corresponding relationship of the image points with the same name may include the corresponding relationship of one or more pixel points.
  • the present disclosure does not specifically limit the selection of the image points with the same name, the feature extraction algorithm, and the corresponding relationship of the image points with the same name.
  • image registration can be achieved using a trained neural network.
  • step S13 at least one first calcification area on the heart image is determined.
  • the heart image is obtained by scanning the target object using an image scanning device.
  • image scanning equipment There are many types of image scanning equipment. Take CT equipment as an example.
  • CT technology X-ray beams are irradiated from multiple directions along a selected tomographic plane of the body. The amount of X-rays transmitted is measured and digitized, and then calculated Obtain the absorption coefficient of each unit volume of the tissue at this level, and then construct an image based on the absorption coefficient.
  • the absorption rate of X-rays is different, so the X-rays that pass through this part will also be different.
  • the penetrating X-rays are converted into the visible light by the detector, and converted into electrical signals by photoelectric conversion, and then converted into data by an analog/digital converter, and input into a computer for processing to generate an image. Therefore, the CT technology can be used to distinguish the difference in the density of each part of the body, and the CT value is used to represent the density of each part, and the unit is Hu (Hounsfield Unit). The higher the CT value, the higher the density of the part.
  • the first calcification area is an area where calcification may occur on the heart image.
  • the heart image is an image obtained by scanning with a CT device
  • an area that satisfies a predetermined CT value threshold can be selected on the heart image as The first calcification area.
  • the predetermined CT threshold can be determined based on the experience of determining the coronary artery calcification area in clinical medicine, so that the calcification area determined in the embodiment of the present disclosure meets actual application requirements and is more accurate.
  • step S14 it is determined that in the first calcification area, a target calcification area having the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image is determined.
  • the coronary blood vessel image is registered with the heart image, and the corresponding relationship between the coronary blood vessel image and at least one image point with the same name in the heart image is obtained. Then, after the first calcification area in the heart image is determined, the corresponding relationship can be used to obtain the calcification area in the coronary blood vessel area in the first calcification area.
  • the coronary blood vessel area here may be a coronary blood vessel. The area in the image where the coronary artery branches are located.
  • the first calcification area is located in the heart image, when the first calcification area is superimposed on the registered coronary blood vessel image, the spatial relationship between the first calcification area and the blood vessel is accurately presented. Then, in the process of determining the target calcification area, specifically, the calcification area that overlaps the coronary blood vessel in the first calcification area can be selected as the target calcification area, which improves the accuracy of determining the calcification area on the coronary blood vessel.
  • the first calcification area corresponding to the coronary blood vessel can be determined as the target calcification area based on the one-to-one correspondence between the image points of the coronary blood vessel and the image points of the same name in the heart image.
  • the acquired coronary vascular images are registered to the heart image, so as to obtain the corresponding relationship of the image points with the same name in the two images; then, the first calcification area is determined on the heart image, and the first calcification area is determined on the heart image.
  • a target calcification area that has a corresponding relationship with the image points of the same name as the coronary blood vessel area in the coronary blood vessel image. Since the determined target calcification area is an area with the same image point correspondence relationship with the coronary blood vessel area, then the target calcification area is the calcification area on the coronary artery branch, therefore, the accuracy of determining the calcification area on the coronary artery branch is improved.
  • it can also accurately display the calcification area on the coronary artery branch.
  • the coronary vascular image is a computed tomography (Computed Tomography Angiography) image
  • the heart image is a computed tomography CT image
  • the CTA image and the CT image It is obtained for image acquisition of the same target object.
  • Both CT image and CTA image imaging equipment can be CT equipment.
  • CT image is obtained by directly scanning the human body using CT equipment.
  • the higher the density of the area, the brighter the image, and vice versa the abnormal areas in the image will be highlighted from the surrounding areas. come out.
  • the density of human diseased tissue is different from its normal tissue density, so CT images can be used to determine the diseased area of each part of the human body.
  • the CTA image is obtained by first injecting a contrast agent into the body, and then scanning the human body with a CT device. Since the density of the contrast agent is higher or lower than the density of the body tissue, the organ or tissue at the injection site can be displayed more prominently on the image.
  • the density of the calcified area of the blood vessel is significantly larger than that of the normal blood vessel, so the location of the calcified area can be easily distinguished using CT images.
  • CT images cannot clearly show the characteristics of blood vessels to meet the needs of medical judgment.
  • the CTA image can make up for this defect.
  • the coronary blood vessels can be displayed very clearly on the image. Therefore, the CT image and CTA image of the same target object are selected as the image data to be processed.
  • the image registration is performed on the coronary blood vessel image and the heart image to obtain the corresponding relationship of at least one image point with the same name in the coronary blood vessel image and the heart image, It includes: determining the coronary blood vessel area in the coronary blood vessel image and the heart area in the heart image; performing image registration on the coronary blood vessel area and the heart area to obtain the coronary blood vessel area and the heart Correspondence of the image points with the same name in the area.
  • the heart area is the main processing object; on the CTA image, the coronary blood vessels are the main processing object. Therefore, the heart area and the coronary blood vessel area as image features can be extracted separately for subsequent operations. After that, the image of the heart area and the image of the coronary vascular area are used for registration, which can reduce the area used for image registration and improve the registration accuracy.
  • the image registration of the coronary blood vessel area and the heart area to obtain the corresponding relationship between the image points of the same name in the coronary blood vessel area and the heart area includes: determining the coronary artery area and the heart area. A first rectangular area in a vascular image, where the first rectangular area includes the smallest rectangular area including the coronary vascular area; determining a second rectangular area in the heart image, the second rectangular area including all The smallest rectangular area of the heart area; image registration is performed on the first rectangular area and the second rectangular area to obtain the correspondence between the image points of the same name in the first rectangular area and the second rectangular area.
  • the heart area in the heart image is the smallest rectangular area in the heart image that contains pixels representing the heart.
  • the maximum value coordinates of the pixels representing the heart on each axis in the heart image are (x max , y max , z max ), and the minimum value coordinates are (x min , y min , z min ).
  • a plane ⁇ max containing the maximum y-axis coordinate and parallel to the plane xoz can be obtained ; a plane ⁇ min containing the minimum y-axis coordinate and parallel to the plane xoz; and a plane ⁇ min containing the maximum z-axis coordinate and parallel to the plane xoy
  • the method of determining the coronary vascular area is similar to that of the heart area.
  • the maximum and minimum values of the pixels of the coronary blood vessels on each coordinate axis can be found on the coronary blood vessel image. Then, use the plane determined by the maximum and minimum to cut the coronary vascular image to obtain the coronary vascular area. The specific method will not be repeated.
  • the use of the heart region and the coronary artery region for registration not only preserves the necessary image features, but also reduces the range of the image to be registered. Therefore, while improving the efficiency of image registration, it also improves the accuracy of image registration. At the same time, the accuracy of the correspondence between the image points with the same name in the two images is improved.
  • the first calcification area includes: an area where a voxel with a CT value greater than 130 Hu is located on the heart image.
  • the method in the embodiment of the present disclosure can improve the accuracy of determining the calcification area in the coronary artery branch.
  • the accurate determination of the calcification area can accurately calculate the coronary artery calcification score, and then accurately determine the coronary artery calcification.
  • the coronary artery calcification score is based on the CT value to determine the degree and scope of coronary artery calcification. It is an indicator that reflects the risk of the heart; in general, the coronary calcification score is the sum of the area of the calcification area larger than 1 mm 2 or 1 pixel multiplied by its maximum density weighting coefficient.
  • the value of the weighting coefficient can be: when the density is less than 130Hu, the weighting coefficient is 0; when the density is 130-199Hu, the weighting coefficient is 1; when the density is 200-299Hu, the weighting coefficient is 2; When the value is 300 ⁇ 399Hu, the weighting coefficient is 3, and when the density is greater than 400, the weighting coefficient is 4. That is, in the image, pixels or areas with a density greater than or equal to 130 appearing at the position of coronary blood vessels will be determined as calcified areas. Therefore, the selection threshold of the first calcification area is greater than 130 Hu.
  • the determining the target calcification area in the first calcification area that has the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image includes: according to the corresponding relationship , Superimpose the first calcification area on the target area on the coronary angiogram, where the target area is the area where the image point of the first calcification area is located; The area where the coronary vascular area overlaps is used as the target calcification area.
  • the first calcification area can be translated, rotated, zoomed, etc., based on the aforementioned corresponding relationship between the image points of the same name, and the first calcification area can be superimposed on the coronary blood vessel image, and the spatial position of the first calcification area and the coronary blood vessel on the image can be analyzed relation.
  • the first calcification area that has an overlapping relationship with the coronary blood vessels is selected as the target calcification area, and the target calcification area can be used to calculate the calcification score.
  • the coronary blood vessel image is a coronary blood vessel image obtained after excluding the blood vessel with the largest blood vessel volume value
  • the image registration is performed on the coronary blood vessel image and the heart image
  • the method further includes: counting the volume value of each blood vessel in the coronary blood vessel image; in the coronary blood vessel image, removing the blood vessel with the largest blood vessel volume value.
  • the coronary vascular image by counting the volume value of each blood vessel, the main coronary blood vessel with the largest volume can be accurately identified.
  • the target calcification area is distributed on the coronary artery branch. After removing the coronary artery trunk, subsequent image processing can reduce unnecessary image features and improve processing efficiency; moreover, it can avoid mistakenly determining the calcification area on the coronary artery trunk as The target calcification area improves the accuracy of determining the target calcification area.
  • the image processing method provided by the embodiments of the present disclosure may be implemented based on a neural network, the neural network including: a first extraction network, a second extraction network, and an image registration network.
  • the determining The coronary blood vessel area in the coronary blood vessel image and the heart area in the heart image include: inputting the CTA image into a first extraction network to determine the coronary blood vessel area; and inputting the CT image into the first extraction network; Second, extract the network to determine the heart area; input the coronary blood vessel area and the heart area into the image registration network to obtain the correspondence between the coronary blood vessel area and the image point of the same name in the heart area.
  • a fixed window can be used to control the image size of the neural network input data. For example, select a rectangle of 256 ⁇ 256 ⁇ 256 pixels as the fixed window of the image, and save the center point of the fixed window. Then, the center value (x cent , y cent , z cent ) of each axis is calculated according to the maximum and minimum coordinates of the pixels representing the heart (or coronary blood vessels), and this point is used as the image center point. Calculated as follows:
  • the image registration network may be a deep neural network, and may include multiple sub-networks, where the first sub-network and the second sub-network are used for image feature extraction.
  • the image registration network may include one or more network layers such as a convolutional layer, a pooling layer, a sampling layer, and an upward convolutional layer.
  • the present disclosure does not limit the specific neural network structure.
  • the internal parameters of the neural network are preset, training samples are used for training, and the difference between the prediction result and the sample label is used to optimize the parameters of the neural network until the difference meets the preset threshold requirements, and the training ends.
  • the trained neural network can improve the accuracy and efficiency of image feature selection and image registration.
  • the present disclosure also provides image processing devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any image processing method provided in the present disclosure.
  • image processing devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any image processing method provided in the present disclosure.
  • Fig. 2 shows a block diagram of an image processing device according to an embodiment of the present disclosure. As shown in Fig. 2, the device 20 includes:
  • the input unit 21 is used to obtain coronary vascular images and heart images
  • the registration unit 22 is configured to perform image registration on the coronary blood vessel image and the heart image to obtain the corresponding relationship of at least one image point with the same name in the coronary blood vessel image and the heart image;
  • the first area determining unit 23 is configured to determine at least one first calcification area on the heart image
  • the target area determining unit 24 is configured to determine a target calcification area in the first calcification area that has the corresponding relationship with the coronary blood vessel area in the coronary blood vessel image.
  • the coronary vascular image is a computed tomography CTA image
  • the heart image is a computed tomography CT image
  • the CTA image and the CT image are for the same target object Obtained from image acquisition.
  • the registration unit 22 includes:
  • the feature extraction subunit is used to determine the coronary blood vessel area in the coronary blood vessel image and the heart area in the heart image.
  • the first registration subunit is used for image registration of the coronary blood vessel area and the heart area to obtain the corresponding relationship between the image points of the same name in the coronary blood vessel area and the heart area.
  • the first registration subunit includes:
  • a first area division subunit configured to determine a first rectangular area in the coronary blood vessel image, the first rectangular area including the smallest rectangular area including the coronary blood vessel area;
  • a second area division subunit configured to determine a second rectangular area in the heart image, the second rectangular area including the smallest rectangular area including the heart area;
  • the second registration subunit is used to perform image registration on the first rectangular area and the second rectangular area to obtain the correspondence between the image points of the same name in the first rectangular area and the second rectangular area.
  • the first calcification area includes: an area where a voxel with a CT value greater than 130 Hu is located on the heart image.
  • the target area determining unit 24 includes:
  • the graphic superimposing unit is configured to superimpose the first calcification area on the target area on the coronary vascular map according to the corresponding relationship, where the target area is the area where the image point with the same name of the first calcification area is located ;
  • the target area determination subunit is configured to use an area overlapping the coronary blood vessel area in the first calcification area as a target calcification area.
  • the image processing device further includes:
  • a statistical unit configured to count the volume values of each blood vessel in the coronary blood vessel image
  • the element removal unit is configured to remove the blood vessel with the largest blood vessel volume value in the coronary blood vessel image.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the embodiment of the present disclosure also proposes a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
  • the embodiments of the present disclosure also provide a computer program product, including computer-readable code.
  • the processor in the device executes the image processing method for implementing the image processing method provided by any of the above embodiments. instruction.
  • the embodiments of the present disclosure also provide another computer program product for storing computer-readable instructions, which when executed, cause the computer to perform the operations of the image processing method provided in any of the foregoing embodiments.
  • the electronic device can be provided as a terminal, server or other form of device.
  • FIG. 3 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
  • the memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the power supply component 806 provides power for various components of the electronic device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
  • the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the electronic device 800.
  • the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 814 may also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, for use in imaging applications.
  • CMOS complementary metal oxide semiconductor
  • CCD charge coupled device
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on a communication standard, such as a wireless network (WiFi), a second-generation mobile communication technology (2G) or a third-generation mobile communication technology (3G), or a combination thereof.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-available A programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as a memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.
  • FIG. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server. 4
  • the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as application programs.
  • the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-mentioned methods.
  • the electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as a Microsoft server operating system (Windows Server TM ), a graphical user interface operating system (Mac OS X TM ) launched by Apple, and a multi-user and multi-process computer operating system (Unix TM ), free and open source Unix-like operating system (Linux TM ), open source Unix-like operating system (FreeBSD TM ) or similar.
  • Microsoft server operating system Windows Server TM
  • Mac OS X TM graphical user interface operating system
  • Unix TM multi-user and multi-process computer operating system
  • FreeBSD TM open source Unix-like operating system
  • a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user’s computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user’s computer). connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
  • FPGA field programmable gate array
  • PDA programmable logic array
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagram can represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
  • Executable instructions can be included in the blocks in the flowchart or block diagram.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software, or a combination thereof.
  • the computer program product is specifically embodied as a computer storage medium.
  • the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.
  • SDK software development kit

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Abstract

本公开涉及一种图像处理方法及装置、电子设备和存储介质，所述方法包括：获取冠脉血管图像和心脏图像；对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；确定所述心脏图像上的至少一个第一钙化区域；确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。本公开实施例可实现提高了对目标钙化区域判定的准确性。

Description

一种图像处理方法及装置、电子设备和存储介质 技术领域
本公开涉及计算机技术领域，尤其涉及一种图像处理方法及装置、电子设备和存储介质。
背景技术
冠心病是心血管疾病的一种，也是世界公认死亡率最高的疾病之一。冠状动脉(冠脉)钙化与冠心病的发病存在相关性。因此，对冠脉钙化情况做出准确判断，例如准确计算钙化积分，将有助于对冠心病患者的患病情况做出预测。
通常，从CT图像中分割出心脏图像后，会在分割出的心脏图像中确定冠脉分支上的钙化区域，然后计算冠脉钙化积分。由于CT图像无法显示血管走势，所以无法准确获得冠脉分支上的钙化区域，进而无法获得准确的冠脉钙化积分。
发明内容
本公开提出了一种图像处理技术方案。
根据本公开的一方面，提供了一种图像处理方法，包括：
对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；
确定所述心脏图像上的至少一个第一钙化区域；
确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
在一种可能的实现方式中，所述冠脉血管图像为计算机体层血管成像CTA图像，所述心脏图像为计算机体层成像CT图像；所述CTA图像和所述CT图像为对同一目标对象进行图像采集得到的。
在一种可能的实现方式中，所述对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系，包括：
确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域；
对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
在一种可能的实现方式中，所述对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系，包括：
确定所述冠脉血管图像中的第一矩形区域，所述第一矩形区域包括包含所述冠脉血管区域的最小矩形区域；
确定所述心脏图像中的第二矩形区域，所述第二矩形区域包括包含所述心脏区域的最小矩形区域；
对所述第一矩形区域和所述第二矩形区域进行图像配准，得到第一矩形区域和第二矩形区域中同名像点的对应关系。
在一种可能的实现方式中，所述第一钙化区域，包括：
所述心脏图像上CT值大于130Hu的体素所在的区域。
在一种可能的实现方式中，所述确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域，包括：
根据所述对应关系，将所述第一钙化区域叠加到所述冠脉血管图上的目标区域，所述目标区域为所述第一钙化区域的同名像点所在的区域；
将所述第一钙化区域中与所述冠脉血管区域重叠的区域，作为目标钙化区域。
在一种可能的实现方式中，所述冠脉血管图像为剔除血管体积值最大的血管后，得到的冠脉血管图像，所述对所述冠脉血管图像和所述心脏图像进行图像配准之前，还包括：统计所述冠脉血管图像中各血管体积值；在所述冠脉血管图像中，去除所述血管体积值最大的血管。
根据本公开的一方面，提供了一种图像处理装置，包括：
输入单元，用于获取冠脉血管图像和心脏图像；
配准单元，用于对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；
第一区域判定单元，用于确定所述心脏图像上的至少一个第一钙化区域；
目标区域确定单元，用于确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
在一种可能的实现方式中，所述冠脉血管图像为计算机体层血管成像CTA图像，所述心脏图像为计算机体层成像CT图像；所述CTA图像和所述CT图像为对同一目标对象进行图像采集得到的。
在一种可能的实现方式中，所述配准单元，包括：
特征提取子单元，用于确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域。
第一配准子单元，用于对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
在一种可能的实现方式中，所述第一配准子单元，包括：
第一区域划分子单元，用于确定所述冠脉血管图像中的第一矩形区域，所述第一矩形区域包括包含所述冠脉血管区域的最小矩形区域；
第二区域划分子单元，用于确定所述心脏图像中的第二矩形区域，所述第二矩形区域包括包含所述心脏区域的最小矩形区域；
第二配准子单元，用于对所述第一矩形区域和所述第二矩形区域进行图像配准，得到第一矩形区域和第二矩形区域中同名像点的对应关系。
在一种可能的实现方式中，所述第一钙化区域，包括：所述心脏图像上CT值大于 130Hu的体素所在的区域。
在一种可能的实现方式中，所述目标区域确定单元，包括：
图形叠加单元，用于根据所述对应关系，将所述第一钙化区域叠加到所述冠脉血管图上的目标区域，所述目标区域为所述第一钙化区域的同名像点所在的区域；
目标区域确定子单元，用于将所述第一钙化区域中与所述冠脉血管区域重叠的区域，作为目标钙化区域。
在一种可能的实现方式中，所述图像处理装置，还包括：
统计单元，用于统计所述冠脉血管图像中各血管体积值；
要素去除单元，用于在所述冠脉血管图像中，去除所述血管体积值最大的血管。
根据本公开的一方面，提供了一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
根据本公开的一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。
在本公开实施例中，将获取的冠脉血管图像配准到心脏图像上，从而获得两个图像中同名像点的对应关系；之后，在心脏图像上确定第一钙化区域，并确定第一钙化区域中，与冠脉血管图像中的冠脉血管区域具备同名像点对应关系的目标钙化区域。由于确定出的目标钙化区域是与冠脉血管区域具备同名像点对应关系的区域，那么，目标钙化区域即为冠脉分支上的钙化区域，因此，提高了冠脉分支上钙化区域判定的准确性，以便准确地计算冠脉钙化积分，也可准确地展示冠脉分支上的钙化区域。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。
图1示出根据本公开实施例的图像处理方法的流程图。
图2示出根据本公开实施例的图像处理装置的框图。
图3示出根据本公开实施例的电子设备的框图。
图4示出根据本公开实施例的电子设备的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
在一种可能的实现方式中，所述图像处理方法可以由终端设备或服务器等电子设备执行，终端设备可以为用户设备(User Equipment，UE)、移动设备、用户终端、终端、蜂窝电话、无绳电话、个人数字助理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备、可穿戴设备等，所述方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。或者，可通过服务器执行所述方法。
图1示出根据本公开实施例的图像处理方法的流程图，如图1所示，所述图像处理方法包括：
在步骤S11中，获取冠脉血管图像和心脏图像。
本公开实施例中，冠脉血管图像中能够清晰地显示冠脉血管信息，而心脏图像中能够清晰地显示心脏信息和各组织密度差异。
冠脉血管图像和心脏图像可以是由用户输入的，也可以是从图像扫描设备接收的。
针对同一扫描对象，图像扫描设备通过不同的图像获取方式，往往能够获取到突出不同特征的图像。在一些实现方式中，图像扫描设备可以是电子计算机断层扫描(Computed Tomography，CT)设备。例如，在对心脏进行CT扫描后，可以获取到能够清晰显示心脏的心脏图像；在碘造影剂的作用下，再对心脏进行CT扫描，可以获取到能够清晰显示冠脉血管的冠脉血管图像。
其中，心脏图像中可以突出显示心脏本体的影像，也可以包括其他组织的影像；同样，在冠脉血管图像中可以突出显示冠脉血管，也可以包含其他组织的影像。
在步骤S12中，对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系。
图像扫描设备在对患者进行多次扫描时，由于患者与图像扫描设备之间的相对位置有可能发生改变。所以目标对象在图像上的位置也会发生变化，导致这些图像上的信息不能完全匹配，进而使得这些图像的信息无法正确合并呈现。因此，可以首先对这些图像进行图像配准。
在图像配准中，可以将相同/不同模式的两幅或多幅图像进行空间变换、结构匹配、 像素叠加，实现图像空间对应。常用的图像配准方法例如可以是基于特征的图像配准方法，这种方法的基本思想是：提取两幅或多幅图像中的图像特征；基于图像特征来进行两幅或多幅图像的配准；以获得两幅或多幅图像中同名像点间的对应关系。
在本公开实施例中，可以在冠脉血管图像和心脏图像中，提取两图中共有的、表现心脏或冠脉血管的特征点。例如，心脏、血管的连接处像点、心脏轮廓上像点等特征点。然后，再通过特征提取算法，实现同名像点提取。同名像点的对应关系可以包括一个或多个像素点的对应关系。本公开不具体对同名像点的选取、特征提取算法、同名像点对应关系进行限定。
在一些实现方式中，图像配准可以利用经过训练的神经网络实现。
冠脉血管图像和心脏图像配准后，为图像间信息叠加提供条件，直观展现钙化区域的位置，使得冠脉钙化的检测更准确。
在步骤S13中，确定所述心脏图像上的至少一个第一钙化区域。
如前文所述，心脏图像是使用图像扫描设备对目标对象进行扫描得到的。图像扫描设备有多种，以CT设备为例，在CT技术中，会以X线束从多个方向沿着身体某一选定断层层面进行照射，测定透过的X线量并数字化后，计算得出该层面组织各个单位容积的吸收系数，然后依据该吸收系数构建图像。
身体各部分由于密度差异，对X射线的吸收率不同，因此透过该部分的X射线也会不同。穿透出来的X射线经探测器转变为该可见光后，由光电转换变为电信号，再经模拟/数字转换器转为数据，输入计算机处理生成图像。所以，利用CT技术可以分辨身体各部分密度的差别，使用CT值来表示各部分密度，其单位为Hu(Hounsfield Unit)。CT值越高，表示该部分的密度越高。
第一钙化区域为心脏图像上可能发生钙化的区域，在本公开实施例中，在心脏图像为通过CT设备扫描得到的图像的情况下，可以在心脏图像上选取满足预定CT值阈值的区域作为第一钙化区域。预定CT阈值可以根据临床医学上对于冠脉钙化区域的判定经验确定，使得本公开实施例确定的钙化区域满足实际应用需要、也更准确。
在步骤S14中，确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
通过上述步骤12，将冠脉血管图像与心脏图像配准，得到了冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系。那么，在确定出心脏图像中的第一钙化区域后，即可根据该对应关系，来得到第一钙化区域中位于冠脉血管区域的钙化区域，这里的冠脉血管区域，可以是冠脉血管图像中冠脉分支所在的区域。
具体来说，由于第一钙化区域位于心脏图像中，所以，将第一钙化区域叠加到配准后的冠脉血管图像上时，使得第一钙化区域与血管的空间关系准确呈现。那么，在确定目标钙化区域的过程中，具体可以选取第一钙化区域中与冠脉血管有交集的钙化区域，作为目标钙化区域，提高了冠脉血管上钙化区域确定的准确性。
此外，还可以依据冠脉血管图像与心脏图像中同名像点的一一对应关系，来确定与 冠脉血管具备对应关系的第一钙化区域，作为目标钙化区域。
在本公开实施例中，将获取的冠脉血管图像配准到心脏图像上，从而获得两个图像中同名像点的对应关系；之后，在心脏图像上确定第一钙化区域，并确定第一钙化区域中，与冠脉血管图像中的冠脉血管区域具备同名像点对应关系的目标钙化区域。由于确定出的目标钙化区域是与冠脉血管区域具备同名像点对应关系的区域，那么，目标钙化区域即为冠脉分支上的钙化区域，因此，提高了冠脉分支上钙化区域判定的准确性，以便准确地计算冠脉钙化积分，也可准确地展示冠脉分支上的钙化区域。
在一种可能的实施方式中，所述冠脉血管图像为计算机体层血管成像CTA(Computed Tomography Angiography)图像，所述心脏图像为计算机体层成像CT图像；所述CTA图像和所述CT图像为对同一目标对象进行图像采集得到的。
CT图像和CTA图像的成像设备均可以为CT设备。不同之处在于，CT图像是使用CT设备直接对人体进行扫描后获得，在CT图像上，密度越高的区域，图像越亮，反之越暗，图像中非正常区域会从周围的区域中突显出来。人体病变组织的密度与其正常时的组织密度不同，因此可以使用CT图像判断出人体各部分的病变区域。CTA图像是先向身体注射造影剂，再使用CT设备对人体进行扫描后获得。由于造影剂的密度高于或低于身体组织密度，可以使得被注射位置的器官或组织在图像上更加突出的显示。
在本公开实施例中，血管的钙化区域较正常血管的密度明显增大，所以使用CT图像能够容易辨别钙化区域的位置。不过，CT图像上不能清楚显示血管的特征，以达到医学判断的需求。CTA图像可以很好的弥补这一缺陷，经过造影剂增强后，冠脉血管可以在图像上非常清晰的显示出来。因此，选取同一目标对象的CT图像和CTA图像作为待处理图像数据。
在一种可能的实施方式中，所述对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系，包括：确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域；对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
基于前文所述，CT图像上，心脏区域是主要处理对象；CTA图像上，冠脉血管是主要处理对象。所以，心脏区域和冠脉血管区域作为图像特征可以分别被提取出来进行后续操作。之后，再用心脏区域的图像和冠脉血管区域的图像进行配准，可以缩小用于图像配准的区域，提高配准精度。
在一种可能的实施方式中，所述对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系，包括：确定所述冠脉血管图像中的第一矩形区域，所述第一矩形区域包括包含所述冠脉血管区域的最小矩形区域；确定所述心脏图像中的第二矩形区域，所述第二矩形区域包括包含所述心脏区域的最小矩形区域；对所述第一矩形区域和所述第二矩形区域进行图像配准，得到第一矩形区域和第二矩形区域中同名像点的对应关系。
心脏图像中的心脏区域是心脏图像中包含表示心脏像素点的最小矩形区域。确定心脏区域时，可以在心脏图像上，找到表示心脏像素点在各坐标轴上的最大值和最小值。然后，用这个最大值和最小值确定的平面对心脏图像进行切割，获得心脏区域。例如，心脏图像中表示心脏的像素在各个轴上的最大值坐标为(x max，y max，z max)，最小值坐标为(x min，y min，z min)。以x轴为例，做包含x轴最大值坐标(x max，0，0)且平行于平面yoz的平面α max；然后，做包含x轴最小值坐标(x min，0，0)且平行于平面yoz的平面α min。同理，可以得到包含y轴最大值坐标且平行于平面xoz的平面β max；包含y轴最小值坐标且平行于平面xoz的平面β min；以及，包含z轴最大值坐标且平行于平面xoy的平面γ max；包含z轴最小值坐标且平行于平面xoy的平面γ min。使用α max、α min、α min、β min、γ max、γ min这些平面将图像切割，可以获得心脏区域。
冠脉血管区域的确定方法与心脏区域的确定类似。可以在冠脉血管图像上找到表示冠脉血管像素点在各坐标轴上的最大值和最小值。之后，用这个最大值和最小值确定的平面对冠脉血管图像进行切割，获得冠脉血管区域。具体方法不再赘述。
使用心脏区域和冠脉血管区域进行配准，既保留了必要的图像特征，又缩小了待配准的图像范围，所以，在提高了图像配准效率的同时，也提高了图像配准精度。同时，提高了两图中的同名像点对应关系的准确度。
在一种可能的实施方式中，所述第一钙化区域，包括：所述心脏图像上CT值大于130Hu的体素所在的区域。
本公开实施例中的方法能够提高确定冠脉分支中钙化区域的准确率。钙化区域的准确判定能够准确计算冠脉钙化积分，进而准确确定冠脉钙化情况。
冠脉钙化积分是以CT值确定冠状动脉钙化的程度与范围。它是反映心脏风险的指标；一般情况下，冠脉钙化积分是大于1mm 2或1像素的钙化区域面积乘以其最大密度加权系数后的和。其中，加权系数的取值可以为：密度小于130Hu时，加权系数为0；密度取值为130～199Hu时，加权系数为1；密度取值为200～299Hu时，加权系数为2；密度取值为300～399Hu时，加权系数为3，密度取值大于400时，加权系数为4。即在图像中，在冠脉血管的位置上出现的密度大于等于130的像素或区域，将被确定为钙化区域。因此，第一钙化区域的选取阈值为大于130Hu。
在一种可能的实施方式中，所述确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域，包括：根据所述对应关系，将所述第一钙化区域叠加到所述冠脉血管图上的目标区域，所述目标区域为所述第一钙化区域的同名像点所在的区域；将所述第一钙化区域中与所述冠脉血管区域重叠的区域，作为目标钙化区域。
可以根据前述同名像点对应关系，对第一钙化区域进行平移、旋转、缩放等操作，将第一钙化区域叠加到冠脉血管图像上，分析图像上第一钙化区域与冠脉血管的空间位置关系。选取与冠脉血管有重叠关系的第一钙化区域，作为目标钙化区域，目标钙化区域可以用于计算钙化积分。
在一种可能的实施方式中，所述冠脉血管图像为剔除血管体积值最大的血管后，得到的冠脉血管图像，所述对所述冠脉血管图像和所述心脏图像进行图像配准之前，还包括：统计所述冠脉血管图像中各血管体积值；在所述冠脉血管图像中，去除所述血管体积值最大的血管。
在冠脉血管图像中，通过统计各个血管的体积值，可以准确辨别其中体积最大的冠脉血管主干。目标钙化区域分布于冠脉分支上，将冠脉主干剔除后再进行后续图像处理，可以减少不必要的图像特征，提高处理效率；而且，可以避免误将冠脉血管主干上的钙化区域确定为目标钙化区域，提高了目标钙化区域判定的准确性。
本公开实施例提供的图像处理方法可以基于神经网络来实现，所述神经网络包括：第一提取网络、第二提取网络和图像配准网络，在一种可能的实施方式中，所述确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域，包括：将所述CTA图像输入第一提取网络，确定所述冠脉血管区域；将所述CT图像输入第二提取网络，确定所述心脏区域；将所述冠脉血管区域和所述心脏区域输入所述图像配准网络中，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
合理的图像尺寸能够有利于神经网络分析效率提升。本公开实施例中，可以利用固定窗口来控制神经网络输入数据的图像尺寸。例如选取256×256×256像素的矩形，作为图像固定窗口，并保存此固定窗口的中心点。然后，根据前述表示心脏(或冠脉血管)的像素的最大值坐标和最小值坐标分别计算每个轴的中心值(x cent，y cent，z cent)，以此点作为图像中心点。计算公式如下：
Figure PCTCN2021122320-appb-000001
Figure PCTCN2021122320-appb-000002
Figure PCTCN2021122320-appb-000003
将图像输入到神经网络时，满足图像中心点与所述固定窗口中心点匹配即可。
在本公开实施例中，图像配准网络可以是深度神经网络，可以包括多个子网络，其中第一子网络和第二子网络用于图像特征提取。本图像配准网络可以包括卷积层、池化层、采样层、向上卷积层等一个或多个网络层，本公开不对具体的神经网络结构进行限制。
对神经网络预设内部参数，使用训练样本进行训练，利用预测结果与样本标签的差异，对于神经网络进行调参优化，直至所述差异满足预设的阈值要求，结束训练。经过训练后的神经网络，可以提高图像特征选取、图像配准的准确性和效率。
对于神经网络的具体预测过程，此处不做赘述。可以理解的是，本公开实施例中的“第一”和“第二”用于区分所描述的对象，而不应当理解为对描述对象的次序等其它限定。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可 以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
此外，本公开还提供了图像处理装置、电子设备、计算机可读存储介质、程序，上述均可用来实现本公开提供的任一种图像处理方法，相应技术方案和描述和参见方法部分的相应记载，不再赘述。
图2示出根据本公开实施例的图像处理装置的框图，如图2所示，所述装置20包括：
输入单元21，用于获取冠脉血管图像和心脏图像；
配准单元22，用于对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；
第一区域判定单元23，用于确定所述心脏图像上的至少一个第一钙化区域；
目标区域确定单元24，用于确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
在一种可能的实现方式中，所述冠脉血管图像为计算机体层血管成像CTA图像，所述心脏图像为计算机体层成像CT图像；所述CTA图像和所述CT图像为对同一目标对象进行图像采集得到的。
在一种可能的实现方式中，所述配准单元22，包括：
特征提取子单元，用于确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域。
第一配准子单元，用于对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
在一种可能的实现方式中，所述第一配准子单元，包括：
第一区域划分子单元，用于确定所述冠脉血管图像中的第一矩形区域，所述第一矩形区域包括包含所述冠脉血管区域的最小矩形区域；
第二区域划分子单元，用于确定所述心脏图像中的第二矩形区域，所述第二矩形区域包括包含所述心脏区域的最小矩形区域；
第二配准子单元，用于对所述第一矩形区域和所述第二矩形区域进行图像配准，得到第一矩形区域和第二矩形区域中同名像点的对应关系。
在一种可能的实现方式中，所述第一钙化区域，包括：所述心脏图像上CT值大于130Hu的体素所在的区域。
在一种可能的实现方式中，所述目标区域确定单元24，包括：
图形叠加单元，用于根据所述对应关系，将所述第一钙化区域叠加到所述冠脉血管图上的目标区域，所述目标区域为所述第一钙化区域的同名像点所在的区域；
目标区域确定子单元，用于将所述第一钙化区域中与所述冠脉血管区域重叠的区域，作为目标钙化区域。
在一种可能的实现方式中，所述图像处理装置，还包括：
统计单元，用于统计所述冠脉血管图像中各血管体积值；
要素去除单元，用于在所述冠脉血管图像中，去除所述血管体积值最大的血管。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
本公开实施例还提供了一种计算机程序产品，包括计算机可读代码，当计算机可读代码在设备上运行时，设备中的处理器执行用于实现如上任一实施例提供的图像处理方法的指令。
本公开实施例还提供了另一种计算机程序产品，用于存储计算机可读指令，指令被执行时使得计算机执行上述任一实施例提供的图像处理方法的操作。
电子设备可以被提供为终端、服务器或其它形态的设备。
图3示出根据本公开实施例的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等终端。
参照图3，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理系 统，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如互补金属氧化物半导体(CMOS)或电荷耦合装置(CCD)图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如无线网络(WiFi)，第二代移动通信技术(2G)或第三代移动通信技术(3G)，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可 编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
图4示出根据本公开实施例的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器。参照图4，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作系统，例如微软服务器操作系统(Windows Server TM)，苹果公司推出的基于图形用户界面操作系统(Mac OS X TM)，多用户多进程的计算机操作系统(Unix TM)，自由和开放原代码的类Unix操作系统(Linux TM)，开放原代码的类Unix操作系统(FreeBSD TM)或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是(但不限于)电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处 理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言一诸如Smalltalk、C++等，以及常规的过程式编程语言一诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络一包括局域网(LAN)或广域网(WAN)一连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中，所述计算机程序产品具体体现为计算机存储介质，在另一个可选实施例中，计算机程序产品具体体现为软件产品，例如软件开发包(Software Development Kit，SDK)等等。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (10)

  1. 一种图像处理方法，其特征在于，包括：
    获取冠脉血管图像和心脏图像；
    对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；
    确定所述心脏图像上的至少一个第一钙化区域；
    确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
  2. 根据权利要求1所述的方法，其特征在于，所述冠脉血管图像为计算机体层血管成像CTA图像，所述心脏图像为计算机体层成像CT图像；
    所述CTA图像和所述CT图像为对同一目标对象进行图像采集得到的。
  3. 根据权利要求1-2任一所述的方法，其特征在于，所述对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系，包括：
    确定所述冠脉血管图像中的冠脉血管区域，以及所述心脏图像中的心脏区域；
    对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系。
  4. 根据权利要求3所述方法，其特征在于，所述对所述冠脉血管区域和心脏区域进行图像配准，得到所述冠脉血管区域和心脏区域中同名像点的对应关系，包括：
    确定所述冠脉血管图像中的第一矩形区域，所述第一矩形区域包括包含所述冠脉血管区域的最小矩形区域；
    确定所述心脏图像中的第二矩形区域，所述第二矩形区域包括包含所述心脏区域的最小矩形区域；
    对所述第一矩形区域和所述第二矩形区域进行图像配准，得到第一矩形区域和第二矩形区域中同名像点的对应关系。
  5. 根据权利要求2所述的方法，其特征在于，所述第一钙化区域，包括：
    所述心脏图像上CT值大于130Hu的体素所在的区域。
  6. 根据权利要求1-5任一所述的方法，其特征在于，所述确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域，包括：
    根据所述对应关系，将所述第一钙化区域叠加到所述冠脉血管图上的目标区域，所述目标区域为所述第一钙化区域的同名像点所在的区域；
    将所述第一钙化区域中与所述冠脉血管区域重叠的区域，作为目标钙化区域。
  7. 根据权利要求1-6任一所述的方法，其特征在于，所述冠脉血管图像为剔除血管体积值最大的血管后，得到的冠脉血管图像；
    所述对所述冠脉血管图像和所述心脏图像进行图像配准之前，还包括：
    统计所述冠脉血管图像中各血管体积值；
    在所述冠脉血管图像中，去除所述血管体积值最大的血管。
  8. 一种图像处理装置，其特征在于，包括：
    输入单元，用于获取冠脉血管图像和心脏图像；
    配准单元，用于对所述冠脉血管图像和所述心脏图像进行图像配准，得到所述冠脉血管图像和所述心脏图像中至少一个同名像点的对应关系；
    第一区域确定单元，用于确定所述心脏图像上的至少一个第一钙化区域；
    目标区域确定单元，用于确定所述第一钙化区域中，与所述冠脉血管图像中的冠脉血管区域具备所述对应关系的目标钙化区域。
  9. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至7中任意一项所述的方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至7中任意一项所述的方法。
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