WO2020177126A1 - Procédé et système de traitement d'image, dispositif informatique, et support de stockage - Google Patents

Procédé et système de traitement d'image, dispositif informatique, et support de stockage Download PDF

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WO2020177126A1
WO2020177126A1 PCT/CN2019/077344 CN2019077344W WO2020177126A1 WO 2020177126 A1 WO2020177126 A1 WO 2020177126A1 CN 2019077344 W CN2019077344 W CN 2019077344W WO 2020177126 A1 WO2020177126 A1 WO 2020177126A1
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class
blood vessel
original image
voxel
probability
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PCT/CN2019/077344
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English (en)
Chinese (zh)
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张保昌
周寿军
李娜
吴宗翰
李迟迟
王澄
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深圳先进技术研究院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/10104—Positron emission tomography [PET]
    • 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 invention belongs to the technical field of image processing, and particularly relates to an image processing method, system, computing device and storage medium.
  • the prior Chinese patent (application publication number CN109102511A) relates to a method, system and electronic device for cerebrovascular segmentation. Its main realization is to perform multi-scale filtering and enhancement processing on the original image containing brain tissue to obtain the enhanced vascular feature image and corresponding The direction vector field of ; establish a finite mixture model and estimate the parameters of the finite mixture model to obtain the class conditional probability; calculate the initial mark field of the original image, and form the Markov random field from the initial mark field and the direction vector field; and then obtain the class prior Probability: Based on the class prior probability and class conditional probability, by maximizing the posterior probability and iterative conditional mode, the cerebrovascular segmentation result is obtained.
  • the purpose of the present invention is to provide an image processing method, system, computing device and storage medium, aiming to solve the problem that the accuracy of cerebrovascular segmentation cannot be effectively improved due to the inaccurate fitting of the vascular tissue distribution interval in the prior art The problem.
  • the present invention provides an image processing method, which includes the following steps:
  • the fitting model is used to fit the gray histogram, and the fitting model is used to simulate the cerebrovascular Distribution or construction of the distribution function of the background distribution;
  • the parameters are iteratively updated.
  • the parameters that are the current iterative update target consist of: the label information corresponding to the voxel that has been first marked and the The unlabeled information corresponding to the first labeled voxel is constructed, the unlabeled information is constructed from the class posterior probability as the update result of the previous iteration, and the class posterior probability is constructed from the distribution function and corresponds to Background class and cerebrovascular class;
  • the class posterior probability corresponding to the voxel perform a second mark on the voxel to indicate that the voxel belongs to the background class or the cerebrovascular class, and obtain the class conditional probability;
  • the marker field is updated by maximizing the corresponding posterior probability to obtain the cerebrovascular segmentation result.
  • obtaining a blood vessel feature map from the original image specifically includes:
  • the multi-scale blood vessel enhancement value is converted into a blood vessel feature value with a meaning of blood vessel prediction probability, and the blood vessel feature map is composed of the blood vessel feature value.
  • determining the brain tissue area from the original image specifically includes:
  • the signal-to-noise ratio enhancement processing is performed on the original image to obtain the brain tissue region, and the signal-to-noise ratio enhancement processing includes decranial processing.
  • the clustering algorithm is a K-means clustering algorithm.
  • the fitting model is a Gaussian mixture model
  • the distribution function is a Gaussian distribution function
  • the two-point potential clump function is used to obtain the energy representation of the voxel to construct the Markov random field.
  • the two-point potential clump function is derived from the mark field obtained by the second mark and the result The construction of the vascular feature map.
  • the marker field is updated to obtain the cerebrovascular segmentation result, specifically:
  • the Bayesian criterion is used Calculate the posterior probability and maximize the posterior probability to update the second marking result of the voxel, thereby updating the marking field, and obtaining the cerebrovascular segmentation result, where N is an integer.
  • the present invention provides an image processing system, which includes:
  • a preprocessing unit for obtaining an original image containing brain tissue; determining a brain tissue area from the original image;
  • the initialization unit is used to use a clustering algorithm to process the gray histogram corresponding to the brain tissue region to obtain a preliminary classification result for preliminarily distinguishing the cerebrovascular and background in the brain tissue region;
  • Preliminary classification results initialize the parameters of the preset fitting model, the fitting model is used to fit the gray histogram, the fitting model is used to simulate the distribution of the cerebral blood vessels or Construction of the distribution function of the background distribution; window width and window level transformation analysis is performed on the original image to obtain voxels in the original image that meet the preset threshold requirements, and perform first marking on these voxels, so The threshold is used to distinguish the background and the cerebrovascular;
  • a feature map calculation unit for obtaining a blood vessel feature map from the original image
  • the segmentation unit is configured to iteratively update the parameters based on a preset iterative update model.
  • the parameter that is the current iterative update target corresponds to the voxel that has been first marked
  • the unlabeled information is constructed from the class posterior probability as the update result of the previous iteration
  • the class posterior probability is constructed from the distribution Function is constructed and corresponds to the background class and the cerebrovascular class; according to the posterior probability of the class corresponding to the voxel, a second label is performed on the voxel to indicate that the voxel belongs to the background class or the Cerebrovascular class, and obtain class conditional probability; combine the marker field obtained from the second label and the blood vessel feature map to construct a Markov random field; construct class prior probability from the Markov random field; based on For the class conditional probability and the class prior probability, in the iterative conditional mode, by
  • the present invention also provides a computing device including a memory and a processor, and the processor implements the steps in the above method when the processor executes the computer program stored in the memory.
  • the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above method are realized.
  • the present invention mainly uses the fitting model to fit the brain tissue area in the original image, and uses the iterative update model to iteratively update the parameters of the fitting model.
  • the marked information and unmarked information generated by the first mark are fully utilized Information, use semi-supervised parameter update to learn the parameters of the fitting model, so that the distribution curve of the fitting model is constantly approaching the gray histogram of the brain tissue area, so that the blood vessel tissue distribution interval can be accurately fitted, thereby improving the brain The accuracy of blood vessel segmentation.
  • FIG. 1 is an implementation flowchart of an image processing method provided by Embodiment 1 of the present invention.
  • Figure 2 is a schematic diagram of FSL-BET processing in the first embodiment of the present invention.
  • Fig. 3 is a statistical histogram of midbrain tissue regions in Example 1 of the present invention.
  • step S108 is a detailed flowchart of step S108 in the second embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an image processing system provided by Embodiment 3 of the present invention.
  • FIG. 6 is a schematic structural diagram of a computing device provided in Embodiment 4 of the present invention.
  • Figure 1 shows the implementation flow of the image processing method provided in the first embodiment of the present invention, which can mainly realize the accurate segmentation of cerebrovascular tissue from the image. For ease of description, only the parts related to the embodiment of the present invention are shown. As follows:
  • step S101 an original image containing brain tissue is obtained.
  • the original image may be a Time of Flight-Magnetic Resonance Angiography (TOF-MRA) image, or other imaging images, such as a computer tomography (Computed Tomography, CT) image Or Positron Emission Computed Tomography (PET) images, etc.
  • TOF-MRA Time of Flight-Magnetic Resonance Angiography
  • CT computer tomography
  • PET Positron Emission Computed Tomography
  • step S102 the brain tissue area is determined from the original image.
  • the original image contains not only the brain tissue area, but also skull, eye tissue, background noise, etc.
  • the subsequent processing is still performed on the original image, the signal-to-noise ratio of the blood vessel is reduced in disguised form, which is not conducive to improvement. Accuracy and precision of subsequent processing.
  • the brain extraction tool (FSL Brain Extraction Tool, FSL-BET) in the medical processing tool FSL can be used to increase the signal-to-noise ratio of the original image to obtain the brain tissue area and the signal-to-noise ratio increase processing It includes decranial processing, eye tissue removal, background noise removal, etc., so as to improve the signal-to-noise ratio of blood vessels, reduce calculation costs, and facilitate more accurate extraction of cerebral blood vessels.
  • step S103 a clustering algorithm is used to process the gray histogram corresponding to the brain tissue region to obtain a preliminary classification result for preliminarily distinguishing the cerebral blood vessels and background in the brain tissue region.
  • the determined brain tissue area can be roughly divided into three gray scale steps: one is cerebrospinal fluid and lateral ventricle, the second is gray matter and white matter, and the third is cerebrovascular.
  • the cerebrospinal fluid and lateral ventricles are all called background.
  • Figure 3 can clearly indicate the approximate distribution range of gray matter and white matter.
  • the left and right sides of the peak are the cerebrospinal fluid and lateral ventricle areas, as well as the cerebrovascular area.
  • the K-MEANS clustering algorithm can be used, the number of clusters is set to 3, and the initial cluster centers are 1/4 point of the peak and valley, peak and valley point, and twice the point of the peak and valley point.
  • clustering algorithms such as: K-MEDOIDS algorithm, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm using hierarchical methods Wait.
  • step S104 the parameters of the preset fitting model are initialized according to the preliminary classification results.
  • the fitting model is used to fit the gray histogram, and the fitting model is used to simulate the distribution of cerebral blood vessels or the background distribution. Distribution function construction.
  • the preset fitting model is a finite mixture model. It is found by testing the fitting effect of various probability density functions in the brain tissue region: Gaussian Mixture Model (GMM) composed of three Gaussian distributions ) It is better to fit the gray distribution of the brain tissue area, so the preferred fitting model is GMM, that is, three Gaussian distributions are used to respectively fit the regions corresponding to the above three gray-scale steps, that is, the first Gaussian distribution simulation Cerebrospinal fluid and lateral ventricle areas, the second Gaussian distribution simulates the gray matter and white matter areas, the third Gaussian distribution simulates the cerebrovascular area, and the proportion of each type of data in the three types of data after clustering w, mean u and The variance ⁇ is used as the initial parameter of GMM.
  • GMM Gaussian Mixture Model
  • I is the gray value
  • Gi is the Gaussian distribution indicator information
  • f Gi is the Gaussian distribution function
  • f G3 (I
  • step S105 perform window width and window level transformation analysis on the original image to obtain voxels in the original image that meet the preset threshold requirements, and perform first labeling on these voxels.
  • the threshold is used to distinguish background and cerebrovascular .
  • the imtool in the Matlab tool can be called to adjust the window width and window level of the original image, and set a tentative threshold for segmentation.
  • the voxels in the original image meet a certain threshold requirement, the voxels can be first marked to indicate that they are initially judged as background. If they meet another threshold requirement, the voxels can be first marked. A marker, which indicates that it was initially judged to be a cerebral blood vessel. There are still voxels that are not first marked in the original image.
  • the parameters are iteratively updated based on the preset iterative update model.
  • the parameters that are the current iterative update target are: the marking information corresponding to the voxel that has been first marked and the first marking has not been performed.
  • the unlabeled information corresponding to the labeled voxel is constructed.
  • the unlabeled information is constructed by the class posterior probability as the update result of the previous iteration.
  • the class posterior probability is constructed by the distribution function and corresponds to the background class and the cerebrovascular class.
  • the iterative update model may be as follows:
  • I j is the gray value of the j-th pixel
  • N( ⁇ ) is the number of voxels in the corresponding area
  • D li is the data belonging to the i-th distribution component
  • k is the number of iterations
  • I j ] k is the class posterior probability of the previous iteration update result.
  • the parameter as the update target of the current iteration is constructed by: the labeled information corresponding to the voxels that have been first labeled and the unlabeled information corresponding to the voxels that have not been first labeled.
  • the construction of probability probability includes:
  • [u i] k + 1 is constructed of unlabeled information flag information, the flag information comprising: I j, I j ⁇ D li and N (D li), without tag information includes: p [G i
  • labeled information includes: I j ,I j ⁇ D li and N(D li ), while unlabeled information includes: p[G i
  • [w i] k + 1 is constructed of unlabeled information flag information, the flag information comprising: N (D li), without tag information includes: p [G i
  • I j ) is constructed by the distribution function, specifically:
  • * may take the value of G 1 , G 2 , G 3 , which is one of the three categories, and ⁇ is the general term for the parameters in each Gaussian distribution, which includes u i , ⁇ i .
  • step S107 according to the class posterior probability corresponding to the voxel, a second mark is performed on the voxel to indicate that the voxel belongs to the background class or the cerebrovascular class, and the class conditional probability is obtained.
  • the body can be The voxel is judged to be the cerebrovascular class (L v ), otherwise it is the background class (L B ), so that the voxel can be second marked to indicate whether the voxel belongs to the cerebrovascular class or the background class, forming an initial label field L 0 .
  • l i ) is f Gi (I
  • the blood vessel feature map is obtained from the original image.
  • the blood vessel feature map can be formed by the gray value of each point of the original image.
  • step S109 the marker field obtained from the second marker and the blood vessel feature map are combined to construct a Markov random field.
  • the domain system of voxels can be defined in the brain tissue region, and the domain system can be a 6-neighbor system;
  • the two-point potential clique function is constructed from the marker field obtained by the second marker and the blood vessel feature map, where:
  • N i 6 neighborhood system of voxels in the i point.
  • ⁇ 1, ⁇ 2 are proportional coefficients
  • V f (i) is the blood vessel score at the i-th voxel in the blood vessel feature map.
  • step S110 the class prior probability P(l i ) is constructed from the Markov random field, which can be specifically:
  • k is the traversal index
  • l k is the label of the k-th random voxel.
  • step S111 based on the class conditional probability and the class prior probability, in the iterative conditional mode, the marker field is updated by maximizing the corresponding posterior probability to obtain the cerebrovascular segmentation result.
  • l i ) can be used, which is equivalent to the aforementioned P(I
  • the fitting model is mainly used to fit the brain tissue area in the original image, and the iterative update model is used to iteratively update the parameters of the fitting model.
  • the label information generated by the first label is fully utilized
  • unlabeled information use semi-supervised parameter update to learn the parameters of the fitting model, so that the distribution curve of the fitting model is constantly approaching the gray histogram of the brain tissue area, so that the distribution interval of the blood vessel tissue can be accurately fitted, thereby Improved the accuracy of cerebrovascular segmentation.
  • the calculation process is performed on the brain tissue area after the skull is removed, which greatly eliminates many irrelevant tissues, improves the signal-to-noise ratio of blood vessels, and reduces the computational cost.
  • this embodiment further provides the following content:
  • step S108 specifically includes:
  • step S401 a multi-scale filter enhancement process is performed on the original image to obtain a primary feature map composed of multi-scale blood vessel enhancement values.
  • the multi-scale filtering technique based on the Hessian matrix can be used to enhance the tubular target in the data.
  • the original image data I and the multi-scale Gaussian kernel are convolved.
  • the point i with coordinates (x, y, z) has a gray value of I ⁇ (i )
  • the corresponding Hessian matrix is calculated as follows:
  • v 3 is redefined at each convolution scale as:
  • ⁇ is a threshold between 0 and 1
  • ⁇ is the filter scale.
  • the enhanced response is calculated by the following vessel response function:
  • step S402 under the threshold constraint condition constructed by the intracranial proportion of blood vessels, the multi-scale blood vessel enhancement value is converted into blood vessel feature values with the significance of blood vessel prediction probability, and the blood vessel feature map is composed of blood vessel feature values.
  • the multi-scale vessel enhancement value v can be transformed as follows:
  • V p ′ represents the collection of vascular enhancement values in the brain tissue region
  • ⁇ is the intracranial proportion of blood vessels (this value is equal to the Gaussian distribution weight w 3 corresponding to the blood vessels in GMM
  • ⁇ ( ⁇ ) is determined by the intracranial proportion of blood vessels.
  • the constructed threshold, the blood vessel characteristic value is V f .
  • the result of multi-scale blood vessel enhancement can be transformed to obtain the characteristic value of the blood vessel so that it has the significance of blood vessel prediction probability and embed it in the Markov random field, which is beneficial to better optimize the segmentation of GMM As a result, high-quality cerebrovascular segmentation is achieved.
  • FIG. 5 shows the structure of the image processing system provided by the third embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown, including:
  • the preprocessing unit 501 is configured to obtain an original image containing brain tissue; determine the brain tissue area from the original image;
  • the initialization unit 502 is configured to use a clustering algorithm to process the gray histogram corresponding to the brain tissue area to obtain a preliminary classification result for preliminarily distinguishing the cerebrovascular and background in the brain tissue area; According to the preliminary classification result, the parameters of the preset fitting model are initialized, the fitting model is used to fit the gray histogram, and the fitting model is used to simulate the distribution of the cerebral blood vessels or Constructing the distribution function of the background distribution; performing window width and window level transformation analysis on the original image to obtain voxels in the original image that meet the preset threshold requirements, and perform first marking on these voxels, The threshold is used to distinguish the background and the cerebral blood vessel;
  • the feature map calculation unit 503 is configured to obtain a blood vessel feature map from the original image.
  • the segmentation unit 504 is configured to iteratively update the parameters based on a preset iterative update model.
  • the parameter that is the current iterative update target is composed of: the voxel that has been first marked The corresponding labeled information and the unlabeled information corresponding to the voxels that are not first labeled are constructed.
  • the unlabeled information is constructed from the class posterior probability as the update result of the previous iteration, and the class posterior probability is constructed by the
  • the distribution function is constructed and corresponds to the background class and the cerebrovascular class; according to the posterior probability of the class corresponding to the voxel, a second label is performed on the voxel to indicate that the voxel belongs to the background class or Describe the cerebrovascular class and obtain the class conditional probability; combine the marker field obtained by the second marker and the blood vessel feature map to construct a Markov random field; construct a class prior probability from the Markov random field; Based on the class conditional probability and the class prior probability, in the iterative conditional mode, the marker field is updated by maximizing the corresponding posterior probability to obtain the cerebrovascular segmentation result.
  • each unit of the image processing system can be implemented by a corresponding hardware or software unit.
  • Each unit can be an independent software and hardware unit, or can be integrated into a software and hardware unit.
  • the present invention is not limited here. .
  • FIG. 6 shows the structure of the computing device provided in the fourth embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown.
  • the computing device in the embodiment of the present invention includes a processor 601 and a memory 602.
  • the processor 601 implements the steps in the foregoing method embodiments when executing the computer program 603 stored in the memory 602, such as steps S101 to S111 shown in FIG.
  • the processor 601 executes the computer program 603
  • the functions of the units in the foregoing device embodiments such as the functions of the units 501 to 504 shown in FIG. 5, are implemented.
  • the computing device in the embodiment of the present invention may be a single computer, or a computer network, or a single processing chip, or a chipset.
  • the processor 601 in the computing device executes the computer program 603 to implement the foregoing methods, reference may be made to the description of the foregoing method embodiments, which will not be repeated here.
  • a computer-readable storage medium stores a computer program, and the computer program implements the steps in the foregoing method embodiments when executed by a processor, for example, as shown in FIG. 1 Steps S101 to S111 are shown. Or, when the computer program is executed by the processor, the functions of the units in the foregoing system embodiments, such as the functions of the units 501 to 504 shown in FIG. 5, are realized.
  • the computer-readable storage medium in the embodiment of the present invention may include any entity or device or recording medium capable of carrying computer program code, such as ROM/RAM, magnetic disk, optical disk, flash memory and other memories.
  • the present invention has been verified on the public data set MIDAS, which contains 109 sets of TOF-MRA clinical data.
  • MIDAS public data set
  • Table 1 The results of the four evaluation measures of the three methods are given.
  • TP, FP, TN, and FN are true cases, false positive cases, true negative cases, and false negative cases, respectively.

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Abstract

La présente invention peut être appliquée au domaine technique des ordinateurs et concerne un procédé et un système de traitement d'image, un dispositif informatique et un support de stockage. Ledit procédé comprend la réalisation d'un ajustement sur une région de tissu cérébral dans une image d'origine à l'aide principalement d'un modèle d'ajustement, la réalisation d'une mise à jour d'itération sur des paramètres du modèle d'ajustement à l'aide d'un modèle de mise à jour d'itération, et dans le processus de mise à jour d'itération, l'utilisation totale d'informations d'étiquette et d'informations de non-étiquette générées par une première étiquette, et l'apprentissage des paramètres du modèle d'ajustement à l'aide d'une mise à jour de paramètre semi-supervisé, de telle sorte qu'une courbe de distribution du modèle d'ajustement s'approche en continu d'un histogramme de niveau de gris de la région de tissu cérébral. De cette manière, l'intervalle de distribution de tissu vasculaire peut être ajusté avec précision, ce qui permet d'améliorer la précision de la segmentation de vaisseau sanguin cérébral.
PCT/CN2019/077344 2019-03-07 2019-03-07 Procédé et système de traitement d'image, dispositif informatique, et support de stockage WO2020177126A1 (fr)

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