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WO1997009690A1 - Criblage dimensionnel de donnees et connectivite floue pour analyse d'image d'irm - Google Patents

Criblage dimensionnel de donnees et connectivite floue pour analyse d'image d'irm Download PDF

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Publication number
WO1997009690A1
WO1997009690A1 PCT/US1996/014500 US9614500W WO9709690A1 WO 1997009690 A1 WO1997009690 A1 WO 1997009690A1 US 9614500 W US9614500 W US 9614500W WO 9709690 A1 WO9709690 A1 WO 9709690A1
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Prior art keywords
dimensional
anatomical structure
connectivity
features
fuzzy
Prior art date
Application number
PCT/US1996/014500
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English (en)
Inventor
Wolfgang Frederick Kraske
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Northrop Grumman Corporation
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Filing date
Publication date
Application filed by Northrop Grumman Corporation filed Critical Northrop Grumman Corporation
Priority to AU69174/96A priority Critical patent/AU6917496A/en
Publication of WO1997009690A1 publication Critical patent/WO1997009690A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/64—Analysis of geometric attributes of convexity or concavity
    • 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
    • G06T7/155—Segmentation; Edge detection involving morphological operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • 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
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20036—Morphological image processing
    • G06T2207/20044—Skeletonization; Medial axis transform
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20101—Interactive definition of point of interest, landmark or seed
    • 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
    • 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
    • G06T2207/30016—Brain

Definitions

  • the present invention relates generally to medical imaging systems and digital signal processing. It relates more particularly to the use of data dimensional sieving and fuzzy connectivity to facilitate analysis and review of three-dimensional medical images such as those produced by magnetic resonance imaging (MRI) devices and the like.
  • MRI magnetic resonance imaging
  • Tomographic imaging techniques for use in medical applications are well known. Examples of such techniques include magnetic resonance imaging (MRI), computer aided tomography (CAT), and positron emission tomography (PET). In each of these techniques, a plurality of cross-sectional two-dimensional images, i.e., slices, of a body portion are generated and processed so as to provide a three-dimensional model of the imaged body portion.
  • MRI magnetic resonance imaging
  • CAT computer aided tomography
  • PET positron emission tomography
  • slices or images taken along planes of interest are generated and then images are printed or otherwise displayed for viewing.
  • the slices viewed may be at various angles with respect to the three-dimensional model. They do not necessarily correspond to the angles of the slides from which the three-dimensional model was originally constructed.
  • the medical diagnostition may review images taken along any desired plane within the three-dimensional model. This provides for a great deal of flexibility in the use of the three-dimensional model as a diagnostic tool.
  • vascular system For example, viewing delicate portions of the vascular system is typically difficult since veins, arteries, and capillaries are intermixed with surrounding tissue. This makes it very difficult to distinguish the desired portions of the vascular system from surrounding tissue. Often, only slight changes in the intensity of the image distinguish a desired anatomical structure from surrounding tissue.
  • veins and arteries are characterized as one-dimensional curvilinear forms, while capillaries exhibit one plus fractal dimensions, typically exhibiting fractional fractal dimensionality.
  • Tumors have three-dimensional fractal forms and exhibit smaller fractal dimensions if metastases are considered.
  • the present invention specifically addresses and alleviates the above-mentioned deficiencies with the prior art. More particularly, the present invention comprises a method for isolating anatomical structures contained within a three-dimensional data set, e.g., a three-dimensional model formed by MRI, a CAT scan, or a PET scan.
  • the method comprises the steps of forming a morphological skeleton of the three-dimensional data set, selecting a seed data point within the morphological skeleton so as to identify a desired anatomical structure to be displayed or analyzed, and utilizing fuzzy connectivity to define additional data points of the desired anatomical structure so as to reconstruct substantially only the desired anatomical structure. Reconstruction of substantially only the desired anatomical structure facilitates the review and analysis of the anatomical structure.
  • the patient's head may be imaged via MRI, CAT, PET scanning techniques or the like to provide a three-dimensional model of substantially the entire head.
  • the three-dimensional data set which defines this model is then processed so as to form a morphological skeleton thereof.
  • An operator selects a seed data point within the morphological skeleton corresponding to the patient's brain. This is typically accomplished by viewing the morphological skeleton on a display such as a CRT.
  • the morphological skeleton maintains all of the data available in the original three-dimensional data set.
  • anatomical structures are separated from one another, based upon the fractal dimensionality thereof.
  • anatomical structures having a fractal dimensionality of less than one dimension are separated from those having a fractal dimensionality of less than two dimensions and the anatomical structures are separated from those having a fractal dimensionality of less than three dimensions.
  • fuzzy connectivity is utilized to define the additional data points which are required to provide a substantially complete image of the brain. Reconstruction of the brain is simply the reverse of the process utilized to form the morphological skeleton. With the use of fuzzy connectivity to define the set of points defining the brain, it appears that all of the features thereof are substantially utilized in the reconstruction process. Reconstruction of the brain without the use of fuzzy connectivity would result in the loss of substantial surface details thereof. For example, the surface texture and even, to a lesser degree, the convolutions of the brain, would tend to be degraded or smoothed.
  • the morphological skeleton is formed by recursive opening and erosion of the three-dimensional data set so as to form a plurality of residuals which define the morphological skeleton.
  • Reconstructing a desired anatomic structure from the morphological skeleton comprises performing the opposite procedure from that utilized to form the morphological skeleton.
  • reconstruction comprises recursive dilation and closing of the morphological skeleton.
  • each step of opening comprises an erosion followed by a dilation and each step of the closing comprises a dilation followed by an erosion.
  • fuzzy connectivity assures that substantially all of the data points associated with the desired anatomical structure are utilized in the reconstruction process.
  • a generally spherical structuring element is utilized in both the formation of the morphological skeleton and the reconstruction process.
  • various other shapes of structuring elements are likewise suitable. Indeed, it has been found that various different shapes of structuring elements are particularly suited for use with various different dimensionalities or shapes of anatomical structures.
  • the seed data point is selected by positioning a cursor at a desired point on an image being displayed upon a monitor.
  • the operator may simply visually identify and manually select a seed within the organ or anatomical structure of interest.
  • various different computer algorithms may be utilized in the selection of such a seed. For example, the operator may simply initiate an algorithm which selects the largest organ within a given volume. Thus, if the operator desires to select the brain for reconstruction, the operator could merely select the largest organ within the head.
  • a cascade of data dimensional sieving filters are used directly with a three-dimensional image from an MRI device or the like to isolate structures such as arteries and veins from surrounding tissue for unobstructed visualization.
  • This cascade of data dimensional sieving filters comprises the use of a generally spherical structuring element, followed by the use of a two-dimensional surface structuring element, followed by the use of a curvilinear structuring element, followed by the use of a point structuring element.
  • a data dimensional sieving algorithm separates the data based upon the dimensional characteristics of the anatomical structures contained therein.
  • the algorithm utilizes filters which resemble geometric constructions such as lines, disks, and spheres, to sieve multi-dimensional features of curves, surfaces, and regions, as well as features of fractal dimensions in between.
  • a hierarchy of dimensional filters is thus utilized to first remove features of less than one fractal dimension, then to remove features of less than two fractal dimensions, and finally to remove features of less than three fractal dimensions from the original three-dimensional data set as the morphological skeleton is being formed.
  • the cascade of filters is used directly with a tomographic image to isolate anatomical structures from surrounding tissues to facilitate analysis and review thereof.
  • the morphological skeleton is formed. This process is ideal for processing data with fractal dimensional components.
  • the recursive formation of the morphological skeleton utilizing alternating opening and erosion transforms a 3.4 dimensional form into .4 dimensional data when a spherical structuring element is utilized.
  • fuzzy connectivity is utilized in the reconstruction of those anatomical structures of interest. Reconstruction of anatomical structures without utilizing fuzzy connectivity results in the loss of significant features such as surface textures and roughness. These features must be reconstructed from the residuals defining the morphological skeleton utilizing fuzzy connectivity. The reconstruction of such anatomical features requires the satisfaction of a fuzzy connectivity criteria such that only those tissue features connected to the dimensional features isolated by the sieving process are utilized.
  • the final result of both the sieving and fuzzy connectivity processes is a classification and clear visualization of the anatomical structures of interest, e.g., tissues and/or tumor pathologies. Additionally, quantification of the volume of organs and tumors as well as other measurements of interest, such as the diameter of arteries and veins, are easily facilitated as a direct result of the use of dimensional sieving and fuzzy connectivity.
  • Connectivity is a mathematical concept which states that a set of points is connected if and only if every pair of points in the set can be connected by a line which is contained within the set.
  • the algorithm described in this invention generalizes this concept of connectivity to the discrete topological grids utilized by a computer to store the digital image data by utilizing fuzzy set operators.
  • a fuzzy set is itself a generalization of a discrete set by defining a function over a set representing degrees of membership such that membership varies from zero which indicates no membership to one which indicates complete membership.
  • this algorithm utilizes a fuzzy generalization of mathematically defined distances between sets as a connectivity criterion. This criterion establishes that if two points or two sets of points are within a specified distance of one another, then they have membership to the same set of points.
  • the prior art attempted to isolate anatomical features from one another based solely upon the intensity of pixels within the three-dimensional data set.
  • the present invention facilitates the distinguishing or isolation of anatomical features based upon such criteria such as size. shape, and intensity of the anatomical feature. Thus, more flexibility in designating those features to isolate is provided and improved accuracy of such isolation is attained.
  • Figure 1 is an illustration of the recursive alternating opening and erosion processes for two dimensions utilized to define the residuals of which the morphological skeleton is constructed;
  • Figure 2 shows the two-dimensional structuring element utilized in the process for forming the morphological skeleton shown in Figure 1;
  • Figure 3 is a chart giving the results of utilizing structuring elements of different forms or dimensionalities upon images of different forms or dimensionalities;
  • Figure 4 shows a representative two-dimensional structuring element utilized in the fuzzy connectivity restructuring process wherein 2r is the major diameter thereof;
  • Figure 5 shows the use of the structuring element of Figure 4 to determine that two points belong to the same set, i.e., a set of data points defining a desired anatomical structure for reconstruction, the two points belong to the same set since when one of the points is located at the center of the structuring element, the other point falls within the bounds defined by the structuring element, wherein the dimension d defines the dimension between adjacent points such that the points fall within the set;
  • Figure 6 shows the use of the restructuring element of Figure 4 to iteratively determine that the set points illustrated are contained within a common set
  • Figure 7 shows the set support function which defines the degree of fuzzy membership for a given pair of points, which is determined by the modified Hausdorff metric for those points.
  • Figure 8 is a block diagram of the conventional morphological data decomposition and reconstruction processes
  • Figure 9 is a block diagram of the morphological data skeletonization process of the present invention.
  • Figure 10 is a block diagram of the morphological data decomposition and selective reconstruction processes of the present invention.
  • Figure 11 is a block diagram of the morphological data dimensional sieving decomposition and selective reconstruction processes utilizing a three-dimension example
  • Figure 12 is a block diagram of the morphological data reconstruction from skeleton process of the present invention.
  • Figure 13 is a block diagram of the morphological data decomposition and selective reconstruction process of the present invention.
  • FIG. 14 is a block diagram of the fuzzy logic process of the present invention. Detailed Description of the Preferred Embodiment
  • FIG. 1 the recursive development of a morphological skeleton utilizing alternating opening and erosion process is shown utilizing a two-dimensional geometric construction, i.e., a square, for purposes of illustration.
  • a two-dimensional example is provided herein, for purposes as illustration, those skilled in the art will appreciate that use of the present invention in medical imaging typically requires the recursive use of a three-dimensional structuring element such as a sphere, a two-dimensional structuring element such as a surface, a one-dimensional structuring element such as a curve, and a zero-dimensional structuring element, i.e., a point.
  • a square 101 having the corners removed therefrom is defined.
  • An octagon 100 as shown in Figure 2, is utilized as the structural element for this example.
  • the corners 102 are the residuals of the opening process for the original square.
  • additional erosion and opening process is performed, progressively smaller squares 102, 103, and 104 is formed.
  • additional residuals 102 are defined.
  • the square is completely eliminated and the collection of residuals defines the desired morphological skeleton 106.
  • d is limited to the integer domain Z of the data and ⁇ is equal to 1.
  • ⁇ defined as a single dilation step followed by a single erosion step.
  • This morphological skeleton contains all of the information contained in the original image.
  • the original image can be reconstructed from the morphological skeleton by reversing the recursive development process, i.e., by substituting dilation and closing for erosion and opening, respectively.
  • dilation and closing for erosion and opening, respectively.
  • data dimensional sieving is performed such that anatomical structures having various dimensionalities are separated from one another in a manner which isolates them and makes them identifiable via computational methodology.
  • those anatomical structures having a fractal dimensionality of less than one dimension are separated from those anatomical structures having a fractal dimensionality of less than two dimensions, both of which are separated from anatomical structures having a fractal dimensionality of less than three dimensions.
  • a desired anatomical structure which has been so isolated and identified can then be reconstructed by reversing the recursive morphological skeleton development sequence described above utilizing only the data points associated with the selected anatomical structure.
  • merely reconstructing the desired anatomical structure results in the loss of significant features such as surface textures and roughness.
  • fuzzy connectivity assures that all of the data points associated with the anatomical structure are utilized in the reconstruction process.
  • fuzzy connectivity defines the entire data set for the desired anatomical structure by utilizing a modified Hausdorff metric, wherein connectivity is defined by the size and shape of the structuring element.
  • the structuring element is first centered upon a seed pixel by the operator.
  • the seed pixel is one which the operator knows is a part of the anatomical structure for which reconstruction is desired. All other pixels contained within the volume defined by the structuring element are then considered to be a part of the anatomical structure being reconstructed. This process is then repeated for each new pixel within the data set until no additional new pixels are found.
  • many different sizes and shapes of structuring elements are suitable, those generally spherical in configuration are preferred.
  • a series of different structuring elements may be utilized in either of the formation of the morphological skeleton or the reconstruction process, as desired, so as to achieve a desired effect.
  • connectivity is a mathematical concept which states that a set of points is connected if and only if every pair of points in the set can be connected by a line contained in the set.
  • the algorithm described in this invention generalizes this concept of connectivity to the discrete topological grids of computers and digital image data with fuzzy set operators.
  • a fuzzy set is itself a generalization of a discrete set by defining a function over a set representing degrees of membership from no membership as represented by a zero to complete membership as represented by a one.
  • This algorithm utilizes convex fuzzy membership, as shown in Figure 7, functions defined over convex set supports.
  • this algorithm uses a fuzzy generalization of mathematically defined distances between sets as a connectivity criterion. This criterion establishes that if two points or two sets of points are within a specified distance of one another, then they have membership to the same set of points. To more precisely define this concept of connectivity, the neighborhood of points and the data must be defined.
  • convexity implies that a line fixed between any two points on the curve of the function must lie on or below the graph of the function:
  • a measure of distance between sets or points g, h can be defined. This metric is used to define points or sets within this distance to be fuzzy connected.
  • FIG. 3 a chart showing the result of utilizing a structuring element of a particular form or dimensionality on an image of a particular form or dimensionality is shown.
  • the chart includes structuring elements of point, segment, disk, and sphere form and images of point, curve, circles, and volume form.
  • utilizing a structuring element defined by a point yields a curve.
  • utilizing a segment in the processing of a curve yields a curve
  • utilizing a disk or sphere in the processing of a curve provides a null product, since a two-dimensional disk or a three-dimensional sphere cannot be utilized to process a one-dimensional curve.
  • the structuring element 200 shown comprises an ellipse having a major diameter of 2r.
  • Those skilled in the art will appreciate that various other shapes are likewise suitable for use as a structuring element.
  • FIG. 5 use of the structuring element to determine if two points are within a common set is shown. This is accomplished by placing the structuring element 202 around one of the points 210 of interest and then determining whether or not the second point of interest 212 lies within the boundary of the structuring element 202. As shown, the second point 212 does lie within the boundary of the first structuring element 202. In order to find additional points which are part of the common set of points, which define the anatomical structure of interest, this process is repeated by placing a structuring element 204 around the second point 212 in order to determine if any points lie within the boundary thereof.
  • this process is repeated so as to define all of the points which belong to a common set of data points which define the anatomical structure of interest.
  • Structuring element 202 formed about point 210 defines point 212 as being included within the data set
  • structuring element 204 formed about point 212 similarly defines point 210 as belonging to the common data set
  • structuring element 206 formed about point 212 defines point 214 as belonging to the common data set.
  • Each point so defined to be within the data set is assigned a fuzzy membership number between zero and one, depending upon the distance between adjacent points, as discussed above.
  • the set of all data points defining a particular anatomical structure of interest are defined such that surface details of the anatomical structure, such as surface smoothness thereof, are maintained during the reconstruction process and are thus included in the reconstructive anatomical structure.
  • an input data array 300 is skeletonized 302 so as to form skeleton 304.
  • Skeleton 304 is then reconstructed 306 so as to provide the original image 308. This process is used in various different data analysis, compression, and data signal processing applications.
  • Morphological data skeletonization is a recursive process wherein erode image n 320 subjected to erosion 322. The product of erosion is then subjected to dilation 323 and in parallel subjected to erosion 324. The product of erosion 324 is erode image n+1 326 which then becomes new erode image n 320 and is iteratively processed. The product of dilation 323 is subjected to subtraction 325 with respect to erode image in 320 so as to form skeleton 327 which is then subjected to addition with full skeleton 304.
  • Input data array 300 is subjected to skeletonization so as to form skeleton 304.
  • Skeleton 304 is used for the selection of a region of interest 310 so as to form edited skeleton 312.
  • Fuzzy connectivity 314 is applied to the edited skeleton 312 to form the edited image 316.
  • Input data array 300 is skeletonized 342 wherein a three-dimensional kernel or structuring element configured as a sphere, for example, is utilized in the skeletonization process.
  • the skeletonization 342 results in the formation of a skeleton 343 having less than three- dimensional features.
  • This skeleton is then subjected to skeletonization 344 utilizing a two-dimensional kernel or structuring element configured as a facette.
  • This two-dimensional skeletonization process 344 results in a skeleton having less than two-dimensional features 345.
  • This skeleton having less than two-dimensional features 345 is then subjected to skeletonization utilizing a one-dimensional kernel or structuring element 346 so as to provide a skeleton having less than one-dimensional features 304.
  • dilation 352 is performed so as to produce dilate image n 353
  • dilate image n 353 and skeleton n 354 are added 356 and the process is iterated by providing the added images as recon n 350.
  • Recon n 360 is subjected to dilation 364 so as to produce dilate image n 368 and seed image n 366.
  • Seed image n is subjected to fuzzy connectivity criteria 370 with skeleton n 362 so as to produce edited skeleton 378.
  • Dilate image n 368 is combined 380 with edited skeleton 378 so as to produce a new recon n 360 and the process is iterated.
  • a seed image n pixel 400 and the skeleton n 402 are operated upon by fuzzy logic 404 utilizing pixel fuzzy logic measure 406, i.e., the selective structuring element, so as to provide pixel fuzzy measure update 408 and set fuzzy connected pixels 410.
  • pixel fuzzy logic measure 406 i.e., the selective structuring element

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

Dans un procédé d'isolement de structures anatomiques présentes dans un ensemble de données en trois dimensions, un squelette morphologique (302) est d'abord constitué à partir de l'ensemble de données en trois dimensions. Ensuite, un point germe (310) est choisi dans l'ensemble du squelette morphologique. Ce point germe se trouve à l'intérieur d'une structure anatomique voulue, faisant l'objet du visionnage et/ou de l'analyse. On utilise la connectivité floue (314) pour définir des points supplémentaires de la structure anatomique désirée, de façon à faciliter une reconstruction se limitant sensiblement à la seule structure anatomique voulue. Cette reconstruction sensiblement limitée à la seule structure anatomique voulue facilite la vision et l'analyse de celle-ci, en réduisant la complexité de l'image et en éliminant les obstacles constitués par les tissus.
PCT/US1996/014500 1995-09-05 1996-09-05 Criblage dimensionnel de donnees et connectivite floue pour analyse d'image d'irm WO1997009690A1 (fr)

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AU69174/96A AU6917496A (en) 1995-09-05 1996-09-05 Data dimensional sieving and fuzzy connectivity for mri image analysis

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US52343895A 1995-09-05 1995-09-05
US08/523,438 1995-09-05

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DE19726827C1 (de) * 1997-06-24 1998-12-03 Siemens Ag Verfahren zum Finden von Objektkonturen in Bildern und dessen Verwendung zum Finden von Organkonturen in Computertomogrammen
WO1999042977A1 (fr) * 1998-02-23 1999-08-26 Algotec Systems Ltd. Procede et systeme de planification automatique d'un trajet
WO2000036565A1 (fr) * 1998-12-16 2000-06-22 Miller Michael I Procede et appareil de traitement d'images comportant des regions representant des objets cibles
EP1262914A1 (fr) * 1998-12-16 2002-12-04 Michael I. Miller Méthode et appareil de traitement d'images avec régions représentant des objets cibles
US6694057B1 (en) 1999-01-27 2004-02-17 Washington University Method and apparatus for processing images with curves
US6754374B1 (en) 1998-12-16 2004-06-22 Surgical Navigation Technologies, Inc. Method and apparatus for processing images with regions representing target objects
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CN106651841A (zh) * 2016-12-02 2017-05-10 北京航星机器制造有限公司 一种用于安检图像复杂度的分析方法
CN111932554A (zh) * 2020-07-31 2020-11-13 青岛海信医疗设备股份有限公司 一种肺部血管分割方法、设备及存储介质
CN112562031A (zh) * 2020-12-09 2021-03-26 山西三友和智慧信息技术股份有限公司 一种基于样本距离重构的核磁共振图像聚类方法

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Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19726827C1 (de) * 1997-06-24 1998-12-03 Siemens Ag Verfahren zum Finden von Objektkonturen in Bildern und dessen Verwendung zum Finden von Organkonturen in Computertomogrammen
WO1999042977A1 (fr) * 1998-02-23 1999-08-26 Algotec Systems Ltd. Procede et systeme de planification automatique d'un trajet
US7167180B1 (en) 1998-02-23 2007-01-23 Algotec Systems Ltd. Automatic path planning system and method
AU746546B2 (en) * 1998-02-23 2002-05-02 Algotec Systems Ltd. Automatic path planning system and method
US6754374B1 (en) 1998-12-16 2004-06-22 Surgical Navigation Technologies, Inc. Method and apparatus for processing images with regions representing target objects
EP1262914A1 (fr) * 1998-12-16 2002-12-04 Michael I. Miller Méthode et appareil de traitement d'images avec régions représentant des objets cibles
WO2000036565A1 (fr) * 1998-12-16 2000-06-22 Miller Michael I Procede et appareil de traitement d'images comportant des regions representant des objets cibles
US6694057B1 (en) 1999-01-27 2004-02-17 Washington University Method and apparatus for processing images with curves
US9123100B2 (en) 2007-11-20 2015-09-01 Olea Medical Method and system for processing multiple series of biological images obtained from a patient
CN106651841A (zh) * 2016-12-02 2017-05-10 北京航星机器制造有限公司 一种用于安检图像复杂度的分析方法
CN106651841B (zh) * 2016-12-02 2020-10-16 北京航星机器制造有限公司 一种用于安检图像复杂度的分析方法
CN111932554A (zh) * 2020-07-31 2020-11-13 青岛海信医疗设备股份有限公司 一种肺部血管分割方法、设备及存储介质
CN111932554B (zh) * 2020-07-31 2024-03-22 青岛海信医疗设备股份有限公司 一种肺部血管分割方法、设备及存储介质
CN112562031A (zh) * 2020-12-09 2021-03-26 山西三友和智慧信息技术股份有限公司 一种基于样本距离重构的核磁共振图像聚类方法

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