WO2000079481A1 - Segmentation en arm utilisant des modeles actifs de contours - Google Patents

Segmentation en arm utilisant des modeles actifs de contours Download PDF

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Publication number
WO2000079481A1
WO2000079481A1 PCT/US2000/017282 US0017282W WO0079481A1 WO 2000079481 A1 WO2000079481 A1 WO 2000079481A1 US 0017282 W US0017282 W US 0017282W WO 0079481 A1 WO0079481 A1 WO 0079481A1
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Prior art keywords
volume
distance function
segmentation
image data
dimensional image
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PCT/US2000/017282
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English (en)
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WO2000079481A9 (fr
Inventor
Liana Lorigo
W. Eric L. Grimson
Olivier Faugeras
Renaud Keriven
Carl-Fredrik Westin
Ronald Kikinis
Original Assignee
Massachusetts Institute Of Technology
Institut National De Recherche En Informatique Et En Automatique
Ecole Nationale Des Ponts Et Chaussees
The Brigham And Women's Hospital, Inc.
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Application filed by Massachusetts Institute Of Technology, Institut National De Recherche En Informatique Et En Automatique, Ecole Nationale Des Ponts Et Chaussees, The Brigham And Women's Hospital, Inc. filed Critical Massachusetts Institute Of Technology
Priority to EP00943090A priority Critical patent/EP1208535A1/fr
Publication of WO2000079481A1 publication Critical patent/WO2000079481A1/fr
Publication of WO2000079481A9 publication Critical patent/WO2000079481A9/fr

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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
    • G06T7/12—Edge-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
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/755—Deformable models or variational models, e.g. snakes or active contours
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20161—Level set
    • 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 invention relates to the field of volumetric three-dimensional image data segmentation, and in particular to MRA segmentation.
  • MRA magnetic resonance angiography
  • MRA magnetic resonance angiography
  • blood vessels appear in MRA images as bright curve-like patterns which may be noisy and have gaps. What is shown is a "maximum intensity projection".
  • the data is a stack of slices where most areas are dark, but vessels tend to be bright. This stack is collapsed into a single image for viewing by performing a projection through the stack that assigns to each pixel in the projection the brightest voxel over all slices. This image shows projections along three orthogonal axes.
  • Thresholding is one possible approach to this segmentation problem and works adequately on the larger vessels.
  • the problem arises in detecting the small vessels. Thresholding cannot be used for the small vessels for several reasons.
  • the voxels may have an intensity that is a combination of the intensities of vessels and background if the vessel is only partially inside the voxel. This sampling artifact is called partial voluming.
  • Other imaging conditions can cause some background areas to be as bright as other vessel areas, complicating threshold selection.
  • the images are often noisy, and methods using local contextual information can be more robust.
  • Mean curvature evolution schemes for segmentation, implemented with level set methods, have become an important approach in computer vision. This approach uses partial differential equations to control the evolution.
  • the invention presents the first implementation of geodesic active contours in 3D.
  • the system and method of the invention use these techniques for automatic segmentation of blood vessels in MRA images.
  • the dimension of the manifold is 1, and its co-dimension is 2.
  • the invention utilizes the fact that the underlying structures in the image are indeed 3D curves and evolves an initial curve into the curves in the data (the vessels).
  • the segmentation techniques of the invention are based on the concept of mean curvature flow, or curve-shortening flow, from the field of differential geometry.
  • the proposed MRA segmentation method uses a mathematical modeling technique that is well-suited to the complicated curve-like structure of blood vessels.
  • the segmentation task is defined as an energy minimization over all 3D curves and uses a level set method to search for a solution.
  • the approach is an extension of previous level set segmentation techniques to higher co- dimension.
  • the process proceeds by iteratively updating v according to
  • v, ⁇ (Vv(x,t), ⁇ 7 v(x,t)) + — Vv(x,t) -Hi — r , the updating terminates at convergence or as g
  • S' is then defined to be the zero level set of the current distance function v' and reinitialize v' to be a distance function to S'.
  • the volume is continually iteratively updated such that a final distance function v is obtained.
  • the first output obtained from this volume is a segmentation of vessels in the MRA data, obtained by computing the zero level set of v.
  • FIG. 1 is a maximum intensity projection of a phase-contrast MRA image of blood vessels in a brain
  • FIG. 2 illustrates level sets of an embedding function u, for a closed curve in R 2 ;
  • FIG. 3 illustrates a single sequence showing eight successive stages of a tubular object undergoing mean curvature flow;
  • FIG. 4A shows a curve having a tangent to C at p, the normal plane, the image-based vector, and its projection onto the normal plane
  • FIG. 4B shows a curve using the ⁇ -level set method
  • FIGs. 5 A-C illustrate an evolving helix under mean curvature flow with additional vector field: target curve, initial level set, level set after evolution with endpoints constrained, respectively;
  • FIG. 6 is an operational block diagram of a MR segmentation system utilizing the invention
  • FIG. 7 is a flowchart of the segmentation algorithm in accordance with the invention
  • FIG. 8 is a flowchart showing the details of the surface and volume initialization portion of the algorithm in accordance with the invention.
  • FIGs. 9A-D illustrate a vertical bar evolving into a segmentation of a first dataset
  • FIG. 10 is a single 3D dataset, the first image in each row is the maximum intensity projection of the raw data, and the second image is the segmentation result from three orthogonal viewpoints;
  • FIG. 11 illustrates an image of a partial segmentation of the first dataset in FIG. 10, the colorscale is continuous from darkest to lightest intensities, with darkest indicating a radius of curvature ⁇ 1mm and lightest indicating a radius of curvature >2mm.
  • mean curvature flow refers to some curve evolving in time so that at each point, the velocity vector normal to the curve is equal to the mean curvature vector.
  • This concept is normally defined for arbitrary generic surfaces, but only curves are necessary for the invention, so the definition has been restricted for purposes of illustration. More formally, let C Constant t > 0 be a family of curves in R 2 or R 3 , N the normal for a given orientation. That is, C is a curve, and t represents the time parameter or the index into the family of curves, not position.
  • the mean curvature flow equation is then given by the vector equation
  • FIG. 2 illustrates level sets of an embedding function u, for a closed curve in R 2 .
  • C is a curve in 3D.
  • FIG. 3 demonstrates this evolution by illustrating evolving curves under mean curvature flow.
  • FIG 3 illustrates a single sequence showing eight successive stages of a tubular object undergoing curve-shortening flow (mean curvature flow), where the curve is the centerline of the tubular shape. The bumps are first smoothed out until the shape approximates a torus, then the torus shrinks to a point.
  • FIG. 4A shows a curve having a tangent to C at p, the normal plane, the image-based vector, and its projection onto the normal plane.
  • FIGs. 5A-C show how underlying image information can attract the evolving tube.
  • FIGs. 5A-C illustrate an evolving helix under mean curvature flow with additional vector field: target curve, initial level set, level set after evolution with endpoints constrained, respectively.
  • the underlying volumetric image data is shown, as a maximum intensity projection in FIG. 5 A. This volume was generated by drawing a cosine curve in the volume, then smoothing with a Gaussian filter.
  • FIG. 5B shows the initial curve, a helix.
  • FIG. 6 is an operational block diagram of a MR segmentation system 600 utilizing the invention.
  • the system includes a conventional MR scanner 602 running in conjunction with a MR computer that generates and stores raw MRA image slices.
  • the MR data is then segmented by a computer 606.
  • a flowchart of the segmentation algorithm in accordance with the invention is shown in FIG. 7.
  • the invention produces 3D surface models of blood vessels based on magnetic resonance angiography (MRA) data.
  • MRA magnetic resonance angiography
  • the patient's head is imaged in a magnetic resonance scanner 602.
  • the image produced is a three-dimensional image. This means that it is actually a stack of many (often
  • the head is this region.
  • the invention utilizes a computer 606 to generate a 3D surface model of the blood vessels in the head, based on this 3D image.
  • This surface model could be displayed and manipulated on a standard computer.
  • the surface model can be viewed by clinicians, radiologists, and other persons.
  • the surface model is often preferable to the raw 3D image in the areas of ease of interpretation, ease of further measurements, incorporation with other anatomical information, and other areas.
  • Blood vessels appear in MRA images as bright curve-like patterns that may be noisy and have gaps.
  • the data is a stack of slices where most areas are dark, but vessels tend to be bright. This stack is collapsed into a single image for viewing by performing a projection through the stack that assigns to each pixel in the projection the brightest voxel over all slices.
  • MRA segmentation is complicated by the presence of imaging artifacts which appear visually similar to true vessel structures and also to partial voluming, the case of a small imaging area having a brightness value that is a combination of the brightness values of vessels and of background because the vessel is only partially inside the area imaged.
  • This specific segmentation problem is part of the high-level problem of developing computerized techniques for the analysis of medical images.
  • Automatic and semi-automatic techniques can potentially assist clinicians and radiologists, saving them much of the time required to manually segment large datasets, or more generally facilitating measurements and interpretation of the images.
  • banding will now be described. Instead of evolving the entire volume, only the portion of the volume within a narrow band of the zero level set is evolved (the current surface). Normally, the band is set to include voxels that are up to 6 voxels away from the surface.
  • the advantage of this technique is efficiency, and the disadvantage is that structures that are outside the band may be missed if the potential function g does not have a large enough capture range to attract the segmentation to these structures.
  • the interpretation of banding is different from that in previous level set methods; they propagate image information from the zero level set to the rest of the bands, while the invention uses image information at each point.
  • the algorithm used in the method of the invention is described.
  • the 3D MRA volume is loaded into the computer.
  • the vessels appear bright, background appears dark.
  • An initial surface S is generated either by thresholding the inputted data set or by using a previously generated surface.
  • a signed distance function to S is then generated.
  • This distance function is a 3D volume, v.
  • FIG. 8 The detailed flowchart of the surface and volume initialization is shown in FIG. 8.
  • the method continues by iteratively updating v according to Equation 5.
  • the algorithm may also incorporate the image scaling term previously described and/or an orientation term.
  • the process terminates at convergence or as determined by the user.
  • S is redefined periodically or when needed, to be the zero level set of the current distance function v' and reinitialize v' to be the distance function to S'.
  • the process continues by updating the volume according to the previous step.
  • the loop above yields a final distance function v.
  • the first output obtained from this volume is a segmentation of the blood vessels, obtained by computing the zero level set of v.
  • the centerlines are obtained as the local minima of the distance function.
  • estimates of vessel diameter are obtained as a by-product of the computation of ⁇ in
  • Equation 5 To control the trade-off between fitting the surface to the image data and enforcing the smoothness constraint on the surface, an image scaling term imscale is added to Equation
  • v, ⁇ (Vv(x ),V 2 v(x,t)) + imscale *(VvV I) * ⁇ Vv(x,t)- H ⁇ -. (7) g
  • For example, if the two vectors point in the same direction, then the brighter region is inside the surface and the darker region is outside. The angle between the vectors is 0, whose cosine is 1 , so the image term is fully counted. However, if they point in opposite directions, the negative weighting prevents the evolving vessel walls from being attracted to image gradients that point in the opposite direction. As customary in level set segmentation methods, the volume v is periodically reinitialized to be a distance function.
  • each point in the volume is set to be its distance to S.
  • this reinitialization is itself a level set method.
  • the surface is propagated outward at constant speed of 1, and the distance at each point is determined to be the time at which the surface crossed that point.
  • a second step propagates the surface inward to obtain the negative distances analogously.
  • FIG. 8 shows a flowchart of a more detailed portion of the algorithm used to generate the initial surface.
  • This initial surface (and thus the initial volume) is normally generated by thresholding the MRA dataset. However, the method does not require that the initial surface be near the target surface but may use any initial surface.
  • FIGs. 9A-D illustrate a vertical bar evolving into the segmentation of the first dataset in FIG. 11.
  • the datasets may be pre-processed to reduce noise and smooth.
  • the segmentations are post-processed to remove any surface patches whose surface area is less than some threshold (a parameter of the method) to eliminate patches corresponding to noise in the original data.
  • the larger principal curvature can be useful in measuring the radii of the vessels for a particular application, since radius is the inverse of curvature. This curvature can be easily computed when the smaller principal curvature is computed for the segmentation.
  • the option to color-code the segmentations can be added based on vessel radii, as estimated from the local larger principal curvature of the tubular surface.
  • FIG. 10 is a single 3D dataset, the first image in each row is the maximum intensity projection of the raw data, and the second image is the segmentation result from three orthogonal viewpoints.
  • a cleaning threshold c indicated the minimum surface area of connected components of the surface to be retained in the post-processing cleaning step.
  • FIG. 10 shows a single 3D data set (in maximum intensity projection) and segmentation result from three orthogonal viewpoints. The result shown is obtained by thresholding the raw data set. Finally, the capability to color-code the vasculature surface is demonstrated based on local curvature. With reference to FIG. 11, it will be appreciated that for a ribbon-like vessel, the flatter sides shows a large radius, and the sharply curved edges show a small radius. In this image of a partial segmentation of the first dataset in FIG. 10, the colorscale is continuous from darkest to lightest intensities, with darkest indicating a radius of curvature ⁇ 1mm and lightest indicating a radius of curvature >2mm. The curvatures output by the evolution have been smoothed by a 3x3x3 filter prior to coloring the surface.

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Abstract

L'invention porte sur un procédé et un système de segmentation d'un ensemble de données d'image volumétriques tridimensionnelles telles que des images d'angiographie par résonance magnétique (ARM). On entre tout d'abord un tel volume, puis on génère une surface S par seuillage des données entrées, puis on génère une fonction de distance v de S assortie d'un signe où v=v(x,t) et S est l'ensemble nul de vo. Le procédé actualise v par itération selon la formule (I), l'actualisation se terminant par une convergence ou étant déterminée par un opérateur. S' est alors défini comme l'ensemble nul de la fonction de distance v' et reinitialise v' comme fonction de distance de S'. Le volume est constamment actualisé par itération jusqu'à obtention de la fonction v de distance finale. Les premières données de sortie obtenues à partir de ce volume sont une segmentation des vaisseaux dans les données d'ARM, obtenue par le calcul de l'ensemble nul de v.
PCT/US2000/017282 1999-06-23 2000-06-23 Segmentation en arm utilisant des modeles actifs de contours WO2000079481A1 (fr)

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Cited By (10)

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WO2002052509A1 (fr) * 2000-12-22 2002-07-04 Koninklijke Philips Electronics N.V. Procede d'analyse d'un ensemble de donnees comprenant une representation volumetrique d'un objet a examiner
WO2003034337A2 (fr) * 2001-10-16 2003-04-24 Koninklijke Philips Electronics N.V. Procede de selection de branches permettant d'orienter une sonde
WO2004079654A2 (fr) * 2003-03-07 2004-09-16 Martin, Philip, John Appareil et procedes de traitement des images
US6845260B2 (en) 2001-07-18 2005-01-18 Koninklijke Philips Electronics N.V. Automatic vessel indentification for angiographic screening
WO2005027053A1 (fr) * 2003-09-18 2005-03-24 Politecnico Di Milano Procede de determination de la surface tridimensionnelle d'un objet
WO2006121410A1 (fr) * 2005-05-11 2006-11-16 Agency For Science, Technology And Research Procede, dispositif et logiciel permettant de segmenter le cerveau a partir de donnees d'imagerie par resonance magnetique
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CN106373097A (zh) * 2016-08-29 2017-02-01 合肥康胜达智能科技有限公司 一种图像处理方法
CN112085723A (zh) * 2020-09-09 2020-12-15 哈尔滨市科佳通用机电股份有限公司 一种货车摇枕弹簧窜出故障自动检测方法

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002052509A1 (fr) * 2000-12-22 2002-07-04 Koninklijke Philips Electronics N.V. Procede d'analyse d'un ensemble de donnees comprenant une representation volumetrique d'un objet a examiner
US6845260B2 (en) 2001-07-18 2005-01-18 Koninklijke Philips Electronics N.V. Automatic vessel indentification for angiographic screening
WO2003034337A2 (fr) * 2001-10-16 2003-04-24 Koninklijke Philips Electronics N.V. Procede de selection de branches permettant d'orienter une sonde
WO2003034337A3 (fr) * 2001-10-16 2004-11-11 Koninkl Philips Electronics Nv Procede de selection de branches permettant d'orienter une sonde
US8922552B2 (en) 2003-01-15 2014-12-30 Koninklijke Philips N.V. Image processing method for automatic adaptation of 3-D deformable model onto a substantially tubular surface of a 3-D object
WO2004079654A2 (fr) * 2003-03-07 2004-09-16 Martin, Philip, John Appareil et procedes de traitement des images
WO2004079654A3 (fr) * 2003-03-07 2004-10-28 Martin Weber Appareil et procedes de traitement des images
WO2005027053A1 (fr) * 2003-09-18 2005-03-24 Politecnico Di Milano Procede de determination de la surface tridimensionnelle d'un objet
US7626584B2 (en) 2003-09-18 2009-12-01 Politecnico Di Milano Method for determining the three-dimensional surface of an object
WO2006121410A1 (fr) * 2005-05-11 2006-11-16 Agency For Science, Technology And Research Procede, dispositif et logiciel permettant de segmenter le cerveau a partir de donnees d'imagerie par resonance magnetique
CN102651130A (zh) * 2012-03-30 2012-08-29 宋怡 水平集图像处理方法
CN106373097A (zh) * 2016-08-29 2017-02-01 合肥康胜达智能科技有限公司 一种图像处理方法
CN112085723A (zh) * 2020-09-09 2020-12-15 哈尔滨市科佳通用机电股份有限公司 一种货车摇枕弹簧窜出故障自动检测方法

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