EP1934936A2 - Procede de segmentation d'une image - Google Patents

Procede de segmentation d'une image

Info

Publication number
EP1934936A2
EP1934936A2 EP06806125A EP06806125A EP1934936A2 EP 1934936 A2 EP1934936 A2 EP 1934936A2 EP 06806125 A EP06806125 A EP 06806125A EP 06806125 A EP06806125 A EP 06806125A EP 1934936 A2 EP1934936 A2 EP 1934936A2
Authority
EP
European Patent Office
Prior art keywords
area
boundary
digital data
disocclusion
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06806125A
Other languages
German (de)
English (en)
Inventor
François Bernard LAUZE
Mads Nielsen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nordic Bioscience AS
Original Assignee
Nordic Bioscience AS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GB0520566A external-priority patent/GB0520566D0/en
Application filed by Nordic Bioscience AS filed Critical Nordic Bioscience AS
Publication of EP1934936A2 publication Critical patent/EP1934936A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 relates to a method of segmenting an image including a background region and an area of interest.
  • lnpainting is a technique that originates from retouching paintings where one wants to recreate lost or damaged structures in a legible way.
  • Digital inpainting uses spatial or frequency information to restore partially damaged/removed images.
  • Geodesic active contours introduce a parameterisation independent formulation. All these models deal only with contours, not with the regions they separate.
  • contour based active contour methods can provide a segmentation of the circle. Although sensitive to the initialisation, region based active contours using statistical parameters of intensity/colour distribution for each region will fail to isolate the circle in the flag from the rest of the image, since the two regions have the same statistics.
  • Figure 1 B the pseudo-classification image, the difficulty comes from the hardly visible edge information.
  • the present inventors have recognised that it would be desirable to provide an alternative method of segmentation, using region based active contours with image inpainting.
  • a method of segmenting an image having a background region and an area of interest, wherein the area of interest occludes part of the background region comprising the steps of: taking a starting set of digital data representative of the image; (1 ) estimating a boundary of the area of interest;
  • the result representative of the degree of disocclusion is additionally determined by the size.of the area within the estimated boundary.
  • the result representative of the degree of disocclusion is biased in a positive way in response to an increase in the difference between the starting set of digital data and the inpainted set of digital data and a decrease in the area within the estimated boundary.
  • the step of moving the position of the estimated boundary comprises using information derived from the result representative of the degree of disocclusion to determine how the position of the estimated boundary should be moved.
  • the step of moving the position of the estimated boundary comprises moving portions of the boundary in subsequent iterations in a direction determined by the factor that yields improvement in the result representative of the degree of disocclusion.
  • the step of moving the position of the estimated boundary in subsequent iterations comprises moving a portion of the boundary to increase the area within the boundary in response to an increase in the result representative of the degree of disocclusion resulting from an increase in .
  • the step of moving the position of the estimated boundary in subsequent iterations comprises moving a portion of the boundary to increase the area within the boundary in response to an increase in the result representative of the degree of disocclusion resulting from an increase in the previous iteration of the difference between the starting set of digital data and the inpainted set of digital data.
  • the result representative of the degree of disocclusion is a generalised statistical moment of the difference between the starting set of digital data and the inpainted set of digital data on the area of interest.
  • the present invention also extends to a method of computing the shape and size of a calcification of a blood vessel by processing an image of at least a part of the blood vessel containing said calcification, which method comprises: taking a starting set of digital data representative of an image of at least part of a blood vessel containing an area of calcification, said area of calcification being set against a background area;
  • the present invention further extends to a method of reconstructing the background of an image having an area of interest in the foreground, comprising: taking a starting set of digital data representative of the image;
  • the present invention also extends to a pre-programmed computational means and an instruction set for performing the above described invention.
  • Figures 1A and 1 B show two examples where segmentation is required for inpainting
  • FIGS. 2A and 2B show an example of the segmentation required in the present invention
  • Figure 3 shows the results of a first experiment where the four square blocks on white are the background
  • Figure 4 shows the results of a second experiment where an image in the foreground is isolated from the background image to enable segmentation;
  • Figure 5 shows the image of the second experiment being defined from a first starting contour; - -
  • Figure 6 shows the various stages of the present invention starting with an image having a background region with an occluding area and ending with an image where the occluding area has been segmented;
  • Figures 7 A to 71 illustrate an example of the present invention when used to determine the shape and size of an area of calcification in a blood vessel
  • Figures 8A to 81 show the example of Figures 7A to 71 illustrating the progression of the area within the boundary
  • Figures 9A to 91 show the example of Figures 7A and 71 illustrating the background image when the area within the boundary is inpainted;
  • Figures 1 OA to 10D illustrate an example of the present invention starting with a small misplaced initial contour
  • Figures 1 1 A to 11 F illustrate an example of the present invention starting with an initially manually placed contour derived from the detection of regions of calcification in an x-ray;
  • Figure 13 illustrates the present invention when used to remove an image of a bus from a background image.
  • the present invention introduces a novel methodology within the framework of active contours. This belongs to the region based family of active contours, but instead of considering that the image domain is partitioned into two (or several regions), the present invention consists in considering the following problem:
  • an area is segmented by differentiating between an area of foreground having different characteristics to the background and the area of background that surrounds the foreground.
  • the image is segmented by differentiation between the foreground and the background as a whole - as if the foreground did not exist.
  • the foreground that requires segmentation may be considered to be an area of interest that occludes or is set against the background.
  • An assumption is made that an image consists of a background region 2 and an area of interest 4, where the area of interest occludes part of the background region. 2.
  • An initial estimate is made of the location of the boundary 6 of the area of interest located in the background region.
  • the background region is assigned a positive function and the area of interest is assigned a negative function.
  • Steps 4 and 5 are repeated such that the assumed area of interest is once again inpainted using information from the background region and the intensity values of the original image and the inpainted image are compared to derive a disocclusion quality measure.
  • Steps 4 to 7 are repeated until the disocclusion quality measure reaches a maximum, thus fulfilling the convergence criteria described below. At this time, the assumed boundary should correlate with the actual boundary of the area of interest.
  • the image may then be segmented into two separate and distinct images (as shown in Figure 2A) as required.
  • disocclusion is used to describe the removal of an area of interest occluding part of the background region.
  • the disocclusion quality measure gives a numerical indication of how successful the boundary estimation and therefore the inpainting has been.
  • the present invention is described with reference to "an image”. It will be apparent that by “taking an image” this involves starting with an image previously acquired by some imaging means, for example, an x-ray image.
  • the starting point of the present invention is digital information representing an image.
  • D be the image domain
  • w the background image defined on D .
  • Ii 0 C (Ub 1 Uf ) . - -
  • the foreground region should be relatively small.
  • the true region ⁇ should be an extremal point of 7( ⁇ ) and we therefore search for a necessary condition for the function /( ⁇ ) to achieve an extremum.
  • the next step is to consider the case of noisy additive signals u h + u f + ⁇ , ⁇ being some noise, as it would be in the case of, for example, X-rays or reflection.
  • the resulting J( ⁇ ) can be seen as a "generalised moment" of u 0 -u b on ⁇ , a moment that we want to maximise.
  • J(Q) is biased by the two following properties:
  • the disocclusion quality measure seeks to optimise itself by finding the maximum difference in intensity between the original image and the inpainted image within the minimum possible area.
  • the present invention uses the computation from Aubert et al in "Image Segmentation using active contours: Calculus of variations or shape gradients? (SIAM Journal of Applied Mathematics, 63(6):2128-2154, 2003)" of 7'( ⁇ ) using shape derivative tools.
  • rthe boundary of ⁇ , N the inward normal to r
  • the Gateaux derivative of /( ⁇ ) in the direction of V is, using the chain rule:
  • the next step is to apply the inpainting operation and compute the shape derivate related terms.
  • F uu denoting the partial differential of F u with respect to its first variable is explicited below as a linear elliptic operator, while F m denotes the shape derivative of F 11 and can be computed as:
  • Lagrangian L being defined by either (3) or (2), one gets: . .
  • the goal is to maximise the "disocclusion criterion".
  • a first order space convex scheme is used to solve this equation, it is ⁇ 'f 1 O)V " ] .
  • the time step ⁇ s chosen at each iteration as 1/ Il F" 11 >o .
  • the discretisation used for the inpainting /( « 0 , ⁇ " ) follows closely the one proposed by Chan and Shen in "Mathematical models for local non texture inpainting” ([SIAM Journal of Applied Mathematics, 62(3), 1019-1043, 2001]).
  • the digital inpainting is computed using a Full Multigrid Scheme, with one V-cycle at each resolution where each level of the V-cycle implements a Full Approximation Scheme (FAS) (following the description given in "A Multigrid tutorial” (SIAM)).
  • FAS Full Approximation Scheme
  • the Grid transfer operations use the ones proposed in "Variational Optical Flow
  • the initial estimate for the boundary to eventually define the contour of the area of interest is in the shape of a giraffe.
  • the invention relies on inpainting an area within the estimated boundary using information from the remainder of the background. Therefore, in this example, information derived from the area outside of the giraffe is used to inpaint the area inside the giraffe. Following this, the disocclusion quality measure is derived based upon the difference in intensity values between the original image and the inpainted image with reference to the size of the area within the giraffe.
  • the boundary is deemed to be split into portions. For each portion a decision is made based on information derived from the disocclusion quality measure. For example, we will first consider the left leg 12 of the giraffe. It is clear from the image that this left leg does not cover the area of interest. Therefore, a decision will be made to have a first attempt at moving the portion of boundary at the bottom of the left leg so that the overall area of the giraffe increases. A move of this nature will result in the same difference of intensity values between the orginal image and the inpainted image as in the previous iteration. As the area within the boundary has increased, the overall disocclusion quality measure will have decreased. Therefore, in the next iteration, it will be apparent that this particular portion of the boundary should be moved to result in a decrease in the overall area of the giraffe.
  • This iterative process will continue for this portion of the boundary until a move of this portion of boundary results in no increase in the difference of intensity values between the original image and the inpainted image, i.e. this portion of the boundary now falls outside the area of interest, and therefore the overall result will be a decrease of the disocclusion quality measure.
  • the estimated boundary is gradually adapted to fit the contour of the area of interest as shown in Figure 5 (middle image).
  • Results are given below from experiments performed on synthetic as well as real data. They present an increasing level of complexity in the image content.
  • experiment 1 is based on an image of four black squares on a white background.
  • the two areas to be segmentised are the white background, following its reconstruction, and the foreground of four black squares.
  • the first row shows a snapshot of the contour evolution at iterations 1 , 7, 15 and 30. From this it can be seen that as the iterations continue, the contours more clearly define the black squares in the foreground.
  • the second row shows the corresponding domain and the last row shows the inpainting result.
  • the circles in row 1 provide the starting point for finding the contours of the areas of segmentation.
  • the contours are then iteratively adapted until the foreground areas have been depicted.
  • the final row shows the result of inpainting within the segmentation.
  • experiment 2 is based on a dark non convex object on a light background with 30% added Gaussian noise.
  • ⁇ - 0.1, p 0.55.
  • the first row shows a snapshot of the contour evolution of iterations 1 , 10, 20 and 30. From this it can be seen that gradually, the mathematical formulae result in the contour clearly defining the foreground shape.
  • the second row shows the corresponding domains and the last one of the inpaintings.
  • experiment 3 uses the same image as experiment 2. It illustrates some stability with respect to the initial contour, as well as the role of the area penaliser exponent p in L .
  • the ⁇ weight in the inpainting was set of 0.1.
  • the initial contour is in the shape of a giraffe, but this is gradually adapted to fit the shape of the foreground object.
  • experiment 4 shows a "pseudo classification" image.
  • This image could represent an image typically found in medical imaging, for example, of an area of calcification.
  • the first row shows the original object and the pseudo classification.
  • the two next rows show snapshots of contour evolution at different iterations, for two different initialisations.
  • the second initialisation while "more creative” is also closer to the true contour and allows for a much faster convergence (260 iterations in the first case, 50 in the second).
  • Figures 7 A-I, Figure 8 A-I and Figures 9 A- I show various iterations of the contour evolution starting with an initial random estimate of the boundary of the area of calcification. As can be seen, as the iterations progress, the boundary is adapted to fit the contour of the area of calcification. This sort of technique would be useful for locating and assessing calcifications found in blood vessels, for example, an aorta.
  • Figures 8A-I show corresponding images of the areas within the boundary.
  • Figures 9A- I show the images of the background with the area within the boundary inpainted using information from the remainder of the background. As can be seen in Figure 91, the area of calcification is no longer visible.
  • Figures 1OA to 1OD illustrates the robustness of the segmentation algorithm described above.
  • the initial boundary provided at the beginning of the algorithm is relatively small and misplacedjn comparison to the area of interest.
  • this initial small curve evolves into a meaningful boundary that can subsequently be used for segmentation.
  • Figures 11 A to 11 F illustrate the results of a segmentation initialised by a calcification detection method.
  • the initial contours shown in Figures 1 1 B and 11 E are applied manually following the detection of areas of calcification using the calcification detection method described in Bruijne in "Shape Particle Guided Tissue Classification". These contours are used as the initial boundaries in the above defined algorithm which is subsequently executed to result in the boundary locating the edge of the area of interest, in this case an area of calcification.
  • the disocclusion quality measure continues increasing until the contour fits the area to be segmented - based on the calculations made following inpainting of the foreground region.

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Abstract

L'invention porte sur un procédé de segmentation d'une image présentant un fond et une zone d'intérêt entourée en partie par le fond. Ledit procédé consiste, partant d'un ensemble de départ de données numériques représentatives de l'image: (1 ) à estimer les lisières de la zone d'intérêt; (2) à figurer la zone d'intérêt à l'intérieur des lisières en utilisant une information provenant du fond; (3) à calculer la différence entre l'ensemble de départ de données numériques et l'ensemble figuré de données numériques pour obtenir un résultat représentatif du niveau de désocclusion de la zone de fond; (4) à déplacer la position estimée des lisières pour produire une nouvelle génération de lisières estimées; (5) à figurer la zone d'intérêt à l'intérieur des nouvelles lisières estimées; (6) à calculer la différence entre l'ensemble de départ de données numériques et l'ensemble figuré de données numériques; et (7) à continuer l'itérations des étapes (4), (5) et (6) jusqu'à l'obtention du niveau maximum de désocclusion; et (8) à utiliser les informations sur les lisières de la zone d'intérêt lorsque le niveau maximum de désocclusion est atteint pour segmenter l'image.
EP06806125A 2005-10-10 2006-10-09 Procede de segmentation d'une image Withdrawn EP1934936A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
GB0520566A GB0520566D0 (en) 2005-10-10 2005-10-10 A method of segmenting an image
GB0520948A GB0520948D0 (en) 2005-10-10 2005-10-14 A method of segmenting an image
PCT/EP2006/009748 WO2007042251A2 (fr) 2005-10-10 2006-10-09 Procede de segmentation d'une image

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