WO2001043047A2 - Imagerie informatique dynamique pour l"osteopathie des visceres et pour la cinetique articulaire - Google Patents

Imagerie informatique dynamique pour l"osteopathie des visceres et pour la cinetique articulaire Download PDF

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Publication number
WO2001043047A2
WO2001043047A2 PCT/BE2000/000145 BE0000145W WO0143047A2 WO 2001043047 A2 WO2001043047 A2 WO 2001043047A2 BE 0000145 W BE0000145 W BE 0000145W WO 0143047 A2 WO0143047 A2 WO 0143047A2
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Prior art keywords
contour
viscera
images
vertices
steps
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PCT/BE2000/000145
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English (en)
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WO2001043047A3 (fr
Inventor
Christian Williame
Georges Finet
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Christian Williame
Georges Finet
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Priority to AU18455/01A priority Critical patent/AU1845501A/en
Publication of WO2001043047A2 publication Critical patent/WO2001043047A2/fr
Priority to US10/167,640 priority patent/US7027650B2/en
Publication of WO2001043047A3 publication Critical patent/WO2001043047A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G06T7/251—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving 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
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • 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
    • G06T2207/30—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
    • G06T2207/30092—Stomach; Gastric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs

Definitions

  • the present invention is related to a method for visually controlling visceral movement or articular kinetics based on medical imaging techniques.
  • the invention is equally related to a computer program for performing said method.
  • the aim is to propose a method and computer program to allow an automatic an repeatable definition of the contour of an object, essentially of an organ, and an automatic calculation of the alteration m the condition of said object, between two or more different images of said obj ect .
  • the starting point of this method and computer software is one or several images m levels of grey (BMP format) containing the viscera m inhalation and exhalation positions. These images have been scanned which may alter them. These images are taken on a film or are directly captured from X-rays or echograms systems. [0004] In order to extract the viscera, the quality of the image obtained has to be improved by increasing the contrast, enhancing the image dynamics and if necessary by smoothing the noise. Once these pre-treatment algorithms have been executed, two other algorithms are run m order to extract the viscera. The first one allows to extract the area of the organ by replenishment of the useful surface (replenishment) . The second one (DDCM) determines the edge by using a technique of active contour. The extraction of the viscera is followed by the determination of the features which provide information about the evolution of the organ between the exhalation and the inhalation.
  • Both methods are started the same way by the user selecting the viscera on the image containing the viscera to be extracted, i.e. by clicking with a mouse inside the viscera or by defining a number of points on the contour of said viscera.
  • the dynamic contour model modifies its shape to evolve towards the desired contour.
  • the driving force leading to the shape deformation is calculated from a so called internal force that is derived from the contour model itself and from an external force that is derived from the main characteristics of the image
  • the mam features of the viscera are determined, such as the perimeter, the area, and the centre of gravity as well as the orientation of the viscera.
  • the positions (inhalation and exhalation) are then compared m terms of coordinates of center of gravity in a given coordinate system and inclination of a given axis, preferably the principal axis of the contour.
  • the movement thus derived is subsequently compared to prescribed physiological movements.
  • the method of the invention is not restricted to physiological movements. Any object having a contour represented on an image can be subjected to the method of the invention.
  • the invention is further related to a computer program for performing the method of the invention.
  • This computer program is a user-friendly, interactive, visual and modular image treatment software which runs on Windows on a PC-type computer.
  • Such basic functions as contrast manipulation, filtering, inter- images operations as well as more complex functions like contour extraction, images superposition, reference search (and specifically bones used as reference considering their stability compared to viscera) are available. All these functions are available from unrolling menus and dialog boxes.
  • This software was developed with Visual C++ m a Microsoft environment. This software is multi- documents and multi-views: several images can simultaneously be loaded and displayed.
  • the file format
  • the main difficulty m the treatment of a vision application is the lack of general theory or universal method offering the answer to several problems at the same time.
  • m order to examine the surface quality of continuous plane products on a high-speed production line [1,2] cannot be used for recognition of characters or for medical diagnosis.
  • every particular application requires a proper survey.
  • contour definition can be accomplished by several ways:
  • Automatic followed by manual .
  • the first step is usually based on simple techniques of contour detection (threshold or region growing) and the second step (manual) modifies the generated contour on the basis of anatomical and pathological knowledge. This approach is very time consuming and the results are not reproducible.
  • All the images refer to shifts occurring from exnalation to inhalation, with the patient standing upright to reproduce the conditions of everyday life.
  • Radiographic and echographic examinations are recorded on videotapes.
  • the tapes are visualised on the screen of the echograph equipped with a camera that allows us to capture the pictures (the first during the inhalation, the other during the exhalation) .
  • These pictures are computerised; the computer memorises the horizontal and vertical shifts and the variation of the inclination axis m each space plan.
  • the goal is to elaborate a general software for image processing that would allow us to extract the viscera on which we want to apply the treatment .
  • the first stage is related to the detection of edges and to the extraction of the viscera after a possible pre-treatment on the image computerised m 256 grey level.
  • the first one allows us to extract the area of the organ by replenishment of the useful surface.
  • the second method determines the edge m using technique of active contour.
  • the extraction of the viscera is followed by the determination of its features at the surface, along the perimeter, at the centre of gravity as well as along its principal axes.
  • the first X-rays image reports the viscera in normal inhalation and normal exhalation positions.
  • the image has to be described as a matrix or some other discrete data structure. Therefore, X-rays are scanned m order to get the numeric image (at the present time, we do not have the possibility to directly capture the image from X-ray or echograms system) . These images, m levels of grey, represent the starting point of the software that we have developed.
  • Edges detection can be performed using convolution or gradient operator, followed by a threshold operation on the gradient m order to decide whether an edge has been found. The pixels that have been identified, as edges must then be linked to form closed curves surrounding regions. Edges m picture are just a preliminary step m the extraction of the features. In fact it does not provide the separated contour of interest for the viscera.
  • K represents the displacement step m pixels.
  • step K If the step K is equal to 1, we will then get the contour and the area of the organ. [0040] When we extract the edge and/or the surface of the organ, we can apply a post treatment on the picture in order to smooth the contour of the organ. Algorithms that we used in post treatment are for example the dilation and the erosion. After the post treatment, we examine data to determine the main features of the viscera such as the perimeter, the area, and the centre of gravity as well as the orientation of the organ.
  • Terzopoulos who introduced the model of the 'Snakes', constructs a contour with connected spline segments and optimizes the contour approximate to a desired form by minimizing an energy function containing internal and external energy.
  • the internal energy corresponds to the energy of curvature of the spline while the external energy is calculated by integrating image features. Additional constraints are also introduced to obtain a better final contour .
  • Another model proposed by Miller the 'Geometrically Deformed Model' or ⁇ GDM ' describes a contour as a set of verticles connected by edges. The energy function is quite different from that used m the Snake model.
  • the energy term depends on an estimation of local curvature and the distance between a vertex and its neighbours; we add an additional term originating from the threshold values for pixels of the picture. This energy function is evaluated only for the vertex positions and not for the trajectory of the connecting edge segments. [0043]
  • Our approach follows Miller's method by adopting the basic structure of the model, i.e., vertices connected by edges. We will describe our dynamic process m term of force, more adequate than the energy of contribution [6]
  • the dynamic contour model modifies its shape to evolve towards the desired contour.
  • the driving force leading to the shape deformation is calculated from internal forces, derived from the shape of the contour model itself, and from an external force field derived from the main characteristics of the image.
  • the internal force will try to minimize local contour curvature while the external forces will try to make the model follow a valley or a ridge through the « landscape
  • An image feature may be simple, like the pixel or voxel grey value, or the magnitude of the grey value gradient but might also be quite complex, like those derived by means of differential geometry.
  • the deformation process is organized m a number of discrete steps; after each of them, the situation is analyzed with respect to position, velocity, and acceleration for each of the vertices. In this evaluation, internal and external forces on a vertex are calculated from the position of the vertex and its neighbours. These forces result m an acceleration, which changes the velocity of the vertex. This velocity determines the displacement of the vertex during the next deformation step. After a given number of deformation steps, a final stable situation will be reached and will be characterized by an equilibrium such that the velocity and the acceleration are nulls for each vertex. When described in term of energies, this situation represents a local minimum m the energy function.
  • the Fig. . represents the structure of the model : an organized contour of vertices connected by a line or a segment .
  • the position of the vertex V L is represented by a vector p x and the edge between V and V 1+1 by a vector d ⁇ (Cartesian coordinates) .
  • the deformation of the model is caused by the combination of forces acting on vertices; the acceleration on the vertex V is noted by a vector a x .
  • v x the speed to the V vertex (not shown on the diagram) .
  • Length d on an edge between segments represents the local resolution of the model : if it is large, the model will not be able to follow the weak variations of energy m the image feature.
  • the length of the edge is evaluated at regular intervals and if necessary, some vertices are withdrawn or inserted while keeping a resolution m a specified range. This is described m the section "Resampling" .
  • the local curvature has a length as well as a direction and gives a measure unique and usable of the angle between the two segments. Besides, the length of the vector local curvature depends solely on this angle, and is not influenced by the length of the two segments joining the vertex.
  • the radial unit vector is given by a rotation of
  • Simple shapes m the absence of external force, can be distorted m shapes having a local curvature minimum everywhere, e.g. a circle (or a symmetrical polygon due to discretization process) .
  • the process does not stop and continuously displaces the vertices towards the center of the model (Fig .9a) .
  • the model shrinks (shrinking) while the local curvature is not affected, contrary to the role of the internal forces acting to reduce the local curvature.
  • Fig.9a m the absence of external force, the contour will be reduced to a single point if we take internal forces proportional to the local curvature .
  • the contour of Fig.9b is facing the same problem. If we apply a force proportional to the local curvature, the contour will become shorter (m term of length) . Besides, as is the case for Fig.9a, the local curvature is not reduced but simply displaced. Fig 9c does not encounter such a problem since this represents a typical situation for which a local curvature reduction is required.
  • the first condition can be filled when using r, for the direction of f m ⁇
  • Fig.11 shows internal force vectors for the shapes shown m Fig.9. The left side describes the internal forces m coordinates r,t ;
  • the local radial force f m r and f m ⁇ provide the resulting force that acts on the vertex only by deforming the model, without any movement of vertices along the contour.
  • A is a multiplication coefficient
  • I(x,y) is the level of brightness of the pixel .
  • W m the weight of vector W m order to favour different research direction for the contour.
  • the chosen vector is the one offering the best results.
  • a high external force is associated to a pixel of the image corresponding to a cavity.
  • An external force of low value is representative of a contour.
  • Regions that have the largest external forces encourage the expansion of the curve, m contrast to the zones presenting weak external forces where the expansion has to stop .
  • Kl , K2 , K3 , K4 are the constants that adjust the speed of expansion of the curve and have the dimensions of the pixel ;
  • FI, F2 , F3 are constants that adjust the sensitivity to partitions and are measured in level of gray.
  • K4 3 FI is a gate to which the model is confronted.
  • the total force/, acting on a vertex is a weighted sum of internal and external forces.
  • the weights can be fixed to default values for a given application but can also be fixed as free parameters by the user.
  • the resampling is evaluated m two steps : the first one checks along of the contour that there is a segment smaller than the minimal length l mn . If it is the case, the segment is removed from the model and the two vertices are replaced by a single one located m the middle (see fig 9a) . The second step verifies along the contour that there is a segment larger than the maximal length / m . When this occurs, this segment is divided m two smaller ones of equal size (to see fig 9b) .
  • the principal axis of a binary object is the inertia axis passing trough the gravity centre of this object and having a minimal total distance with all the pixels of the object.
  • This axis divides the surface m two equal parts, and around which the surface is balanced.
  • This axis also passes through one of the contour pixels; its orientation is along the direction of the maximum extent of the object.
  • minus axis ⁇ perpendicular
  • Table II DDCM : Features comparison between exhalation and inhalation. 300T1 and 300T2: automatic detection, mainl and main2 : manual cuts.
  • ⁇ l 1-2 : represents the variation m degree of the angle of the exhalation towards the inhalation between the result (image 1) of the original image 192-1 and the result (image 2) of 192-2.
  • ⁇ l represents tne variation m degree of the angle of the exhalation towards the inhalation determined manually by G. Finet and Ch . Williame. ⁇ l : gives the gap m degree between ⁇ l and ⁇ l . ⁇ 2, ⁇ 2 and ⁇ 2 give results for images 231-1 and 231-2.
  • gaps ⁇ are minimal between results that we found and those gotten by osteopaths.
  • the gap ⁇ is included m the interval of error determined by G. Finet and Ch. Williame at the time of the statistical study of error tests and margin of error [3] .
  • Results of this study have shown that an organised dynamics does exist on visceral level : viscera move on a specific way under diaphragmal pressure.
  • stomach trouble for example
  • beginning pictures are the X-ray, what provokes a loss of information at the time of the transformation m images.
  • Our objective is that the software runs directly with echograms and X-ray machines, what alleviates the phase of the pre- treatment and permits by the same opportunity to treat several viscera at a time.
  • This software must be completely automatic m order to facilitate the task to users and permits to note anomalies directly at the patient.
  • a last point is to allow the software to animate the dynamics of viscera, detect anomalies and propose solutions.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Artificial Intelligence (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
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  • Apparatus For Radiation Diagnosis (AREA)
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Abstract

La présente invention concerne des procédés informatiques pour étudier les altérations des mouvements des objets, des mouvements physiologiques, c"est-à-dire de la dynamique des viscères, des viscères abdominaux et uro-génitaux ou des parties des viscères. Ces altérations sont organisées et répétées régulièrement et présentent un rythme déterminé par la respiration. Ce procédé consiste à saisir deux images du viscère concerné dans un état d"inhalation maximale et dans un état d"exhalation maximale respectivement, à améliorer la qualité de ces images en utilisant des techniques d"amélioration des images, à sélectionner le viscère sur un écran, à définir les contours de ce viscère sur les deux images et à calculer l"amplitude du mouvement du viscère entre les positions d"inhalation et d"exhalation.
PCT/BE2000/000145 1999-12-10 2000-12-08 Imagerie informatique dynamique pour l"osteopathie des visceres et pour la cinetique articulaire WO2001043047A2 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
AU18455/01A AU1845501A (en) 1999-12-10 2000-12-08 Dynamic computing imagery for visceral osteopathy and for articular kinetics
US10/167,640 US7027650B2 (en) 1999-12-10 2002-06-10 Dynamic computing imagery, especially for visceral osteopathy and for articular kinetics

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US17022299P 1999-12-10 1999-12-10
US60/170,222 1999-12-10

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5446836A (en) * 1992-10-30 1995-08-29 Seiko Epson Corporation Polygon rasterization

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Title
KASS M ET AL: "SNAKES: ACTIVE CONTOUR MODELS" PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION. LONDON, JUNE 8 - 11, 1987, WASHINGTON, IEEE COMP. SOC. PRESS, US, vol. CONF. 1, 8 June 1987 (1987-06-08), pages 259-268, XP000971219 cited in the application *
LUJAN A E ET AL: "A METHOD FOR INCORPORATING ORGAN MOTION DUE THO BREATHING INTO 3D DOSE CALCULATIONS" MEDICAL PHYSICS, AMERICAN INSTITUTE OF PHYSICS. NEW YORK, US, vol. 26, no. 5, May 1999 (1999-05), pages 715-720, XP000898856 ISSN: 0094-2405 *
PARK J ET AL: "MODEL-BASED ANALYSIS OF CARDIAC MOTION FROM TAGGED MRI DATA" PROCEEDINGS OF THE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS. WINSTON-SALEM, JUNE 11 - 12, 1994, LOS ALAMITOS, IEEE COMP. SOC. PRESS, US, vol. SYMP. 7, 11 June 1994 (1994-06-11), pages 40-45, XP000499313 ISBN: 0-8186-6257-3 *
REN Z ET AL: "ANALYSIS AND CHARACTERIZATION OF THE LEFT VENTRICULAR WALL MOTION BY MEANS OF 3D-MODELS" PROCEEDINGS OF THE COMPUTERS IN CARDIOLOGY MEETING. LEUVEN, SEPT. 12 - 15, 1987, WASHINGTON, IEEE COMP. SOC. PRESS, US, vol. MEETING 14, 12 September 1987 (1987-09-12), pages 643-646, XP000093082 *
UDUPA J K ET AL: "ANALYSIS OF IN VIVO 3-D INTERNAL KINEMATICS OF THE JOINTS OF THE FOOT" IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, IEEE INC. NEW YORK, US, vol. 45, no. 11, 1 November 1998 (1998-11-01), pages 1387-1396, XP000786949 ISSN: 0018-9294 *

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WO2001043047A3 (fr) 2002-06-13

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