Image analysis – Image transformation or preprocessing – Changing the image coordinates
Reexamination Certificate
1999-01-15
2002-04-16
Mehta, Bhavesh (Department: 2721)
Image analysis
Image transformation or preprocessing
Changing the image coordinates
C382S284000, C382S282000, C382S132000, C128S922000
Reexamination Certificate
active
06373998
ABSTRACT:
BACKGROUND OF THE INVENTION
The invention concerns the field of processing of comparable digital images, for the purpose of detecting (or determining) dimensional variations. They may be “two-dimensional” (2D) images, in which case the variation will be termed surface variation, or “three-dimensional” (3D) images, and in this case the variation will be termed volume variation.
The invention applies more particularly, but not exclusively, to images termed medical images, and especially to the analysis of comparable digital images of regions of the brain, in order to study areas of interest comprising, for example, lesions or tumours, or active anatomical structures such as the heart or the ventricles of the brain. By comparable images, there is meant images taken either of substantially identical regions of the same “subject” at different moments, or of substantially identical regions of two separate “subjects”, or even of a single image and the associated image symmetrized with respect to a plane (or also termed “chiral”), when the region analyzed has a certain degree of symmetry.
In many fields it is very important to make comparative analyses of regions in order to see their evolution over time. This is especially the case in the field of high precision welding. But it is even more the case in the medical field, where the detection of lesions and/or following the course of their evolution is absolutely essential in order to adapt a treatment to a patient or to carry out clinical tests, for example. By evolution, there is meant any modification of a region, whether it is of the deformation type (mass effect) and/or of the transformation type (structural modification without deformation).
In the medical field, a set of image data forming an n-dimensional (nD) image is obtained by means of such apparatus as X-ray scanners or nuclear magnetic resonance apparatuses (MRI), or more generally any type of apparatus capable of acquiring images with variations in intensity. Each elementary part of a region represented by an nD image is defined by
n
spatial co-ordinates and an intensity (measured magnitude).
Thus, in the case of an MRI, the 3D image of a region observed consists of a multiplicity of stacked 2D sections, in which the variations in intensity represent the proton density of the tissues.
Techniques are already known which make it possible to detect and/or estimate variations in volume in active regions:—S. A. Roll, A. C. F. Colchester, L. D. Griffin, P. E. Summers, F. Bello, B. Sharrack, and D. Leibfritz, “Volume estimation of synthetic multiple sclerosis lesions: An evaluation of methods”, in the 3rd Annual Meeting of the Society of Magnetic Resonance, p. 120, Nice, France, August 1994; and
C. Roszmanith, H. Handels, S. J. Pöppl, E. Rinast, and H. D. Weiss, “Characterization and classification of brain tumours in three-dimensional MR image sequences”, in Visualization in Biomedical Computing, VBC′96, Hamburg, Germany, September 1996.
These techniques, termed “segmentation” techniques, consist in delineating (or attributing a contour to) an area of interest on two images of an active region, which are spaced in time, then subtracting the “volumes” contained within the two contours in order to estimate the variation in volume of the area of interest within the time interval separating the two images.
These techniques are particularly difficult to put into practice in the case of 3D images, owing to the difficulty encountered when delineating the area of interest. Moreover, the volume measurement is carried out by counting reference volume elements (voxels), of very small size, contained in a closed contour of an area of interest, the dimension of which is generally very large compared with that of a voxel. This counting can only be carried out by (semi-)automatic methods such as, for example, that termed “3D snakes”, which are difficult to put into practice for the non-specialist such as is generally the practitioner who carries out the analysis of the images.
The result is that the uncertainty of the measurement of the volume of an area of interest is very often greater than the estimated variation in volume, which reduces the interest of such volume measurements to a considerable extent. The accuracy of these measurements is even poorer when man has to intervene, since the measurement is then dependent on the observer.
Moreover, the areas of interest are frequently difficult to detect, owing to the fact that the materials of which they consist are not always well contrasted in the images.
SUMMARY OF THE INVENTION
The aim of the present invention is therefore to improve the situation in this field of processing of digital images of active regions.
To this end, it proposes an electronic image processing device which comprises:
registration means making it possible to determine a registration transformation between one of the images and the other, starting from the two sets of image data,
sampling means operating according to this registration in order to re-sample a first of the two sets of image data into a third set of image data relating to the same image, and able to be superposed directly, sample by sample, on the second set of image data, and
processing means which operate starting from the second and third sets of image data in order to obtain therefrom at least one set of difference data, representing differences between superposable areas of interest of the images constituted respectively by the said second and third sets of image data.
Here, the expression “difference” should be taken in the wider sense, that is to say that it may be a question of the appearance of a new area of interest, or of a modification/transformation of a known area of interest. More generally, any type of difference between the two images is concerned here.
According to another feature of the invention, the processing means comprise a calculation module to determine firstly a deformation vector field, from the second and third sets of image data, in such a manner as to make it possible to provide the set of difference data.
Preferably, the processing means comprise first calculation means for applying to the deformation vector field at least a first operator so as to provide the set of difference data, which is then termed a first set of difference data.
The processing means may also comprise second calculation means for applying to the deformation vector field a second operator, different from the first operator, so as to provide another set of difference data, which is then termed a second set of difference data.
In this way, two sets of difference data are obtained which include complementary information on the areas of interest.
The processing means may additionally comprise third calculation means for applying to the deformation vector field a third operator, a composition of the first and second operators, so as to provide another set of difference data, which is then termed a third set of difference data. This makes it possible to obtain other information on the areas of interest, complementary to those obtained with a single operator, and moreover much less subject to noise interference, and consequently more precise, owing to the fact that the respective contributions of the “noise” generated by the application of these operators are decorrelated.
Consequently, the contrast of the areas of interest is significantly improved, which makes it possible to detect them more easily.
The first and second operators are advantageously selected from a group comprising an operator of the modulus type and an operator based on partial derivatives, of the divergence or Jacobian type, for example.
The modulus type operator will provide information more particularly representing movements, while the operator based on partial derivatives will provide information representing more particularly growth or diminution (volume variation or mass effect).
According to yet another feature of the invention, the processing means may comprise detection means in order to transform each first, second and
Calmon Guillaume
Thirion Jean-Philippe
Inria Institut National DN Recherche en Informatique et en Autom
Kassa Yosef
Mehta Bhavesh
Rabin & Berdo P.C.
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