Image processing device and method for detecting developing lesions

• US 20050141757A1
• Filed: 10/04/2002
• Published: 06/30/2005
• Est. Priority Date: 10/12/2001
• Status: Active Grant

• Current Assignee: Inria Institut National de Recherche EN Informatique et EN Automatique
• Original Assignee: Inria Institut National de Recherche EN Informatique et EN Automatique
• Inventors: Malandain, Gregoire, Ayache, Nicholas, Stoeckel, Jonathan, Rey, David
• Granted Patent: US 7,822,240 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/30 Determination of transform parameters for the alignment of images, i.e. image registration

Abstract

The invention concerns an image processing device, including an input for receiving a time series of data sets representing comparable volume digital images, each data having a position component and an intensity component, pre-processing means (18) for modifying the data sets so as to obtain images updated in position and intensity, and comparative processing means (20) for examining sets of time series of image elements and to detect therein signs of variations. The processing means (20) include a modeling function (12) for adjusting a parametric model separately on some of the sets of time series of image elements, to obtain pairs of data (image element, time) and a function for statistical analysis (14) of said pairs of data to isolate the pairs of data representing a significant variation.

Claims

1. An Image processing device, comprising;

an input for receiving a time series of data sets representing comparable volume-based digital images, each data item comprising a position component and an intensity component,

preprocessing means (8;

18) for modifying the data sets so as to obtain images registered geometrically and intensity-wise, and

means of comparative processing (10;

20), able to examine sets of time series of image elements, so as to detect signs of variation therein, characterized in that the processing means (20) comprise;

a modeling function (12), capable of fitting a parametric model separately over certain at least of the sets of time series of image elements, thereby providing information pairs of the kind;

image element, time; and

a function (14) for statistical analysis of these information pairs, which is able to isolate those of the information pairs that are representative of a significant variation.

2. The device as claimed in claim 1, characterized in that the parametric model is of the asymmetric Gaussian type, comprising a maximum amplitude parameter, a minimum amplitude parameter, a time at the peak parameter, a reference width parameter and a dissymmetry parameter.

3. The device as claimed in claim 2, characterized in that the parametric model comprised a mean model whose parameters are established on the basis of sets of time series of image elements measured over developing pathological zones.

4. The device as claimed in claim 3, characterized in that said parameters of the mean model are obtained on the basis of sets of time-normalized and amplitude-normalized time series.

5. The device as claimed in claim 1, characterized in that the modeling function (12) furthermore comprised a general linear model applied to each set o time series of image elements so as to obtain, for each of said sets of series, a linear combination of at least the mean model (X₁) and a constant (X₂) according to a vector (β

) comprising at least a first and a second coefficient.

6. The device as claimed in claim 5, characterized in that the statistical analysis function (14) comprises an operation of the T-test type revealing connected pathologically developing zone where said first coefficient is significantly different from zero.

7. The device as claimed in claim 1, characterized in that the modeling function (12) furthermore comprises fitting at least one parametric model for at least one time series of image elements.

8. The device as claimed in claim 7, characterized in that at least one parameter of the model is fixed on the basis of sets of time-normalized and amplitude-normalized time series of image elements.

9. The device as claimed in claim 7, characterized in that at least one parameter of the model is obtained on the basis of the set of the time series of said image element.

10. The device as claimed in claim 1, characterized in that the statistical analysis function (14) comprises repeating, for at least one series in a different time order from said image element, the modeling function (12) comprising fitting a parametric model for a time series of an image element.

11. The device as claimed in claim 10, characterized in that the statistical analysis function (14) comprises the determination of groups of neighboring image elements corresponding to series of image elements having at least on of the parameters of their model greater than a given threshold, these groups of neighboring image elements forming developing pathological zones of a size determined by the statistical analysis function.

12. The device as claimed in claim 11, characterized in that the statistical analysis function (14) furthermore comprises a distribution of said developing pathological zones according to their size and a determination of significant developing pathological zones.

13. The device as claimed in claim 3, characterized in that the modeling function (12) comprises an optimization function applied to the parameters of the parametric model.

14. The device as claimed in claim 1, in which the preprocessing comprises a rigid registration, followed by a correction of the temporal bias, characterized in that the correction of the temporal bias comprises the construction of a 3D joint histogram of the intensities of images taken pairwise.

15. The device as claimed in claim 14, characterized in that the correction of the temporal bias comprises the construction of a regression line on said histogram by a robust orthogonal least squares technique.

16. The device as claimed in claim 14, characterized in that the correction of the temporal bias comprises the consequential correction of one of the images with respect to the other.

30. The method as claimed in claim 1, in which step b—

comprises performing a rigid registration, followed by a correction of the temporal bias, characterized in that step b—

comprises performing the correction of the temporal bias by the construction of a 3D joint histogram of the intensities of image taken pairwise.

31. The method as claimed in claim 30, characterized in that step b—

comprises performing the correction of the temporal bias by the construction of a regression line on said histogram by a robust orthogonal least squares technique.

32. The method as claimed in claim 30, characterized in that step b—

comprises performing the correction of the temporal bias by the consequential correction of one of the images with respect to the other.

17. A method of image processing, comprising the following steps:

a—

receiving a time series of data sets representing comparable volume-based digital images, each data item comprising a position component and an intensity component,

b—

modifying the data sets so as to obtain images registered geometrically and intensity-wise, characterized in that it comprises the complementary steps,

c—

fitting a parametric model (140) separately over certain at least of the sets of time series of image elements, thereby providing information pairs of the kind;

image element, time,

d—

isolating those of the information pairs that are representative of a significant variation by statistical analysis (156).

18. The method as claimed in claim 17, characterized in that the parametric model of step c—

is of the asymmetric Gaussian type, comprising a maximum amplitude parameter, a minimum amplitude parameter, a time at the peak parameter, a reference width parameter and a dissymmetry parameter.

19. The method as claimed in claim 18, characterized in that the parametric model of step c—

comprises a mean model whose parameters are established on the basis of sets of time series of image elements measured over developing pathological zones.

20. The method as claimed in claim 19, characterized in that said parameters of the mean model of step c—

are obtained on the basis of sets of time-normalized and amplitude-normalized time series.

21. The method as claimed in claim 17, characterized in that step c—

comprises c1—

applying a general linear model to each set of time series of image elements so as to obtain, for each of said sets of series, a linear combination of at least the mean model and a constant according to a vector comprising at least a first and second coefficient (142).

22. The method as claimed in claim 21, characterized in that step d—

comprises d1—

applying to the information pairs an operation of the T-test type revealing connected pathologically developing zones where said first coefficient is significantly different from zero (152).

23. The method as claimed in claim 17, characterized in that step c—

comprises c1—

fitting at least one parametric model for at least one time series of image elements.

24. The method as claimed in claim 23, characterized in that step c1—

furthermore comprises fixing at least one parameter of models on the basis of sets of time-normalized and amplitude-normalized time series.

25. The method as claimed in claim 23, characterized in that step c1—

comprises obtaining at least one parameter of the model on the basis of the set of the time series of said image element.

26. The method as claimed in claim 17, characterized in that step d—

comprises d1—

repeating step c—

for at least one series in a different time order from said image element.

27. The method as claimed in claim 26, characterized in that step d—

comprises

d2—

determining groups of neighboring image elements corresponding to series of image elements having at least one of the parameters of their model greater than a given threshold,

d3—

determining the size of developing pathological zones formed by these groups of neighboring image elements.

28. The method as claimed in claim 23, characterized in that step d—

comprises

d4—

distributing said developing pathological zones according to their size,

d5—

determining the significant developing pathological zones.

29. The method as claimed in claim 19, characterized in that step c—

comprises optimizing the parameters of the parametric model.