Method for computer-aided detection of three-dimensional lesions

• US 20030223627A1
• Filed: 10/16/2002
• Published: 12/04/2003
• Est. Priority Date: 10/16/2001
• Status: Active Grant

• Current Assignee: University of Chicago
• Original Assignee: University of Chicago
• Inventors: Yoshida, Hiroyuki, Rubin, David, Nappi, Janne, Lan, Li, Maceneaney, Peter, Dachman, Abraham, Masutani, Yoshitaka
• Granted Patent: US 7,379,572 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30032 Colon polyp
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: G06T 7/41 based on statistical description of texture

Abstract

A method, system, and computer program product for identifying at least one three-dimensionally extended lesion within a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ. The method includes: (1) generating a set of voxels representing a total scanned volume from a set of cross-sectional images of the target organ; (2) performing segmentation to extract a set of voxels representing the volumetric region from the set of voxels representing the total scanned volume; (3) detecting a set of candidate lesions based on geometric feature values of each voxel in the set of voxels representing the volumetric region; and (4) selecting the at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric, morphologic, and texture feature values of each lesion in the set of candidate lesions.

Claims

1. A method of processing a set of cross-sectional images defining a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ, comprising:

obtaining a set of voxels representing a total scanned volume from the set of cross-sectional images of the target organ; and

performing segmentation to extract a set of voxels representing the volumetric region from the set of voxels representing the total scanned volume.

2. The method of claim 1, wherein the obtaining step comprises:

determining corresponding pixels on adjacent images in the set of cross-sectional images of the target organ; and

connecting said corresponding pixels to obtain a set of voxels representing the total scanned volume.

3. The method of claim 1, wherein the performing step comprises:

generating a first set of segmentation voxels by thresholding the set of voxels representing the total scanned volume with a value characteristic of the target organ;

performing organ-based analysis of the first set of segmentation voxels to obtain a second set of segmentation voxels; and

determining an intersection of the first set of segmentation voxels and the second set of segmentation voxels to obtain the set of voxels representing the volumetric region encompassing the inner surface, the outer surface, and the intervening tissue of the target organ.

4. The method of claim 3, wherein the step of performing organ-based analysis comprises:

selecting an air-seed voxel;

designating spatially connected voxels in a neighborhood of the air-seed voxel to be in the second set of segmentation voxels, if a set of predetermined conditions is satisfied; and

repeating the selecting and designating steps until a number of voxels designated in the second set of segmentation voxels decreases by more that a predetermined percentage of a number of voxels included in a prior designating step.

5. The method of claim 4, wherein the repeating step comprises:

repeating the selecting and designating steps until a number of voxels designated in the second set of segmentation voxels decreases by more than a predetermined number.

6. The method of claim 4, wherein the designating step comprises:

determining as the predetermined conditions, (1) if each voxel in the region of spatially connected voxels is not in the first set of segmentation voxels, and (2) if each voxel in the region of spatially connected voxels has an associated voxel value and voxel gradient, each below predetermined values associated with walls of the target organ.

7. The method of claim 4, further comprising:

designating a region of surface voxels to be in the second set of segmentation voxels;

repeating the designating step until the region of surface voxels intersects with more than a first predetermined percentage of a layer of voxels in the first set of segmentation voxels; and

repeating the preceding designating step until the region of surface voxels intersects with less than a second predetermined percentage of the layer of voxels in the first set of segmentation voxels.

8. The method of claim 4, wherein the air-seed voxel selecting step comprises:

selecting a voxel that (1) is not in the first set of segmentation voxels, (2) has a voxel value less than a predetermined value, (3) is not in the second set of segmentation voxels, and (4) is not close to a boundary of the total scanned volume.

9. The method of claim 1, further comprising:

detecting a set of candidate lesions based on geometric feature values at each voxel in the set of voxels representing the volumetric region;

selecting at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric feature values and morphologic feature values of each candidate lesion in the set of candidate lesions; and

outputting a set of voxels representing the at least one three-dimensionally extended lesion selected in the selecting step.

10. The method of claim 9, wherein the detecting step comprises:

calculating geometric feature values for each voxel in the set of voxels representing the volumetric region;

generating a set of initial candidate lesions using the geometric feature values calculated in the calculating step; and

clustering the set of initial candidate lesions to form the set of candidate lesions.

11. The method of claim 10, wherein the calculating step comprises:

smoothing the volumetric region to generate volumetric regions at multiple scales;

determining a volumetric shape index, for each voxel in the set of voxels representing the volumetric region, on at least one scale; and

determining a volumetric curvedness value, for each voxel in the set of voxels representing the volumetric region, on the at least one scale.

12. The method of claim 10, wherein the step of generating a set of initial candidate lesions comprises:

identifying a set of seed voxels having a volumetric shape index value in a first predefined range and a volumetric curvedness value in a second predefined range;

determining a grow region of spatially connected voxels adjacent to a seed voxel in the set of seed voxels;

applying conditional morphological dilation to the grow region to obtain an enhanced grow region;

designating the enhanced grow region as an initial candidate lesion in the set of initial candidate lesions; and

repeating the preceding determining, applying, and designating steps for each seed voxel in the set of seed voxels.

13. The method of claim 12, wherein the step of applying conditional morphological dilation comprises:

expanding the grow region by morphological dilation until at least a boundary of an expanded region reaches a boundary of the target organ;

measuring a growth rate of the grow region during the expanding step; and

designating the grow region at a minimum growth rate as the enhanced grow region.

14. The method of claim 10, wherein the clustering step comprises:

merging initial candidate lesions in the set of initial candidate lesions that are located within a predetermined distance of each other to obtain a reduced set of candidate lesions;

grouping the reduced set of candidate lesions to obtain a set of lesion clusters; and

removing lesion clusters having a total volume below a predetermined minimum volume from the set of lesion clusters to obtain the set of candidate lesions.

15. The method of claim 9, wherein the selecting step comprises:

selecting a set of true-positive lesions from the set of candidate lesions; and

outputting the set of true-positive lesions as the at least one three-dimensionally extended lesion.

16. The method of claim 15, wherein the step of selecting a set of true-positive lesions comprises:

calculating at least one feature value for each voxel in a set of voxels representing the set of candidate lesions;

calculating statistics of the at least one feature value for each lesion in the set of candidate lesions; and

partitioning the set of candidate lesions into a set of false-positive lesions and the set of true-positive lesions based on analysis of the statistics of the at least one feature value calculated in the preceding calculating step.

17. The method of claim 16, wherein the step of calculating at least one feature value comprises:

calculating a gradient concentration feature value for each voxel in the set of voxels representing the set of candidate lesions;

calculating at least one of volumetric shape index value, volumetric curvedness value, and gradient of voxel value for each voxel in the set of voxels representing the set of candidate lesions; and

identifying a set of voxels having a feature value in a predefined range to generate a restricted set of candidate lesions; and

calculating at least one of gradient concentration feature value, volumetric shape index value, volumetric curvedness value, and gradient of voxel value for the set of voxels representing the restricted set of candidate lesions.

18. The method of claim 16, wherein the step of calculating statistics of the at least one feature value comprises:

determining at least one of mean, minimum, maximum, variance, standard deviation, skewness, kurtosis, and ratio of minimum to maximum, using feature values of all voxels in each candidate lesion in the enhanced set of candidate lesions.

19. The method of claim 16, wherein the partitioning step comprises:

partitioning the set of candidate lesions using at least one of a linear discriminant classifier, a quadratic discriminant classifier, a neural network, and a support vector machine.

20. The method of claim 17, wherein the step of calculating the gradient concentration feature comprises:

determining a gradient vector of voxel values for each voxel in the set of voxels representing the enhanced set of candidate lesions; and

calculating a concentration of the gradient vector at each voxel in the set of voxels representing the enhanced set of candidate lesions.

21. The method of claim 1, wherein the obtaining step comprises:

obtaining the set of voxels representing the total scanned volume from a set of cross-sectional computed-tomographic images of a colon.

22. The method of claim 1, wherein the performing step comprises:

generating a first set of segmentation voxels by thresholding the set of voxels representing the total scanned volume with a value characteristic of a colon;

performing colon-based analysis of the first set of segmentation voxels to obtain a second set of segmentation voxels; and

determining an intersection of the first set of segmentation voxels and the second set of segmentation voxels to obtain the set of voxels representing the volumetric region encompassing the inner surface, the outer surface, and the intervening tissue of the colon.

23. The method of claim 1, wherein the step of performing segmentation comprises:

removing voxels representing organs other than the target organ from the set of voxels representing the total scanned volume.

36. A computer program product configured to store plural computer program instructions which, when executed by a computer, cause the computer to perform the steps recited in any one of claims 1-35.

37. A system configured to process a set of cross-sectional images defining a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ by performing the steps recited in any one of claims 1-23.

24. A method of identifying at least one three-dimensionally extended lesion from a set of voxels representing a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ, comprising:

detecting a set of candidate lesions based on geometric feature values at each voxel in the set of voxels representing the volumetric region;

selecting the at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric feature values and morphologic feature values of each candidate lesion in the set of candidate lesions; and

outputting a set of voxels representing the at least one three-dimensionally extended lesion selected in the selecting step.

25. The method of claim 24, wherein the detecting step comprises:

calculating geometric feature values for each voxel in the set of voxels representing the volumetric region;

generating a set of initial candidate lesions using the geometric feature values calculated in the calculating step; and

clustering the set of initial candidate lesions to form the set of candidate lesions.

26. The method of claim 25, wherein the calculating step comprises:

smoothing the volumetric region to generate volumetric regions at multiple scales;

determining a volumetric shape index, for each voxel in the set of voxels representing the volumetric region, on at least one scale; and

determining a volumetric curvedness value, for each voxel in the set of voxels representing the volumetric region, on the at least one scale.

27. The method of claim 25, wherein the step of generating a set of initial candidate lesions comprises:

identifying a set of seed voxels having a volumetric shape index value in a first predefined range and a volumetric curvedness value in a second predefined range;

determining a grow region of spatially connected voxels adjacent to a seed voxel in the set of seed voxels;

applying conditional morphological dilation to the grow region to obtain an enhanced grow region;

designating the enhanced grow region as an initial candidate lesion in the set of initial candidate lesions; and

repeating the preceding determining, applying, and designating steps for each seed voxel in the set of seed voxels.

28. The method of claim 27, wherein the step of applying conditional morphological dilation comprises:

expanding the grow region by morphological dilation until at least a boundary of an expanded region reaches a boundary of the target organ;

measuring a growth rate of the grow region during the expanding step; and

designating the grow region at a minimum growth rate as the enhanced grow region.

29. The method of claim 25, wherein the clustering step comprises:

merging initial candidate lesions in the set of initial candidate lesions that are located within a predetermined distance of each other to obtain a reduced set of candidate lesions;

grouping the reduced set of candidate lesions to obtain a set of lesion clusters; and

removing lesion clusters having a total volume below a predetermined minimum volume from the set of lesion clusters to obtain the set of candidate lesions.

30. The method of claim 24, wherein the selecting step comprises:

selecting a set of true-positive lesions from the set of candidate lesions; and

outputting the set of true-positive lesions as the at least one three-dimensionally extended lesion.

31. The method of claim 30, wherein the step of selecting a set of true-positive lesions comprises:

calculating at least one feature value for each voxel in a set of voxels representing the set of candidate lesions;

calculating statistics of the at least one feature value for each lesion in the set of candidate lesions; and

partitioning the set of candidate lesions into a set of false-positive lesions and the set of true-positive lesions based on analysis of the statistics of the at least one feature value calculated in the preceding calculating step.

32. The method of claim 31, wherein the step of calculating at least one feature value comprises:

calculating a gradient concentration feature value for each voxel in the set of voxels representing the set of candidate lesions;

calculating at least one of volumetric shape index value, volumetric curvedness value, and gradient of voxel value for each voxel in the set of voxels representing the set of candidate lesions; and

identifying a set of voxels having a feature value in a predefined range to generate a restricted set of candidate lesions; and

calculating at least one of gradient concentration feature value, volumetric shape index value, volumetric curvedness value, and gradient of voxel value for the set of voxels representing the restricted set of candidate lesions.

33. The method of claim 31, wherein the step of calculating statistics of the at least one feature value comprises:

determining at least one of mean, minimum, maximum, variance, standard deviation, skewness, kurtosis, and ratio of minimum to maximum, using feature values of all voxels in each candidate lesion in the enhanced set of candidate lesions.

34. The method of claim 31, wherein the partitioning step comprises:

partitioning the set of candidate lesions using at least one of a linear discriminant classifier, a quadratic discriminant classifier, a neural network, and a support vector machine.

35. The method of claim 32, wherein the step of calculating the gradient concentration feature comprises:

determining a gradient vector of voxel values for each voxel in the set of voxels representing the enhanced set of candidate lesions; and

calculating a concentration of the gradient vector at each voxel in the set of voxels representing the enhanced set of candidate lesions.

38. A system configured to identify at least one three-dimensionally extended lesion within a thick volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ by performing the steps recited in any one of claims 24-35.

39. A signal representing a three-dimensional segmentation of an organ, the signal derived from a set of cross-sectional images of said organ, comprising:

a first signal portion representing an internal surface of said organ;

a second signal portion representing an external surface of said organ; and

a third signal portion representing tissue located between said internal surface and said external surface of said organ.

40. The signal of claim 39, wherein the first signal portion is obtained by executing the steps of:

determining corresponding pixels on adjacent images in the set of cross-sectional images;

connecting said corresponding pixels to obtain a set of voxels representing a total scanned volume;

removing voxels representing organs other than the said organ from the set of voxels representing the total scanned volume to obtain a set of voxels representing a reduced volume; and

performing organ-based analysis of set of voxels representing the reduced volume to obtain the first signal portion.

41. The signal of claim 39, wherein the second signal portion is obtained by executing the steps of:

determining corresponding pixels on adjacent images in the set of cross-sectional images;

connecting said corresponding pixels to obtain a set of voxels representing a total scanned volume;

removing voxels representing organs other than the said organ from the set of voxels representing the total scanned volume to obtain a set of voxels representing a reduced volume; and

generating the second signal portion by thresholding the set of voxels representing the reduced volume with values characteristic of the external surface of said organ.

42. The signal of claim 39, wherein the third signal portion is obtained by executing the steps of:

determining corresponding pixels on adjacent images in the set of cross-sectional images;

connecting said corresponding pixels to obtain a set of voxels representing a total scanned volume;

removing voxels representing organs other than the said organ from the set of voxels representing the total scanned volume to obtain a set of voxels representing a reduced volume; and

performing organ-based analysis of set of voxels representing the reduced volume to obtain the third signal portion.

43. A signal representing a three-dimensional segmentation of an organ, the signal formed by executing the steps of:

obtaining a set of cross-sectional images of said organ;

determining corresponding pixels on adjacent images in the set of cross-sectional images;

connecting said corresponding pixels to obtain a set of voxels representing a total scanned volume; and

extracting a signal representing the three-dimensional segmentation of said organ from the set of voxels representing the total scanned volume.