QUANTIFICATION METHOD OF THE FEATURE OF A TUMOR AND AN IMAGING METHOD OF THE SAME

• US 20110181614A1
• Filed: 01/25/2010
• Published: 07/28/2011
• Est. Priority Date: 01/25/2010
• Status: Active Grant

• Current Assignee: Amcad Biomed Corporation
• Original Assignee: Amcad Biomed Corporation
• Inventors: Liu, Chung Wei, Wu, Ming Hsun, Chen, Argon, Ho, Ming Chih, Tsai, Po Wei, Tai, Hao Chih, Chen, Chiung Nein, Wu, Hsin-Jung, Chen, Wen Hwa, Chang, King Jen
• Granted Patent: US 8,948,474 B2
• Time in Patent Office: Days
• US Class Current: 345/595
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/41 based on statistical description of texture

Abstract

A quantification method and an imaging method are disclosed, capable of quantifying the margin feature, the cysts feature, the calcifications feature, the echoic feature and the heterogenesis feature of a tumor, and capable of imaging the margin feature, the cysts feature, the calcifications feature and the heterogenesis feature of a tumor. The quantification method and the imaging method calculate the moving variance of the gray scale of each of the pixel points based on the gradient value of the gray scale of these pixel points. Then, depending on the purpose of the quantification method or the imaging method, the maximum value, the minimum value, the mean value, and the standard deviation of the moving variance of the gray scale of these pixel points are calculated, respectively. At final, with the definition of the threshold value and the imaging rule, the above features of the tumor are quantified or imaged.

Claims

1. A quantification method of the margin feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) retrieving a center of gravity of the tumor contour annular region, defining a section line extending outwardly from the center of gravity and penetrating the tumor contour annular region, and providing a measured line segment being on the section line and in the tumor contour annular region;

(D) calculating the moving variance of the gray scale of each of the plurality pixel points on the measured line segment; and

(E) quantifying the margin feature of the tumor on the section line, based on the moving variance of the gray scale of each of the plurality pixel points on the measured line segment.

2. The quantification method as claimed in claim 1, wherein the step (D) includes a step (D1), calculating the standard deviation of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

3. The quantification method as claimed in claim 2, wherein the step (D) includes a step (D2) following the step (D1), calculating the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

4. The quantification method as claimed in claim 3, wherein the step (E) includes a step (E1), defining a threshold value for identifying the blur of the margin feature of the tumor on the section line, based on the standard deviation and the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

5. The quantification method as claimed in claim 1, further comprises a step (F) following the step (E), changing the orientation of the section line to scan the whole margin of the tumor, for quantifying all the margin feature of the tumor.

6. An imaging method of the margin feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) retrieving a center of gravity of the tumor contour annular region, defining a section line extending outwardly from the center of gravity and penetrating the tumor contour annular region, and providing a measured line segment being on the section line and in the tumor contour annular region;

(D) calculating the moving variance of the gray scale of each of the plurality pixel points on the measured line segment; and

(E) defining a margin imaging upper limit and a margin imaging lower limit based on the moving variance of the gray scale of each of the plurality pixel points on the measured line segment, and imaging the margin feature of the tumor on the section line in coordination with a rainbow level.

7. The imaging method as claimed in claim 6, wherein the step (D) includes a step (D1), calculating the standard deviation of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

8. The imaging method as claimed in claim 7, wherein the step (D) includes a step (D2) following the step (D1), calculating the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

9. The imaging method as claimed in claim 8, wherein the step (E) includes a step (E1), defining the margin imaging upper limit and the margin imaging lower limit, based on the standard deviation and the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

10. The imaging method as claimed in claim 9, wherein the margin imaging upper limit is the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment, plus 3 times of the standard deviation of the moving variance of the gray scale of the plurality pixel points on the measured line segment;

and the margin imaging lower limit is the mean value of the moving variance of the gray scale of the plurality pixel points on the measured line segment, minus 3 times of the standard deviation of the moving variance of the gray scale of the plurality pixel points on the measured line segment.

11. The imaging method as claimed in claim 6, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a pixel point on the measured line segment having the moving variance of the gray scale larger than or equal to the margin imaging upper limit, the pixel point is covered by a red block while the margin feature of the tumor is imaged.

12. The imaging method as claimed in claim 6, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a pixel point on the measured line segment having the moving variance of the gray scale smaller than or equal to the margin imaging lower limit, the pixel point is covered by a purple block while the margin feature of the tumor is imaged.

13. The imaging method as claimed in claim 6, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a pixel point on the measured line segment having the moving variance of the gray scale between the margin imaging upper limit and the margin imaging lower limit, the pixel point is covered by a block having the color selected from the rainbow level corresponding to the relation among the moving variance of the gray scale of the pixel point, the margin imaging upper limit and the margin imaging lower limit, while the margin feature of the tumor is imaged.

14. The imaging method as claimed in claim 6, further comprises a step (F) following the step (E), changing the orientation of the section line to scan the whole margin of the tumor, for imaging all the margin feature of the tumor.

15. A quantification method of the cysts feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) calculating both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region; and

(D) quantifying the cysts feature of the tumor in the tumor inner region, based on both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region.

16. The quantification method as claimed in claim 15, wherein the step (D) includes a step (D1), defining a threshold value for calculating the ratio of the region having the cysts feature over the tumor inner region, based on both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region.

17. An imaging method of the cysts feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) calculating both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region; and

(D) defining a cysts imaging upper limit and a cysts imaging lower limit based on both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region, and imaging the cysts feature of the tumor in the tumor inner region.

18. The imaging method as claimed in claim 17, wherein the cysts imaging upper limit is the minimum value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region, plus 0.1 times of the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

and the cysts imaging lower limit is the minimum value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region.

19. The imaging method as claimed in claim 17, when a pixel point in the tumor inner region having the gradient value of the gray scale between the cysts imaging upper limit and the cysts imaging lower limit, the pixel point is covered by a peach block while the cysts feature of the tumor is imaged.

46. The imaging method as claimed in claim 17, further comprises the below steps following the step (D):

(E) defining a plurality of reference masks from the plurality pixel points in the tumor inner region, each of the plurality of reference masks including a reference pixel point and plural pixels points adjacent to the reference pixel point, wherein the gradient value of the gray scale of the reference pixel point is between the cysts imaging upper limit and the cysts imaging lower limit; and

(F) covering the reference pixel point and all the plural pixels points with a peach block while the gradient value of the gray scale of at least one of the plural pixels points is between the cysts imaging upper limit and the cysts imaging lower limit and the cysts feature of the tumor is imaged.

47. The imaging method as claimed in claim 46, further comprises the step (G) following the step (F):

(G) removing the peach block covering the reference pixel point and all the plural pixels points while only gradient value of the gray scale of the reference pixel point is between the cysts imaging upper limit and the cysts imaging lower limit and cysts feature of the tumor is imaged.

20-45. -45. (canceled)

48. A quantification method of the calcifications feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) calculating both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

(D) retrieving a cysts region in the tumor inner region based on both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

(E) calculating the maximum value, the standard deviation, and the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region; and

(F) quantifying the calcifications feature of the tumor in the tumor inner region, based on the maximum value, the standard deviation, and the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region.

49. The quantification method as claimed in claim 48, wherein the step (F) includes a step (F1), defining a threshold value for calculating the ratio of the region having the calcifications feature over the tumor inner region, based on the maximum value, the standard deviation, and the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region.

50. An imaging method of the calcifications feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) calculating both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

(D) retrieving a cysts region in the tumor inner region based on both the minimum value and the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

(E) calculating the maximum value, the standard deviation, and the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region, based on the gradient value of the gray scale of each of the plurality pixel points in the tumor inner region but outside the cysts region; and

(F) defining a calcifications imaging upper limit and a calcifications imaging lower limit based on the maximum value, the standard deviation, and the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region, and imaging the calcifications feature of the tumor in the tumor inner region.

51. The imaging method as claimed in claim 50, wherein the calcifications imaging upper limit is the maximum value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region;

and the calcifications imaging lower limit is the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region, plus 2.8 times of the standard deviation of the gradient value of the gray scale of the plurality pixel points in the tumor inner region but outside the cysts region.

52. The imaging method as claimed in claim 50, when a pixel point in the tumor inner region but outside the cysts region having the gradient value of the gray scale between the calcifications imaging upper limit and the calcifications imaging lower limit, the pixel point is covered by a yellow block while the calcifications feature of the tumor is imaged.

53. A quantification method of the echoic feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) calculating the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region;

(D) selecting a reference block in the tumor external region, and calculating the mean value of the gradient value of the gray scale of the plurality pixel points in the reference block based on the gradient value of each of the gray scale of the plurality pixel points in the reference block; and

(E) quantifying the echoic feature of the tumor based on the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region and the mean value of the gradient value of the gray scale of the plurality pixel points in the reference block.

54. The quantification method as claimed in claim 53, the echoic feature of the tumor is quantified by dividing the difference between the mean value of the gradient value of the gray scale of the plurality pixel points in the tumor inner region and the mean value of the gradient value of the gray scale of the plurality pixel points in the reference block with the mean value of the gradient value of the gray scale of the plurality pixel points in the reference block.

55. The quantification method as claimed in claim 53, the tumor has the hyperechoic feature while a resulting value of the quantification method is larger than or equal to 0.

56. The quantification method as claimed in claim 53, the tumor has the hypoechoic feature while a resulting value of the quantification method is smaller than 0.

57. A quantification method of the heterogenesis feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) defining a plurality of reference masks from the plurality pixel points in the tumor inner region, each of the plurality of reference masks including a reference pixel point and plural pixels points adjacent to the reference pixel point;

(D) calculating the local mean and the local variance of the gradient value of the gray scale of the reference mask for each of the plurality of reference masks;

(E) calculating the variance of local mean, the mean of local variance, and the variance of local variance of the gradient value of the gray scale of the reference mask of the plurality of reference masks; and

(F) quantifying the heterogenesis feature of the tumor by the calculation of a heterogenesis index for each of the plurality of the reference masks, based on at least one selected from a group consisted of the variance of local mean, the mean of local variance, and the variance of local variance of the gradient value of the gray scale of the reference mask for each of the plurality of reference masks.

58. The quantification method as claimed in claim 57, wherein each of the plurality reference masks includes 25 pixel points.

59. The quantification method as claimed in claim 57, wherein the step (F) includes a step (F1), defining a threshold value for calculating the ratio of the region having the heterogenesis feature over the tumor inner region, based on the heterogenesis index for each of the plurality of the reference masks.

60. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the variance of local mean.

61. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the mean of local variance.

62. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the variance of local variance.

63. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the ratio of the variance of local variance divided by the variance of local mean.

64. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the ratio of the mean of local variance divided by the variance of local variance.

65. The quantification method as claimed in claim 57, wherein the heterogenesis index is a function of the ratio of the mean of local variance divided by the variance of local mean.

66. An imaging method of the heterogenesis feature of a tumor, applying on a gray scale image consisting of a plurality of pixel points and displaying at least one tumor therein, comprising the steps of:

(A) retrieving a tumor contour and a tumor contour annular region from the gray scale image, wherein the tumor contour is in the tumor contour annular region;

(B) displaying the tumor contour over the gray scale image for defining a tumor inner region and a tumor external region on the gray scale image;

(C) defining a plurality of reference masks from the plurality pixel points in the tumor inner region, each of the plurality of reference masks including a reference pixel point and plural pixels points adjacent to the reference pixel point;

(D) calculating the variance of the gradient value of the gray scale of the reference mask for each of the plurality of reference masks;

(E) calculating the mean variance of the gradient value of the gray scale of the reference mask of the plurality of reference masks;

(F) calculating of a heterogenesis index for each of the plurality of the reference masks, based on the variance of the gradient value of the gray scale of the reference mask for each of the plurality of reference masks;

(G) calculating the maximum value, the minimum value, the mean value, and the standard deviation of the heterogenesis indices of the plurality of the reference masks, based on the heterogenesis index for each of the plurality of the reference masks; and

(H) defining a heterogenesis imaging upper limit and a heterogenesis imaging lower limit based on the maximum value, the minimum value, the mean value, and the standard deviation of the heterogenesis indices of the plurality of the reference masks, and imaging the heterogenesis feature of the tumor in the tumor inner region in coordination with a rainbow level.

67. The imaging method as claimed in claim 66, wherein the heterogenesis imaging upper limit is the maximum value of the heterogenesis indices of the plurality of the reference masks;

and the heterogenesis imaging lower limit is the mean value of the heterogenesis indices of the plurality of the reference masks, minus 0.1 times of the standard deviation of the heterogenesis indices of the plurality of the reference masks.

68. The imaging method as claimed in claim 66, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a reference mask having the heterogenesis index equal to the heterogenesis imaging upper limit, the reference pixel point of the reference mask is covered by a red block while the heterogenesis feature of the tumor is imaged.

69. The imaging method as claimed in claim 66, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a reference mask having the heterogenesis index smaller than or equal to the heterogenesis imaging lower limit, the reference pixel point of the reference mask is covered by a purple block while the heterogenesis feature of the tumor is imaged.

70. The imaging method as claimed in claim 66, wherein the rainbow level is a level changing continuously from red to purple, through orange, yellow, green, blue, and indigo;

and when a reference mask having the heterogenesis index between the heterogenesis imaging upper limit and the heterogenesis imaging lower limit, the reference pixel point of the reference mask is covered by a block having the color selected from the rainbow level corresponding to the relation among the heterogenesis index of the reference mask, the heterogenesis imaging upper limit and the heterogenesis imaging lower limit, while the heterogenesis feature of the tumor is imaged.

71. The imaging method as claimed in claim 66, wherein each of the plurality reference masks includes 25 pixel points.