Method and apparatus for the early detection of tissue pathology using wavelet transformation

• US 6,415,046 B1
• Filed: 10/07/1999
• Issued: 07/02/2002
• Est. Priority Date: 10/07/1999
• Status: Expired due to Fees

• Current Assignee: Edmund Kenneth Sr. Kerut
• Original Assignee: Edmund Kenneth Sr. Kerut
• Inventors: Kerut, Edmund Kenneth Sr.
• Primary Examiner(s): Johns, Andrew W.
• Assistant Examiner(s): Nakhjavan, Shervin K
• Application Number:
  US09/414,123
• Time in Patent Office: 999 Days
• Field of Search: 382/128, 382/130, 382/131, 382/132, 382/133, 382/240, 382/248, 382/276
• US Class Current: 382/128
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method and apparatus for the early detection of tissue pathology using wavelet decomposition, such as Cardiomyopathy, for the early detection and diagnosis of disease using multi-dimensional non-invasive imaging techniques. The apparatus for early detection of tissue pathology includes a tissue texture quantifier apparatus having a wavelet decomposer for decomposing a region-of-interest (ROI) of a digitized image of anatomical tissue into vertical, horizontal and diagonal detail coefficients and a tissue pathology evaluator having a standard reference model. The tissue texture quantifier further includes a wavelet calculator to calculate the energy content for each of the vertical detail coefficient, the horizontal detail coefficient and the diagonal detail coefficient. Moreover, the tissue texture quantifier includes a summer for summing the energy content of the vertical detail coefficient, the horizontal detail coefficient and the diagonal detail coefficient and a normalizer for normalizing all energy content and the summed energy content.

Claims

1. A pathological tissue texture quantifier apparatus for use with a computing device comprising:

means for wavelet decomposing a region-of-interest (ROI) of a digitized “

raw”

image of anatomical tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

means for calculating an energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D); and

,

means for summing the energy content of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail cofficient (D).

2. The apparatus of claim 1, further comprising means for summing the energy content of said vertical detail coefficient and said horizontal detail coefficient.

3. The apparatus of claim 2, further comprises:

means for normalizing said energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI); and

,

means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.

4. The apparatus of claim 3, further comprising means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient and said horizontal detail coefficient.

5. The apparatus of claim 4, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N

 





Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

6. The apparatus of claim 5, wherein said normalizing means normalizes said energy content with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

7. The apparatus of claim 4, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

8. The apparatus of claim 7, wherein said normalizing means normalizes said energy content with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

9. The apparatus of claim 1, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

10. The apparatus of claim 1, wherein said digitized “

raw”

image of said anatomical tissue is a digitized “

raw”

ultrasound image file.

11. The apparatus of claim 1, wherein said digitized “

raw”

image of said anatomical tissue is a digitized “

raw”

two-dimensional echocardiographic image of myocardium during end-diastole.

12. The apparatus of claim 11, wherein said two-dimensional echocardiographic image is substantially devoid of endocardial and epicardial borders.

13. The apparatus of claim 11, wherein said two-dimensional echocardiographic image is of a parasternal short axis image at a level of papillary muscles during said end-diastole.

14. The apparatus of claim 1, wherein said region-of-interest has a small two-dimensional pixel resolution.

15. The apparatus of claim 1, wherein said anatomical tissue is mammal tissue.

16. The apparatus of claim 1, wherein said anatomical tissue is Homo sapien tissue.

17. The apparatus of claim 1, wherein said anatomical tissue is muscle tissue.

18. The apparatus of claim 1, wherein said anatomical tissue is organ tissue.

19. The apparatus of claim 1, wherein said anatomical tissue is myocardium.

20. The apparatus of claim 1, wherein said digitized “

raw”

image of said anatomical tissue is derived from multi-dimensional non-invasive imaging techniques wherein said multi-dimensional non-invasive imaging techniques include any one of magnetic resonance imaging (MRI), computer axial tomography (CAT Scan), multi-dimensional ultrasound imaging, and radionuclide imaging.

21. A pathological tissue texture quantifier apparatus for use with a computing device comprising:

a wavelet decomposer to decompose a region-of-interest of an ultrasound image of anatomical tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

a wavelet energy calculator to calculate an energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D) of said region-of-interest of said ultrasound image of anatomical tissue;

a wavelet energy summer to sum the energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue; and

,

a wavelet normalizer to calculate a normalized energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) and a normalized energy content for said sum of said energy content of said vertical detail coefficient, said hzdetail coefficient and said diagonal detail coefficient.

22. The apparatus of claim 21, wherein said wavelet energy summer further sums the energy content of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue;

and, wherein said a wavelet normalizer further calculates a normalized energy content for said energy content of said vertical detail coefficient and said horizontal detail coefficient.

23. The apparatus of claim 21, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N

 





Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

24. The apparatus of claim 23, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

25. The apparatus of claim 21, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

26. The apparatus of claim 21, wherein said ultrasound image of said anatomical tissue is a digitized “

raw”

ultrasound image file.

27. The apparatus of claim 21, wherein said ultrasound image of said anatomical tissue is a digitized “

raw”

ultrasound image file of a two-dimensional echocardiographic image of myocardium during end-diastole.

28. The apparatus of claim 27, wherein said two-dimensional echocardiographic image is substantially devoid of endocardial and epicardial borders.

29. The apparatus of claim 27, wherein said two-dimensional echocardiographic image is of a parasternal short axis image at a level of papillary muscles during said end-diastole.

30. The apparatus of claim 21, wherein said region-of-interest has a two-dimensional pixel resolution of 16×

16.

31. The apparatus of claim 21, wherein said anatomical tissue is mammal tissue.

32. The apparatus of claim 21, wherein said anatomical tissue is Homo sapien tissue.

33. The apparatus of claim 21, wherein said anatomical tissue is muscle tissue.

34. The apparatus of claim 21, wherein said anatomical tissue is organ tissue.

35. The apparatus of claim 21, wherein said anatomical tissue is myocardium.

36. The apparatus of claim 21, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

37. The apparatus of claim 36, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

38. A pathological tissue texture quantifier apparatus for use with a computing device comprising:

a wavelet decomposer to decompose a region-of-interest of a digitized “

raw”

image of anatomical tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

a wavelet energy calculator to calculate an energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D) of said region-of-interest;

a wavelet energy summer to sum the energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI); and

,

a wavelet normalizer to calculate a normalized energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D) of said region-of-interest (ROI) and a normalized energy content for said sum of said energy content of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D).

39. The apparatus of claim 38, wherein said wavelet energy summer further sums the energy content of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI);

and, wherein said a wavelet normalizer further calculates a normalized energy content for said energy content of said vertical detail coefficient and said horizontal detail coefficient.

40. The apparatus of claim 38, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N

 





Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

41. The apparatus of claim 40, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

42. The apparatus of claim 38, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

43. The apparatus of claim 38, wherein said digitized “

raw”

image of said anatomical tissue is derived from multi-dimensional non-invasive imaging techniques wherein said multi-dimensional non-invasive imaging techniques include any one of magnetic resonance imaging (MRI), computer axial tomography (CAT Scan), multi-dimensional ultrasound imaging, and radionuclide imaging.

44. The apparatus of claim 38, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels-within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

45. The apparatus of claim 44, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

46. A method for quantifying pathological tissue texture comprising the steps of:

(a) wavelet decomposing a region-of-interest (ROI) of an ultrasound image of anatomical tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

(b) calculating an energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D) of said region-of-interest of said ultrasound image of anatomical tissue;

(c) summing the energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue;

(d) normalizing each of the energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D) of said region-of-interest (ROI); and

,

(e) normalizing said sum of said energy content of said vertical detail coefficient (V), saidhorizontal detail coefficient (H) and said diagonal detail coefficient (D).

47. The method of claim 46, wherein said step of (c) further comprises the step of:

(c1) summing the energy content of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical tissue.

48. The method of claim 46, wherein said step of (e) further comprises the step of:

(e1) normalizing the sum of the energy content of said vertical detail coefficient and said horizontal detail coefficient.

49. The method of claim 46, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

50. The method of claim 49, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

51. The method of claim 46, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

52. The method of claim 46, wherein said ultrasound image of said anatomical tissue is a digitized “

raw”

ultrasound image file.

53. The method of claim 46, wherein said ultrasound image of said anatomical tissue is a digitized “

raw”

ultrasound image file of a two-dimensional echocardiographic image of myocardium during end-diastole.

54. The method of claim 53, wherein said two-dimensional echocardiographic image is substantially devoid endocardial and epicardial borders.

55. The method of claim 53, wherein said two-dimensional echocardiographic image is of a parasternal short axis image at a level of papillary muscles during said end-diastole.

56. The method of claim 46, wherein said region-of-interest has a two-dimensional pixel resolution of 16×

16.

57. The method of claim 46, wherein said anatomical tissue is mammal tissue.

58. The method of claim 46, wherein said anatomical tissue is Homo sapien tissue.

59. The method of claim 46, wherein said anatomical tissue is muscle tissue.

60. The method of claim 46, wherein said anatomical tissue is organ tissue.

61. The method of claim 46, wherein said anatomical tissue is myocardium.

62. The method of claim 46, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

63. The method of claim 62, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

64. A pathological tissue detector apparatus adapted to interface with an ultrasound imaging machine comprising;

(a) a region-of-interest (ROI) selector operable to select a region-of-interest, having a two-dimensional pixel resolution, from a digitized “

raw”

ultrasound image of anatomical suspect tissue;

(b) a wavelet decomposer to decompose said region-of-interest of said ultrasound image of anatomical suspect tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

(c) a wavelet energy calculator to calculate an energy content of each of said vertical, horizontal and diagonal detail coefficients (V, H, D) in said region-of-interest of said ultrasound image of anatomical suspect tissue; and

,

(d) a tissue pathology evaluator which compares said energy content of said each of said vertical, horizontal and diagonal detail coefficients (V, H, D) in said region-of-interest with energies of vertical, horizontal and diagonal detail coefficients of a standard reference model for a corresponding anatomical suspect tissue.

65. The apparatus of claim 64, further comprising:

(e) a wavelet energy summer to sum the energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical suspect tissue; and

,

(f) a wavelet normalizer to calculate a normalized energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI) and a normalized energy content for said sum of said energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.

66. The apparatus of claim 65, wherein said wavelet energy summer further sums the energy content of said vertical detail coefficient and said horizontal detail coefficient of said region-of-interest (ROI) of said ultrasound image of anatomical suspect tissue;

and, wherein said wavelet normalizer further calculates a normalized energy content for said energy content of said vertical detail coefficient and said horizontal detail coefficient.

67. The apparatus of claim 65, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}$

i=1



Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

68. The apparatus of claim 67, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

69. The apparatus of claim 65, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

70. The apparatus of claim 69, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

71. The apparatus of claim 64, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

72. The apparatus of claim 64, wherein said ultrasound image of anatomical suspect tissue is a digitized “

raw”

ultrasound image file.

73. The apparatus of claim 64, wherein said ultrasound image of anatomical suspect tissue is a digitized “

raw”

ultrasound image file of a two-dimensional echocardiographic image of myocardium during end-diastole.

74. The apparatus of claim 73, wherein said two-dimensional echocardiographic image is substantially devoid endocardial and epicardial borders.

75. The apparatus of claim 73, wherein said two-dimensional echocardiographic image is of a parasternal short axis image at a level of papillary muscles during said end-diastole.

76. The apparatus of claim 64, wherein said region-of-interest has a two-dimensional pixel resolution of 16×

16.

77. The apparatus of claim 64, wherein said anatomical suspect tissue is mammal tissue.

78. The apparatus of claim 64, wherein said anatomical suspect tissue is Homo sapien tissue.

79. The apparatus of claim 64, wherein said anatomical suspect tissue is muscle tissue.

80. The apparatus of claim 64, wherein said anatomical suspect tissue is organ tissue.

81. The apparatus of claim 64, wherein said anatomical suspect tissue is myocardium.

82. The apparatus of claim 81, further comprising:

(e) an echocardiograph machine interface to receive said digitized “

raw”

ultrasound image file from an echocardiograph machine.

83. The apparatus of claim 81, further comprising:

(e) an RGB-grayscale converter which converts said digitized “

raw”

ultrasound image file to a grayscale format.

84. A method of detecting and diagnosing tissue abnormalities comprising the steps of:

(a) performing an ultrasound imaging procedure on anatomical suspect tissue;

(b) obtaining a digitized “

raw”

ultrasound image signal of a two-dimensional image;

(c) selecting a region-of-interest (ROI) having a predetermined two-dimensional pixel resolution from said digitized “

raw”

ultrasound image signal;

(d) decomposing the selected ROI image using a two-dimensional wavelet decomposition method;

(e) calculating an energy content of each first level detail coefficient of a plurality of different first level detail coefficients of the selected ROI; and

,

(f) comparing each energy content of each first level detail coefficient with a standard reference model in order to differentiate normal from abnormal tissue.

85. The method of claim 84, further comprising the step of:

(d) converting said digitized “

raw”

ultrasound image signal to a grayscale image.

86. The method of claim 84, wherein said plurality of different first level detail coefficients includes vertical, horizontal and diagonal detail coefficients (V1, H1, D1) and said energy content of each of said vertical V1, horizontal H1 and diagonal D1 detail coefficients in said region-of-interest is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V1, H1, or D1;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value of a given first level detail coefficient.

87. The method of claim 84, wherein said plurality of different first level detail coefficients includes vertical, horizontal and diagonal detail coefficients (V1, H1, D1) and said energy content of each of said vertical V1, horizontal H1 and diagonal D1 detail coefficients in said region-of-interest is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V1, H1, or D1;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value of a given first level detail coefficient.

88. A method of detecting cardiomyopathy comprising the steps of:

(a) selecting a region-of-interest (ROI), having a two-dimensional pixel resolution, from a digitized “

raw”

ultrasound image file of myocardium;

(b) decomposing said region-of-interest into vertical, horizontal and diagonal detail coefficients;

(c) calculating energy of each of said vertical, horizontal and diagonal detail coefficients (V, H, D) in said region-of-interest (ROI); and

,

(d) comparing said energy of said each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest with energies of vertical, horizontal and diagonal detail coefficients of a standard reference model for said myocardium.

89. The method of claim 88, wherein said energy of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value of a given first level detail coefficient.

90. The method of claim 89, further comprising:

(e) summing the energy of said vertical detail coefficient, said horizontal detail component and said diagonal detail coefficient of said region-of-interest and wherein said comparing step of (d) further comprises the step of;

(d1) comparing said sum of said energy in said region-of-interest with a sum of energies of vertical detail coefficient, horizontal detail coefficient and diagonal detail coefficient of said standard reference model.

91. The method of claim 90, further comprises the step of:

(f) normalizing said energy of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest with an energy of a non-decomposed image of said ROI and

wherein said comparing step of (d) further comprises the step of;

(d2) comparing the normalized energy in said region-of-interest with normalized energies of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said standard reference model.

92. The method of claim 91, wherein said energy of said non-decomposed image of said region-of-interest is defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi2

wherein O represents the non-decomposed ROI image;

i=the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value for the non-decomposed ROI image.

93. The method of claim 91, wherein said energy of said non-decomposed image (O) of said region-of-interest (ROI) is defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

94. The method of claim 88, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

95. The method of claim 88, wherein said digitized “

raw”

ultrasound image file is a two-dimensional echocardiographic image during end-diastole.

96. The method of claim 88, wherein said digitized “

raw”

ultrasound image file is a two-dimensional echocardiographic image during end-diastole is substantially devoid endocardial and epicardial boarders.

97. The method of claim 88, wherein said digitized “

raw”

ultrasound image file is a two-dimensional echocardiographic image of a parasternal short axis image at a level of papillary muscles during end-diastole.

98. The method of claim 88, wherein said two dimensional pixel resolution is a 16×

16 pixel resolution.

99. The method of claim 88, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

100. A method of detecting and diagnosing early cardiomyopathy includes the following:

(a) performing an echocardiograph procedure on an end-diastolic heart wherein an end-diastolic heart is a relaxed heart;

(b) obtaining a digitized “

raw”

ultrasound image signal of a two-dimensional echocardiographic image;

(c) selecting a region-of-interest (ROI) having a predetermined two-dimensional pixel resolution from said digitized “

raw”

ultrasound image signal;

(d) converting said digitized “

raw”

ultrasound image signal to a grayscale image;

(e) decomposing the selected ROI image using a two-dimensional wavelet decomposition method;

(f) calculating an energy content of each first level detail coefficient of a plurality of different first level detail coefficients of the selected ROI; and

,

(g) comparing each energy content of each first level detail coefficient with a standard reference model in order to differentiate normal from abnormal tissue.

101. The method of claim 100, wherein said plurality of different first level detail coefficients includes vertical, horizontal and diagonal detail coefficients (V1, H1, D1) and the energy content of each of said vertical V1, horizontal H1 and diagonal D1 detail coefficients in said region-of-interest is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V1, H1, or D1;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value of a given first level detail coefficient.

102. The method of claim 101, further comprising:

(h) summing the energy content of said vertical detail coefficient, said horizontal detail component and said diagonal detail coefficient of said region-of-interest and

wherein said comparing step of (g) further comprises the step of;

(g1) comparing said sum of said energy content in said region-of-interest with a sum of energies of vertical detail coefficient, horizontal detail coefficient and diagonal detail coefficient of said standard reference model.

103. The method of claim 102, further comprises the step of:

(i) normalizing said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest with an energy content of a non-decomposed image of said ROI and

wherein said comparing step of (g) further comprises the step of;

(g2) comparing the normalized energy content in said region-of-interest with normalized energies of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said standard reference model.

104. The method of claim 103, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

105. The method of claim 103, wherein said energy content are normalized with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

106. The apparatus of claim 105, wherein said normalizing means normalizes said energy content with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

107. The method of claim 100, wherein said plurality of different first level detail coefficients includes vertical, horizontal and diagonal detail coefficients (V1, H1, D1) and the energy content of each of said vertical V1, horizontal H1 and diagonal D1 detail coefficients in said region-of-interest is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V1, H1, or D1;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value of a given first level detail coefficient.

108. A pathological tissue texture quantifier apparatus for use with a computing device comprising:

means for wavelet decomposing a region-of-interest (ROI) of a digitized image of anatomical tissue into vertical, horizontal and diagonal detail coefficients (V, H, D);

means for calculating the energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D); and

,

means for summing the energy content of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D).

109. The apparatus of claim 108, further comprising means for summing the energy content of said vertical detail coefficient and said horizontal detail coefficient.

110. The apparatus of claim 109, further comprises:

means for normalizing said energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI); and

,

means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.

111. The apparatus of claim 110, further comprising means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient and said horizontal detail coefficient.

112. The apparatus of claim 111, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

113. The apparatus of claim 112, wherein said normalizing means normalizes said energy content with a energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N

 





IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

114. The apparatus of claim 111, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum ^{}$

N

 





Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

115. The apparatus of claim 108, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

116. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a digitized ultrasound image.

117. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a digitized two-dimensional echocardiographic image of myocardium during end-diastole.

118. The apparatus of claim 117, wherein said two-dimensional echocardiographic image is substantially devoid of endocardial and epicardial borders.

119. The apparatus of claim 117, wherein said two-dimensional echocardiographic image is of a parasternal short axis image at a level of papillary muscles during said end-diastole.

120. The apparatus of claim 108, wherein said region-of-interest has a small two-dimensional pixel resolution.

121. The apparatus of claim 108, wherein said anatomical tissue is mammal tissue.

122. The apparatus of claim 108, wherein said anatomical tissue is Homo sapien tissue.

123. The apparatus of claim 108, wherein said anatomical tissue is muscle tissue.

124. The apparatus of claim 108, wherein said anatomical tissue is organ tissue.

125. The apparatus of claim 108, wherein said anatomical tissue is myocardium.

126. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is derived from multi-dimensional non-invasive imaging techniques wherein said multi-dimensional non-invasive imaging techniques include any one of magnetic resonance imaging (MRI), computer axial tomography (CAT Scan), multi-dimensional ultrasound imaging and radionuclide imaging.

127. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a two-dimensional digitized “

raw”

image of said anatomical tissue.

128. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a digitized “

raw”

image of said anatomical tissue.

129. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a digitized image of said anatomical tissue having been subjected to digital image processing.

130. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a digitized image of said anatomical tissue having been subjected to a smoothing function.

131. The apparatus of claim 108, wherein said digitized image of said anatomical tissue is a video signal of said anatomic tissue.

132. A pathological tissue texture quantifier apparatus for use with a computing device comprising:

means for wavelet decomposing a region-of-interest (ROI) of a digitized image of anatomical tissue into vertical, horizontal and diagonal detail coefficients(V, H, D);

means for calculating the energy content for each of said vertical detail coefficient (V), said horizontal detail coefficient (H) and said diagonal detail coefficient (D); and

,

means for summing the energy content of said vertical detail coefficient (V) and said horizontal detail cofficient (H).

133. The apparatus of claim 132, further comprising means for summing the energy content of said vertical detail coefficient, said horizontal detail coefficient and said detail coefficient.

134. The apparatus of claim 133, further comprises:

means for normalizing said energy content for each of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient of said region-of-interest (ROI); and

,

means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient, said horizontal detail coefficient and said diagonal detail coefficient.

135. The apparatus of claim 134, further comprising means for normalizing said energy content for the sum of said energy content of said vertical detail coefficient and said horizontal detail coefficient.

136. The apparatus of claim 135, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi2

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

137. The apparatus of claim 136, wherein said normalizing means normalizes said energy content with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi2

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

138. The apparatus of claim 135, wherein said energy content of each of said vertical, horizontal and diagonal detail coefficients in said region-of-interest (ROI) is defined by $E_z=\left(1/N\right)*\sum _{}^{}$

i=1N



Izi

wherein z=V, H, or D;

i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_zi=the pixel intensity value for a respective one said vertical, horizontal and diagonal detail coefficients.

139. The apparatus of claim 138, wherein said normalizing means normalizes said energy content with an energy content of a non-decomposed image (O) of said region-of-interest (ROI) defined by $E_O=\left(1/N\right)*\sum _{}^{}$

i=1N



IOi

wherein i=is the number of the pixel from 1 to N wherein N=the number of pixels within the ROI; and

I_Oi=the pixel intensity value of said non-decomposed image of said region-of-interest (ROI).

140. The apparatus of claim 132, wherein said vertical, horizontal and diagonal detail coefficients are first level detail coefficients of a two-dimensional Haar wavelet decomposition method.

141. The apparatus of claim 132, wherein said digitized image of said anatomical tissue is derived from multi-dimensional non-invasive imaging techniques wherein said multi-dimensional non-invasive imaging techniques include any one of magnetic resonance imaging (MRI), computer axial tomography (CAT Scan), multi-dimensional ultrasound imaging and radionuclide imaging.

142. The apparatus of claim 132, wherein said digitized image of said anatomical tissue is a two-dimensional digitized “

raw”

image of said anatomical tissue.

143. The apparatus of claim 132, wherein said digitized image of said anatomical tissue is a digitized “

raw”

image of said anatomical tissue.

144. The apparatus of claim 132, wherein said digitized image of said anatomical tissue is a digitized image of said anatomical tissue having been subjected to digital image processing.

145. The apparatus of claim 132, wherein said digitized image of said anatomical tissue is a digitized image of said anatomical tissue having been subjected to a smoothing function.