Within-sample variance classification of samples

• US 20030087456A1
• Filed: 10/02/2002
• Published: 05/08/2003
• Est. Priority Date: 10/08/2001
• Status: Abandoned Application

• Current Assignee: Inlight Solutions, Inc.
• Original Assignee: Inlight Solutions, Inc.
• Inventors: Jones, Howland D.T., Nixon, Kristin A., Robinson, M. Ries, Gardner, Craig M., Hull, Edward L.
• Application Number:
  US10/262,692
• Publication Number:
  US 20030087456A1
• Time in Patent Office: Days
• US Class Current: 436/171
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01N 21/35 using infrared light G01N21/39 takes precedence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus and method for infrared spectral analysis of samples to determine if the samples are normal or abnormal or to otherwise classify the sample. More specifically, the apparatus and method classify the sample on the basis of attenuation of infrared radiation at different wavelengths using a within-sample variance model. Further, the method and apparatus can include merging the output of multivariate classification models with the within-sample variance model applied to the infrared spectra sample such that their combined output results in a classification accuracy that is greater than any single model. The invention is useful in classifying, for example, biological samples such as human tissue, including cervical cells.

Claims

1. A method of classifying a sample, comprising:

a. Determining an optical characteristic of the sample at a plurality of measurement events, wherein a measurement event is a determination of the optical characteristic of a spatial portion of the sample made at a time, and wherein at least one of the time and the spatial are different from the times and regions of other measurement events;

b. Evaluating a variance among the determined optical characteristics; and

c. Classifying the sample according to the variance.

45. A method as in claim 1, wherein the sample comprises a biological sample.

52. A method as in claim 1, wherein the sample comprises a cervical cell sample.

59. A method as in claim 1, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

66. A method as in claim 1, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

2. A method of classifying a sample according to a within-sample variance classification model, comprising:

a. Determining a sample response spectrum for each of a plurality of regions of the sample;

b. Determining a variance among the sample response spectra; and

c. Classifying the sample according to the variance and the within-sample variance model.

3. A method as in claim 2, wherein determining a variance comprises determining the standard deviation, determining the median absolute deviation, determining the mean absolute deviation, determining the square of the standard deviation, or a combination thereof.

4. A method as in claim 2, wherein the within-sample variance model comprises a classification model based on a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a variance and a corresponding classification.

5. A method as in claim 4, wherein a spectrum-reference pair comprises a variance among a plurality of sample response spectra of a reference sample and a corresponding classification of the reference sample.

6. A method as in claim 2, wherein the within-sample variance model comprises a classification model based on LDA, QDA, neural network, unsupervised classification, CART, k-nearest neighbors, or a combination thereof.

7. A method as in claim 2, wherein the within-sample variance model comprises a classification model based on PCA or PLS scores of a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a variance and a corresponding classification.

8. A method as in claim 7, wherein the within-sample variance model comprises a classification model based on LDA, QDA, neural network, unsupervised classification, CART, k-nearest neighbors, or a combination thereof.

9. A method as in claim 2, wherein determining the sample response spectrum comprises:

a. Directing radiation to each of the plurality of regions;

b. Determining the interaction with the radiation of each region as a function of radiation characteristic.

10. A method as in claim 9, wherein the radiation characteristic comprises wavelength.

11. A method as in claim 9, wherein determining the interaction comprises determining the absorption of radiation, determining the elastic scattering of incident radiation, determining the inelastic scattering of incident radiation, determining the transmission of incident radiation, or a combination thereof.

46. A method as in claim 2, wherein the sample comprises a biological sample.

53. A method as in claim 2, wherein the sample comprises a cervical cell sample.

60. A method as in claim 2, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

67. A method as in claim 2, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

12. A method of making a sample classification system, comprising:

a. Determining a plurality of spectrum-reference pairs, where each spectrum-reference pair comprises;

i. A variance among a plurality of sample response spectra; and

ii. A corresponding classification;

b. Establishing the sample classification system from a multivariate model based on the plurality of spectrum-reference pairs.

13. A method as in claim 12, wherein each sample response spectrum comprises an optical characteristic of a region of a sample, determined as a function of incident radiation wavelength.

14. A method as in claim 13, wherein the optical characteristic comprises absorption of radiation incident on the region, elastic scattering of radiation incident on the region, inelastic scattering of radiation incident on the region, transmission of radiation incident on the region, or a combination thereof.

15. A method as in claim 12, wherein the variance comprises the standard deviation, the median absolute deviation, the mean absolute deviation, the square of the standard deviation, or a combination thereof.

47. A method as in claim 12, wherein the sample comprises a biological sample.

54. A method as in claim 12, wherein the sample comprises a cervical cell sample.

61. A method as in claim 12, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

68. A method as in claim 12, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

16. A method of classifying a sample according to a within-sample variance classification model, comprising:

a. Determining a sample response spectrum for each of a plurality of regions of the sample;

b. Determining a first variance metric among the sample response spectra;

c. Determining a second variance metric among the sample response spectra; and

d. Classifying the sample according to the first variance metric, the second variance metric, and the within-sample variance model.

17. A method as in claim 16, wherein determining a first variance metric comprises determining the standard deviation, determining the median absolute deviation, determining the mean absolute deviation, determining the square of the standard deviation, or combinations thereof.

18. A method as in claim 16, wherein the within-sample variance model comprises a classification model based on a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a first variance metric, a second variance metric, and a corresponding classification.

19. A method as in claim 16, wherein the within-sample variance model comprises a classification model based on PCA or PLS scores of a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a first variance metric, a second variance metric, and a corresponding classification.

20. A method as in claim 16, wherein determining the sample response spectrum comprises:

a. Directing radiation to the region;

b. Determining the interaction with the radiation of the region as a function of a radiation characteristic.

21. A method as in claim 20, wherein the radiation characteristic comprises wavelength.

22. A method as in claim 20, wherein determining the interaction comprises determining the interaction as a function of the wavenumber of radiation, for a plurality of wavenumbers from about 400 to about 14,000 cm⁻

1.

23. A method as in claim 20, wherein determining the interaction comprises determining the absorption of radiation, determining the elastic scattering of incident radiation, determining the inelastic scattering of incident radiation, determining the transmission of incident radiation, or a combination thereof.

24. A method according to claim 16, wherein the within-sample variance model comprises a combination of the first and second within-sample variance models, wherein:

a. the first within-sample variance model comprises a multivariate model based on the first variance metric determined for a plurality of references, each with a corresponding classification;

b. the second within-sample variance model comprises a multivariate model based on the second variance metric determined for a plurality of references, each with a corresponding classification.

25. A method according to claim 24, wherein the combination comprises a voting mechanism.

48. A method as in claim 16, wherein the sample comprises a biological sample.

55. A method as in claim 16, wherein the sample comprises a cervical cell sample.

62. A method as in claim 16, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

69. A method as in claim 16, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

26. A method of classifying a sample according to a within-sample variance classification model, comprising:

a. Determining a sample response spectrum for each of a plurality of regions of the sample;

b. Determining a plurality of variance metrics among the sample response spectra;

c. Classifying the sample according to the plurality of variance metrics and the within-sample variance model.

27. A method as in claim 26, wherein determining a plurality of variance metrics comprises determining one or more of the standard deviation, the median absolute deviation, the mean absolute deviation, the square of the standard deviation, or a combination thereof.

28. A method as in claim 26, wherein the within-sample variance model comprises a classification model based on a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a plurality of variance metrics and a corresponding classification.

29. A method as in claim 26, wherein the within-sample variance model comprises a classification model based on PCA or PLS scores of a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a plurality of variance metrics and a corresponding classification.

30. A method as in claim 26, wherein determining the sample response spectrum comprises:

a. Directing radiation to the region;

b. Determining the interaction with the radiation of the region as a function of radiation characteristic.

31. A method as in claim 30, wherein the radiation characteristic comprises wavelength.

32. A method as in claim 30, wherein determining the interaction comprises determining the absorption of radiation, determining the elastic scattering of incident radiation, determining the inelastic scattering of incident radiation, determining the transmission of incident radiation, or a combination thereof.

33. A method according to claim 26, wherein the within-sample variance model comprises a combination of a plurality of within-sample variance models, wherein each of the plurality of within-sample variance models comprises a multivariate model based on one of the plurality of variance metrics determined for a plurality of references, each with a corresponding classification.

34. A method according to claim 33, wherein the combination comprises a voting mechanism.

36. A method as in claim 32, wherein the second classification method comprises a mean spectrum classification method.

37. A method as in claim 32, wherein determining a variance comprises determining the standard deviation, determining the median absolute deviation, determining the mean absolute deviation, determining the square of the standard deviation, or a combination thereof.

38. A method as in claim 32, wherein the within-sample variance model comprises a classification model based on a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a variance and a corresponding classification.

39. A method as in claim 32, wherein a spectrum-reference pair comprises a variance among a plurality of sample response spectra of a reference sample and a corresponding classification of the reference sample.

40. A method as in claim 32, wherein the within-sample variance model comprises a classification model based on PCA or PLS scores of a plurality of spectrum-reference pairs, wherein a spectrum-reference pair comprises a variance and a corresponding classification.

41. A method as in claim 32, wherein determining the sample response spectrum comprises:

a. Directing radiation to each of the plurality of regions;

b. Determining the interaction with the radiation of each region as a function of radiation characteristic.

42. A method as in claim 41, wherein the radiation characteristic comprises wavelength.

43. A method as in claim 41, wherein determining the interaction comprises determining the absorption of radiation, determining the scattering of incident radiation, determining the transmission of incident radiation, or a combination thereof.

49. A method as in claim 26, wherein the sample comprises a biological sample.

56. A method as in claim 26, wherein the sample comprises a cervical cell sample.

63. A method as in claim 26, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

70. A method as in claim 26, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

35. A method of classifying a sample, comprising:

a. Determining a sample response spectrum for each of a plurality of regions of the sample;

b. Determining a variance among the sample response spectra;

c. Determining a variance classification of the sample according to the variance and the within-sample variance model;

d. Determining a second classification of the sample according to another classification method;

e. Classifying the sample according to a combination of the variance classification and the second classification.

50. A method as in claim 35, wherein the sample comprises a biological sample.

57. A method as in claim 35, wherein the sample comprises a cervical cell sample.

64. A method as in claim 35, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

71. A method as in claim 35, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

44. An apparatus for classifying a sample, comprising:

a. A source of radiation;

b. Means for directing the radiation to each of a plurality of regions of the sample;

c. Means for detecting the interaction of each of the plurality of regions with the radiation;

d. Means for determining a variance among the regions'"'"' interactions;

e. A multivariate model that classifies the sample based on the determined variance.

51. An apparatus as in claim 44, wherein the sample comprises a biological sample.

58. An apparatus as in claim 44, wherein the sample comprises a cervical cell sample.

65. An apparatus as in claim 44, wherein the sample comprises a cervical cell sample deposited in a substantially monolayer.

72. An apparatus as in claim 44, wherein the sample comprises a cervical cell sample deposited on a slide that is substantially transparent to both IR and visible light, stained, and coverslipped.

73. A method of classifying a sample, comprising:

a. Determining a sample response spectrum of the sample;

b. Determining a first classification of the sample according to a first multivariate classification method;

c. Determining a second classification of the sample according to a second multivariate classification method;

d. Classifying the sample according to a combination of the first classification and the second classification.