Classification and characterization of tissue through features related to adipose tissue

• US 6,587,702 B1
• Filed: 01/21/2000
• Issued: 07/01/2003
• Est. Priority Date: 01/22/1999
• Status: Expired due to Term

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Instrumentation Metrics Inc.
• Inventors: Acosta, George M., Makarewicz, Marcy R., Ruchti, Timothy L., Hazen, Kevin H.
• Primary Examiner(s): Shaver, Kevin
• Assistant Examiner(s): KREMER, MATTHEW J
• Application Number:
  US09/489,617
• Time in Patent Office: 1,257 Days
• Field of Search: 600/309-310, 600/322-324, 600/306, 600/473-475, 356/39-42
• US Class Current: 600/310
• 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 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/4872 Body fat
• 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: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A non-invasive system for characterizing and classifying the state and structure of a tissue sample operates on a near infrared absorbance spectrum of in vivo tissue. A method that uses near-infrared spectral measurements to characterize and classify the state and structure of tissue sampled based on absorbance features related to fat in adipose tissue is provided. Also provided is a method of estimating skin fold thickness. The approach provides information about sources of tissue variability and is therefore useful for establishing the general category of the tissue structure. Categorization of subjects on the basis of the determination is suitable for further spectral analysis and the measurement of biological and chemical compounds, such as blood analytes. The invention further provides a method of estimating percent body fat based on a skin fold thickness estimate. The invention also provides an apparatus for measuring the absorbance spectra coupled with a digital processor for performing the required analyses.

Claims

1. A non-invasive method of characterizing and classifying the state and structure of tissue on the basis of spectral absorbance features related to fat in the subcutaneous tissue comprising the steps of:

providing a calibration set of exemplary measurements;

measuring NIR absorbance spectrum of a skin tissue sample;

detecting outliers, wherein said outliers are invalid measurements caused by spectral variation due to any of instrument malfunction poor sampling and subjects outside of said calibration set;

preprocessing said NIR spectrum, wherein said preprocessing step includes one or more transformations that attenuate noise and instrumental variation without affecting signal of interest, including any of wavelength selection, scaling, normalization, smoothing, derivatives, and filtering;

providing a basis set, wherein said basis set comprises a pure component spectrum of water and one of animal fat, for feature extraction and preprocessing; and

extracting features, whereby features of measurements relevant to classification are determined.

2. The method of claim 1, wherein said spectrum is denoted by a vector m∈

R^N of absorbance values pertaining to a set of N wavelengths λ

∈

R^N.

3. The method of claim 1, wherein said NIR spectral measurements are in the wavelength region of approximately 1100 to 2500 nm.

4. The method of claim 1, wherein said outlier detection step employs principal components analysis, and residual analysis to detect spectral outliers.

5. The method of claim 4, wherein said outlier detection step further comprises the steps of:

projecting a spectrum m onto a plurality of eigenvectors, contained in a matrix o, said matrix o being previously developed through principal components analysis of said calibration set, where ${\mathrm{xpc}}_o=\underset{k=1}{\stackrel{7}{\sum <br><br>}}<br><br>{\mathrm{mo}}_k,$

 

and where o_k is the k^th column of the matrix o;

determining the residual q, according to

6. The method of claim 4, wherein said feature extraction step comprises principal component analysis.

7. The method of claim 6, wherein said feature extraction step comprises the steps of:

truncating said spectrum m at the wavelength region of approximately 1100-2500 nm;

projecting said truncated spectrum onto a plurality, p_k, of eigenvectors, where said eigenvectors were previously calculated through principal component analysis of said calibration set;

wherein said projection produces a 1 by N vector of scores, xpc; and

applying a Discriminant function whereby said samples are classified on the basis of the first M scores, wherein said scores are rotated through a cross product with a Discriminant, w, to produce a scalar, L.

8. The method of claim 7, wherein M=3.

9. The method of claim 1, wherein said feature extraction step comprises any mathematical transformation that enhances a quality or aspect of sample measurement for interpretation to represent concisely properties and characteristics of the tissue measurement site.

10. The method of claim 9, wherein said feature extraction step comprises normalizing the magnitude of absorbance bands of fat in adipose tissue.

11. The method of claim 9, wherein said feature extraction step comprises comparing water and fat absorbance spectra of said sample to water and fat absorbance spectra of said calibration set.

12. The method of claim 9, wherein said features are represented in a vector z∈

R^M that is determined from a preprocessed measurement through;

13. The method of claim 12, wherein individual features are divided into two categories comprising:

abstract features that do not necessarily have a specific interpretation related to a physical system; and

simple features that are derived from an a priori understanding of a sample and that can be related directly to a physical phenomenon.

14. The method of claim 1, further comprising the step of:

classifying said sample according to predefined categories of fatness and leanness.

15. The method of claim 14, wherein said spectrum is limited to any of the wavelength regions of approximately 1100-1350 nm and approximately 1650-1800 nm.

16. The method of claim 15, wherein said feature extraction step comprises the step of:

normalizing said limited spectrum.

17. The method of claim 16, wherein said normalizing step comprise the steps of:

projecting said spectrum of water on said limited spectrum according to

18. The method of claim 17, wherein class membership is defined by a continuum of grades, and wherein a set of membership functions map a feature space into an interval [0,1] for each class and wherein an assigned grade of “

1”

represents a highest degree of class membership.

19. The method of claim 18, wherein the mapping from the feature space to a vector of class memberships is given by:

20. The method of claim 19, wherein a membership function is represented by $y={}^{}$

-12

σ

2

(z-z_)2,

where y is the degree of membership in a fuzzy subset, z is the feature used to determine membership, {overscore (z)} is the center of a fuzzy subset, and σ

is the standard deviation.

21. The method of claim 19, wherein said membership vector provides the degree of class membership in each of said predefined classes.

22. The method of claim 14, wherein said classification step comprises the steps of:

measuring similarity of at least one feature to said predefined categories; and

assigning membership in said predefined categories.

23. The method of claim 22, wherein said assigning step uses mutually exclusive classes and assigns each sample to one class.

24. The method of claim 23, wherein said assigning step further comprises the steps of:

mapping said sample to one of said predefined classes;

applying a decision rule to assign class membership.

25. The method of claim 24, wherein said mapping step is given by:

26. The method of claim 24, wherein limits for said mapping and said decision rule are determined from a calibration set of exemplary measurements and corresponding reference values of fat and lean through a classification calibration procedure.

27. The method of claim 26, wherein said classification calibration comprises any of linear Discriminant Analysis, SIMCA, k nearest neighbor, fuzzy classification artificial neural networks.

28. The method of claim 27, wherein said mapping step is given by

29. The method of claim 28, wherein said assigning step employs a decision rule, wherein said decision rule is

If L>

{overscore (L)}, said sample is assigned to a first of said two classes;

If L≦

{overscore (L)}, said sample is assigned to a second of said two classes.

30. The method of claim 23, wherein said categories are “

fat” and

“

lean” and

where L_fat corresponds to a representative value for said “

fat”

class and L_lean corresponds to a representative value for said “

lean”

class; and

wherein said class assignment is based on the closeness of L to L_fat and L_lean.

31. The method of claim 30, wherein a distance d_fat of L to L_fat is measured by

32. The method of claim 3, wherein said decision rule is:

If d_lean<

d_fat, said sample is classified as “

lean;

”

If d_lean≧

d_fat, said sample is classified as “

fat.”

33. The method of claim 22, wherein said assigning step uses a fuzzy classification system that allows class membership in more than one class simultaneously.

34. The method of claim 1, further comprising the step of:

estimating the thickness of a skinfold, said skinfold comprising a layer of adipose tissue.

35. The method of claim 34, wherein said estimating step uses any of preprocessed spectra and extracted features.

36. The method of claim 35, wherein said estimating step further comprises the step of:

providing a calibration model to map said preprocessed spectrum through a mapping to an estimate of skin fold thickness.

37. The method of claim 36, wherein said mapping is linear.

38. The method of claim 37, wherein said skin fold thickness estimate is determined according to $\hat{y}=\sum ^{}_{}$

Nk=1

wc,k

xk;

given the preprocessed spectrum x, and the calibration model w_c, where w_c,k is the k^th element of w_c and ŷ

is the skin fold thickness estimate.

39. The method of claim 38, wherein said calibration model employs factor analysis to decompose a high-dimensional (redundant) data set comprising absorbance, intensity or reflectance measurements at a plurality of wavelengths to significant factors representing the majority of variation within said data set;

and

wherein said calibration model includes factors that capture variation in said spectra correlated with variation in skin fold thickness.

40. The method of claim 39, further comprising the steps of;

projecting said samples into a resulting factor space to produce a set of scores for each sample; and

applying multiple linear regression to model the relationship between said scores and said skin fold thickness.

41. The method of claim 36, wherein said mapping is non-linear.

42. The method of claim 41, wherein said non-linear mapping is specified through any of artificial neural networks and non-linear partial least squares regression.

43. The method of claim 34, further comprising the step of:

estimating the body composition of a subject.

44. The method of claim 43, wherein said body composition estimating step comprises the step of;

mapping a skin fold thickness estimate, a sex estimate and an age estimate to an estimate of the percent body fat of said subject according to;

45. The method of claim 44, wherein said model f( ) is determined by applying a nonlinear regression method to a calibration set of exemplary measurements.

46. The method of claim 43, wherein said spectrum is limited to three wavelengths.

47. The method of claim 46, wherein said three wavelengths are 1124, 1210, and 1276 nm.

48. The method of claim 47, wherein said feature extraction step comprises the step of:

normalizing said limited spectrum.

49. The method of claim 48, wherein said normalizing step comprises the steps of:

projecting said spectrum of water onto two of said three wavelengths; and

subtracting said pure water spectrum from said limited spectrum at the third of said wavelengths according to

 

z=x−

mp,

where z is a final spectrum.

50. The method of claim 48, further comprising the step of:

estimating the percent bodyfat according to

51. The method of claim 34, further comprising the step of:

performing a blood analyte prediction.

52. An apparatus for non-invasively characterizing and classifying the state and structure of a skin tissue sample based on spectral absorbance features related to the absorbance of fat in the subcutaneous tissue of a subject comprising:

means for generating near (NIR) energy;

means for separating said generated NIR energy into a plurality of wavelength regions;

an optical interface comprising;

means for transmitting said NIR energy from said wavelength separating means towards a target measurement site on a subject; and

means for collecting NIR energy emanating from said measurement site;

means for detecting said collected energy and converting said collected energy to a voltage;

means for converting said voltage to a digital value; and

means for analyzing said digital value whereby said analysis results in any of a characterization and a classification of said skin tissue sample.

53. The apparatus of claim 52, wherein said energy source and said wavelength separating means together comprise an array of LED'"'"'s surrounding said detecting means in a radial fashion, each of said LED'"'"'s and said detecting means having a lateral edge and wherein each of said LED'"'"'s successively emits energy at a specific wavelength in a set of pre-selected wavelengths.

54. The apparatus of claim 53, wherein said set of pre-selected wavelengths includes 1100 nm, 1208 nm, 1210 nm, 1275 nm, 1350 nm, 1650 nm, 1720 nm, 1760 nm.

55. The apparatus of claim 53, wherein said lateral edge of any of said LED'"'"'s comprises a point of illumination and said lateral edge of said detecting means comprises a point of detection, and wherein a distance between said point of illumination and said point of detection is approximately 1-3 mm.

56. The apparatus of claim 55, wherein said LED array and said detecting means are coupled with said measurement site by means of staring optics and a lens system.

57. The apparatus of claim 52, wherein said energy source is a quartz halogen lamp wherein said lamp transmits energy in the wavelength range of approximately 1100-2500 nm.

58. The apparatus of claim 57, wherein said wavelength separating means is any of a monochromator and an interferometer.

59. The apparatus of claim 52 wherein said transmission means is any of a light pipe, a fiber-optic probe, a lens system, and a light-directing mirror system.

60. The apparatus of claim 59, wherein a point of illumination is set through any of a focusing lens and a fiber-optic probe.

61. The apparatus of claim 52, wherein said energy detecting means comprises InGaAs detectors.

62. The apparatus of claim 52, wherein said digitizing means is a 16-bit A/D converter.

63. The apparatus of claim 52, wherein said energy collecting means comprises any of at least one staring optical detector and at least one fiber-optic probe.

64. The apparatus of claim 60, wherein a point of detection is set through any of a staring optical detector or a fiber-optic probe.

65. The apparatus of claim 52, wherein said means for analysis comprises a digital processor programmed to perform a tissue characterization procedure;

wherein said digital value is processed by said relative age estimation procedure and whereby said tissue sample is characterized.