Within-sample variance classification of samples
First Claim
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.
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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.
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Citations
73 Claims
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1. A method of classifying a sample, comprising:
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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. - View Dependent Claims (45, 52, 59, 66)
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2. A method of classifying a sample according to a within-sample variance classification model, comprising:
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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. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 46, 53, 60, 67)
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12. A method of making a sample classification system, comprising:
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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. - View Dependent Claims (13, 14, 15, 47, 54, 61, 68)
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16. A method of classifying a sample according to a within-sample variance classification model, comprising:
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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. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 48, 55, 62, 69)
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26. A method of classifying a sample according to a within-sample variance classification model, comprising:
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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. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 49, 56, 63, 70)
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35. A method of classifying a sample, comprising:
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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. - View Dependent Claims (50, 57, 64, 71)
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44. An apparatus for classifying a sample, comprising:
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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. - View Dependent Claims (51, 58, 65, 72)
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73. A method of classifying a sample, comprising:
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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.
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Specification