CN103340635A - Optical parameter and blood glucose concentration three-dimensional correlation calculation method based on OCT - Google Patents

Abstract

The invention relates to an optical parameter and blood glucose concentration three-dimensional correlation calculation method based on the OCT. The method includes the following steps that blood glucose concentration changes of a measured target are controlled; a plurality of blood glucose concentration values at different times and OCT three-dimensional data of the skin tissue are acquired; the OCT three-dimensional data of the skin tissue acquired at different times form OCT three-dimensional data vectors, and the blood glucose concentration values form blood glucose concentration value vectors; the OCT three-dimensional data vectors are aligned to obtain OCT three-dimensional data aligned vectors; an optical parameter three-dimensional distribution vector of the skin is calculated; according to the optical parameter three-dimensional distribution vector of the skin and the blood glucose concentration value vectors, the three-dimensional correlation between the optical parameters of the skin and the blood glucose concentration value is calculated. According to the optical parameter and blood glucose concentration three-dimensional correlation calculation method based on the OCT, the three-dimensional correlation between the optical parameters of the skin tissue and the blood glucose concentration value can be calculated and is used for guiding detecting area selecting of the OCT noninvasive blood glucose detection technology, and accuracy of an OCT noninvasive blood glucose concentration detection system is improved.

Description

Computational methods based on the optical parametric of OCT and the three-dimensional dependency of blood sugar concentration

Technical field

The present invention relates to the measurement of blood sugar concentration field, particularly relate to the computational methods of the three-dimensional dependency of a kind of optical parametric based on OCT and blood sugar concentration.

Background technology

Diabetes are frequently-occurring diseases of mid-aged population, and along with the raising of people's living standard, the sickness rate of diabetes also rises day by day, and World Health Organization (WHO) classifies diabetes, tumor and cardiovascular and cerebrovascular disease as worldwide three disaster diseases together.Seeking method that a kind of blood sugar concentration detects has very great significance for prevention and the treatment of diabetes.The blood sugar concentration detection method is most widely used at present is to have wound to measure.Having wound to detect the main method of using is to rely on electrochemical method that patient is referred to that blood detects the blood glucose concentration value that obtains patient.This method can realize the blood glucose concentration value in a certain moment is detected, but equally also has some shortcomings.Such as, extraction refers to that blood is relatively more painful, measuring blood concentration needs consumptive material etc.Also have some Wicresoft's detection methods in addition, Wicresoft's detection method mainly is to detect blood sugar concentration by detecting the tissue fluid of extracting from skin, and this method can alleviate patient's misery, but equally patient has been caused certain wound.To sum up, device and correlation method that noinvasive detects blood sugar concentration are highly significant.

Studies show that, the corresponding relation of the light intensity that utilizes optical means to detect to return from skin reflex and the scattering coefficient of skin histology, and the inner blood glucose concentration value of the scattering coefficient of skin histology and biological tissue is closely related.The corresponding relation of the light intensity that following single scattered light strength formula can approximate description be returned from skin reflex and the scattering coefficient of skin histology:

I _R＝I _oexp[-(μ _a+μ _s)L]

I wherein _RBe the light intensity of returning from skin reflex, I _oFor projecting the light intensity of skin, μ _aBe the absorptance of skin histology, μ _sBe the scattering coefficient of skin histology, L is the total optical path of light transdermal.As can be seen from the above equation, the light intensity of returning from skin reflex is the exponential damping with scattering coefficient and absorptance.

In skin histology, the refractive index of body fluid and the refractive index of organelle there are differences.This difference can cause skin histology to the scattering of light phenomenon.Glucose is a main ingredient of body fluid, and when blood sugar concentration changed, the refractive index of body fluid also can change thereupon, and this can cause tissue scatter's coefficient to change.At near infrared band, the scattering coefficient that glucose causes changes wants the variation of specific absorptivity much bigger, so the variation of blood sugar concentration mainly is to cause the variation of skin histology scattering coefficient rather than the variation of absorptance.This shows that optical detection skin histology scattering coefficient can be used as the important means that Woundless blood sugar detects.

Skin histology is on organizational structure, comprise a lot of sweat glands, oils and fats gland and blood vessel, these organizational structuries are very strong for the absorption of infrared light, therefore using OCT(Optical Coherence Tomography, when optical coherence tomography) gathering the data relevant with skin histology, can very weak or disappearance at these organizational structure regional signals.When using optical means to carry out the blood glucose concentration value detection, need to consider that these strong absorptive tissues parts are to the influence to the blood sugar test precision of whole signal.Therefore, be necessary to find the rule that the different tissues structural region is changed by blood glucose concentration value in the skin histology, thereby when detecting blood glucose, noinvasive avoids strong absorptive tissue structure, acquisition realizes the method that changes by rayed skin detection blood sugar concentration with the measurement data of the highly sensitive tissue of change of blood sugar.

OCT is continue ultra sonic imaging, X ray CT (Computed Tomography, computed tomography), MRI(Magnetic Resonance Imaging, nuclear magnetic resonance) biomedical imaging technology of new generation afterwards, be the product that low coherence interference technology, confocal microscope principle and superhet Detection Techniques combine, can realize that non-intruding, high sensitivity, high-resolution carry out imaging to tissue.The ultimate principle that it utilizes low-coherent light to interfere, organize the different depth aspect can accurately measure catoptrical amplitude and relative phase to the backscatter signals of the low-coherent light of incident by detection of biological, obtain the microstructure features on the organization internal depth direction, pass through horizontal scanning again, can obtain data and the image of biological tissue's two dimension or three dimensional structure.The OCT two dimension that obtains by scanning or three-dimensional data can use the single scattered light strength formula to calculate scattering coefficient with change in depth.

Relevant for adopting OCT to carry out the method for Woundless blood sugar measurement of concetration, still in existing OCT measuring blood concentration technology, have only and adopt the stratiform Information Monitoring to carry out the technology that blood sugar concentration is analyzed at present.Prior art thinks that skin is layer structure, on an aspect, its blood sugar concentration is consistent, so, the method of existing OCT commercial measurement blood sugar concentration, be with skin in certain search coverage, measure optical parametric layer by layer, with the set of the optical parametric of each layer and form an optical parametric with the curve that skin depth changes, judge the variation of blood sugar concentration by the variation of this curve then; That is to say, prior art is just carried out the optical parametric accumulation and is surveyed optical parametric along with the changes of blood glucose rule in the skin depth direction, but skin histology is not a kind of simple layer structure, but a kind of labyrinth in three dimensions, the method for therefore existing this Woundless blood sugar measurement of concetration can't obtain dependency Changing Pattern in three dimensions between optical parametric and the blood sugar concentration.

Summary of the invention

Based on this, be necessary to provide a kind of computational methods based on optical parametric and the three-dimensional dependency of blood sugar concentration of OCT that can access the Changing Pattern in three dimensions of dependency between optical parametric and the blood sugar concentration.

A kind of based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, comprise the steps:

The blood sugar concentration of step (1), control measurand changes;

Step (2), in the blood sugar concentration change procedure of measurand, gather several different constantly blood glucose concentration value and the OCT three-dimensional data of skin histology;

The OCT three-dimensional data of step (3), skin histology that difference is gathered constantly constitutes OCT three-dimensional data vector, and blood glucose concentration value constitutes the blood glucose concentration value vector;

Step (4), obtain OCT three-dimensional data alignment vector according to skin surface characteristic information alignment OCT three-dimensional data vector;

Step (5), according to OCT three-dimensional data alignment vector calculation skin optical parameter distributed in three dimensions vector;

Step (6), go out the three-dimensional dependency of skin optical parameter and blood glucose concentration value according to skin optical parameter distributed in three dimensions vector sum blood glucose concentration value vector calculation.

Therein among embodiment, in the blood sugar concentration conversion step of described control measurand, the blood sugar concentration of control measurand changes by making measurand take in glucose or feed realizes.

Among embodiment, constitute in the OCT three-dimensional data vector step in the OCT of the described skin histology that difference is gathered constantly three-dimensional data therein, also comprise the steps:

It is right with the blood glucose concentration value vector that described OCT three-dimensional data vector and blood glucose concentration value are constituted OCT three-dimensional data vector.

Therein among embodiment, described according to OCT three-dimensional data alignment vector calculation skin optical parameter distributed in three dimensions vector step in, comprise the steps:

An OCT three-dimensional data element in step a, the selection OCT three-dimensional data alignment vector;

Step b, selection three-dimensional rectangle frame size;

Step c, at the coordinate points P(x of skin histology, y z) locates, and uses selected three-dimensional rectangle frame to take the three-dimensional subdata of an OCT from this OCT three-dimensional data element;

Steps d, in the three-dimensional subdata of OCT, along degree of depth Z direction the three-dimensional subdata of OCT is averaged in the XOY plane parallel with skin surface, obtain one dimension OCT data and corresponding position coordinates thereof;

Step e, calculate P(x, y, skin optical parameter z) according to one dimension OCT data and corresponding position coordinates thereof;

Step f, repeatedly repeating step c, steps d and step e choose different coordinate points P ' (x ', y ', z ') at every turn, obtain the skin optical parameter on all three-dimensional coordinates of skin histology and constitute skin three-dimensional optical parameter;

Repeating step c obtains the skin three-dimensional optical parameter of all elements in the OCT three-dimensional data alignment vector and constitutes skin optical parameter distributed in three dimensions vector to step f behind other OCT three-dimensional data elements in step g, the selection OCT three-dimensional data alignment vector.

Among embodiment, in described step e, adopt the least-squares linear regression method to calculate P(x therein, y, z) the skin optical parameter of some position.

Therein among embodiment, in the described three-dimensional dependency step that goes out skin optical parameter and blood glucose concentration value according to skin optical parameter distributed in three dimensions vector calculation, comprise the steps:

Step (1), select skin optical parameter distributed in three dimensions vector at coordinate points P(x, y, the optical parametric vector of z) locating;

Step (2), coordinates computed point P(x, y, the dependency between the optical parametric of z) locating vector and the blood glucose concentration value vector, and be kept in the three-dimensional correlation data;

Step (3), repeating step (1) and step (2) travel through the three-dimensional dependency that all coordinate points obtain skin optical parameter and blood glucose concentration value.

Therein among embodiment, in described step (2), go out dependency between optical parametric vector and the blood glucose concentration value vector according to Pearson's correlation calculations.

Above-mentioned based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, gathered several different constantly blood glucose concentration value and the OCT three-dimensional data of skin histology and to have constituted OCT three-dimensional data vector right with the blood glucose concentration value vector, by right further processing obtains skin optical parameter distributed in three dimensions vector to OCT three-dimensional data vector and blood glucose concentration value vector, and finally calculate the three-dimensional dependency of skin optical parameter and blood glucose concentration value.Above-mentioned optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration based on OCT can obtain the optical parametric of skin histology in three dimensions and distribute, obtain the three-dimensional dependency between skin histology optical parametric and the blood glucose concentration value simultaneously, this three-dimensional dependency can be used for instructing the selection of OCT Woundless blood sugar detection technique surveyed area.

Description of drawings

Fig. 1 is skin optical parameter and the three-dimensional relevance algorithms flow chart of blood glucose concentration value of an embodiment;

Fig. 2 is that OCT three-dimensional data vector and the blood glucose concentration value vector of an embodiment is to sketch map;

Fig. 3 is single group OCT three-dimensional data alignment front and back contrast of an embodiment, and left side figure is sketch map before the alignment, and right side figure is alignment back sketch map;

Fig. 4 is the skin optical parameter distributed in three dimensions vector calculation flow chart of an embodiment;

Fig. 5 is that the three-dimensional rectangle frame of an embodiment takes the three-dimensional subdata sketch map of an OCT;

Fig. 6 is that the OCT three-dimensional data that is taken by the three-dimensional rectangle frame of an embodiment on average obtains the one-dimensional signal sketch map in the XY direction;

Fig. 7 is skin optical parameter and the three-dimensional correlation calculations flow chart of blood glucose concentration value of an embodiment;

Fig. 8 is that the single coordinate points dependency of an embodiment is described sketch map;

Fig. 9 is the skin optical parameter that obtains of the experiment of an embodiment and the three-dimensional dependency sketch map of blood glucose concentration value.

The specific embodiment

For the method that solves present Woundless blood sugar measurement of concetration can't obtain the problem of dependency Changing Pattern in three dimensions between optical parametric and the blood sugar concentration, present embodiment provides a kind of optical parametric based on OCT and the computational methods of the three-dimensional dependency of blood sugar concentration and the device of measuring blood concentration.Below in conjunction with specific embodiment, to carrying out concrete description based on the optical parametric of OCT and the computational methods of the three-dimensional dependency of blood sugar concentration.

Please refer to Fig. 1, the computational methods based on the optical parametric of OCT and the three-dimensional dependency of blood sugar concentration that present embodiment provides comprise the steps:

Step S110: the blood sugar concentration of control measurand changes.In this step, can allow measurand oral glucose or feed, the blood glucose concentration value of control measurand changes.

Step S120: in the blood sugar concentration change procedure of measurand, gather the blood glucose concentration value in several different moment and the OCT three-dimensional data of skin histology.T1 in the blood glucose concentration value change procedure, t2 ..., tn gathers OCT three-dimensional data T1 constantly respectively successively, T2 ..., Tn and blood glucose concentration value G1, G2 ..., Gn.The skin histology that the OCT three-dimensional data specifically just refers to measurand in three dimensions each point to reflection of light intensity.

Step S130: the OCT three-dimensional data of the skin histology that difference is gathered constantly constitutes OCT three-dimensional data vector, blood glucose concentration value constitutes the blood glucose concentration value vector, and it is right with the blood glucose concentration value vector that OCT three-dimensional data vector and blood glucose concentration value are constituted OCT three-dimensional data vector.Please refer to Fig. 2, with t1, t2 ..., the OCT three-dimensional data T1 that tn collects respectively constantly successively, T2 ..., Tn and blood glucose concentration value G1, G2 ..., Gn formation OCT three-dimensional data vector T1, T2 ..., Tn } and the blood glucose concentration value vector G1, G2 ..., Gn }.Simultaneously, for OCT three-dimensional data vector T1, T2 ..., Tn } and the blood glucose concentration value vector G1, G2 ..., Gn } and keep the corresponding relation of synchronization, it is right with the blood glucose concentration value vector they can be formed OCT three-dimensional data vector.

Step S140: obtain OCT three-dimensional data alignment vector according to skin surface characteristic information alignment OCT three-dimensional data vector.Because OCT three-dimensional data vector { T1, T2,, Tn } in element be collected in different constantly, and in difference constantly, measurand is unavoidably understood the phenomenon that occurrence positions moves, although this position move can by take some measures control to very small, even move also can be to OCT three-dimensional data vector { T1, T2 small position again,, Tn } in data cause and seriously influence.Therefore, we be necessary with OCT three-dimensional data vector T1, T2 ..., Tn } in the data that comprise of all elements carry out " registration process ".So-called " registration process " process is as follows:

(a), from OCT three-dimensional data vector T1, T2 ..., Tn } and element T of middle selection _k, use image processing method to find this OCT three-dimensional data T _kIn the skin surface position of each A-Scan.Wherein the A-Scan data refer at T _kIn X Y coordinate one-dimensional data on the degree of depth Z direction fixedly the time.

(b), according to the skin surface positional information, the skin surface of each A-Scan is snapped to first Data Position of A-Scan, obtain the three-dimensional align data T' of OCT _k

(c), repeating step (a) and step (b) obtain OCT three-dimensional data alignment vector T'1, T'2 ..., T'n }.

Please refer to Fig. 3, OCT three-dimensional data vector T1, T2 ..., Tn } through " registration process " obtain afterwards OCT three-dimensional data alignment vector T'1, T'2 ..., T'n }.

Step S150: according to OCT three-dimensional data alignment vector calculation skin optical parameter distributed in three dimensions vector.Please refer to Fig. 4, this step further comprises the steps:

An OCT three-dimensional data element T in step a, the selection OCT three-dimensional data alignment vector ' _m

Step b, selection three-dimensional rectangle frame size.Here the size of rectangle frame comprises length l, width w and height h.

Step c, at the coordinate points P(x of skin histology, y z) locates, and uses selected three-dimensional rectangle frame to take the three-dimensional subdata of an OCT from this OCT three-dimensional data element.Please refer to Fig. 5, (z) sentencing coordinate points P is that three-dimensional data selects the center of rectangle frame R to take the three-dimensional subdata T' of OCT for x, y at coordinate points P _Mp

Steps d, in the three-dimensional subdata of OCT, along degree of depth Z direction the three-dimensional subdata of OCT is averaged in the XOY plane parallel with skin surface, obtain one dimension OCT data and corresponding position coordinates thereof.And further obtain the corresponding relation of one dimension OCT data and position coordinates.Wherein one dimension OCT data S can be expressed as s1, s2 ..., s _h, obtain simultaneously space, S place Z direction coordinate position z1, z2 ..., z _h.Please refer to Fig. 6, according to one dimension OCT data S=s1, s2 ..., s _hAnd z1, z2 ..., z _hCan obtain the curve that the three-dimensional subdata meansigma methods of OCT changes with skin histology degree of depth Z.

Step e, calculate P(x, y, skin optical parameter z) according to one dimension OCT data and corresponding position coordinates thereof.Carry out least-squares linear regression according to one dimension OCT data S and obtain slope value as the optical parametric (that is scattering coefficient) of this coordinate position, formula is as follows:

$\left\{\begin{array}{c}b=\frac{\underset{i=1}{\overset{h}{\mathrm{\Σ}}}{s}_i{z}_i-h\stackrel{\‾}{s}\stackrel{\‾}{z}}{\underset{i=0}{\overset{h}{\mathrm{\Σ}}}{z}_i^2-{h\stackrel{\‾}{z}}^2}\\ a=\stackrel{\‾}{s}-b\stackrel{\‾}{z}\end{array}\right.$

Wherein, slope value is b, and intercept is a,

Be the average of S,

Be the coordinate position average.

Step f, repeatedly repeating step c, steps d and step e choose different coordinate points P ' (x ', y ', z ') at every turn, obtain the skin optical parameter on all three-dimensional coordinates of skin histology and constitute skin three-dimensional optical parameter O _m

Repeating step c is to step f behind other OCT three-dimensional data elements in step g, the selection OCT three-dimensional data alignment vector, obtain the skin three-dimensional optical parameter of all elements in the OCT three-dimensional data alignment vector and constitute skin optical parameter distributed in three dimensions vector { O1, O2 ..., On }.

Step S160: the three-dimensional dependency that goes out skin optical parameter and blood glucose concentration value according to skin optical parameter distributed in three dimensions vector sum blood glucose concentration value vector calculation.Please refer to Fig. 7, Fig. 8 and Fig. 9, this step further comprises the steps:

Step (1), select skin optical parameter distributed in three dimensions vector O1, O2 ..., On } and at coordinate points P(x, y, the optical parametric of z) locating vector Up=O1p, O2p ..., Onp };

Step (2), coordinates computed point (x, y, the dependency between the optical parametric of z) locating vector and the blood glucose concentration value vector, and be kept in the correlation data.According to Pearson's dependency, utilized following formula here during the dependency between calculating optical parameter vector and blood glucose concentration value vector:

$R_p=\frac{\mathrm{n\Σ}{O}_{\mathrm{ip}}{G}_{\mathrm{ip}}-\mathrm{\Σ}{O}_{\mathrm{ip}}\mathrm{\Σ}{G}_{\mathrm{ip}}}{\sqrt{\mathrm{n\Σ}{O}_{\mathrm{ip}}^2-{\left(\mathrm{\Σ}{O}_{\mathrm{ip}}\right)}^2}\sqrt{\mathrm{n\Σ}{G}_{\mathrm{ip}}^2-{\left(\mathrm{\Σ}{G}_{\mathrm{ip}}\right)}^2}}$

Wherein, R _PBe the dependency between optical parametric vector and the blood glucose concentration value vector, O _IpBe skin optical parameter distributed in three dimensions vector O _iAt coordinate points P(x, y, the optical parametric vector of z) locating, G _IpBe blood glucose concentration value vector G _iAt coordinate points P(x, y, the blood glucose concentration value of z) locating.

Step (3), repeating step (1) and step (2) travel through the three-dimensional dependency that all coordinate points obtain skin optical parameter and blood glucose concentration value.

It is right with the blood glucose concentration value vector that above-mentioned optical parametric based on OCT and the computational methods of the three-dimensional dependency of blood sugar concentration have been gathered the OCT three-dimensional data of the blood glucose concentration value in several different moment and skin histology and constituted OCT three-dimensional data vector, by right further processing obtains skin optical parameter distributed in three dimensions vector to OCT three-dimensional data vector and blood glucose concentration value vector, and finally calculate the three-dimensional dependency of skin optical parameter and blood glucose concentration value.Above-mentioned optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration based on OCT can obtain the optical parametric of skin histology in three dimensions and distribute, obtain the three-dimensional dependency between skin histology optical parametric and the blood glucose concentration value simultaneously, this three-dimensional dependency can be used for instructing the selection of OCT Woundless blood sugar detection technique calibration phase surveyed area.

The above embodiment has only expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (7)

1. the computational methods based on optical parametric and the three-dimensional dependency of blood sugar concentration of OCT is characterized in that, comprise the steps:

The blood sugar concentration of step (1), control measurand changes;

Step (2), in the blood sugar concentration change procedure of measurand, gather several different constantly blood glucose concentration value and the OCT three-dimensional data of skin histology;

The OCT three-dimensional data of step (3), skin histology that difference is gathered constantly constitutes OCT three-dimensional data vector, and blood glucose concentration value constitutes the blood glucose concentration value vector;

Step (4), obtain OCT three-dimensional data alignment vector according to skin surface characteristic information alignment OCT three-dimensional data vector;

Step (5), according to OCT three-dimensional data alignment vector calculation skin optical parameter distributed in three dimensions vector;

Step (6), go out the three-dimensional dependency of skin optical parameter and blood glucose concentration value according to skin optical parameter distributed in three dimensions vector sum blood glucose concentration value vector calculation.

According to claim 1 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, in the blood sugar concentration conversion step of described control measurand, the blood sugar concentration of control measurand changes by making measurand take in glucose or feed realization.

According to claim 1 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, constitute in the OCT three-dimensional data vector step in the OCT of the described skin histology that difference is gathered constantly three-dimensional data, also comprise the steps:

It is right with the blood glucose concentration value vector that described OCT three-dimensional data vector and blood glucose concentration value are constituted OCT three-dimensional data vector.

According to claim 1 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, described according to OCT three-dimensional data alignment vector calculation skin optical parameter distributed in three dimensions vector step in, comprise the steps:

An OCT three-dimensional data element in step a, the selection OCT three-dimensional data alignment vector;

Step b, selection three-dimensional rectangle frame size;

Step c, at the coordinate points P(x of skin histology, y z) locates, and uses selected three-dimensional rectangle frame to take the three-dimensional subdata of an OCT from this OCT three-dimensional data element;

Steps d, in the three-dimensional subdata of OCT, along degree of depth Z direction the three-dimensional subdata of OCT is averaged in the XOY plane parallel with skin surface, obtain one dimension OCT data and corresponding position coordinates thereof;

Step e, calculate P(x, y, skin optical parameter z) according to one dimension OCT data and corresponding position coordinates thereof;

Step f, repeatedly repeating step c, steps d and step e choose different coordinate points P ' (x ', y ', z ') at every turn, obtain the skin optical parameter on all three-dimensional coordinates of skin histology and constitute skin three-dimensional optical parameter;

Repeating step c obtains the skin three-dimensional optical parameter of all elements in the OCT three-dimensional data alignment vector and constitutes skin optical parameter distributed in three dimensions vector to step f behind other OCT three-dimensional data elements in step g, the selection OCT three-dimensional data alignment vector.

According to claim 4 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, in described step e, adopt the least-squares linear regression method to calculate P(x, y, z) the skin optical parameter of some position.

According to claim 1 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, in the described three-dimensional dependency step that goes out skin optical parameter and blood glucose concentration value according to skin optical parameter distributed in three dimensions vector calculation, comprise the steps:

Step (1), select skin optical parameter distributed in three dimensions vector at coordinate points P(x, y, the optical parametric vector of z) locating;

Step (2), coordinates computed point P(x, y, the dependency between the optical parametric of z) locating vector and the blood glucose concentration value vector, and be kept in the three-dimensional correlation data;

Step (3), repeating step (1) and step (2) travel through the three-dimensional dependency that all coordinate points obtain skin optical parameter and blood glucose concentration value.

According to claim 6 based on OCT optical parametric and the computational methods of the three-dimensional dependency of blood sugar concentration, it is characterized in that, in described step (2), go out dependency between optical parametric vector and the blood glucose concentration value vector according to Pearson's correlation calculations.

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