CN106372596A - Biological information collection device - Google Patents

Abstract

The present invention provides a biological information collection device. The device comprises a cell identification module and a biosensor; the cell identification module is configured to determine the biological species; and the biosensor is formed by dried porous materials. The biological information collection device improves the permeability of a reaction layer carrier and realizes the more uniform permeation so as to obtain a biosensor with convenient, rapid, high-sensitivity and high-performance measurement.

Description

A kind of biological information acquisition device

Technical field

The present invention relates to field of biosensors is and in particular to a kind of biological information acquisition device.

Background technology

In recent years, medical diagnosiss scene it would be desirable to access rapid, easy, accurately measure device.But, in note In the existing method carrying, during to Sensor section addition sample solution, if for example sample solution is blood, by using note Emitter carries out taking blood, generally by using centrifugal separator etc., carries out separating the operation that visible component is hemocyte and blood plasma, And regarding necessary as using the sequence of operations that the instruments such as allotter, imbibition glass tubing add to Sensor section.? In these methods, take blood to need the special technical ability such as medical skill using syringe, and centrifugation operation needs are special Instrument and technical ability, general family and there is no the individual of these technology can not use the method when oneself measures.In addition, in order to Examined solution is carried out quantitation, the instruments such as allotter be there is a problem of as necessary various operations so-called miscellaneous.

Content of the invention

For solving the above problems, the present invention is intended to provide a kind of biological information acquisition device.

The purpose of the present invention employs the following technical solutions to realize:

A kind of biological information acquisition device, including cell recognition module and biosensor, described cell recognition module is used To determine biological species, described biosensor is constituted by porous material is dried, above-mentioned biosensor is that have importing examination The sample lead-in portion of sample solution and the developer layer being configured with expansion said sample solution, containing by opening up on above-mentioned developer layer Open said sample solution and keep the labelled reagent holding part of labelled reagent with the drying regime that can elute, and can be with The Immobilized reagents that analyte combines and not elute and fixed on this developer layer containing the reagent in order to participate in reacting Part, imports sample solution in said sample lead-in portion, reaches labelled reagent holding part by being impregnated with above-mentioned developer layer, on State sample solution and elute labelled reagent while partly moving to above-mentioned Immobilized reagents, so that analyte and labelled reagent And immobilized reagent is reacted and is constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

The invention has the benefit that improve the impregnability of conversion zone carrier it is achieved that being more uniformly impregnated with, and then Achieve simplicity and rapid and can be with the biosensor of the high performance mensure of high sensitivity.

Brief description

Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, can also obtain according to the following drawings Other accompanying drawings.

Fig. 1 is biosensor structure schematic diagram of the present invention；

Fig. 2 is the structural representation of cell recognition module.

Reference:

Cell recognition module 1, Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, Classification and Identification unit 13.

Specific embodiment

In conjunction with following application scenarios, the invention will be further described.

Application scenarios 1

Referring to Fig. 1, Fig. 2, a kind of biological information acquisition device of an embodiment of this application scene, including cell recognition Module and biosensor, described cell recognition module is used for determining biological species, described biosensor is by being dried porous Material is constituted, and above-mentioned biosensor is that have to import the sample lead-in portion of sample solution and to be configured with expansion said sample molten The developer layer of liquid, containing by launching said sample solution the drying regime holding mark eluting on above-mentioned developer layer The labelled reagent holding part of note reagent, and can be combined with analyte and not wash containing the reagent in order to participate in reacting The Immobilized reagents part carrying and being fixed on this developer layer, imports sample solution in said sample lead-in portion, by leaching Above-mentioned developer layer reaches labelled reagent holding part thoroughly, and it is solid to mentioned reagent that said sample solution elutes labelled reagent Determining partly moves, so that analyte and labelled reagent and immobilized reagent are reacted and are constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

Preferably, in said sample lead-in portion configuration mesh structure.

This preferred embodiment can increase contact area.

Preferably, in said sample lead-in portion or being labelling sample holding part not equal to being that sample imports On the position of surface, there is the cell shrinkage agent holding part of contractive cell composition.

The measurement of this preferred embodiment is more accurate.

Preferably, described cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is used for distinguishing the back of the body in the cell image being gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is used for the textural characteristics of cell image are extracted；Described classification Recognition unit 13 is used for utilizing grader to realize to cell image Classification and Identification according to textural characteristics.

This preferred embodiment constructs the unit structure of cell recognition module 1.

Preferably, described Methods of Segmentation On Cell Images unit 11 includes image conversion subunit, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

(1) image conversion subunit, for being converted into gray level image by the cell image of collection；

(2) noise remove subelement, for carrying out denoising to gray level image, comprising:

For pixel (x, y), choose its 3 × 3 neighborhood s_x,y(2n+1) the neighborhood l of × (2n+1)_x,y, n be more than Integer equal to 2；

Whether it is that boundary point judges first to pixel, given threshold t, t ∈ [13,26], calculate pixel (x, y) With its neighborhood s_x,yIn each pixel gray scale difference value, and be compared with threshold value t, if gray scale difference value is more than the number of threshold value t More than or equal to 6, then pixel (x, y) is boundary point, and otherwise, pixel (x, y) is non-boundary point；

If (x, y) is boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{q(i,j)\&element;\[q(x,y)-1.5\mathrm{\σ},q(x,y)+1.5\mathrm{\σ}\]}q(i,j)}{k}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the ash of noise reduction preceding pixel point (x, y) Angle value, σ is pixel (x, y) neighborhood l_x,yInterior gray value mark is poor, and q (i, j) ∈ [q (x, y) -1.5 σ, q (x, y)+1.5 σ] represents Neighborhood l_x,yInterior gray value falls within the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ], and k represents neighborhood l_x,yInterior gray value falls within The quantity of the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ]；

If (x, y) is non-boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)q(i,j)}{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the ash at q (i, j) representative image midpoint (i, j) place Angle value, w (i, j) is neighborhood l_x,yThe corresponding Gauss weight of interior point (i, j)；

(3) coarse segmentation subelement, for slightly being drawn to the background in the cell image after denoising, Cytoplasm, nucleus Point, particularly as follows:

Each pixel (x, y) is represented with four dimensional feature vectors:

$\stackrel{\→}{u}(x,y)=\[h(x,y),h_{ave}(x,y),h_{med}(x,y),h_{sta}(x,y)\]$

In formula, h (x, y) represents the gray value of (x, y), h_ave(x, y) represents its neighborhood s_x,yGray average, h_med(x, y) generation Table its neighborhood s_x,yGray scale intermediate value, h_sta(x, y) represents its neighborhood s_x,yGray variance；

Background, Cytoplasm, nucleus three class are divided into using k-means clustering procedure；

(4) nuclear centers demarcate subelement, for demarcating to nuclear centers:

Nucleus approximate region is obtained by coarse segmentation subelement, if nuclear area comprises n point: (x₁,y₁),…,(x_n, y_n), this region is carried out with intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

$x_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{x}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{x}_i}{n})$

$y_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{y}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{y}_i}{n})$

(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, Cytoplasm；

Build from nuclear centers (x_z, y_z) arrive nucleus and Cytoplasm boundary point (x_p,y_p) directed line segmentDistanceRepresent and round downwards；

Along line segment, sampling is carried out with unit length and can obtain dis_pIndividual pointIf adopting The coordinate of sampling point is not integer, and its gray value is obtained by surrounding pixel linear interpolation；

Point (x_i,y_i) place along line segment direction gray scale difference:

hd(x_i,y_i)=h (x_i-1,y_i-1)-h(x_i,y_i)

Definition gray scale difference inhibition function:

$y\left(x\right)=\left\{\begin{array}{ccc}x& if& x\≤0\\ 0.5x& if& x>0\end{array}\right.$

Point (x_i,y_i) place along line segment direction gradient gra (x_i,y_i):

$gra(x_i,y_i)=\frac{\left|y\left(hd\right(x_i,y_i\left)\right)\right|+\left|y\left(hd\right(x_{i+1},y_{i+!}\left)\right)\right|}{2}$

Choose the maximum value point of gradient as nucleus and cytoplasmic precise edge.

This preferred embodiment arranges noise remove subelement, and effective integration center pixel is closed on the space of neighborhood territory pixel Property and grey similarity carrying out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, and adopts Gaussian filter is weighted to gray value filtering, and is changing violent borderline region, row bound keeps filtering, is conducive to image The holding at edge；Nucleus and Cytoplasm coarse contour are extracted using k mean cluster, can effectively remove the interference of noise；Setting is thin Subelement is demarcated at karyon center, is easy to subsequently nucleus and Cytoplasm profile are accurately positioned；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the interference to edge graph for the inflammatory cell, can accurately extract nucleus and Cytoplasm side Edge.

Preferably, the described textural characteristics to cell image extract, comprising:

(1) the Gray co-occurrence matrix of cell image, described comprehensive ash is asked for based on improved gray level co-occurrence matrixes method Degree co-occurrence matrix embodies cell textural characteristics in different directions:

Be located at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes on 135 ° of four directions be respectively h (x, y, d, 0 °), h (x, y, d, 45 °), h (x, y, d, 90 °), h (x, y, d, 135 °), corresponding matrix element project is x₁、x₂、x₃、x₄, then Gray is common The computing formula of raw matrix is:

H (x, y, d)=w₁h(x,y,d,0°)+w₂h(x,y,d,45°)+w₃h(x,y,d,90°)+w₄h(x,y,d,135°)

Gray co-occurrence matrix element number is:

$x=\underset{i=1}{\overset{4}{\σ}}{w}_i{x}_i$

In formula, d represents distance, the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides To in each direction on the corresponding contrast level parameter of gray level co-occurrence matrixes calculate, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively d_i, average isThen weight coefficient w_iComputing formula be:

$w_i=\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}/\underset{i=1}{\overset{4}{\σ}}\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}$

(2) four textural characteristics parameters needed for being obtained using described Gray co-occurrence matrix and matrix element project: Contrast, variance and, energy and average；

(3) described four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.

This preferred embodiment is based on improved gray level co-occurrence matrixes method, asks for cytological map by the way of setting weight coefficient The Gray co-occurrence matrix of picture, and then extract textural characteristics on specified four direction for the cell, solve due to outside dry Disturb the textural characteristics ginseng of the cell that (impact that causes as lighting angle when cell image gathers, flowing interference of gas etc.) causes Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast, Variance and, energy and four textural characteristics of average, eliminate redundancy and the characteristic parameter repeating；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image is processed.

In this application scenarios, given threshold t=13, d=2, image denoising effect improves 5% relatively, cell image The extraction accuracy of feature improves 8%.

Application scenarios 2

Referring to Fig. 1, Fig. 2, a kind of biological information acquisition device of an embodiment of this application scene, including cell recognition Module and biosensor, described cell recognition module is used for determining biological species, described biosensor is by being dried porous Material is constituted, and above-mentioned biosensor is that have to import the sample lead-in portion of sample solution and to be configured with expansion said sample molten The developer layer of liquid, containing by launching said sample solution the drying regime holding mark eluting on above-mentioned developer layer The labelled reagent holding part of note reagent, and can be combined with analyte and not wash containing the reagent in order to participate in reacting The Immobilized reagents part carrying and being fixed on this developer layer, imports sample solution in said sample lead-in portion, by leaching Above-mentioned developer layer reaches labelled reagent holding part thoroughly, and it is solid to mentioned reagent that said sample solution elutes labelled reagent Determining partly moves, so that analyte and labelled reagent and immobilized reagent are reacted and are constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

Preferably, in said sample lead-in portion configuration mesh structure.

This preferred embodiment can increase contact area.

Preferably, in said sample lead-in portion or being labelling sample holding part not equal to being that sample imports On the position of surface, there is the cell shrinkage agent holding part of contractive cell composition.

The measurement of this preferred embodiment is more accurate.

Preferably, described cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is used for distinguishing the back of the body in the cell image being gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is used for the textural characteristics of cell image are extracted；Described classification Recognition unit 13 is used for utilizing grader to realize to cell image Classification and Identification according to textural characteristics.

This preferred embodiment constructs the unit structure of cell recognition module 1.

Preferably, described Methods of Segmentation On Cell Images unit 11 includes image conversion subunit, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

(1) image conversion subunit, for being converted into gray level image by the cell image of collection；

(2) noise remove subelement, for carrying out denoising to gray level image, comprising:

For pixel (x, y), choose its 3 × 3 neighborhood s_x,y(2n+1) the neighborhood l of × (2n+1)_x,y, n be more than Integer equal to 2；

Whether it is that boundary point judges first to pixel, given threshold t, t ∈ [13,26], calculate pixel (x, y) With its neighborhood s_x,yIn each pixel gray scale difference value, and be compared with threshold value t, if gray scale difference value is more than the number of threshold value t More than or equal to 6, then pixel (x, y) is boundary point, and otherwise, pixel (x, y) is non-boundary point；

If (x, y) is boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{q(i,j)\&element;\[q(x,y)-1.5\mathrm{\σ},q(x,y)+1.5\mathrm{\σ}\]}q(i,j)}{k}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the ash of noise reduction preceding pixel point (x, y) Angle value, σ is pixel (x, y) neighborhood l_x,yInterior gray value mark is poor, and q (i, j) ∈ [q (x, y) -1.5 σ, q (x, y)+1.5 σ] represents Neighborhood l_x,yInterior gray value falls within the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ], and k represents neighborhood l_x,yInterior gray value falls within The quantity of the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ]；

If (x, y) is non-boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)q(i,j)}{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the ash at q (i, j) representative image midpoint (i, j) place Angle value, w (i, j) is neighborhood l_x,yThe corresponding Gauss weight of interior point (i, j)；

(3) coarse segmentation subelement, for slightly being drawn to the background in the cell image after denoising, Cytoplasm, nucleus Point, particularly as follows:

Each pixel (x, y) is represented with four dimensional feature vectors:

$\stackrel{\→}{u}(x,y)=\[h(x,y),h_{ave}(x,y),h_{med}(x,y),h_{sta}(x,y)\]$

In formula, h (x, y) represents the gray value of (x, y), h_ave(x, y) represents its neighborhood s_x,yGray average, h_med(x, y) generation Table its neighborhood s_x,yGray scale intermediate value, h_sta(x, y) represents its neighborhood s_x,yGray variance；

Background, Cytoplasm, nucleus three class are divided into using k-means clustering procedure；

(4) nuclear centers demarcate subelement, for demarcating to nuclear centers:

Nucleus approximate region is obtained by coarse segmentation subelement, if nuclear area comprises n point: (x₁,y₁),…,(x_n, y_n), this region is carried out with intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

$x_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{x}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{x}_i}{n})$

$y_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{y}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{y}_i}{n})$

(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, Cytoplasm；

Build from nuclear centers (x_z,y_z) arrive nucleus and Cytoplasm boundary point (x_p,y_p) directed line segmentDistanceRepresent and round downwards；

Along line segment, sampling is carried out with unit length and can obtain dis_pIndividual point (x₁,y₁) ...,If sampling The coordinate of point is not integer, and its gray value is obtained by surrounding pixel linear interpolation；

Point (x_i,y_i) place along line segment direction gray scale difference:

hd(x_i,y_i)=h (x_i-1,y_i-1)-h(x_i,y_i)

Definition gray scale difference inhibition function:

$y\left(x\right)=\left\{\begin{array}{ccc}x& if& x\≤0\\ 0.5x& if& x>0\end{array}\right.$

Point (x_i,y_i) place along line segment direction gradient gra (x_i,y_i):

$gra(x_i,y_i)=\frac{\left|y\left(hd\right(x_i,y_i\left)\right)\right|+\left|y\left(hd\right(x_{i+1},y_{i+!}\left)\right)\right|}{2}$

Choose the maximum value point of gradient as nucleus and cytoplasmic precise edge.

This preferred embodiment arranges noise remove subelement, and effective integration center pixel is closed on the space of neighborhood territory pixel Property and grey similarity carrying out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, and adopts Gaussian filter is weighted to gray value filtering, and is changing violent borderline region, row bound keeps filtering, is conducive to image The holding at edge；Nucleus and Cytoplasm coarse contour are extracted using k mean cluster, can effectively remove the interference of noise；Setting is thin Subelement is demarcated at karyon center, is easy to subsequently nucleus and Cytoplasm profile are accurately positioned；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the interference to edge graph for the inflammatory cell, can accurately extract nucleus and Cytoplasm side Edge.

Preferably, the described textural characteristics to cell image extract, comprising:

(1) the Gray co-occurrence matrix of cell image, described comprehensive ash is asked for based on improved gray level co-occurrence matrixes method Degree co-occurrence matrix embodies cell textural characteristics in different directions:

Be located at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes on 135 ° of four directions be respectively h (x, y, d, 0 °), h (x, y, d, 45 °), h (x, y, d, 90 °), h (x, y, d, 135 °), corresponding matrix element project is x₁、x₂、x₃、x₄, then Gray is common The computing formula of raw matrix is:

H (x, y, d)=w₁h(x,y,d,0°)+w₂h(x,y,d,45°)+w₃h(x,y,d,90°)+w₄h(x,y,d,135°)

Gray co-occurrence matrix element number is:

$x=\underset{i=1}{\overset{4}{\σ}}{w}_i{x}_i$

In formula, d represents distance, the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides To in each direction on the corresponding contrast level parameter of gray level co-occurrence matrixes calculate, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively d_i, average isThen weight coefficient w_iComputing formula be:

$w_i=\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}/\underset{i=1}{\overset{4}{\σ}}\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}$

(2) four textural characteristics parameters needed for being obtained using described Gray co-occurrence matrix and matrix element project: Contrast, variance and, energy and average；

(3) described four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.

This preferred embodiment is based on improved gray level co-occurrence matrixes method, asks for cytological map by the way of setting weight coefficient The Gray co-occurrence matrix of picture, and then extract textural characteristics on specified four direction for the cell, solve due to outside dry Disturb the textural characteristics ginseng of the cell that (impact that causes as lighting angle when cell image gathers, flowing interference of gas etc.) causes Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast, Variance and, energy and four textural characteristics of average, eliminate redundancy and the characteristic parameter repeating；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image is processed.

In this application scenarios, given threshold t=15, d=2, image denoising effect improves 6% relatively, cell image The extraction accuracy of feature improves 8%.

Application scenarios 3

Referring to Fig. 1, Fig. 2, a kind of biological information acquisition device of an embodiment of this application scene, including cell recognition Module and biosensor, described cell recognition module is used for determining biological species, described biosensor is by being dried porous Material is constituted, and above-mentioned biosensor is that have to import the sample lead-in portion of sample solution and to be configured with expansion said sample molten The developer layer of liquid, containing by launching said sample solution the drying regime holding mark eluting on above-mentioned developer layer The labelled reagent holding part of note reagent, and can be combined with analyte and not wash containing the reagent in order to participate in reacting The Immobilized reagents part carrying and being fixed on this developer layer, imports sample solution in said sample lead-in portion, by leaching Above-mentioned developer layer reaches labelled reagent holding part thoroughly, and it is solid to mentioned reagent that said sample solution elutes labelled reagent Determining partly moves, so that analyte and labelled reagent and immobilized reagent are reacted and are constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

Preferably, in said sample lead-in portion configuration mesh structure.

This preferred embodiment can increase contact area.

Preferably, in said sample lead-in portion or being labelling sample holding part not equal to being that sample imports On the position of surface, there is the cell shrinkage agent holding part of contractive cell composition.

The measurement of this preferred embodiment is more accurate.

Preferably, described cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is used for distinguishing the back of the body in the cell image being gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is used for the textural characteristics of cell image are extracted；Described classification Recognition unit 13 is used for utilizing grader to realize to cell image Classification and Identification according to textural characteristics.

This preferred embodiment constructs the unit structure of cell recognition module 1.

Preferably, described Methods of Segmentation On Cell Images unit 11 includes image conversion subunit, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

(1) image conversion subunit, for being converted into gray level image by the cell image of collection；

(2) noise remove subelement, for carrying out denoising to gray level image, comprising:

For pixel (x, y), choose its 3 × 3 neighborhood s_x,y(2n+1) the neighborhood l of × (2n+1)_x,y, n be more than Integer equal to 2；

Whether it is that boundary point judges first to pixel, given threshold t, t ∈ [13,26], calculate pixel (x, y) With its neighborhood s_x,yIn each pixel gray scale difference value, and be compared with threshold value t, if gray scale difference value is more than the number of threshold value t More than or equal to 6, then pixel (x, y) is boundary point, and otherwise, pixel (x, y) is non-boundary point；

If (x, y) is boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{q(i,j)\&element;\[q(x,y)-1.5\mathrm{\σ},q(x,y)+1.5\mathrm{\σ}\]}q(i,j)}{k}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the ash of noise reduction preceding pixel point (x, y) Angle value, σ is pixel (x, y) neighborhood l_x,yInterior gray value mark is poor, and q (i, j) ∈ [q (x, y) -1.5 σ, q (x, y)+1.5 σ] represents Neighborhood l_x,yInterior gray value falls within the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ], and k represents neighborhood l_x,yInterior gray value falls within The quantity of the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ]；

If (x, y) is non-boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)q(i,j)}{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the ash at q (i, j) representative image midpoint (i, j) place Angle value, w (i, j) is neighborhood l_x,yThe corresponding Gauss weight of interior point (i, j)；

(3) coarse segmentation subelement, for slightly being drawn to the background in the cell image after denoising, Cytoplasm, nucleus Point, particularly as follows:

Each pixel (x, y) is represented with four dimensional feature vectors:

$\stackrel{\→}{u}(x,y)=\[h(x,y),h_{ave}(x,y),h_{med}(x,y),h_{sta}(x,y)\]$

In formula, h (x, y) represents the gray value of (x, y), h_ave(x, y) represents its neighborhood s_x,yGray average, h_med(x, y) generation Table its neighborhood s_x,yGray scale intermediate value, h_sta(x, y) represents its neighborhood s_x,yGray variance；

Background, Cytoplasm, nucleus three class are divided into using k-means clustering procedure；

(4) nuclear centers demarcate subelement, for demarcating to nuclear centers:

Nucleus approximate region is obtained by coarse segmentation subelement, if nuclear area comprises n point: (x₁,y₁),…,(x_n, y_n), this region is carried out with intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

$x_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{x}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{x}_i}{n})$

$y_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{y}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{y}_i}{n})$

(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, Cytoplasm；

Build from nuclear centers (x_z,y_z) arrive nucleus and Cytoplasm boundary point (x_p,y_p) directed line segmentDistanceRepresent and round downwards；

Along line segment, sampling is carried out with unit length and can obtain dis_pIndividual pointIf adopting The coordinate of sampling point is not integer, and its gray value is obtained by surrounding pixel linear interpolation；

Point (x_i,y_i) place along line segment direction gray scale difference:

hd(x_i,y_i)=h (x_i-1,y_i-1)-h(x_i,y_i)

Definition gray scale difference inhibition function:

$y\left(x\right)=\left\{\begin{array}{ccc}x& if& x\≤0\\ 0.5x& if& x>0\end{array}\right.$

Point (x_i,y_i) place along line segment direction gradient gra (x_i,y_i):

$gra(x_i,y_i)=\frac{\left|y\left(hd\right(x_i,y_i\left)\right)\right|+\left|y\left(hd\right(x_{i+1},y_{i+!}\left)\right)\right|}{2}$

Choose the maximum value point of gradient as nucleus and cytoplasmic precise edge.

This preferred embodiment arranges noise remove subelement, and effective integration center pixel is closed on the space of neighborhood territory pixel Property and grey similarity carrying out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, and adopts Gaussian filter is weighted to gray value filtering, and is changing violent borderline region, row bound keeps filtering, is conducive to image The holding at edge；Nucleus and Cytoplasm coarse contour are extracted using k mean cluster, can effectively remove the interference of noise；Setting is thin Subelement is demarcated at karyon center, is easy to subsequently nucleus and Cytoplasm profile are accurately positioned；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the interference to edge graph for the inflammatory cell, can accurately extract nucleus and Cytoplasm side Edge.

Preferably, the described textural characteristics to cell image extract, comprising:

(1) the Gray co-occurrence matrix of cell image, described comprehensive ash is asked for based on improved gray level co-occurrence matrixes method Degree co-occurrence matrix embodies cell textural characteristics in different directions:

Be located at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes on 135 ° of four directions be respectively h (x, y, d, 0 °), h (x, y, d, 45 °), h (x, y, d, 90 °), h (x, y, d, 135 °), corresponding matrix element project is x₁、x₂、x₃、x₄, then Gray is common The computing formula of raw matrix is:

H (x, y, d)=w₁h(x,y,d,0°)+w₂h(x,y,d,45°)+w₃h(x,y,d,90°)+w₄h(x,y,d,135°)

Gray co-occurrence matrix element number is:

$x=\underset{i=1}{\overset{4}{\σ}}{w}_i{x}_i$

In formula, d represents distance, the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides To in each direction on the corresponding contrast level parameter of gray level co-occurrence matrixes calculate, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively d_i, average isThen weight coefficient w_iComputing formula be:

$w_i=\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}/\underset{i=1}{\overset{4}{\σ}}\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}$

(2) four textural characteristics parameters needed for being obtained using described Gray co-occurrence matrix and matrix element project: Contrast, variance and, energy and average；

(3) described four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.

This preferred embodiment is based on improved gray level co-occurrence matrixes method, asks for cytological map by the way of setting weight coefficient The Gray co-occurrence matrix of picture, and then extract textural characteristics on specified four direction for the cell, solve due to outside dry Disturb the textural characteristics ginseng of the cell that (impact that causes as lighting angle when cell image gathers, flowing interference of gas etc.) causes Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast, Variance and, energy and four textural characteristics of average, eliminate redundancy and the characteristic parameter repeating；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image is processed.

In this application scenarios, given threshold t=18, d=3, image denoising effect improves 7% relatively, cell image The extraction accuracy of feature improves 7%.

Application scenarios 4

Referring to Fig. 1, Fig. 2, a kind of biological information acquisition device of an embodiment of this application scene, including cell recognition Module and biosensor, described cell recognition module is used for determining biological species, described biosensor is by being dried porous Material is constituted, and above-mentioned biosensor is that have to import the sample lead-in portion of sample solution and to be configured with expansion said sample molten The developer layer of liquid, containing by launching said sample solution the drying regime holding mark eluting on above-mentioned developer layer The labelled reagent holding part of note reagent, and can be combined with analyte and not wash containing the reagent in order to participate in reacting The Immobilized reagents part carrying and being fixed on this developer layer, imports sample solution in said sample lead-in portion, by leaching Above-mentioned developer layer reaches labelled reagent holding part thoroughly, and it is solid to mentioned reagent that said sample solution elutes labelled reagent Determining partly moves, so that analyte and labelled reagent and immobilized reagent are reacted and are constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

Preferably, in said sample lead-in portion configuration mesh structure.

This preferred embodiment can increase contact area.

Preferably, in said sample lead-in portion or being labelling sample holding part not equal to being that sample imports On the position of surface, there is the cell shrinkage agent holding part of contractive cell composition.

The measurement of this preferred embodiment is more accurate.

Preferably, described cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is used for distinguishing the back of the body in the cell image being gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is used for the textural characteristics of cell image are extracted；Described classification Recognition unit 13 is used for utilizing grader to realize to cell image Classification and Identification according to textural characteristics.

This preferred embodiment constructs the unit structure of cell recognition module 1.

Preferably, described Methods of Segmentation On Cell Images unit 11 includes image conversion subunit, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

(1) image conversion subunit, for being converted into gray level image by the cell image of collection；

(2) noise remove subelement, for carrying out denoising to gray level image, comprising:

For pixel (x, y), choose its 3 × 3 neighborhood s_x,y(2n+1) the neighborhood l of × (2n+1)_x,y, n be more than Integer equal to 2；

Whether it is that boundary point judges first to pixel, given threshold t, t ∈ [13,26], calculate pixel (x, y) With its neighborhood s_x,yIn each pixel gray scale difference value, and be compared with threshold value t, if gray scale difference value is more than the number of threshold value t More than or equal to 6, then pixel (x, y) is boundary point, and otherwise, pixel (x, y) is non-boundary point；

If (x, y) is boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{q(i,j)\&element;\[q(x,y)-1.5\mathrm{\σ},q(x,y)+1.5\mathrm{\σ}\]}q(i,j)}{k}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the ash of noise reduction preceding pixel point (x, y) Angle value, σ is pixel (x, y) neighborhood l_x,yInterior gray value mark is poor, and q (i, j) ∈ [q (x, y) -1.5 σ, q (x, y)+1.5 σ] represents Neighborhood l_x,yInterior gray value falls within the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ], and k represents neighborhood l_x,yInterior gray value falls within The quantity of the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ]；

If (x, y) is non-boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)q(i,j)}{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the ash at q (i, j) representative image midpoint (i, j) place Angle value, w (i, j) is neighborhood l_x,yThe corresponding Gauss weight of interior point (i, j)；

(3) coarse segmentation subelement, for slightly being drawn to the background in the cell image after denoising, Cytoplasm, nucleus Point, particularly as follows:

Each pixel (x, y) is represented with four dimensional feature vectors:

$\stackrel{\→}{u}(x,y)=\[h(x,y),h_{ave}(x,y),h_{med}(x,y),h_{sta}(x,y)\]$

In formula, h (x, y) represents the gray value of (x, y), h_ave(x, y) represents its neighborhood s_x,yGray average, h_med(x, y) generation Table its neighborhood s_x,yGray scale intermediate value, h_sta(x, y) represents its neighborhood s_x,yGray variance；

Background, Cytoplasm, nucleus three class are divided into using k-means clustering procedure；

(4) nuclear centers demarcate subelement, for demarcating to nuclear centers:

Nucleus approximate region is obtained by coarse segmentation subelement, if nuclear area comprises n point: (x₁,y₁),…,(x_n, y_n), this region is carried out with intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

$x_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{x}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{x}_i}{n})$

$y_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{y}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{y}_i}{n})$

(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, Cytoplasm；

Build from nuclear centers (x_z,y_z) arrive nucleus and Cytoplasm boundary point (x_p,y_p) directed line segmentDistanceRepresent and round downwards；

Along line segment, sampling is carried out with unit length and can obtain dis_pIndividual pointIf adopting The coordinate of sampling point is not integer, and its gray value is obtained by surrounding pixel linear interpolation；

Point (x_i,y_i) place along line segment direction gray scale difference:

hd(x_i,y_i)=h (x_i-1,y_i-1)-h(x_i,y_i)

Definition gray scale difference inhibition function:

$y\left(x\right)=\left\{\begin{array}{ccc}x& if& x\≤0\\ 0.5x& if& x>0\end{array}\right.$

Point (x_i,y_i) place along line segment direction gradient gra (x_i,y_i):

$gra(x_i,y_i)=\frac{\left|y\left(hd\right(x_i,y_i\left)\right)\right|+\left|y\left(hd\right(x_{i+1},y_{i+!}\left)\right)\right|}{2}$

Choose the maximum value point of gradient as nucleus and cytoplasmic precise edge.

This preferred embodiment arranges noise remove subelement, and effective integration center pixel is closed on the space of neighborhood territory pixel Property and grey similarity carrying out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, and adopts Gaussian filter is weighted to gray value filtering, and is changing violent borderline region, row bound keeps filtering, is conducive to image The holding at edge；Nucleus and Cytoplasm coarse contour are extracted using k mean cluster, can effectively remove the interference of noise；Setting is thin Subelement is demarcated at karyon center, is easy to subsequently nucleus and Cytoplasm profile are accurately positioned；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the interference to edge graph for the inflammatory cell, can accurately extract nucleus and Cytoplasm side Edge.

Preferably, the described textural characteristics to cell image extract, comprising:

(1) the Gray co-occurrence matrix of cell image, described comprehensive ash is asked for based on improved gray level co-occurrence matrixes method Degree co-occurrence matrix embodies cell textural characteristics in different directions:

Be located at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes on 135 ° of four directions be respectively h (x, y, d, 0 °), h (x, y, d, 45 °), h (x, y, d, 90 °), h (x, y, d, 135 °), corresponding matrix element project is x₁、x₂、x₃、x₄, then Gray is common The computing formula of raw matrix is:

H (x, y, d)=w₁h(x,y,d,0°)+w₂h(x,y,d,45°)+w₃h(x,y,d,90°)+w₄h(x,y,d,135°)

Gray co-occurrence matrix element number is:

$x=\underset{i=1}{\overset{4}{\σ}}{w}_i{x}_i$

In formula, d represents distance, the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides To in each direction on the corresponding contrast level parameter of gray level co-occurrence matrixes calculate, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively d_i, average isThen weight coefficient w_iComputing formula be:

$w_i=\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}/\underset{i=1}{\overset{4}{\σ}}\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}$

(2) four textural characteristics parameters needed for being obtained using described Gray co-occurrence matrix and matrix element project: Contrast, variance and, energy and average；

(3) described four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.

This preferred embodiment is based on improved gray level co-occurrence matrixes method, asks for cytological map by the way of setting weight coefficient The Gray co-occurrence matrix of picture, and then extract textural characteristics on specified four direction for the cell, solve due to outside dry Disturb the textural characteristics ginseng of the cell that (impact that causes as lighting angle when cell image gathers, flowing interference of gas etc.) causes Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast, Variance and, energy and four textural characteristics of average, eliminate redundancy and the characteristic parameter repeating；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image is processed.

In this application scenarios, given threshold t=20, d=4, image denoising effect improves 8% relatively, cell image The extraction accuracy of feature improves 6%.

Application scenarios 5

Referring to Fig. 1, Fig. 2, a kind of biological information acquisition device of an embodiment of this application scene, including cell recognition Module and biosensor, described cell recognition module is used for determining biological species, described biosensor is by being dried porous Material is constituted, and above-mentioned biosensor is that have to import the sample lead-in portion of sample solution and to be configured with expansion said sample molten The developer layer of liquid, containing by launching said sample solution the drying regime holding mark eluting on above-mentioned developer layer The labelled reagent holding part of note reagent, and can be combined with analyte and not wash containing the reagent in order to participate in reacting The Immobilized reagents part carrying and being fixed on this developer layer, imports sample solution in said sample lead-in portion, by leaching Above-mentioned developer layer reaches labelled reagent holding part thoroughly, and it is solid to mentioned reagent that said sample solution elutes labelled reagent Determining partly moves, so that analyte and labelled reagent and immobilized reagent are reacted and are constituted,

By mentioned reagent immobilization partly middle measure above-mentioned labelled reagent binding capacity, will be in said sample solution The analyte containing carries out qualitative or quantitative biosensor.

Preferably, in said sample lead-in portion configuration mesh structure.

This preferred embodiment can increase contact area.

Preferably, in said sample lead-in portion or being labelling sample holding part not equal to being that sample imports On the position of surface, there is the cell shrinkage agent holding part of contractive cell composition.

The measurement of this preferred embodiment is more accurate.

Preferably, described cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is used for distinguishing the back of the body in the cell image being gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is used for the textural characteristics of cell image are extracted；Described classification Recognition unit 13 is used for utilizing grader to realize to cell image Classification and Identification according to textural characteristics.

This preferred embodiment constructs the unit structure of cell recognition module 1.

Preferably, described Methods of Segmentation On Cell Images unit 11 includes image conversion subunit, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

(1) image conversion subunit, for being converted into gray level image by the cell image of collection；

(2) noise remove subelement, for carrying out denoising to gray level image, comprising:

For pixel (x, y), choose its 3 × 3 neighborhood s_x,y(2n+1) the neighborhood l of × (2n+1)_x,y, n be more than Integer equal to 2；

Whether it is that boundary point judges first to pixel, given threshold t, t ∈ [13,26], calculate pixel (x, y) With its neighborhood s_x,yIn each pixel gray scale difference value, and be compared with threshold value t, if gray scale difference value is more than the number of threshold value t More than or equal to 6, then pixel (x, y) is boundary point, and otherwise, pixel (x, y) is non-boundary point；

If (x, y) is boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{q(i,j)\&element;\[q(x,y)-1.5\mathrm{\σ},q(x,y)+1.5\mathrm{\σ}\]}q(i,j)}{k}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the ash of noise reduction preceding pixel point (x, y) Angle value, σ is pixel (x, y) neighborhood l_x,yInterior gray value mark is poor, and q (i, j) ∈ [q (x, y) -1.5 σ, q (x, y)+1.5 σ] represents Neighborhood l_x,yInterior gray value falls within the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ], and k represents neighborhood l_x,yInterior gray value falls within The quantity of the point of interval [q (x, y) -1.5 σ, q (x, y)+1.5 σ]；

If (x, y) is non-boundary point, then carry out following noise reduction process:

$h(x,y)=\frac{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)q(i,j)}{{\σ}_{(i,j)\&element;l_{x,y}}w(i,j)}$

In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the ash at q (i, j) representative image midpoint (i, j) place Angle value, w (i, j) is neighborhood l_x,yThe corresponding Gauss weight of interior point (i, j)；

(3) coarse segmentation subelement, for slightly being drawn to the background in the cell image after denoising, Cytoplasm, nucleus Point, particularly as follows:

Each pixel (x, y) is represented with four dimensional feature vectors:

$\stackrel{\→}{u}(x,y)=\[h(x,y),h_{ave}(x,y),h_{med}(x,y),h_{sta}(x,y)\]$

In formula, h (x, y) represents the gray value of (x, y), h_ave(x, y) represents its neighborhood s_x,yGray average, h_med(x, y) generation Table its neighborhood s_x,yGray scale intermediate value, h_sta(x, y) represents its neighborhood s_x,yGray variance；

Background, Cytoplasm, nucleus three class are divided into using k-means clustering procedure；

(4) nuclear centers demarcate subelement, for demarcating to nuclear centers:

Nucleus approximate region is obtained by coarse segmentation subelement, if nuclear area comprises n point: (x₁,y₁),…,(x_n, y_n), this region is carried out with intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

$x_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{x}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{x}_i}{n})$

$y_z=\frac{1}{2}(\frac{{\σ}_{i=1}^n{y}_{i}h(x_i,y_i)}{{\σ}_{i=1}^{n}h(x_i,y_i)}+\frac{{\σ}_{i=1}^n{y}_i}{n})$

(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, Cytoplasm；

Build from nuclear centers (x_z,y_z) arrive nucleus and Cytoplasm boundary point (x_p,y_p) directed line segmentDistanceRepresent and round downwards；

Along line segment, sampling is carried out with unit length and can obtain dis_pIndividual pointIf adopting The coordinate of sampling point is not integer, and its gray value is obtained by surrounding pixel linear interpolation；

Point (x_i,y_i) place along line segment direction gray scale difference:

hd(x_i,y_i)=h (x_i-1,y_i-1)-h(x_i,y_i)

Definition gray scale difference inhibition function:

$y\left(x\right)=\left\{\begin{array}{ccc}x& if& x\≤0\\ 0.5x& if& x>0\end{array}\right.$

Point (x_i,y_i) place along line segment direction gradient gra (x_i,y_i):

$gra(x_i,y_i)=\frac{\left|y\left(hd\right(x_i,y_i\left)\right)\right|+\left|y\left(hd\right(x_{i+1},y_{i+!}\left)\right)\right|}{2}$

Choose the maximum value point of gradient as nucleus and cytoplasmic precise edge.

This preferred embodiment arranges noise remove subelement, and effective integration center pixel is closed on the space of neighborhood territory pixel Property and grey similarity carrying out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, and adopts Gaussian filter is weighted to gray value filtering, and is changing violent borderline region, row bound keeps filtering, is conducive to image The holding at edge；Nucleus and Cytoplasm coarse contour are extracted using k mean cluster, can effectively remove the interference of noise；Setting is thin Subelement is demarcated at karyon center, is easy to subsequently nucleus and Cytoplasm profile are accurately positioned；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the interference to edge graph for the inflammatory cell, can accurately extract nucleus and Cytoplasm side Edge.

Preferably, the described textural characteristics to cell image extract, comprising:

(1) the Gray co-occurrence matrix of cell image, described comprehensive ash is asked for based on improved gray level co-occurrence matrixes method Degree co-occurrence matrix embodies cell textural characteristics in different directions:

Be located at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes on 135 ° of four directions be respectively h (x, y, d, 0 °), h (x, y, d, 45 °), h (x, y, d, 90 °), h (x, y, d, 135 °), corresponding matrix element project is x₁、x₂、x₃、x₄, then Gray is common The computing formula of raw matrix is:

H (x, y, d)=w₁h(x,y,d,0°)+w₂h(x,y,d,45°)+w₃h(x,y,d,90°)+w₄h(x,y,d,135°)

Gray co-occurrence matrix element number is:

$x=\underset{i=1}{\overset{4}{\σ}}{w}_i{x}_i$

In formula, d represents distance, the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides To in each direction on the corresponding contrast level parameter of gray level co-occurrence matrixes calculate, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively d_i, average isThen weight coefficient w_iComputing formula be:

$w_i=\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}/\underset{i=1}{\overset{4}{\σ}}\frac{1}{\left|d_i-\stackrel{\&overbar;}{d}\right|+1}$

(2) four textural characteristics parameters needed for being obtained using described Gray co-occurrence matrix and matrix element project: Contrast, variance and, energy and average；

(3) described four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.

This preferred embodiment is based on improved gray level co-occurrence matrixes method, asks for cytological map by the way of setting weight coefficient The Gray co-occurrence matrix of picture, and then extract textural characteristics on specified four direction for the cell, solve due to outside dry Disturb the textural characteristics ginseng of the cell that (impact that causes as lighting angle when cell image gathers, flowing interference of gas etc.) causes Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast, Variance and, energy and four textural characteristics of average, eliminate redundancy and the characteristic parameter repeating；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image is processed.

In this application scenarios, given threshold t=26, d=2, image denoising effect improves 7.5% relatively, cytological map Extraction accuracy as feature improves 8%.

Finally it should be noted that above example is only in order to illustrating technical scheme, rather than the present invention is protected The restriction of shield scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. a kind of biological information acquisition device, is characterized in that, including cell recognition module and biosensor, described cell recognition Module is used for determining biological species, described biosensor is constituted by porous material is dried, and above-mentioned biosensor is that have Import the sample lead-in portion of sample solution and be configured with the developer layer launching said sample solution, containing by above-mentioned expansion Said sample solution is launched on layer and keeps the labelled reagent holding part of labelled reagent with the drying regime that can elute, and The reagent that can be combined with analyte and not elute and fixed on this developer layer containing the reagent in order to participate in reacting Immobilization part, imports sample solution in said sample lead-in portion, reaches labelled reagent holding by being impregnated with above-mentioned developer layer Part, said sample solution elutes labelled reagent and partly moves to above-mentioned Immobilized reagents so as analyte and Labelled reagent and immobilized reagent are reacted and are constituted,

By in the mentioned reagent immobilization partly middle binding capacity measuring above-mentioned labelled reagent, containing in said sample solution Analyte carry out qualitative or quantitative biosensor.

2. a kind of biological information acquisition device according to claim 1, is characterized in that, in the configuration of said sample lead-in portion Network structure.

3. a kind of biological information acquisition device according to claim 2, is characterized in that, in said sample lead-in portion or Be labelling sample holding part not equal to being on the position of sample lead-in portion side, there is the thin of contractive cell composition Born of the same parents' contracting agent holding part.