CN106244420A - A kind of producing device of high-density biochip - Google Patents

Abstract

A kind of producing device of high-density biochip, including cell recognition module with for making the spotting needle of biochip, described cell recognition module is used for determining biological species, the needle body of the described spotting needle for making biochip is that many arris lean on body, and the end face of needle point is plane, and center is shaped with circular depressed, needle body lower end axially has 26 clearance channels, each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, communicates with depression.The invention have the benefit that can efficiently complete high-density biochip makes.

Description

A kind of producing device of high-density biochip

Technical field

The present invention relates to biological field, be specifically related to the producing device of a kind of high-density biochip.

Background technology

Chip spotting needle is the critical component making high-density gene chip, the quality of point sample on gene chip, size, close Degree significant portion designs and fineness with depending on spotting needle.Main employing has solid spotting needle and utilizes rainbow Inhale the various spotting needles of principle.Solid spotting needle is a kind of traditional method, and its principle is utilize spotting needle needle surface attached Put forth effort to pick sample solution, then contact with slide surface and sample liquid is placed in slide surface.This needle point economy and durability is not enough The point that part is total is relatively small, sampling amount is little, and every sub-sampling can only be put and put once, is therefore unfavorable for large-scale fast fast-growing Produce.Further, since every the most all needs to take a sample, and each sampling amount also can change, thus affects the equal of point sample size Even property and quality.

Summary of the invention

For solving the problems referred to above, it is desirable to provide the producing device of a kind of high-density biochip.

The purpose of the present invention realizes by the following technical solutions:

The producing device of a kind of high-density biochip, including cell recognition module with for making the point sample of biochip Pin, described cell recognition module is used for determining that biological species, the needle body of the described spotting needle for making biochip are polygon Rib leans on body, and the end face of needle point is plane, and center is shaped with circular depressed, and needle body lower end axially has 2-6 clearance channel, each clearance channel Communicate in axle center, and the top that bottom land end caves in needle point, communicate with depression.

The invention have the benefit that can efficiently complete high-density biochip makes.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, 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, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is spotting needle 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.

Detailed description of the invention

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

Application scenarios 1

See Fig. 1, Fig. 2, the producing device of a kind of high-density biochip of an embodiment of this application scene, including Cell recognition module and for making the spotting needle of biochip, described cell recognition module is used for determining biological species, described Being that many arris lean on body for making the needle body of the spotting needle of biochip, the end face of needle point is plane, and center is shaped with circular depressed, Needle body lower end axially has 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, with recessed Fall into and communicate.

Preferably, the tail end sidewall of spotting needle is provided with the ledge torr of Polygonal column shape.

This preferred embodiment is easy to the operation of spotting needle.

Preferably, a length of 1.5-5cm of needle body, diameter 0.8-3cm of spotting needle；A diameter of 8-400 μm of needle point end face, A diameter of 5-350 μm of central concave, cup depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μ m。

This preferred embodiment is more suitable for commercial production.

Preferably, described cell recognition module 1 includes that Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification are known Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is for distinguishing the back of the body in the cell image gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is for extracting the textural characteristics of cell image；Described classification Recognition unit 13 is for utilizing grader to realize 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 that image changes subelement, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

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

(2) noise remove subelement, for gray level image is carried out denoising, including:

For pixel, (x y), chooses its neighborhood S of 3 × 3_x,y(2N+1) the neighborhood L of × (2N+1)_x,y, N is for being more than Integer equal to 2；

First whether be that boundary point judges to pixel, set threshold value T, T ∈ [13,26], calculate pixel (x, y) With its neighborhood S_x,yIn the gray scale difference value of each pixel, and compare 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 (x, y) is boundary point to pixel, and otherwise, (x y) is non-boundary point to pixel；

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

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

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

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

$h(x,y)=\frac{{\Σ}_{(i,j)\∈L_{x,y}}w(i,j)q(i,j)}{{\Σ}_{(i,j)\∈L_{x,y}w(i,j)}}$

In formula, (x y) is pixel (x, gray value y), q (i, j) representative image midpoint (i, j) ash at place after noise reduction to h Angle value, (i j) is neighborhood L to w_x,yInterior point (i, j) corresponding Gauss weight；

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

By each pixel (x, y) represents 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, (x y) represents (x, gray value y), h to 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；

K-means clustering procedure is used to be divided into background, Cytoplasm, nucleus three class；

(4) nuclear centers demarcates subelement, for demarcating nuclear centers:

Nucleus approximate region is obtained, if nuclear area comprises n point: (x by coarse segmentation subelement₁,y₁),…,(x_n, y_n), this region is carried out 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；

Carry out sampling along line segment 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 is along the gray scale difference of line segment direction:

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 is along the gradient gra (x of line segment direction_i,y_i):

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

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 closes on the space of neighborhood territory pixel Property and grey similarity carry out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, use Gaussian filter is weighted filtering to gray value, and at the borderline region that change is violent, row bound keeps filtering, beneficially image The holding at edge；Use K mean cluster to extract nucleus and Cytoplasm coarse contour, can effectively remove the interference of noise；Arrange thin Subelement is demarcated at karyon center, it is simple to follow-up be accurately positioned nucleus and Cytoplasm profile；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the inflammatory cell interference to edge graph, it is possible to accurately extract nucleus and Cytoplasm limit Edge.

Preferably, the described textural characteristics to cell image extracts, including:

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

Be located at 0 °, 45 °, 90 °, 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 altogether 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, and the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides The contrast level parameter that the gray level co-occurrence matrixes on each direction in is corresponding calculates, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively D_i, average isI=1,2,3,4, then weight coefficient w_iComputing formula be:

$w_i=\frac{1}{|D_i-\stackrel{\‾}{D}|+1}/\underset{i=1}{\overset{4}{\Σ}}\frac{1}{|D_i-\stackrel{\‾}{D}|+1}$

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

(3) described four textural characteristics parameters are normalized, the normalized textural characteristics value of final acquisition.

This preferred embodiment, based on the gray level co-occurrence matrixes method improved, uses the mode arranging weight coefficient to ask for cytological map The Gray co-occurrence matrix of picture, and then extract cell textural characteristics on appointment four direction, solve owing to outside is done Disturb the textural characteristics ginseng of the cell that (cause such as lighting angle when cell image gathers impact, the flowing interference etc. of gas) 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 the characteristic parameter of redundancy and repetition；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image processes.

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

Application scenarios 2

See Fig. 1, Fig. 2, the producing device of a kind of high-density biochip of an embodiment of this application scene, including Cell recognition module and for making the spotting needle of biochip, described cell recognition module is used for determining biological species, described Being that many arris lean on body for making the needle body of the spotting needle of biochip, the end face of needle point is plane, and center is shaped with circular depressed, Needle body lower end axially has 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, with recessed Fall into and communicate.

Preferably, the tail end sidewall of spotting needle is provided with the ledge torr of Polygonal column shape.

This preferred embodiment is easy to the operation of spotting needle.

Preferably, a length of 1.5-5cm of needle body, diameter 0.8-3cm of spotting needle；A diameter of 8-400 μm of needle point end face, A diameter of 5-350 μm of central concave, cup depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μ m。

This preferred embodiment is more suitable for commercial production.

Preferably, described cell recognition module 1 includes that Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification are known Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is for distinguishing the back of the body in the cell image gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is for extracting the textural characteristics of cell image；Described classification Recognition unit 13 is for utilizing grader to realize 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 that image changes subelement, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

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

(2) noise remove subelement, for gray level image is carried out denoising, including:

For pixel, (x y), chooses its neighborhood S of 3 × 3_x,y(2N+1) the neighborhood L of × (2N+1)_x,y, N is for being more than Integer equal to 2；

First whether be that boundary point judges to pixel, set threshold value T, T ∈ [13,26], calculate pixel (x, y) With its neighborhood S_x,yIn the gray scale difference value of each pixel, and compare 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 (x, y) is boundary point to pixel, and otherwise, (x y) is non-boundary point to pixel；

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

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

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

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

$h(x,y)=\frac{{\mathrm{\Σ}}_{(i,j)\∈L_{x,y}}w(i,j)q(i,j)}{{\mathrm{\Σ}}_{(i,j)\∈L_{x,y}}w(i,j)}$

In formula, (x y) is pixel (x, gray value y), q (i, j) representative image midpoint (i, j) ash at place after noise reduction to h Angle value, (i j) is neighborhood L to w_x,yInterior point (i, j) corresponding Gauss weight；

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

By each pixel (x, y) represents 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, (x y) represents (x, gray value y), h to 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；

K-means clustering procedure is used to be divided into background, Cytoplasm, nucleus three class；

(4) nuclear centers demarcates subelement, for demarcating nuclear centers:

Nucleus approximate region is obtained, if nuclear area comprises n point: (x by coarse segmentation subelement₁,y₁),…,(x_n, y_n), this region is carried out intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

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

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

(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；

Carry out sampling along line segment with unit length and can obtain dis_pIndividual point (x₁,y₁) ...,If sampled point Coordinate be not integer, its gray value is obtained by surrounding pixel linear interpolation；

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

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 is along the gradient gra (x of line segment direction_i,y_i):

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

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 closes on the space of neighborhood territory pixel Property and grey similarity carry out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, use Gaussian filter is weighted filtering to gray value, and at the borderline region that change is violent, row bound keeps filtering, beneficially image The holding at edge；Use K mean cluster to extract nucleus and Cytoplasm coarse contour, can effectively remove the interference of noise；Arrange thin Subelement is demarcated at karyon center, it is simple to follow-up be accurately positioned nucleus and Cytoplasm profile；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the inflammatory cell interference to edge graph, it is possible to accurately extract nucleus and Cytoplasm limit Edge.

Preferably, the described textural characteristics to cell image extracts, including:

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

Be located at 0 °, 45 °, 90 °, 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 altogether 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, and the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides The contrast level parameter that the gray level co-occurrence matrixes on each direction in is corresponding calculates, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively D_i, average isI=1,2,3,4, then weight coefficient w_iComputing formula be:

$w_i=\frac{1}{|D_i-\stackrel{\‾}{D}|+1}/\underset{i=1}{\overset{4}{\Σ}}\frac{1}{|D_i-\stackrel{\‾}{D}|+1}$

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

(3) described four textural characteristics parameters are normalized, the normalized textural characteristics value of final acquisition.

This preferred embodiment, based on the gray level co-occurrence matrixes method improved, uses the mode arranging weight coefficient to ask for cytological map The Gray co-occurrence matrix of picture, and then extract cell textural characteristics on appointment four direction, solve owing to outside is done Disturb the textural characteristics ginseng of the cell that (cause such as lighting angle when cell image gathers impact, the flowing interference etc. of gas) 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 the characteristic parameter of redundancy and repetition；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image processes.

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

Application scenarios 3

See Fig. 1, Fig. 2, the producing device of a kind of high-density biochip of an embodiment of this application scene, including Cell recognition module and for making the spotting needle of biochip, described cell recognition module is used for determining biological species, described Being that many arris lean on body for making the needle body of the spotting needle of biochip, the end face of needle point is plane, and center is shaped with circular depressed, Needle body lower end axially has 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, with recessed Fall into and communicate.

Preferably, the tail end sidewall of spotting needle is provided with the ledge torr of Polygonal column shape.

This preferred embodiment is easy to the operation of spotting needle.

Preferably, a length of 1.5-5cm of needle body, diameter 0.8-3cm of spotting needle；A diameter of 8-400 μm of needle point end face, A diameter of 5-350 μm of central concave, cup depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μ m。

This preferred embodiment is more suitable for commercial production.

Preferably, described cell recognition module 1 includes that Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification are known Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is for distinguishing the back of the body in the cell image gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is for extracting the textural characteristics of cell image；Described classification Recognition unit 13 is for utilizing grader to realize 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 that image changes subelement, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

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

(2) noise remove subelement, for gray level image is carried out denoising, including:

For pixel, (x y), chooses its neighborhood S of 3 × 3_x,y(2N+1) the neighborhood L of × (2N+1)_x,y, N is for being more than Integer equal to 2；

First whether be that boundary point judges to pixel, set threshold value T, T ∈ [13,26], calculate pixel (x, y) With its neighborhood S_x,yIn the gray scale difference value of each pixel, and compare 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 (x, y) is boundary point to pixel, and otherwise, (x y) is non-boundary point to pixel；

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

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

In formula, h (x, y) be after noise reduction pixel ((x y) is noise reduction preceding pixel point (x, ash y) to q for x, gray value y) Angle value, σ is pixel (x, y) neighborhood L_x,yInterior gray value mark is poor, q (i, j) ∈ [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] represent Neighborhood L_x,yInterior gray value fall within interval [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] point, k represents neighborhood L_x,yInterior gray value falls within Interval

[q (and x, y)-1.5 σ, q (x, y)+1.5 σ] the quantity of point；

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

$h(x,y)=\frac{{\mathrm{\Σ}}_{(i,j)\∈L_{x,y}}w(i,j)q(i,j)}{{\mathrm{\Σ}}_{(i,j)\∈L_{x,y}}w(i,j)}$

In formula, (x y) is pixel (x, gray value y), q (i, j) representative image midpoint (i, j) ash at place after noise reduction to h Angle value, (i j) is neighborhood L to w_x,yInterior point (i, j) corresponding Gauss weight；

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

By each pixel (x, y) represents 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, (x y) represents (x, gray value y), h to 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；

K-means clustering procedure is used to be divided into background, Cytoplasm, nucleus three class；

(4) nuclear centers demarcates subelement, for demarcating nuclear centers:

Nucleus approximate region is obtained, if nuclear area comprises n point: (x by coarse segmentation subelement₁,y₁),…,(x_n, y_n), this region is carried out intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

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

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

(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；

Carry out sampling along line segment 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 is along the gray scale difference of line segment direction:

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 is along the gradient gra (x of line segment direction_i,y_i):

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

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 closes on the space of neighborhood territory pixel Property and grey similarity carry out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, use Gaussian filter is weighted filtering to gray value, and at the borderline region that change is violent, row bound keeps filtering, beneficially image The holding at edge；Use K mean cluster to extract nucleus and Cytoplasm coarse contour, can effectively remove the interference of noise；Arrange thin Subelement is demarcated at karyon center, it is simple to follow-up be accurately positioned nucleus and Cytoplasm profile；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the inflammatory cell interference to edge graph, it is possible to accurately extract nucleus and Cytoplasm limit Edge.

Preferably, the described textural characteristics to cell image extracts, including:

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

Be located at 0 °, 45 °, 90 °, 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 altogether 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, and the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides The contrast level parameter that the gray level co-occurrence matrixes on each direction in is corresponding calculates, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively D_i, average isI=1,2,3,4, then weight coefficient w_iComputing formula be:

$w_i=\frac{1}{|D_i-\stackrel{\‾}{D}|+1}/\underset{i=1}{\overset{4}{\Σ}}\frac{1}{|D_i-\stackrel{\‾}{D}|+1}$

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

(3) described four textural characteristics parameters are normalized, the normalized textural characteristics value of final acquisition.

This preferred embodiment, based on the gray level co-occurrence matrixes method improved, uses the mode arranging weight coefficient to ask for cytological map The Gray co-occurrence matrix of picture, and then extract cell textural characteristics on appointment four direction, solve owing to outside is done Disturb the textural characteristics ginseng of the cell that (cause such as lighting angle when cell image gathers impact, the flowing interference etc. of gas) 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 the characteristic parameter of redundancy and repetition；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image processes.

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

Application scenarios 4

See Fig. 1, Fig. 2, the producing device of a kind of high-density biochip of an embodiment of this application scene, including Cell recognition module and for making the spotting needle of biochip, described cell recognition module is used for determining biological species, described Being that many arris lean on body for making the needle body of the spotting needle of biochip, the end face of needle point is plane, and center is shaped with circular depressed, Needle body lower end axially has 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, with recessed Fall into and communicate.

Preferably, the tail end sidewall of spotting needle is provided with the ledge torr of Polygonal column shape.

This preferred embodiment is easy to the operation of spotting needle.

Preferably, a length of 1.5-5cm of needle body, diameter 0.8-3cm of spotting needle；A diameter of 8-400 μm of needle point end face, A diameter of 5-350 μm of central concave, cup depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μ m。

This preferred embodiment is more suitable for commercial production.

Preferably, described cell recognition module 1 includes that Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification are known Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is for distinguishing the back of the body in the cell image gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is for extracting the textural characteristics of cell image；Described classification Recognition unit 13 is for utilizing grader to realize 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 that image changes subelement, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

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

(2) noise remove subelement, for gray level image is carried out denoising, including:

For pixel, (x y), chooses its neighborhood S of 3 × 3_x,y(2N+1) the neighborhood L of × (2N+1)_x,y, N is for being more than Integer equal to 2；

First whether be that boundary point judges to pixel, set threshold value T, T ∈ [13,26], calculate pixel (x, y) With its neighborhood S_x,yIn the gray scale difference value of each pixel, and compare 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 (x, y) is boundary point to pixel, and otherwise, (x y) is non-boundary point to pixel；

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

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

In formula, h (x, y) be after noise reduction pixel ((x y) is noise reduction preceding pixel point (x, ash y) to q for x, gray value y) Angle value, σ is pixel (x, y) neighborhood L_x,yInterior gray value mark is poor, q (i, j) ∈ [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] represent Neighborhood L_x,yInterior gray value fall within interval [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] point, k represents neighborhood L_x,yInterior gray value falls within Interval

[q (and x, y)-1.5 σ, q (x, y)+1.5 σ] the quantity of point；

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

$h(x,y)=\frac{{\Σ}_{(i,j)\∈L_{x,y}}w(i,j)q(i,j)}{{\Σ}_{(i,j)\∈L_{x,y}}w(i,j)}$

In formula, (x y) is pixel (x, gray value y), q (i, j) representative image midpoint (i, j) ash at place after noise reduction to h Angle value, (i j) is neighborhood L to w_x,yInterior point (i, j) corresponding Gauss weight；

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

By each pixel (x, y) represents 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, (x y) represents (x, gray value y), h to 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；

K-means clustering procedure is used to be divided into background, Cytoplasm, nucleus three class；

(4) nuclear centers demarcates subelement, for demarcating nuclear centers:

Nucleus approximate region is obtained, if nuclear area comprises n point: (x by coarse segmentation subelement₁,y₁),…,(x_n, y_n), this region is carried out intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

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

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

(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；

Carry out sampling along line segment 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 is along the gray scale difference of line segment direction:

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 is along the gradient gra (x of line segment direction_i,y_i):

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

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 closes on the space of neighborhood territory pixel Property and grey similarity carry out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, use Gaussian filter is weighted filtering to gray value, and at the borderline region that change is violent, row bound keeps filtering, beneficially image The holding at edge；Use K mean cluster to extract nucleus and Cytoplasm coarse contour, can effectively remove the interference of noise；Arrange thin Subelement is demarcated at karyon center, it is simple to follow-up be accurately positioned nucleus and Cytoplasm profile；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the inflammatory cell interference to edge graph, it is possible to accurately extract nucleus and Cytoplasm limit Edge.

Preferably, the described textural characteristics to cell image extracts, including:

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

Be located at 0 °, 45 °, 90 °, 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 altogether 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, and the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides The contrast level parameter that the gray level co-occurrence matrixes on each direction in is corresponding calculates, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively D_i, average isI=1,2,3,4, then weight coefficient w_iComputing formula be:

$w_i=\frac{1}{|D_i-\stackrel{\‾}{D}|+1}/\underset{i=1}{\overset{4}{\Σ}}\frac{1}{|D_i-\stackrel{\‾}{D}|+1}$

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

(3) described four textural characteristics parameters are normalized, the normalized textural characteristics value of final acquisition.

This preferred embodiment, based on the gray level co-occurrence matrixes method improved, uses the mode arranging weight coefficient to ask for cytological map The Gray co-occurrence matrix of picture, and then extract cell textural characteristics on appointment four direction, solve owing to outside is done Disturb the textural characteristics ginseng of the cell that (cause such as lighting angle when cell image gathers impact, the flowing interference etc. of gas) 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 the characteristic parameter of redundancy and repetition；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image processes.

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

Application scenarios 5

See Fig. 1, Fig. 2, the producing device of a kind of high-density biochip of an embodiment of this application scene, including Cell recognition module and for making the spotting needle of biochip, described cell recognition module is used for determining biological species, described Being that many arris lean on body for making the needle body of the spotting needle of biochip, the end face of needle point is plane, and center is shaped with circular depressed, Needle body lower end axially has 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, with recessed Fall into and communicate.

Preferably, the tail end sidewall of spotting needle is provided with the ledge torr of Polygonal column shape.

This preferred embodiment is easy to the operation of spotting needle.

Preferably, a length of 1.5-5cm of needle body, diameter 0.8-3cm of spotting needle；A diameter of 8-400 μm of needle point end face, A diameter of 5-350 μm of central concave, cup depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μ m。

This preferred embodiment is more suitable for commercial production.

Preferably, described cell recognition module 1 includes that Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification are known Other unit 13；Described Methods of Segmentation On Cell Images unit 11 is for distinguishing the back of the body in the cell image gathered by cell image acquisition module Scape, nucleus and Cytoplasm；Described feature extraction unit 12 is for extracting the textural characteristics of cell image；Described classification Recognition unit 13 is for utilizing grader to realize 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 that image changes subelement, noise remove subelement, coarse segmentation Subelement, nuclear centers demarcate subelement, Accurate Segmentation subelement, particularly as follows:

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

(2) noise remove subelement, for gray level image is carried out denoising, including:

For pixel, (x y), chooses its neighborhood S of 3 × 3_x,y(2N+1) the neighborhood L of × (2N+1)_x,y, N is for being more than Integer equal to 2；

First whether be that boundary point judges to pixel, set threshold value T, T ∈ [13,26], calculate pixel (x, y) With its neighborhood S_x,yIn the gray scale difference value of each pixel, and compare 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 (x, y) is boundary point to pixel, and otherwise, (x y) is non-boundary point to pixel；

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

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

In formula, h (x, y) be after noise reduction pixel ((x y) is noise reduction preceding pixel point (x, ash y) to q for x, gray value y) Angle value, σ is pixel (x, y) neighborhood L_x,yInterior gray value mark is poor, q (i, j) ∈ [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] represent Neighborhood L_x,yInterior gray value fall within interval [q (and x, y)-1.5 σ, q (x, y)+1.5 σ] point, k represents neighborhood L_x,yInterior gray value falls within Interval

[q (and x, y)-1.5 σ, q (x, y)+1.5 σ] the quantity of point；

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

$h(x,y)=\frac{{\mathrm{\Σ}}_{\left(ij\right)\∈L_{x,y}}w(i,j)q(i,j)}{{\mathrm{\Σ}}_{(i,j)\∈L_{x,y}}w(i,j)}$

In formula, (x y) is pixel (x, gray value y), q (i, j) representative image midpoint (i, j) ash at place after noise reduction to h Angle value, (i j) is neighborhood L to w_x,yInterior point (i, j) corresponding Gauss weight；

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

By each pixel (x, y) represents 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, (x y) represents (x, gray value y), h to 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；

K-means clustering procedure is used to be divided into background, Cytoplasm, nucleus three class；

(4) nuclear centers demarcates subelement, for demarcating nuclear centers:

Nucleus approximate region is obtained, if nuclear area comprises n point: (x by coarse segmentation subelement₁,y₁),…,(x_n, y_n), this region is carried out intensity-weighted demarcation and geometric center is demarcated, take its meansigma methods as nuclear centers (x_z,y_z):

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

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

(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；

Carry out sampling along line segment with unit length and can obtain dis_pIndividual point (x₁,y₁) ...,If sampled point Coordinate be not integer, its gray value is obtained by surrounding pixel linear interpolation；

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

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 is along the gradient gra (x of line segment direction_i,y_i):

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

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 closes on the space of neighborhood territory pixel Property and grey similarity carry out noise reduction process, flat site in the picture, in neighborhood, grey scale pixel value is more or less the same, use Gaussian filter is weighted filtering to gray value, and at the borderline region that change is violent, row bound keeps filtering, beneficially image The holding at edge；Use K mean cluster to extract nucleus and Cytoplasm coarse contour, can effectively remove the interference of noise；Arrange thin Subelement is demarcated at karyon center, it is simple to follow-up be accurately positioned nucleus and Cytoplasm profile；Accurate Segmentation subelement fills Divide and make use of directional information, overcome the inflammatory cell interference to edge graph, it is possible to accurately extract nucleus and Cytoplasm limit Edge.

Preferably, the described textural characteristics to cell image extracts, including:

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

Be located at 0 °, 45 °, 90 °, 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 altogether 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, and the span of d is [2,4], w_iFor weight coefficient, i=1,2,3,4, it is by four sides The contrast level parameter that the gray level co-occurrence matrixes on each direction in is corresponding calculates, if the gray level co-occurrence matrixes on four direction Corresponding contrast level parameter is respectively D_i, average isI=1,2,3,4, then weight coefficient w_iComputing formula be:

$w_i=\frac{1}{|D_i-\stackrel{\‾}{D}|+1}/\underset{i=1}{\overset{4}{\Σ}}\frac{1}{|D_i-\stackrel{\‾}{D}|+1}$

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

(3) described four textural characteristics parameters are normalized, the normalized textural characteristics value of final acquisition.

This preferred embodiment, based on the gray level co-occurrence matrixes method improved, uses the mode arranging weight coefficient to ask for cytological map The Gray co-occurrence matrix of picture, and then extract cell textural characteristics on appointment four direction, solve owing to outside is done Disturb the textural characteristics ginseng of the cell that (cause such as lighting angle when cell image gathers impact, the flowing interference etc. of gas) 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 the characteristic parameter of redundancy and repetition；To described four textural characteristics ginseng Number is normalized, and the Classification and Identification facilitating follow-up cell image processes.

In this application scenarios, setting threshold value T=26, d=2, image denoising effect improves 7.5% relatively, cytological map As the extraction accuracy of feature improves 8%.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of 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 producing device for high-density biochip, is characterized in that, including cell recognition module with for making biological core The spotting needle of sheet, described cell recognition module is used for determining biological species, the pin of the described spotting needle for making biochip Body is that many arris lean on body, and the end face of needle point is plane, and center is shaped with circular depressed, and needle body lower end axially has 2-6 clearance channel, Each clearance channel communicates in axle center, and the top that bottom land end caves in needle point, communicates with depression.

The producing device of a kind of high-density biochip the most according to claim 1, is characterized in that, at the tail end of spotting needle The ledge torr of Polygonal column shape it is provided with on sidewall.

The producing device of a kind of high-density biochip the most according to claim 2, is characterized in that, the needle body of spotting needle is long Degree is 1.5-5cm, diameter 0.8-3cm；A diameter of 8-400 μm of needle point end face, a diameter of 5-350 μm of central concave, depression The degree of depth is 0.6-3.8mm；The a length of 2-8mm of clearance channel, width is 15-300 μm.