CN106244420B - A kind of producing device of high-density biochip - Google Patents
A kind of producing device of high-density biochip Download PDFInfo
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Abstract
A kind of producing device of high-density biochip, spotting needle including cell recognition module and for making biochip, the cell recognition module is used to determine biological species, the needle body for making the spotting needle of biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess, needle body lower end is axially provided with 2-6 clearance channel, each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, communicates with recess.Beneficial effects of the present invention are：High-density biochip production can efficiently be completed.
Description
Technical field
The present invention relates to biological fields, and in particular to a kind of producing device of high-density biochip.
Background technique
Chip spotting needle is the critical component for making high-density gene chip, the quality, size of point sample on genetic chip, close
Degree significant portion is designed depending on spotting needle and fineness.What is mainly used in the world at present has solid spotting needle and using rainbow
Inhale the various spotting needles of principle.Solid spotting needle is a kind of traditional method, and principle is to utilize the attached of spotting needle needle surface
Put forth effort to pick sample solution, is then contacted with slide surface and sample liquid is placed in slide surface.This needle point economy and durability is insufficient
Place is that total point is relatively small, sampling amount is small, and every sub-sampling, which can only be put, to be set once, therefore is unfavorable for large-scale fast fast-growing
It produces.In addition, once all need to take a sample due to every, and each sampling amount can also change, thus influence the equal of point sample size
Even property and quality.
Summary of the invention
To solve the above problems, the present invention is intended to provide a kind of producing device of high-density biochip.
The purpose of the present invention is realized using following technical scheme：
A kind of producing device of high-density biochip, the point sample including cell recognition module and for making biochip
Needle, the cell recognition module are used to determine that biological species, the needle body for making the spotting needle of biochip are polygon
Rib leans on body, and the end face of needle point is plane, and center is formed with circular recess, and needle body lower end is axially provided with 2-6 clearance channel, each clearance channel
It communicates in axle center, and the top that slot bottom end is recessed to needle point, is communicated with recess.
Beneficial effects of the present invention are：High-density biochip production can efficiently be completed.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is spotting needle schematic diagram of the present invention；
Fig. 2 is the structural schematic diagram of cell recognition module.
Appended drawing 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 producing device of high-density biochip of one embodiment of this application scene, including
Cell recognition module and spotting needle for making biochip, the cell recognition module is used to determine biological species, described
The needle body of spotting needle for making biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess,
Needle body lower end is axially provided with 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, and recessed
It falls into and communicates.
Preferably, the protrusion frame torr of Polygonal column shape is provided on the tail end side wall of spotting needle.
This preferred embodiment is convenient for the operation of spotting needle.
Preferably, the needle body length of spotting needle is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm,
The diameter of central concave is 5-350 μm, cup depth 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μ
m。
This preferred embodiment is more suitable for industrial production.
Preferably, the cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge
Other unit 13；The Methods of Segmentation On Cell Images unit 11 is used to distinguish the back in the cell image acquired by cell image acquisition module
Scape, nucleus and cytoplasm；The feature extraction unit 12 is for extracting the textural characteristics of cell image；The classification
Recognition unit 13 is used to be realized using classifier to cell image Classification and Identification according to textural characteristics.
This preferred embodiment constructs the unit structure of cell recognition module 1.
Preferably, the 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, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N be greater than
Integer equal to 2；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] is calculated pixel (x, y)
With its neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if gray scale difference value is greater 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, following noise reduction process is carried out：
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 are pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] is indicated
Neighborhood Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value is fallen within
The quantity of the point in section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
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)
Angle value, w (i, j) are neighborhood Lx,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, specially：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) generation
Its neighborhood of table Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,
yn), intensity-weighted calibration is carried out to the region and geometric center is demarcated, takes its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If sampling
The coordinate of point is not integer, and gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
This preferred embodiment is arranged noise remove subelement, and the space of effective integration center pixel and neighborhood territory pixel is closed on
Property and grey similarity carry out noise reduction process, flat site in the picture, grey scale pixel value is not much different in neighborhood, uses
Gaussian filter is weighted filtering to gray value, 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, the interference of noise can be effectively removed；Setting is thin
Subelement is demarcated at karyon center, is accurately positioned convenient for subsequent to nucleus and cytoplasm profile；Accurate Segmentation subelement fills
Divide and directional information is utilized, overcomes interference of the inflammatory cell to edge graph, can accurately extract nucleus and cytoplasm side
Edge.
Preferably, the textural characteristics to cell image extract, including：
(1) the Gray co-occurrence matrix of cell image, the comprehensive ash are sought based on improved gray level co-occurrence matrixes method
Degree co-occurrence matrix embodies the textural characteristics of cell 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 be X1、X2、X3、X4, then Gray is total
The calculation 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：
In formula, d indicates that distance, the value range of d are [2,4], wiFor weighting coefficient, i=1,2,3,4, by four sides
The corresponding contrast level parameter of gray level co-occurrence matrixes in each direction in calculates, if the gray level co-occurrence matrixes on four direction
Corresponding contrast level parameter is respectively Di, mean value isI=1,2,3,4, then weighting coefficient wiCalculation formula be：
(2) four textural characteristics parameters needed for being obtained using the Gray co-occurrence matrix and matrix element project：
Contrast, variance and energy and mean value；
(3) four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.
This preferred embodiment is based on improved gray level co-occurrence matrixes method, seeks cytological map by the way of weighting coefficient is arranged
The Gray co-occurrence matrix of picture, and then textural characteristics of the cell on specified four direction are extracted, it solves since outside is dry
Disturb the textural characteristics ginseng of cell caused by (influence caused by lighting angle, the flowing of gas interference etc. when such as cell image acquisition)
Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast,
Variance and four textural characteristics of energy and mean value, eliminate redundancy and duplicate characteristic parameter；Four textural characteristics are joined
Number is normalized, and facilitates the Classification and Identification processing of subsequent cell image.
In this application scenarios, given threshold T=13, d=2, image denoising effect is opposite to improve 5%, cell image
The extraction accuracy of feature improves 8%.
Application scenarios 2
Referring to Fig. 1, Fig. 2, a kind of producing device of high-density biochip of one embodiment of this application scene, including
Cell recognition module and spotting needle for making biochip, the cell recognition module is used to determine biological species, described
The needle body of spotting needle for making biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess,
Needle body lower end is axially provided with 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, and recessed
It falls into and communicates.
Preferably, the protrusion frame torr of Polygonal column shape is provided on the tail end side wall of spotting needle.
This preferred embodiment is convenient for the operation of spotting needle.
Preferably, the needle body length of spotting needle is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm,
The diameter of central concave is 5-350 μm, cup depth 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μ
m。
This preferred embodiment is more suitable for industrial production.
Preferably, the cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge
Other unit 13；The Methods of Segmentation On Cell Images unit 11 is used to distinguish the back in the cell image acquired by cell image acquisition module
Scape, nucleus and cytoplasm；The feature extraction unit 12 is for extracting the textural characteristics of cell image；The classification
Recognition unit 13 is used to be realized using classifier to cell image Classification and Identification according to textural characteristics.
This preferred embodiment constructs the unit structure of cell recognition module 1.
Preferably, the 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, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N be greater than
Integer equal to 2；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] is calculated pixel (x, y)
With its neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if gray scale difference value is greater 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, following noise reduction process is carried out：
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 are pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] is indicated
Neighborhood Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value is fallen within
The quantity of the point in section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
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)
Angle value, w (i, j) are neighborhood Lx,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, specially：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) generation
Its neighborhood of table Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,
yn), intensity-weighted calibration is carried out to the region and geometric center is demarcated, takes its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If sampled point
Coordinate be not integer, gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
This preferred embodiment is arranged noise remove subelement, and the space of effective integration center pixel and neighborhood territory pixel is closed on
Property and grey similarity carry out noise reduction process, flat site in the picture, grey scale pixel value is not much different in neighborhood, uses
Gaussian filter is weighted filtering to gray value, 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, the interference of noise can be effectively removed；Setting is thin
Subelement is demarcated at karyon center, is accurately positioned convenient for subsequent to nucleus and cytoplasm profile；Accurate Segmentation subelement fills
Divide and directional information is utilized, overcomes interference of the inflammatory cell to edge graph, can accurately extract nucleus and cytoplasm side
Edge.
Preferably, the textural characteristics to cell image extract, including：
(1) the Gray co-occurrence matrix of cell image, the comprehensive ash are sought based on improved gray level co-occurrence matrixes method
Degree co-occurrence matrix embodies the textural characteristics of cell 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 be X1、X2、X3、X4, then Gray is total
The calculation 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：
In formula, d indicates that distance, the value range of d are [2,4], wiFor weighting coefficient, i=1,2,3,4, by four sides
The corresponding contrast level parameter of gray level co-occurrence matrixes in each direction in calculates, if the gray level co-occurrence matrixes on four direction
Corresponding contrast level parameter is respectively Di, mean value isI=1,2,3,4, then weighting coefficient wiCalculation formula be：
(2) four textural characteristics parameters needed for being obtained using the Gray co-occurrence matrix and matrix element project：
Contrast, variance and energy and mean value；
(3) four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.
This preferred embodiment is based on improved gray level co-occurrence matrixes method, seeks cytological map by the way of weighting coefficient is arranged
The Gray co-occurrence matrix of picture, and then textural characteristics of the cell on specified four direction are extracted, it solves since outside is dry
Disturb the textural characteristics ginseng of cell caused by (influence caused by lighting angle, the flowing of gas interference etc. when such as cell image acquisition)
Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast,
Variance and four textural characteristics of energy and mean value, eliminate redundancy and duplicate characteristic parameter；Four textural characteristics are joined
Number is normalized, and facilitates the Classification and Identification processing of subsequent cell image.
In this application scenarios, given threshold T=15, d=2, image denoising effect is opposite to improve 6%, cell image
The extraction accuracy of feature improves 8%.
Application scenarios 3
Referring to Fig. 1, Fig. 2, a kind of producing device of high-density biochip of one embodiment of this application scene, including
Cell recognition module and spotting needle for making biochip, the cell recognition module is used to determine biological species, described
The needle body of spotting needle for making biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess,
Needle body lower end is axially provided with 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, and recessed
It falls into and communicates.
Preferably, the protrusion frame torr of Polygonal column shape is provided on the tail end side wall of spotting needle.
This preferred embodiment is convenient for the operation of spotting needle.
Preferably, the needle body length of spotting needle is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm,
The diameter of central concave is 5-350 μm, cup depth 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μ
m。
This preferred embodiment is more suitable for industrial production.
Preferably, the cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge
Other unit 13；The Methods of Segmentation On Cell Images unit 11 is used to distinguish the back in the cell image acquired by cell image acquisition module
Scape, nucleus and cytoplasm；The feature extraction unit 12 is for extracting the textural characteristics of cell image；The classification
Recognition unit 13 is used to be realized using classifier to cell image Classification and Identification according to textural characteristics.
This preferred embodiment constructs the unit structure of cell recognition module 1.
Preferably, the 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, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N be greater than
Integer equal to 2；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] is calculated pixel (x, y)
With its neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if gray scale difference value is greater 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, following noise reduction process is carried out：
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 are pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] is indicated
Neighborhood Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value is fallen within
Section
The quantity of the point of [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
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)
Angle value, w (i, j) are neighborhood Lx,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, specially：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) generation
Its neighborhood of table Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,
yn), intensity-weighted calibration is carried out to the region and geometric center is demarcated, takes its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If sampling
The coordinate of point is not integer, and gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
This preferred embodiment is arranged noise remove subelement, and the space of effective integration center pixel and neighborhood territory pixel is closed on
Property and grey similarity carry out noise reduction process, flat site in the picture, grey scale pixel value is not much different in neighborhood, uses
Gaussian filter is weighted filtering to gray value, 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, the interference of noise can be effectively removed；Setting is thin
Subelement is demarcated at karyon center, is accurately positioned convenient for subsequent to nucleus and cytoplasm profile；Accurate Segmentation subelement fills
Divide and directional information is utilized, overcomes interference of the inflammatory cell to edge graph, can accurately extract nucleus and cytoplasm side
Edge.
Preferably, the textural characteristics to cell image extract, including：
(1) the Gray co-occurrence matrix of cell image, the comprehensive ash are sought based on improved gray level co-occurrence matrixes method
Degree co-occurrence matrix embodies the textural characteristics of cell 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 be X1、X2、X3、X4, then Gray is total
The calculation 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：
In formula, d indicates that distance, the value range of d are [2,4], wiFor weighting coefficient, i=1,2,3,4, by four sides
The corresponding contrast level parameter of gray level co-occurrence matrixes in each direction in calculates, if the gray level co-occurrence matrixes on four direction
Corresponding contrast level parameter is respectively Di, mean value isI=1,2,3,4, then weighting coefficient wiCalculation formula be：
(2) four textural characteristics parameters needed for being obtained using the Gray co-occurrence matrix and matrix element project：
Contrast, variance and energy and mean value；
(3) four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.
This preferred embodiment is based on improved gray level co-occurrence matrixes method, seeks cytological map by the way of weighting coefficient is arranged
The Gray co-occurrence matrix of picture, and then textural characteristics of the cell on specified four direction are extracted, it solves since outside is dry
Disturb the textural characteristics ginseng of cell caused by (influence caused by lighting angle, the flowing of gas interference etc. when such as cell image acquisition)
Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast,
Variance and four textural characteristics of energy and mean value, eliminate redundancy and duplicate characteristic parameter；Four textural characteristics are joined
Number is normalized, and facilitates the Classification and Identification processing of subsequent cell image.
In this application scenarios, given threshold T=18, d=3, image denoising effect is opposite to improve 7%, cell image
The extraction accuracy of feature improves 7%.
Application scenarios 4
Referring to Fig. 1, Fig. 2, a kind of producing device of high-density biochip of one embodiment of this application scene, including
Cell recognition module and spotting needle for making biochip, the cell recognition module is used to determine biological species, described
The needle body of spotting needle for making biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess,
Needle body lower end is axially provided with 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, and recessed
It falls into and communicates.
Preferably, the protrusion frame torr of Polygonal column shape is provided on the tail end side wall of spotting needle.
This preferred embodiment is convenient for the operation of spotting needle.
Preferably, the needle body length of spotting needle is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm,
The diameter of central concave is 5-350 μm, cup depth 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μ
m。
This preferred embodiment is more suitable for industrial production.
Preferably, the cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge
Other unit 13；The Methods of Segmentation On Cell Images unit 11 is used to distinguish the back in the cell image acquired by cell image acquisition module
Scape, nucleus and cytoplasm；The feature extraction unit 12 is for extracting the textural characteristics of cell image；The classification
Recognition unit 13 is used to be realized using classifier to cell image Classification and Identification according to textural characteristics.
This preferred embodiment constructs the unit structure of cell recognition module 1.
Preferably, the 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, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N be greater than
Integer equal to 2；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] is calculated pixel (x, y)
With its neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if gray scale difference value is greater 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, following noise reduction process is carried out：
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 are pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] is indicated
Neighborhood Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value is fallen within
Section
The quantity of the point of [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
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)
Angle value, w (i, j) are neighborhood Lx,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, specially：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) generation
Its neighborhood of table Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,
yn), intensity-weighted calibration is carried out to the region and geometric center is demarcated, takes its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If sampling
The coordinate of point is not integer, and gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
This preferred embodiment is arranged noise remove subelement, and the space of effective integration center pixel and neighborhood territory pixel is closed on
Property and grey similarity carry out noise reduction process, flat site in the picture, grey scale pixel value is not much different in neighborhood, uses
Gaussian filter is weighted filtering to gray value, 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, the interference of noise can be effectively removed；Setting is thin
Subelement is demarcated at karyon center, is accurately positioned convenient for subsequent to nucleus and cytoplasm profile；Accurate Segmentation subelement fills
Divide and directional information is utilized, overcomes interference of the inflammatory cell to edge graph, can accurately extract nucleus and cytoplasm side
Edge.
Preferably, the textural characteristics to cell image extract, including：
(1) the Gray co-occurrence matrix of cell image, the comprehensive ash are sought based on improved gray level co-occurrence matrixes method
Degree co-occurrence matrix embodies the textural characteristics of cell 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 be X1、X2、X3、X4, then Gray is total
The calculation 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：
In formula, d indicates that distance, the value range of d are [2,4], wiFor weighting coefficient, i=1,2,3,4, by four sides
The corresponding contrast level parameter of gray level co-occurrence matrixes in each direction in calculates, if the gray level co-occurrence matrixes on four direction
Corresponding contrast level parameter is respectively Di, mean value isI=1,2,3,4, then weighting coefficient wiCalculation formula be：
(2) four textural characteristics parameters needed for being obtained using the Gray co-occurrence matrix and matrix element project：
Contrast, variance and energy and mean value；
(3) four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.
This preferred embodiment is based on improved gray level co-occurrence matrixes method, seeks cytological map by the way of weighting coefficient is arranged
The Gray co-occurrence matrix of picture, and then textural characteristics of the cell on specified four direction are extracted, it solves since outside is dry
Disturb the textural characteristics ginseng of cell caused by (influence caused by lighting angle, the flowing of gas interference etc. when such as cell image acquisition)
Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast,
Variance and four textural characteristics of energy and mean value, eliminate redundancy and duplicate characteristic parameter；Four textural characteristics are joined
Number is normalized, and facilitates the Classification and Identification processing of subsequent cell image.
In this application scenarios, given threshold T=20, d=4, image denoising effect is opposite to improve 8%, cell image
The extraction accuracy of feature improves 6%.
Application scenarios 5
Referring to Fig. 1, Fig. 2, a kind of producing device of high-density biochip of one embodiment of this application scene, including
Cell recognition module and spotting needle for making biochip, the cell recognition module is used to determine biological species, described
The needle body of spotting needle for making biochip is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess,
Needle body lower end is axially provided with 2-6 clearance channel, and each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, and recessed
It falls into and communicates.
Preferably, the protrusion frame torr of Polygonal column shape is provided on the tail end side wall of spotting needle.
This preferred embodiment is convenient for the operation of spotting needle.
Preferably, the needle body length of spotting needle is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm,
The diameter of central concave is 5-350 μm, cup depth 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μ
m。
This preferred embodiment is more suitable for industrial production.
Preferably, the cell recognition module 1 includes Methods of Segmentation On Cell Images unit 11, feature extraction unit 12, classification knowledge
Other unit 13；The Methods of Segmentation On Cell Images unit 11 is used to distinguish the back in the cell image acquired by cell image acquisition module
Scape, nucleus and cytoplasm；The feature extraction unit 12 is for extracting the textural characteristics of cell image；The classification
Recognition unit 13 is used to be realized using classifier to cell image Classification and Identification according to textural characteristics.
This preferred embodiment constructs the unit structure of cell recognition module 1.
Preferably, the 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, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N be greater than
Integer equal to 2；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] is calculated pixel (x, y)
With its neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if gray scale difference value is greater 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, following noise reduction process is carried out：
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 are pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] is indicated
Neighborhood Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value is fallen within
Section
The quantity of the point of [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
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)
Angle value, w (i, j) are neighborhood Lx,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, specially：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) generation
Its neighborhood of table Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,
yn), intensity-weighted calibration is carried out to the region and geometric center is demarcated, takes its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If sampled point
Coordinate be not integer, gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
This preferred embodiment is arranged noise remove subelement, and the space of effective integration center pixel and neighborhood territory pixel is closed on
Property and grey similarity carry out noise reduction process, flat site in the picture, grey scale pixel value is not much different in neighborhood, uses
Gaussian filter is weighted filtering to gray value, 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, the interference of noise can be effectively removed；Setting is thin
Subelement is demarcated at karyon center, is accurately positioned convenient for subsequent to nucleus and cytoplasm profile；Accurate Segmentation subelement fills
Divide and directional information is utilized, overcomes interference of the inflammatory cell to edge graph, can accurately extract nucleus and cytoplasm side
Edge.
Preferably, the textural characteristics to cell image extract, including：
(1) the Gray co-occurrence matrix of cell image, the comprehensive ash are sought based on improved gray level co-occurrence matrixes method
Degree co-occurrence matrix embodies the textural characteristics of cell 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 be X1、X2、X3、X4, then Gray is total
The calculation 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：
In formula, d indicates that distance, the value range of d are [2,4], wiFor weighting coefficient, i=1,2,3,4, by four sides
The corresponding contrast level parameter of gray level co-occurrence matrixes in each direction in calculates, if the gray level co-occurrence matrixes on four direction
Corresponding contrast level parameter is respectively Di, mean value isI=1,2,3,4, then weighting coefficient wiCalculation formula be：
(2) four textural characteristics parameters needed for being obtained using the Gray co-occurrence matrix and matrix element project：
Contrast, variance and energy and mean value；
(3) four textural characteristics parameters are normalized, finally obtain normalized texture eigenvalue.
This preferred embodiment is based on improved gray level co-occurrence matrixes method, seeks cytological map by the way of weighting coefficient is arranged
The Gray co-occurrence matrix of picture, and then textural characteristics of the cell on specified four direction are extracted, it solves since outside is dry
Disturb the textural characteristics ginseng of cell caused by (influence caused by lighting angle, the flowing of gas interference etc. when such as cell image acquisition)
Numerical value has the problem of bigger difference in different directions, improves the precision of cell image texture feature extraction；Selected contrast,
Variance and four textural characteristics of energy and mean value, eliminate redundancy and duplicate characteristic parameter；Four textural characteristics are joined
Number is normalized, and facilitates the Classification and Identification processing of subsequent cell image.
In this application scenarios, given threshold T=26, d=2, image denoising effect is opposite to improve 7.5%, cytological map
As the extraction accuracy of feature improves 8%.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (3)
1. a kind of producing device of high-density biochip, characterized in that including cell recognition module and for making biological core
The spotting needle of piece, the cell recognition module is used to determine biological species, described for making the needle of the spotting needle of biochip
Body is that polygon rib leans on body, and the end face of needle point is plane, and center is formed with circular recess, and needle body lower end is axially provided with 2-6 clearance channel,
Each clearance channel communicates in axle center, and the top that slot bottom end is recessed to needle point, communicates with recess；
The cell recognition module includes Methods of Segmentation On Cell Images unit, feature extraction unit, Classification and Identification unit；The cytological map
As cutting unit is used to distinguish background, nucleus and the cytoplasm in the cell image acquired by cell image acquisition module；Institute
Feature extraction unit is stated for extracting to the textural characteristics of cell image；The Classification and Identification unit is used for according to texture spy
Sign is realized using classifier to cell image Classification and Identification；
The Methods of Segmentation On Cell Images unit includes image conversion subunit, noise remove subelement, coarse segmentation subelement, nucleus
Subelement, Accurate Segmentation subelement are demarcated in center, specially：
(1) image conversion subunit, for converting gray level image for the cell image of acquisition；
(2) noise remove subelement is used to carry out denoising to gray level image, including：
For pixel (x, y), its 3 × 3 neighborhood S is chosenx,yThe neighborhood L of (2N+1) × (2N+1)x,y, N is more than or equal to 2
Integer；
It whether is first that boundary point judges to pixel, given threshold T, T ∈ [13,26] calculates pixel (x, y) and its
Neighborhood Sx,yIn each pixel gray scale difference value, and be compared with threshold value T, if number of the gray scale difference value greater than threshold value T is greater than
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, following noise reduction process is carried out：
In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, and q (x, y) is the gray value of noise reduction preceding pixel point (x, y),
σ is pixel (x, y) neighborhood Lx,yInterior gray value mark is poor, and q (i, j) ∈ [+1.5 σ of q (x, y) -1.5 σ, q (x, y)] indicates neighborhood
Lx,yInterior gray value falls within the point of section [+1.5 σ of q (x, y) -1.5 σ, q (x, y)], and k indicates neighborhood Lx,yInterior gray value falls within section
The quantity of the point of [+1.5 σ of q (x, y) -1.5 σ, q (x, y)]；
If (x, y) is non-boundary point, following noise reduction process is carried out：
In formula, h (x, y) is the gray value of pixel (x, y) after noise reduction, the gray value at q (i, j) representative image midpoint (i, j),
W (i, j) is neighborhood Lx,yThe corresponding Gauss weight of interior point (i, j)；
(3) coarse segmentation subelement, for carrying out thick division, tool to the background in the cell image after denoising, cytoplasm, nucleus
Body is：
Each pixel (x, y) is indicated with four dimensional feature vectors：
In formula, h (x, y) represents the gray value of (x, y), have(x, y) represents its neighborhood Sx,yGray average, hmed(x, y) represents it
Neighborhood Sx,yGray scale intermediate value, hsta(x, y) represents its neighborhood Sx,yGray variance；
Background, cytoplasm, nucleus three classes 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 includes n point：(x1,y1),…,(xn,yn),
Intensity-weighted calibration and geometric center calibration are carried out to the region, take its average value as nuclear centers (xz,yz)：
(5) Accurate Segmentation subelement, for carrying out Accurate Segmentation to nucleus, cytoplasm；
It constructs from nuclear centers (xz,yz) arrive nucleus and cytoplasm boundary point (xp,yp) directed line segmentDistanceIt indicates to be rounded downwards；
It carries out sampling available dis with unit length along line segmentpA point (x1,y1) ...,If the seat of sampled point
Mark is not integer, and gray value is obtained by surrounding pixel linear interpolation；
Point (xi,yi) at along line segment direction gray scale difference：
hd(xi,yi)=h (xi-1,yi-1)-h(xi,yi)
Define gray scale difference inhibition function：
Point (xi,yi) at along line segment direction gradient gra (xi,yi)：
It chooses the maximum value point of gradient and is used as nucleus and cytoplasmic precise edge.
2. a kind of producing device of high-density biochip according to claim 1, characterized in that in the tail end of spotting needle
The protrusion frame torr of Polygonal column shape is provided on side wall.
3. a kind of producing device of high-density biochip according to claim 2, characterized in that the needle body of spotting needle is long
Degree is 1.5-5cm, diameter 0.8-3cm；The diameter of needle point end face is 8-400 μm, and the diameter of central concave is 5-350 μm, recess
Depth is 0.6-3.8mm；The length of clearance channel is 2-8mm, and width is 15-300 μm.
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