CN106108832A - A kind of in-vivo information acquiring apparatus - Google Patents

Abstract

A kind of in-vivo information acquiring apparatus, including cell recognition module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include: information acquiring section, electric power source, Magnetic Sensor portion, umber of pulse count section, umber of pulse judging part and power cut control portion.The invention have the benefit that and reliably the switching on or off of power supply of the in-vivo information acquiring apparatus being introduced inside a subject can be switched over simple structure.

Description

A kind of in-vivo information acquiring apparatus

Technical field

The present invention relates to acquisition of information field, be specifically related to a kind of in-vivo information acquiring apparatus.

Background technology

In recent years, in the field of endoscope, it is proposed that a kind of receiving image pickup part, illumination in the shell of capsule shape Portion, sending part etc. and the capsule type endoscope of deglutition type that constitutes, wherein, above-mentioned image pickup part obtains image subject within to be believed Breath, the shooting position of image pickup part is illuminated by above-mentioned Lighting Division, the image that above-mentioned sending part wireless transmission is obtained by image pickup part Information.From the mouth as the patient of subject, swallow this capsule type endoscope this capsule type endoscope is imported to subject Internal.Then, until the period naturally drained, this capsule type endoscope is carried out along with its vermicular movement in body cavity Endoceliac image is shot successively while movement, and to the image information acquired in external wireless transmission.

Summary of the invention

For solving the problems referred to above, it is desirable to provide a kind of in-vivo information acquiring apparatus.

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

A kind of in-vivo information acquiring apparatus, including cell recognition module and data obtaining module, described cell recognition Module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

The invention have the benefit that can be with simple structure reliably to the organism being introduced inside a subject Switching on or off of the power supply of internal information acquisition device switches over.

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 data obtaining module structural representation 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

Seeing Fig. 1, Fig. 2, a kind of in-vivo information acquiring apparatus of an embodiment of this application scene, including cell Identification module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

Preferably, described data obtaining module also includes being spaced test section, and this interval test section detection passes from above-mentioned magnetic The output gap of the pulse signal in sensor portion,

Described umber of pulse count section at the output gap detected by above-mentioned interval test section not less than base set in advance In the case of quasi-interval, the output number of above-mentioned pulse signal is updated.

Data can be updated by this preferred embodiment in time.

Preferably, described interval test section is made up of enumerator.

It is more accurate that this preferred embodiment obtains information.

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):

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

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

This preferred embodiment arranges noise remove subelement, and effective integration center pixel 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:

$X=\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{w}_i{X}_i$

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:

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

(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

Seeing Fig. 1, Fig. 2, a kind of in-vivo information acquiring apparatus of an embodiment of this application scene, including cell Identification module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

Preferably, described data obtaining module also includes being spaced test section, and this interval test section detection passes from above-mentioned magnetic The output gap of the pulse signal in sensor portion,

Described umber of pulse count section at the output gap detected by above-mentioned interval test section not less than base set in advance In the case of quasi-interval, the output number of above-mentioned pulse signal is updated.

Data can be updated by this preferred embodiment in time.

Preferably, described interval test section is made up of enumerator.

It is more accurate that this preferred embodiment obtains information.

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 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):

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

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

This preferred embodiment arranges noise remove subelement, and effective integration center pixel 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:

$X=\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{w}_i{X}_i$

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:

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

(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

Seeing Fig. 1, Fig. 2, a kind of in-vivo information acquiring apparatus of an embodiment of this application scene, including cell Identification module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

Preferably, described data obtaining module also includes being spaced test section, and this interval test section detection passes from above-mentioned magnetic The output gap of the pulse signal in sensor portion,

Described umber of pulse count section at the output gap detected by above-mentioned interval test section not less than base set in advance In the case of quasi-interval, the output number of above-mentioned pulse signal is updated.

Data can be updated by this preferred embodiment in time.

Preferably, described interval test section is made up of enumerator.

It is more accurate that this preferred embodiment obtains information.

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):

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

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

(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 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):

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

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

This preferred embodiment arranges noise remove subelement, and effective integration center pixel 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:

$X=\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{w}_i{X}_i$

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:

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

(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

Seeing Fig. 1, Fig. 2, a kind of in-vivo information acquiring apparatus of an embodiment of this application scene, including cell Identification module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

Preferably, described data obtaining module also includes being spaced test section, and this interval test section detection passes from above-mentioned magnetic The output gap of the pulse signal in sensor portion,

Described umber of pulse count section at the output gap detected by above-mentioned interval test section not less than base set in advance In the case of quasi-interval, the output number of above-mentioned pulse signal is updated.

Data can be updated by this preferred embodiment in time.

Preferably, described interval test section is made up of enumerator.

It is more accurate that this preferred embodiment obtains information.

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):

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

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

(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 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):

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

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

This preferred embodiment arranges noise remove subelement, and effective integration center pixel 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:

$X=\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{w}_i{X}_i$

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:

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

(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

Seeing Fig. 1, Fig. 2, a kind of in-vivo information acquiring apparatus of an embodiment of this application scene, including cell Identification module and data obtaining module, described cell recognition module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports control corresponding with the detection state of this magnetic signal Signal processed；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, its judge the umber of pulse counted to get by above-mentioned umber of pulse count section be whether predetermined number with On；

Power cut control portion, it is being judged as have input the feelings of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under condition, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

Preferably, described data obtaining module also includes being spaced test section, and this interval test section detection passes from above-mentioned magnetic The output gap of the pulse signal in sensor portion,

Described umber of pulse count section at the output gap detected by above-mentioned interval test section not less than base set in advance In the case of quasi-interval, the output number of above-mentioned pulse signal is updated.

Data can be updated by this preferred embodiment in time.

Preferably, described interval test section is made up of enumerator.

It is more accurate that this preferred embodiment obtains information.

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, w (i) it is neighborhood l_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 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):

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

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

This preferred embodiment arranges noise remove subelement, and effective integration center pixel 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:

$X=\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{w}_i{X}_i$

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:

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

(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. an in-vivo information acquiring apparatus, is characterized in that, including cell recognition module and data obtaining module, described carefully Born of the same parents' identification module is used for determining that biological species, described data obtaining module include:

Information acquiring section, it obtains organism internal information；

Electric power source, it is for providing electric power to above-mentioned information acquiring section；

Magnetic Sensor portion, its detection, from the magnetic signal of outside input, exports and corresponding with the detection state of this magnetic signal controls letter Number；

Umber of pulse count section, the umber of pulse of the pulse signal from above-mentioned Magnetic Sensor portion is counted by it；

Umber of pulse judging part, it judges whether the umber of pulse counted to get by above-mentioned umber of pulse count section is more than predetermined number；

Power cut control portion, it is being judged as have input the situation of the pulse of more than predetermined number by above-mentioned umber of pulse judging part Under, above-mentioned electric power source is provided to the electric power that above-mentioned information acquiring section is carried out and switches to dissengaged positions from offer state.

A kind of in-vivo information acquiring apparatus the most according to claim 1, is characterized in that, described data obtaining module is also Including interval test section, this interval test section detects the output gap of the pulse signal from above-mentioned Magnetic Sensor portion,

Described umber of pulse count section is between the output gap detected by above-mentioned interval test section is not less than benchmark set in advance In the case of every, the output number of above-mentioned pulse signal is updated.

A kind of in-vivo information acquiring apparatus the most according to claim 2, is characterized in that, described interval test section is by counting Number device is constituted.