CN106651946A - High-definition blood cell image analysis processing system - Google Patents
High-definition blood cell image analysis processing system Download PDFInfo
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- CN106651946A CN106651946A CN201611235731.XA CN201611235731A CN106651946A CN 106651946 A CN106651946 A CN 106651946A CN 201611235731 A CN201611235731 A CN 201611235731A CN 106651946 A CN106651946 A CN 106651946A
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- 238000010191 image analysis Methods 0.000 title claims abstract description 11
- 210000000601 blood cell Anatomy 0.000 title claims abstract description 8
- 238000012545 processing Methods 0.000 title description 2
- 239000008280 blood Substances 0.000 claims abstract description 69
- 210000004369 blood Anatomy 0.000 claims abstract description 67
- 238000012360 testing method Methods 0.000 claims abstract description 16
- 238000001228 spectrum Methods 0.000 claims abstract description 6
- 230000008859 change Effects 0.000 claims abstract description 5
- 238000012937 correction Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000004075 alteration Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 3
- 239000000203 mixture Substances 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 4
- 239000007788 liquid Substances 0.000 description 4
- 230000005693 optoelectronics Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 241001062009 Indigofera Species 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 238000004159 blood analysis Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001429 stepping effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
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- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Dispersion Chemistry (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Geometry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention provides a high-definition blood cell image analysis system. The system is characterized in comprising an image obtaining module which carries out segmented photographing to obtain images of a blood sample test tube and judging blood sample boundaries by analyzing spectrum colour difference change points, for the obtained blood sample image of each part; an image splicing module which splices the obtained partial blood sample images and corrects the spliced part according to a correction coefficient, thereby obtaining a complete image of blood; an image analysis module which analyzes positions of the boundaries of the whole blood sample image and calculates the length of various segments of compositions of the blood sample image; and an image denoising module which denoises the blood sample image and automatically stores the blood sample image.
Description
Technical field
The present invention relates to blood testing field, specifically, is related to a kind of high-resolution Blood Corpuscle Image analyzing and processing system
System.
Background technology
At present clinical examination equipment is mainly based upon the inspection equipment of wet-chemical mode, and this equipment is uniform for liquid stream
Property, optical path accurate degree have high demands, therefore instrument and equipment complex structure, volume are big, price is high, environmental suitability is poor, are only suitable for
In lab analysis, it is unsuitable for outdoor detection, and detection process is complicated, detection speed is slower, especially carries energetically in country at present
Advocate the moment of national basic public health service project, it is a be easy to carry, test speed is high, strong environmental adaptability and have
The high blood analysis detecting system of accuracy of detection has fabulous Research Significance and application and popularizations value.
This patent does not adopt microscope, does not use reagent, not affected by environment, high degree of automation, be applicable to high temperature,
High and cold, plateau, the outdoor four seasons all can use.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of high-resolution Blood Corpuscle Image analysis process system, detection speed
Degree is fast, with preferable accuracy of detection.
The present invention is adopted the following technical scheme that and realizes goal of the invention：
A kind of high-resolution Blood Corpuscle Image analysis process system, it is characterised in that include：
Image collection module：Segmentation is taken pictures and obtains the image of blood sample test tube, and for the blood sample figure of every part acquisition
As judging blood sample line of demarcation by analysis spectrum aberration change point；
Image mosaic module：The part-blood sampled images of acquisition are spliced, and splicing part is entered by correction factor
Row amendment, the final complete image for obtaining blood；
Image analysis module：Each marginal position of whole blood sample image is analyzed, and calculates each segment components of blood sample image
Length；
Image goes dry module：Blood sample image carries out denoising, and automatically saves.
Used as the further restriction to the technical program, described image goes dry module to include:
On the basis of the complete image for obtaining, two threshold values are given：D and N, makes H (i) represent the gray value of ith pixel,
If first pixel does not have a noise point, the normal gray value in H (1) position, it is assumed that pixel count is M, then for pixel i from 1 to
M-1, if
| H (i+1)-H (i) | ＜ d
Prove that gray value is continually varying, H (i+1) is not belonging to the noise point that reject, if
| H (i+1)-H (i) | ＞ d
Illustrate larger in the gray-value variation of i+1 pixel, then may be noise point；
How many calculate the gray value for starting a continuous pixel from i+1 pixel to meet
| H (i+n)-H (i) | ＞ d；N is positive number
If continuous number of pixels is less than threshold values N, i.e. n<N, then illustrate that these pixels are noise points；
For noise point, if noise point is discontinuous, i.e., spuious noise point, using directly rejecting, and with both sides
The mean value of adjacent normal grey scale pixel value replacing, if that is, i-th point is noise point, with i-th -1 and the ash of i+1
The mean value of angle value is replacing；If i-th point and the i-th+n point is not noise point, and i+1 ... the i+n-1 points between it are
Noise point, i.e., continuous noise point, then the grey scale pixel value computing formula of k-th noise point be：
H (i+k)=H (i)+(H (i+n)-H (i))/nk, k=1,2 ..., n-1.
Compared with prior art, advantages of the present invention and good effect are：The present invention is in different positions to blood sample
Test tube is taken pictures, the marginal position of blood of automatic identification various pieces and the length of each segment components；Finally splice blood
The image of each part of liquid sample tube, and splicing part is modified by correction factor, it is final to obtain the complete of blood
Image, and denoising is carried out to Blood Image, so as to obtaining assay and automatically saving, the high definition of image is obtained,
Blood sample test tube is layered marginal position and the length computation of each segment components is accurate, and accuracy of detection is higher.
Description of the drawings
Fig. 1 is the structural representation of the present invention.
Fig. 2 is the Structure explosion diagram of the present invention.
Fig. 3 is the schematic diagram of connecting plate, white led lamps, CDD imageing sensors and the blue LED lamp of the present invention.
Fig. 4 is the structural representation of the blood sample objective table of the present invention.
Fig. 5 is the functional-block diagram of the present invention.
Fig. 6 is the circuit diagram of the controller of the present invention.
Fig. 7 is the circuit diagram of the LCDs connection of the present invention.
Fig. 8 is the keyboard connection circuit diagram of the present invention.
Fig. 9 is the circuit diagram of the electric-motor drive unit of the present invention.
Figure 10 is the peripheral hardware print interface circuit figure of the present invention.
Figure 11 is the circuit diagram of the RS232 communication units of the present invention.
In figure, the motors of 1- first, 2- encoders, 3- base plates, 4- shaft couplings, 5- leading screws, 6- gag lever posts, 7- limit switches,
8- screw sliders, 9- supports, 10- blood sample objective tables, 11- connecting plates, 12- cartridge type bearings, 13- baffle plates, 14- white light LEDs
Lamp, 15- blue LED lamps, 16-CDD imageing sensors, 17- friction mandrels, the motors of 18- second.
Specific embodiment
Below in conjunction with the accompanying drawings, a specific embodiment of the present invention is described in detail, it is to be understood that of the invention
Protection domain do not limited by specific embodiment.
As shown in figs. 1-11, the present invention includes image collection module：Segmentation is taken pictures and obtains the image of blood sample test tube, and
Blood sample line of demarcation is judged by analysis spectrum aberration change point for the blood sample image that every part obtains
The concrete operations equipment that image collection module is adopted includes base plate 3, and the one end of the base plate 3 is provided with the first motor 1, institute
State and encoder 2 is connected with the first motor 1, the output shaft of first motor 1 connects one end of leading screw 5, institute by shaft coupling 4
The other end for stating leading screw 5 is connected by cartridge type bearing 12 with baffle plate 13, and the baffle plate 13 is fixed on the other end of the base plate 3,
Gag lever post 6 is connected with the baffle plate 13, gag lever post 6 can prevent screw mandrel slide block 8 from rotating, the gag lever post 6 passes through screw slider
8, leading screw 5 described in the upper ring cap of the screw slider 8, the affixed blood sample objective table 10 in the top of the screw slider 8, the blood
Sample stage middle part is provided with axial trough 1002, and the bottom of blood sample objective table 10 is provided with motor slot 1003, the motor slot
The second motor 18 is fixed with 1003, the output shaft of second motor 18 is connected with one end of friction mandrel 17, the friction mandrel
17 in axial trough 1002, and affixed support 9 on the base plate 3 is connected with connecting plate 11, on the connecting plate on the support 9
White LED lamp 14, CDD imageing sensors 16 and blue LED lamp 15 are provided with, the base plate 3 is provided with three limit switches 7, are limited
Bit switch 7 adopts the position of photoelectric tube, three limit switches 7 to arrange according to the actual requirements, the CDD imageing sensors and three
The individual limit switch connects controller, and the controller connects respectively RS232 communication units, electric-motor drive unit, liquid crystal
Display screen, peripheral hardware printing interface, thermal printer, keyboard and bluetooth module and wireless network module.
The controller adopts the pin 27 and pin 28 of STC15W series monolithics, the controller to connect described
RS232 communication units, the pin 9 of the controller and the connection described image collecting unit of pin 10, the pin of the controller
45-48 connects the electric-motor drive unit, and the pin 35 and pin 36 of the controller connect the LCDs, the control
The pin 16-18 of device processed connects the peripheral hardware printing interface, and the pin 2-8 and pin 13-15 of the controller connect the key
Disk, the pin 11 of the controller, pin 38 and pin 39 are connected respectively three photoelectric tubes, the controller
Pin 61 and pin 62 connect the bluetooth module, and the pin 49 and pin 50 of the controller connect the wireless network mould
Block, the RS232 communication units connect the cable network module.First motor 1 is electric using 220v exchange steppings first
Machine.The bluetooth module is adopted using Risym cc2541 low-power consumption 4.0 with wireless network module described in base plate bluetooth serial ports module
With the USR-C322 wireless network modules of Jinan someone Science and Technology Ltd..The cable network module is using Jinan someone science and technology
The USR-TCP232-24 modules of Co., Ltd.
Running of the present invention comprises the steps：
(1) system initialization, blood sample objective table is moved to initial position, and the test tube that will be equipped with blood sample is put into blood
Liquid sample stage, LED blue lamps work, objective table intermediate friction axle is rotated, and is driven and is rotated equipped with blood sample test tube so that blood
Blood in liquid sample tube is layered, and ccd image sensor is started working；
(2) blood sample objective table initially enters ccd image sensor shooting area, the figure that ccd image sensor shoots
Picture is in liquid crystal display screen display, and white lamp is started working, and blue lamp keeps working condition；
(3) blood sample objective table enters into setting position 1, and the image that position 1 shoots can be in LCDs
The position of first layer boundary and the second layering boundary in blood sample test tube, ccd image sensor starts to gather blood sample
Product test tube Part I image herein, after the completion of collection, by analyzing spectrum aberration change point blood sample boundary line position, light is judged
The detection method of spectrum chromatic aberration belongs to prior art, will not be described here；Objective table continues into setting position 2, position 2
It is on a display screen it can be seen that the layering boundary of the blood sample the 3rd with blood sample, the 4th layering boundary, the 5th layering boundary
With the position of the 6th layering boundary, while white lamp quits work, prevent from affecting the layering of blood sample the 3rd of identification blood sample test tube
Boundary, the 4th layering boundary, the 5th layering boundary and the 6th layering boundary, blue lamp keeps working condition, ccd image sensor to open
Begin collection blood sample test tube Part II image herein, and automatic identification blood sample boundary line position；Objective table is continued into
At setting position 3, position 3 is on a display screen it can be seen that the blood sample the 7th with blood sample is layered the position of boundary, indigo plant
Lamp keeps working condition, gathers blood sample test tube Part III image herein, and automatic identification blood sample line of demarcation.
Image mosaic module：The part-blood sampled images of acquisition are spliced, and splicing part is entered by correction factor
Row amendment, the final complete image for obtaining blood；
Image analysis module：Each marginal position of whole blood sample image is analyzed, and calculates each segment components of blood sample image
Length；
Image goes dry module：Blood sample image carries out denoising, and automatically saves.
The step (1) comprises the steps：
(1.1) motor is rotated, and drives leading screw to rotate, the slide block movement on leading screw, and objective table is moved into initial bit
Put；
(1.2) spindle motor that rubs is rotated, and drives friction mandrel to rotate, and friction mandrel drives blood sample test tube to rotate.
When the blood sample objective table of the step (3) enters into setting position 1, slide block touches optoelectronic switch one, light
Electric switch one quits work to controller sending signal, controller controlled motor driver element, and motor stops operating, loading
Platform stop motion；
When the blood sample objective table of the step (3) enters into setting position 2, slide block touches optoelectronic switch two, light
Electric switch two quits work to controller sending signal, controller controlled motor driver element, and motor stops operating, loading
Platform stop motion；When the blood sample objective table of the step (3) enters into setting position 3, slide block touches optoelectronic switch three,
Optoelectronic switch three quits work to controller sending signal, controller controlled motor driver element, and motor stops operating, and carries
Thing platform stop motion；
Described image goes dry module to include:
On the basis of the complete blood sample image for obtaining, two threshold values are given：D and N, makes H (i) represent the ash of ith pixel
Angle value, if first pixel does not have noise point, the normal gray value in H (1) position, it is assumed that pixel count is M, then for pixel i
From 1 to M-1, if
| H (i+1)-H (i) | ＜ d
Prove that gray value is continually varying, H (i+1) is not belonging to the noise point that reject, if
| H (i+1)-H (i) | ＞ d
Illustrate larger in the gray-value variation of i+1 pixel, it is possible to be noise point；
How many calculate the gray value for starting a continuous pixel from i+1 pixel to meet
| H (i+n)-H (i) | ＞ d；N is positive number
If continuous number of pixels is less than threshold values N, i.e. n<N, then illustrate that these pixels are noise points；
For noise point, if noise point is discontinuous, i.e., spuious noise point, using directly rejecting, and with both sides
Replacing, such as i-th point is noise point to the mean value of adjacent normal grey scale pixel value, then with i-th -1 and the gray value of i+1
Mean value replacing；If i-th point and the i-th+n point is not noise point, and i+1 ... the i+n-1 points between it are noises
Point, i.e., continuous noise point, then the grey scale pixel value computing formula of k-th noise point be：
H (i+k)=H (i)+(H (i+n)-H (i))/nk, k=1,2 ..., n-1.
Claims (2)
1. a kind of high-resolution Blood Corpuscle Image analysis process system, it is characterised in that include：
Image collection module：Segmentation obtains the image of blood sample test tube, and the blood sample image obtained for every part passes through to divide
Analysis spectrum aberration change point judges blood sample line of demarcation；
Image mosaic module：The part-blood sampled images of acquisition are spliced, and splicing part is repaiied by correction factor
Just, the final complete image for obtaining blood；
Image analysis module：Each marginal position of whole blood sample image is analyzed, and calculates the length of each segment components of blood sample image
Degree；
Image goes dry module：Blood sample image carries out denoising, and automatically saves.
2. high-resolution Blood Corpuscle Image analysis process system according to claim 1, it is characterised in that described image is gone
Dry module includes:
On the basis of the complete image for obtaining, two threshold values are given：D and N, makes H (i) represent the gray value of ith pixel, if
First pixel does not have noise point, then the normal gray value in H (1) position, it is assumed that pixel count is M, then for pixel i is from 1 to M-1,
If
| H (i+1)-H (i) | ＜ d
Prove that gray value is continually varying, H (i+1) is not belonging to the noise point that reject, if
| H (i+1)-H (i) | ＞ d
Illustrate larger in the gray-value variation of i+1 pixel, then may be noise point；
How many calculate the gray value for starting a continuous pixel from i+1 pixel to meet
| H (i+n)-H (i) | ＞ d；N is positive number
If continuous number of pixels is less than threshold values N, i.e. n<N, then illustrate that these pixels are noise points；
For noise point, if noise point is discontinuous, i.e., spuious noise point is adopted and directly rejected, and adjacent with both sides
Normal grey scale pixel value mean value replacing, if that is, i-th point is noise point, with i-th -1 and the gray value of i+1
Mean value replacing；If i-th point and the i-th+n point is not noise point, and i+1 ... the i+n-1 points between it are noises
Point, i.e., continuous noise point, then the grey scale pixel value computing formula of k-th noise point be：
H (i+k)=H (i)+(H (i+n)-H (i))/nk, k=1,2 ..., n-1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611235731.XA CN106651946A (en) | 2016-12-28 | 2016-12-28 | High-definition blood cell image analysis processing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611235731.XA CN106651946A (en) | 2016-12-28 | 2016-12-28 | High-definition blood cell image analysis processing system |
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| Publication Number | Publication Date |
|---|---|
| CN106651946A true CN106651946A (en) | 2017-05-10 |
Family
ID=58833098
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
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| CN201611235731.XA Pending CN106651946A (en) | 2016-12-28 | 2016-12-28 | High-definition blood cell image analysis processing system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108519479A (en) * | 2018-04-19 | 2018-09-11 | 湖南乐准智芯生物科技有限公司 | A kind of online self-diagnosis system of biochip reaction process |
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| US20090087071A1 (en) * | 2007-09-26 | 2009-04-02 | Terrence Chen | Method and system for scale-based vessel enhancement in X-ray angiography |
| CN201903507U (en) * | 2010-10-30 | 2011-07-20 | 张红侠 | Blood cell analyzer |
| CN103778609A (en) * | 2014-01-22 | 2014-05-07 | 南京航空航天大学 | Binarization image and gray level image efficient denoising method based on pixel continuity judgment |
| CN103776768A (en) * | 2013-10-25 | 2014-05-07 | 北京倍肯恒业科技发展有限责任公司 | Dry blood cell analytical method |
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2016
- 2016-12-28 CN CN201611235731.XA patent/CN106651946A/en active Pending
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|---|---|---|---|---|
| US20090087071A1 (en) * | 2007-09-26 | 2009-04-02 | Terrence Chen | Method and system for scale-based vessel enhancement in X-ray angiography |
| CN201903507U (en) * | 2010-10-30 | 2011-07-20 | 张红侠 | Blood cell analyzer |
| CN103776768A (en) * | 2013-10-25 | 2014-05-07 | 北京倍肯恒业科技发展有限责任公司 | Dry blood cell analytical method |
| CN103778609A (en) * | 2014-01-22 | 2014-05-07 | 南京航空航天大学 | Binarization image and gray level image efficient denoising method based on pixel continuity judgment |
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| 宓超 等: "《装卸机器视觉及其应用》", 31 January 2016, 上海科学技术出版社 * |
| 崔秀美: "基于定量离心分层技术的快速血液检测关键技术与装置研究", 《中国博士学位论文全文数据库 医药卫生科技辑》 * |
| 徐科 等: "《金属表面质量在线检测技术》", 31 October 2016, 北京冶金工业出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108519479A (en) * | 2018-04-19 | 2018-09-11 | 湖南乐准智芯生物科技有限公司 | A kind of online self-diagnosis system of biochip reaction process |
| CN108519479B (en) * | 2018-04-19 | 2024-02-13 | 湖南乐准智芯生物科技有限公司 | On-line self-diagnosis device for biochip reaction process |
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Application publication date: 20170510 |