CN106446582A - Image processing method - Google Patents
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- CN106446582A CN106446582A CN201610907074.2A CN201610907074A CN106446582A CN 106446582 A CN106446582 A CN 106446582A CN 201610907074 A CN201610907074 A CN 201610907074A CN 106446582 A CN106446582 A CN 106446582A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/7243—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages
- H04M1/72439—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages for image or video messaging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
- G06V10/422—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention relates to an image processing method. The method includes the steps of obtaining a human body surface image through an image obtaining device of a mobile terminal; extracting an image gray value and a chromatic value from the human body surface image; generating a first image gray value curve and a first image chromatic value curve according to the image gray value and the chromatic value; conducting curve data analysis on the first image gray value curve and the first image chromatic value curve respectively to obtain human body detection data, wherein the human body detection data includes a heart rate value, a blood pressure value, a blood viscosity value and a blood oxygen saturation degree value; generating first alarm information when the human body detection data exceeds a first threshold value or is smaller than a second threshold value; calculating the first change rate of the human body detection data, and generating second alarm information if the first change rate exceeds a third threshold value; calculating the change tendency according to multiple human body detection data, and generating third alarm information if the change tendency conforms to the first change tendency.
Description
Technical field
The present invention relates to a kind of image processing method, more particularly, to a kind of processing method of surface images.
Background technology
With the development in epoch, the live and work rhythm of people is more and more faster, leads to body excessively to be overdrawed.And if
Need to learn in time the Financial cost of some data (the such as rhythm of the heart, blood pressure, blood viscosity and blood oxygen saturation) of human body and when
Between cost be very high, and cannot be carried out long term data comparison analysis.
Content of the invention
The purpose of the present invention is the defect for prior art, provides a kind of image processing method, thus economical and low become
The detection data of this acquisition human body.
For achieving the above object, the invention provides a kind of image processing method, methods described includes：
Step 1, the image acquiring device using mobile terminal obtains body surface image；
Step 2, extracts image intensity value and chromatic value from described body surface image；
Step 3, generates the first image intensity value curve and the first image chroma according to image intensity value and chromatic value respectively
Value curve；
Step 4, carries out curve data and divides to described first image gray value curve and the first pattern colour angle value curve respectively
Analysis, thus obtaining human detection data, described human detection data includes heart rate value, pressure value, hyperlipidemia angle value and blood oxygen
Intensity value；
Step 5, when described human detection data exceedes first threshold, or during less than Second Threshold, generates the first report
Alarming information；
Step 6, calculates the first rate of change of described human detection data, if described first rate of change is more than the 3rd threshold
Value, then generate the second warning message；
Step 7, according to repeatedly described human detection data, calculates variation tendency, if described variation tendency meets first
During variation tendency, then generate the 3rd warning message.
Further, described step 1 specifically includes：Step 1, obtains the body of human body finger tip using the photographic head of mobile terminal
Table image.
Further, described step 2 specifically includes, and described body surface image is converted to the numeral of YUV color space
Image, extracts Y value and UV value from described digital picture.
Further, obtain heart rate value in described step 4 to specifically include：
Small echo is selected to carry out wavelet decomposition to described first image gray value curve；
Threshold value quantizing is carried out to the wavelet conversion coefficient after decomposing；
According to the coefficient reconstruct small echo after described threshold value quantizing, form the second image intensity value curve；
Real-time heart rate data is calculated according to the crest number of described second image intensity value curve.
Further, obtain pressure value in described step 4 to specifically include：
Select small echo that described first image gray-value variation curve is carried out with wavelet decomposition, and to the wavelet transformation after decomposing
Coefficient carries out threshold value quantizing；
According to the coefficient reconstruct small echo after described threshold value quantizing, form the second image intensity value change curve；
Real-time heart rate data is calculated according to the crest number of described second image intensity value change curve；
Blood pressure data is calculated with the relation of blood pressure and described real-time heart rate data according to heart rate.
Further, obtain hyperlipidemia angle value in described step 4 to specifically include：
Determine the relative value of hematocrit according to described first image chromatic value curve, relative according to hematocrit
It is worth to hyperlipidemia angle value.
Further, obtain oximetry value in described step 4 to specifically include：
Light intensity rate of change is obtained according to described first image gray value curve, blood oxygen is calculated according to described light intensity rate of change and satisfies
And angle value.
Further, the first rate of change in described step 6 is mean square deviation rate of change.
Further, in described step 7, calculate variation tendency specifically, being spaced according to detection time, the unit of account time
Amplitude of variation, if the first variation tendency continuing to increase or persistently reducing, will surpass when reaching next unit interval
Cross the 4th threshold value or be less than the 5th threshold value, then generate the 3rd warning message.
Further, also include after described step 7：Step 8, described variation tendency formation visualized graphs are represented.
Image processing method of the present invention it is achieved that get the detection number of human body with low time cost and Financial cost
According to.
Brief description
Fig. 1 is the flow chart of image processing method of the present invention.
Specific embodiment
Below by drawings and Examples, technical scheme is described in further detail.
Fig. 1 is the flow chart of image processing method of the present invention, as illustrated, the present invention specifically includes following steps：
Step 101, the image acquiring device using mobile terminal obtains body surface image；
Specifically utilize the surface images of the photographic head acquisition human body finger tip of mobile terminal.
In order to save the Financial cost of image acquisition, so make use of the photographic head of mobile terminal, photographic head can be moved
Photographic head that dynamic terminal has been configured is it is also possible to by other photographic head external for mobile terminal (such as high-definition camera or special take the photograph
As head) gathering body surface image.
Preferably, in order to not make, the brightness of surface images is too low or gray value is too high, so body surface image
Collection point preferably can be higher with light transmittance, and this allows for, and body surface is thin as far as possible, and preferably collection point is finger tip.
Step 102, extracts image intensity value and chromatic value in body surface image；
Specifically include, body surface image is converted to the digital picture of YUV color space, extract Y from digital picture
Value and UV value.
Because being analogue signal using the body surface image that photographic head collects, need to convert analog signals into
The image of digital signal.And Digital Image Data can be a lot of image spaces, such as RGB image space or YUV image space.
If for the gradation data easily gathering image, certainly preferably with YUV image space, such Y value is exactly image
Gray value, UV value is chromatic value.
Step 103, generates the first image intensity value curve and the first pattern colour according to image intensity value and chromatic value respectively
Angle value curve；
The foundation of the first image intensity value curve needs the gray value of continuous multiple images just can generate, therefore in step
In rapid 101, the body surface image of collection should be continuous multiple images frame.Thus identification obtains the gray value of multiple images,
Thus generate the first image intensity value curve.Equally, the first pattern colour angle value curve is generated according to pattern colour angle value.
Specifically, body surface image is mainly the gray scale gathering blood color.
Step 104, carries out curve data to described first image gray value curve and the first pattern colour angle value curve respectively
Analysis, thus obtain human detection data；
First image intensity value curve and the first pattern colour angle value curve carry out curve data analysis it is simply that to blood color
The pulse wave that obtains of light and shade mutation analysises, by heart rate value, pressure value, blood viscosity are finally given to the analysis of pulse wave
Value and oximetry value.
Specifically, obtain heart rate value to specifically include：Small echo is selected to carry out wavelet decomposition to the first image intensity value curve；Right
Wavelet conversion coefficient after decomposition carries out threshold value quantizing；According to the coefficient reconstruct small echo after threshold value quantizing, form the second image ash
Angle value curve；Real-time heart rate data is calculated according to the crest number of the second image intensity value curve.
Video data, during collection, is inevitably disturbed by all kinds noise, and common noise is done
Disturbing source mainly has following three kinds：The first is myoelectricity noise, is that the frequency being caused by physical activity or muscular tone is usual
Between 5 hertz to 2000 hertz；Second is power frequency noise, is the spatial electromagnetic interference being produced by supply network and its equipment
In the reaction of human body, it is the interference of fixed frequency, frequency is typically more than 50 hertz；The third is baseline drift, is by human body
The low-frequency disturbance that breathing, limb activity etc. cause, somewhat violent limb motion is serious by causing heart rate waveform signal to occur
Change, frequency is typically between 0.05 hertz to 2 hertz.Myoelectricity noise and baseline drift are important interference sources, in this example
Method using wavelet threshold denoising.Wavelet function changes in the range of finite time, and meansigma methodss are 0.
Choose a wavelet function and determine the level N of a wavelet decomposition, then the first image intensity value curve is entered
Row N shell wavelet decomposition, obtains wavelet coefficient, and wherein N is positive integer.Specifically, the first image intensity value curve is averagely decomposed
Become the part gray value curve of several times；Small echo is alignd with the starting point of part gray value curve, calculates very first time portion
Divide the approximation ratio of gray value curve and wavelet function, that is, calculate wavelet conversion coefficient, wavelet conversion coefficient means that more greatly
Part gray value curve is more close with the waveform of selected wavelet function；When then by mobile along time shafts for a wavelet function unit
Between, calculate the wavelet conversion coefficient of the part gray value curve of next time, bent until covering whole first image intensity value
Line.
For each layer of high frequency coefficient, select a threshold value to carry out quantification treatment, obtain new wavelet coefficient.
Low frequency coefficient according to wavelet decomposition n-th layer and the 1st layer of high frequency coefficient to n-th layer after quantification treatment,
Carry out the wavelet inverse transformation of the first image intensity value curve, obtain the second image intensity value curve.
Because cardiac motion result in the waveform running of a blood arrival fingerstick capillary each time, work as blood capillary
During congestive state, in blood, oxygen content increases, and blood color is in cerise, and average gray value is relatively low, and consumes blood in body
After oxygen in liquid, blood becomes kermesinus, and the period of change therefore calculating blood color just can calculate the week of cardiac motion
Phase, i.e. heart rate.Crest number according to the second image intensity value change curve and the ratio of acquisition time, can calculate blood per second
The number of times of liquid color change, then be multiplied by 60 number of times being blood color per minute change, thus to real-time heart rate data.
Obtain pressure value to specifically include：Small echo is selected to carry out wavelet decomposition to the first image intensity value change curve, and right
Wavelet conversion coefficient after decomposition carries out threshold value quantizing；According to the coefficient reconstruct small echo after threshold value quantizing, form the second image ash
Angle value change curve；Real-time heart rate data is calculated according to the crest number of the second image intensity value change curve；According to heart rate
It is calculated blood pressure data with the relation of blood pressure and real-time heart rate data.
Blood pressure data includes systolic pressure data and diastolic blood pressure data.Systolic pressure, diastolic pressure and heart rate have dependency, according to
Systolic pressure=0.1736 × heart rate+105.34 and real-time heart rate data calculate systolic pressure data, and according to diastolic pressure=
0.378 × heart rate-(20-M) and real-time heart rate data calculate diastolic blood pressure data, and wherein M is the integer in the range of 0 to 5.
Obtain hyperlipidemia angle value to specifically include：The relative of hematocrit is determined according to the first pattern colour angle value curve
Value, relative according to hematocrit is worth to blood viscosity value.
Because user's blood viscosity has dependency with the relative value of hematocrit, hematocrit change causes finger
The colourity change of sharp color, the colourity change of finger tip color is by the every two field picture in human body finger tip surface images video data
Colourity embodied.Therefore, it can determine that the hemocyte in user's blood accounts for whole blood by the colourity change of finger tip color
In percent volume to volume, that is, obtain hematocrit, so that it is determined that the data of blood viscosity.
Obtain oximetry value to specifically include：Light intensity rate of change is obtained according to the first image intensity value curve, according to light
Strong rate of change calculates oximetry value.
In near-infrared region, when two-beam detects tissue, only consider the impact of reduced hemoglobin and HbO2 Oxyhemoglobins,
Therefore blood oxygen can be carried out using the secondary light source signal of the first light signal of the R component in rgb color space and G component
The measurement of saturation.
Because the optical signal that light is returned after blood can be attenuated, and the rate of change of transmitted light intensity and reflective light intensity
It is directly proportional to absorptance, therefore can calculate blood oxygen saturation using light intensity rate of change.
Step 105, when human detection data exceedes first threshold, or during less than Second Threshold, generates the first warning
Information；
Specifically, if in human detection data (heart rate value, pressure value, hyperlipidemia angle value and oximetry value)
Certain or a few data exceed first threshold set in advance, or less than Second Threshold predetermined in advance, then generate the
One warning message.
Because by each one high first threshold of different human detection data settings, if the human detection detecting
Data then needs to generate the first warning message higher than first threshold；In the same manner by one ratio of each different human detection data setting
Relatively low Second Threshold, if the human detection data detecting is less than Second Threshold, needs to generate the first warning message.
Specifically, the generation of the first warning message is because that human detection data is excessive or too small, might have risk,
So the first warning message generating can be to remind to check UP, remind and remove examination in hospital etc..
Step 106, calculates the first rate of change of human detection data, if the first rate of change, more than the 3rd threshold value, is given birth to
Become the second warning message；
Specifically, the first rate of change is mean square deviation rate of change.
Human detection data (heart rate value, pressure value, hyperlipidemia angle value and oximetry value) each time is all lonely
Vertical data, such that it is able to carry out time-domain analyses by human detection data.
For example, obtain, using mean square deviation rate of change, the first rate of change that human detection data detects every time, if the first change
Rate excessive (the such as first rate of change is more than the 3rd threshold value), then need to generate the second warning message.
Specifically, the generation of the second warning message is because that the rate of change of human detection data is too big, might have risk,
So the second warning message generating can be to remind to check UP, remind and remove examination in hospital etc..
Step 107, according to multiple human detection data, calculates variation tendency, if variation tendency meets the first change and becomes
During gesture, then generate the 3rd warning message.
What the data processing of step 106 obtained is the situation of change of human detection data, but does not learn specific change
Change trend.And the purpose of the data processing of step 107 is desirable to obtain the variation tendency of human detection data.
Calculate variation tendency specifically, according to detection time interval, the amplitude of variation of unit of account time, if persistently increased
Plus or the first variation tendency of persistently reducing, the 4th threshold value will be exceeded or be less than the 5th threshold when reaching next unit interval
Value, then generate the 3rd warning message.
It is possible to carry out time-domain analyses when having multiple human detection data, specific time-domain analyses need to press
According to the concrete time of multiple human detection data, because the time interval of the different generations of human detection data is different,
So being accomplished by calculating the first variation tendency according to time interval.Such as variation tendency is that human detection data becomes larger, meter
Calculate the next unit interval detection time when, human detection data is likely to be breached more than the 4th threshold value；Or human detection number
According to being gradually reduced, calculate the next unit interval detection time when, human detection data is likely less than the 5th threshold value, thus
Need to generate the 3rd warning message.
Specifically, the generation of the 3rd warning message is because the too Gao Houtai that the variation tendency of human detection data can become
Low it is possible to can occurrence risk, so generate the 3rd warning message can be remind check UP, remind remove examination in hospital etc.
Deng.
Step 108, variation tendency formation visualized graphs are represented.
The step for meaning be according to time domain distribution, representing of multiple human detection data visualization thus may be used
Intuitively to be observed it is seen that the situation of change/rate of change of specific qualitative/quantitative by people, and possible variation tendency.
Image processing method of the present invention it is achieved that get the detection number of human body with low time cost and Financial cost
According to.
Professional should further appreciate that, each example describing in conjunction with the embodiments described herein
Unit and algorithm steps, can be hard in order to clearly demonstrate with electronic hardware, computer software or the two be implemented in combination in
Part and the interchangeability of software, generally describe composition and the step of each example in the above description according to function.
These functions to be executed with hardware or software mode actually, the application-specific depending on technical scheme and design constraint.
Professional and technical personnel can use different methods to each specific application realize described function, but this realization
It is not considered that it is beyond the scope of this invention.
The step of the method in conjunction with the embodiments described herein description or algorithm can be with hardware, computing device
Software module, or the combination of the two is implementing.Software module can be placed in random access memory (RAM), internal memory, read only memory
(ROM), electrically programmable ROM, electrically erasable ROM, depositor, hard disk, moveable magnetic disc, CD-ROM or technical field
In interior known any other form of storage medium.
Above-described specific embodiment, has been carried out to the purpose of the present invention, technical scheme and beneficial effect further
Describe in detail, be should be understood that the specific embodiment that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, all any modification, equivalent substitution and improvement within the spirit and principles in the present invention, done etc., all should comprise
Within protection scope of the present invention.
Claims (10)
1. a kind of image processing method is it is characterised in that methods described includes：
Step 1, the image acquiring device using mobile terminal obtains body surface image；
Step 2, extracts image intensity value and chromatic value from described body surface image；
Step 3, generates the first image intensity value curve according to image intensity value and chromatic value respectively and the first pattern colour angle value is bent
Line；
Step 4, carries out curve data analysis to described first image gray value curve and the first pattern colour angle value curve respectively, from
And obtaining human detection data, described human detection data includes heart rate value, pressure value, hyperlipidemia angle value and blood oxygen saturation
Value；
Step 5, when described human detection data exceedes first threshold, or during less than Second Threshold, generates the first alarm signal
Breath；
Step 6, calculates the first rate of change of described human detection data, if described first rate of change is more than the 3rd threshold value,
Generate the second warning message；
Step 7, according to repeatedly described human detection data, calculates variation tendency, if described variation tendency meets the first change
During trend, then generate the 3rd warning message.
2. method according to claim 1 is it is characterised in that described step 1 specifically includes：Step 1, using mobile terminal
Photographic head obtain human body finger tip surface images.
3. method according to claim 1 is it is characterised in that described step 2 specifically includes, by described body surface image
Be converted to the digital picture of YUV color space, extract Y value and UV value from described digital picture.
4. method according to claim 1 specifically includes it is characterised in that obtaining heart rate value in described step 4：
Small echo is selected to carry out wavelet decomposition to described first image gray value curve；
Threshold value quantizing is carried out to the wavelet conversion coefficient after decomposing；
According to the coefficient reconstruct small echo after described threshold value quantizing, form the second image intensity value curve；
Real-time heart rate data is calculated according to the crest number of described second image intensity value curve.
5. method according to claim 1 specifically includes it is characterised in that obtaining pressure value in described step 4：
Select small echo that described first image gray-value variation curve is carried out with wavelet decomposition, and to the wavelet conversion coefficient after decomposing
Carry out threshold value quantizing；
According to the coefficient reconstruct small echo after described threshold value quantizing, form the second image intensity value change curve；
Real-time heart rate data is calculated according to the crest number of described second image intensity value change curve；
Blood pressure data is calculated with the relation of blood pressure and described real-time heart rate data according to heart rate.
6. method according to claim 1 specifically includes it is characterised in that obtaining hyperlipidemia angle value in described step 4：
Determine the relative value of hematocrit according to described first image chromatic value curve, obtained according to the relative value of hematocrit
To blood viscosity.
7. method according to claim 1 specifically includes it is characterised in that obtaining oximetry value in described step 4：
Light intensity rate of change is obtained according to described first image gray value curve, blood oxygen saturation is calculated according to described light intensity rate of change
Value.
8. method according to claim 1 is it is characterised in that the first rate of change in described step 6 changes for mean square deviation
Rate.
9. method according to claim 1 is it is characterised in that in described step 7, calculate variation tendency specifically, according to
Detection time is spaced, the amplitude of variation of unit of account time, if the first variation tendency continuing to increase or persistently reducing, when
Reach next unit interval to exceed the 4th threshold value or be less than the 5th threshold value, then generate the 3rd warning message.
10. method according to claim 1 is it is characterised in that also include after described step 7：Step 8, by described change
Change trend forms visualized graphs and represents.
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| CN109259748A (en) * | 2018-08-17 | 2019-01-25 | 西安电子科技大学 | The system and method for handset processes face video extraction heart rate signal |
| TWI695169B (en) * | 2019-05-28 | 2020-06-01 | 眾匯智能健康股份有限公司 | Color comparison area selection method |
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