CN106023151A - Traditional Chinese medicine tongue manifestation object detection method in open environment - Google Patents

Abstract

The invention discloses a traditional Chinese medicine tongue manifestation object detection method in an open environment, and relates to medical image processing. The method comprises the following steps: 1, inputting an image A acquired in the open environment; 2, obtaining a correction image B by performing color correction on the acquired image A; 3, performing image segmentation on the correction image B obtained in the second step; 4, performing area feature determination on an image C obtained in the third step; and 5, performing texture feature determination on a candidate tongue body area D obtained in the fourth step. First of all, preprocessing of the color correction is performed on the image so that an influence brought by an external light source color temperature is reduced; then multiple communicating areas are obtained by segmenting the image; the candidate tongue body area is obtained by performing the feature determination on each communicating area; and finally, determination is carried out through comparing area texture features, and thus whether the candidate tongue body area is a tongue manifestation is determined. The purpose of performing object detection on the tongue manifestation in the image is finally realized, i.e., whether the tongue manifestation exists in a current picture is determined, and if so, where a tongue manifestation object is is determined.

Description

Tongue object detection method under a kind of open environment

Technical field

The present invention relates to Medical Image Processing, especially relate to Tongue object detection method under a kind of open environment.

Background technology

The tongue image that picture of the tongue presents after i.e. people stretches out tongue, the acquisition of Traditional Chinese Medicine picture of the tongue relies primarily on doctor's naked eyes and sees Examining, in recent years, the development of information technology has promoted Tongue analysis to objectify, digitized and the process of automatization.Both at home and abroad Scholar has carried out many useful explorations to this, and develops some Analysis of Lingual Picture systems, and (Jiang depends on to achieve preferable effect I. computerized Evolution of Tongue Inspection of TCM system [J]. China's combination of Chinese and Western medicine magazine, 2000,20 (2): 145-147；Cai Yihang, Liu Changjiang, Shen Lansun. the design [J] of novel tongue image analysis. observation and control technology, 2005,24 (5): 34-36.), but the bat of these systems Take the photograph what environment was usually fixed, i.e. shooting, collecting in the environment of airtight, illumination are stablized, and these equipment and instruments are the most stupid Weight, the most portable, price costly, have certain limitation (Liu Feng. in auxiliary Chinese patent medicine using system of Chinese medicine Analysis of Lingual Picture research [D]. Xiamen: Xiamen University, master thesis, 2007.).Along with smart mobile phone, panel computer etc. are mobile Popularizing of equipment, carries out tongue image acquisition by mobile device, it is thus achieved that personal health information gradually becomes under open natural environment It it is a developing direction.But thing followed problem is, owing to gathering picture of the tongue under open environment, there is light source color temperature, light The impact of many uncertain factors such as power, shooting angle, equipment difference so that the final image obtained is with fixed environment phase Ratio, often there is larger difference, follow-up analysis brought difficulty in the picture of the tongue image of collection, even due to photographer, The image gathered may not be available for the picture of the tongue image analyzed.Therefore, when under open environment, collection picture of the tongue is analyzed, The serious forgiveness of enhancing system and robustness seem particularly significant.Before picture of the tongue is analyzed, image is carried out picture of the tongue target inspection Survey and be favorably improved the follow-up analysis speed of system and accuracy rate, it helps doctor of traditional Chinese medicine observes tongue intuitively, accurately, easily As, improve the speed of diagnosis.Whether picture of the tongue target detection purpose is to judge to exist in the image gathered suitably to be available for follow-up point The picture of the tongue of analysis, if there is picture of the tongue in the picture gathered, need to detect which position at image.The standard of the target detection of picture of the tongue Really property directly affects the serious forgiveness of whole system.

At present, the research that launches for Tongue target detection under open environment is the most few, connects most with the inventive method A kind of based on mobile terminal the Tongue that near technology designs for Xiamen Qiangben Science and Technology Co., Ltd analyzes system (Wang Bo Bright. a kind of Tongue based on mobile terminal analyzes system: Chinese invention patent is open, 2014200304657 [P] .2014- 12-03.), the picture of the tongue to mobile device collection detects, analyzes.Although this system achieves picture of the tongue detection function, but also deposits In place of some shortcomings:

1, needing target cutting fritter to be detected, be transformed into hough space ballot, algorithm is complicated, and detection speed is slow；And Mobile device is used to carry out tongue image acquisition, detection under open environment, often higher to the requirement of real-time of processing procedure；

2, processing procedure needs to describe picture of the tongue model by more parameter, more complicated, it is achieved relatively difficult；

3, need the training mass data of supervision, need to mark manually foreground and background, thus set up detection Model, training process difficulty is big；

When 4, carrying out data training, it is desirable to sample is the most, high to the dependency of data set.

Summary of the invention

Present invention aims to existing collection during picture of the tongue under open environment and there is many weak points, As: the impact of many uncertain factors such as light source color temperature, light intensity, shooting angle, equipment difference so that the final figure obtained As compared with fixed environment, often there is larger difference in the picture of the tongue image of collection, follow-up analysis is brought difficulty, even due to The reason of photographer, may not be available for the problem such as picture of the tongue image analyzed in the image of collection.The present invention provides one to open Putting Tongue object detection method under environment, it is suitable that the method can quickly and accurately judge whether exist in the image gathered The picture of the tongue being available for subsequent analysis, if gather image in there is picture of the tongue, it is judged that picture of the tongue is in the position of image.

The present invention comprises the following steps:

1) the image A that input gathers under open environment；

2) the image A gathered is carried out color correction, obtain correction chart as B；

In step 2) in, the concrete grammar that the described image A to gathering carries out color correction is as follows:

(1) under standard photoenvironment, gather picture of the tongue image S1, calculate tri-Color Channel averages of RGB of picture of the tongue image S1 Respectively with overall average K of picture of the tongue image S1_sRatio cc_r、α_g、α_b:

${\mathrm{\α}}_r=\frac{R_{avgs}}{K_s}$

${\mathrm{\α}}_g=\frac{G_{avgs}}{K_s}$

${\mathrm{\α}}_b=\frac{B_{avgs}}{K_s}$

Wherein, overall average K of picture of the tongue image S1_s=(R_avgs+G_avgs+B_avgs)/3；R_avgs、G_avgs、B_avgsIt is respectively mark The average of tri-Color Channels of RGB of picture of the tongue image S1 is gathered under quasi-photoenvironment；It can be D65 that described standard illumination gathers environment Light source, colour temperature is 6500K；

(2) adjust the average of tri-Color Channels of RGB of image A as the following formula, obtain correction chart as B；

K=(R_avg+G_avg+B_avg)/3

$R_d=\frac{K\×{\mathrm{\α}}_r}{R_{avg}}{R}_s$

$G_d=\frac{K\×{\mathrm{\α}}_g}{G_{avg}}{G}_s$

$B_d=\frac{K\×{\mathrm{\α}}_b}{B_{avg}}{B}_s$

Wherein, K is the overall average of image A, R_avg、G_avg、B_avgRespectively tri-Color Channels of RGB of image A is equal Value；R_d、G_d、B_dFor correction chart as the value of tri-Color Channels of RGB of each pixel of B, R_s、G_s、B_sFor each pixel of image A The value of tri-Color Channels of RGB, α_r、α_g、α_bBy step 2) (1st) ratio partly tried to achieve；

3) to step 2) correction chart that obtains carries out image segmentation as B, and concrete grammar is as follows:

(1) correction image B is transformed into gray space image fB1, (sees document: OHTSU according to maximum variance between clusters N.A threshold selection method from gray-level histograms[J].System Man& Cybernetics IEEE Transactions on, 1979,9 (1): 62-66.) gray space image fB1 is carried out threshold value to divide Cut, obtain splitting image B1 ', and use morphology operations to smooth connected domain segmentation image B1 ', obtain image B1；

In step 3) in (1st) part, described correction image B is transformed into gray space image fB1, according between maximum kind Variance method carries out Threshold segmentation to gray space image fB1, obtains splitting image B1 ', and segmentation image B1 ' is used morphology Computing smooths connected domain, obtains specifically comprising the following steps that of image B1

A) correction image B is transformed into gray space image fB1；

B) for gray space image fB1, if span G=[0, L-1] of gray scale G of gray space image fB1, respectively The probability that gray value occurs is P_i, threshold value is T, and threshold value T is divided into f after gray space image fB1 is carried out binaryzation₀And f₁: f₀= [0, T], f₁=[T+1, L-1], f₀And f₁Probability be respectivelyAnd α₁=1-α₀, average gray value is respectivelyWithThen f₀And f₁Maximum between-cluster variance be: g²(T)=α₀(μ₀- μ)²+α₁(μ₁-μ)²=α₀α₁(μ₀-μ₁)², wherein μ=Σ iP_i, obtain threshold value T when g takes maximum, to gray space image FB1 carries out Threshold segmentation, must split RGB value f of image B1 ' pixel_{X, y}(r, g, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& iffB1(x,y)>T\\ (0,0,0)& else\end{array}\right.$

Wherein, (x, y) represents the value of gray space image fB1 pixel to fB1, and T is threshold value；

C) use the closed operation in morphology operations to smooth connected domain segmentation image B1 ', depend on according to following two formula Secondary calculating, obtains image B1；Pixel value g1 in image B1 (x, y) be:

G1 (x, y)=erode (dilate (f1 (x, y), element))

G1 (x, y)=bitwise_not (g1 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B1 ', element is defined as the structural elements in morphology operations to f1 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations； Bitwise_not is defined as the inversion operation of each pixel to image.

(2) correction image B is transformed into hsv color spatial image fB2, the H passage of image fB2 is carried out Threshold segmentation, Obtain splitting image B2 ', and use morphology operations to smooth connected domain segmentation image B2 ', obtain image B2；

In step 3) in (2nd) part, described correction image B is transformed into hsv color spatial image fB2, to image fB2 H passage carry out Threshold segmentation, obtain split image B2 ', and to segmentation image B2 ' use morphology operations smooth connected domain, Obtain specifically comprising the following steps that of image B2

A) correction image B is transformed into hsv color spatial image fB2；

B) use following formula that image fB2 is carried out hue threshold segmentation, obtain splitting RGB value f of image B2 ' pixel_{X, y} (r, g, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& if{h}_{x,y}\∈\[T_1,T_2\]\\ (0,0,0)& else\end{array}\right.$

Wherein, h_{X, y}Represent H passage pixel value in image fB2, T₁And T₂Represent the threshold value set；

C) use the closed operation in morphology operations to smooth connected domain segmentation image B2 ', depend on according to following two formula Secondary calculating, obtains image B2；Pixel value g2 in image B2 (x, y) be:

G2 (x, y)=erode (dilate (f2 (x, y), element))

G2 (x, y)=bitwise_not (g2 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B2 ', element is defined as the structural elements in morphology operations to f2 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations； Bitwise_not is defined as the inversion operation of each pixel to image.

(3) use RGB trichroism component variance method (see document: Jiang Yiwu. computerized Evolution of Tongue Inspection of TCM system [J]. in China Doctor trained in Western medicine combines magazine, and 2000,20 (2): 145-147) correction chart is carried out Threshold segmentation as B, obtain splitting image B3 ', and right Segmentation image B3 ' uses morphology operations to smooth connected domain, obtains image B3.

In step 3) in (3rd) part, described employing RGB trichroism component variance method carries out Threshold segmentation to correction chart as B, Obtain splitting image B3 ', and use morphology operations to smooth connected domain segmentation image B3 ', obtain image B3 concrete step Rapid as follows:

A) for correction chart as B, it is assumed that its size is m × n, the RGB value of each pixel in correction image B is carried out Normalization operates, and its span is [0,1], uses following formula to correction chart as each pixel in B calculates RGB tri-colouring component (m, n), and splits as B variance gate to correction chart, obtains splitting RGB value f of image B3 ' pixel_{M, n}(r, g, b):

Gate (m, n)=(r_{M, n}-g_{M, n})+(b_{M, n}-g_{M, n})×6+(r_{M, n}+g_{M, n}+b_{M, n})/3

$f_{m,n}(r,g,b)=\left\{\begin{array}{cc}(1,1,1)& ifgate(m,n)<0.627\\ (0,0,0)& else\end{array}\right.$

Wherein, r_{M, n}Represent that correction chart is as pixel (m, the value of R passage n), g in B_{M, n}Represent that correction chart is as picture in B Vegetarian refreshments (m, the value of G passage n), b_{M, n}Represent that correction chart is as pixel (m, the value of channel B n) in B；

B) use the closed operation in morphology operations to smooth connected domain segmentation image B3 ', calculate according to formula below, To image B3；Pixel value g3 in image B3 (x, y) be:

G3 (x, y)=erode (dilate (f3 (x, y), element))

Wherein, (x, y) for the pixel value in segmentation image B3 ', element is defined as the structural elements in morphology operations to f3 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations.

(4) image B1, image B2, tri-images of image B3 are carried out logic "and" operation, obtain image C.

4) to step 3) the image C that obtains carries out provincial characteristics judgement, and concrete grammar is as follows:

(1) for each connected domain in image C, the convex closure (S of this connected domain is calculated_i)；

(2) each convex closure (S is calculated_i) areaArea area with image C_CRatio:Delete the area ratio connected domain less than 0.02；

(4) each connected domain convex closure (S is calculated_i) barycenter (C_i) and image C center (C₀) Euclidean distance:

$dis=\sqrt{{(C_{ix}-C_{0x})}^2+{(C_{iy}-C_{0y})}^2};$

Wherein, C_ix、C_iyRepresent convex closure (S respectively_i) abscissa of barycenter and vertical coordinate, C_0xAnd C_0yRepresent respectively in image C The abscissa of the heart and vertical coordinate；

(4) each connected domain convex closure (S is calculated_i) length-width ratio scale=w/h of minimum enclosed rectangle；

Wherein w represents the width of minimum enclosed rectangle, and h represents the length of minimum enclosed rectangle；

(5) each connected domain convex closure (S is calculated_i) 7 Hu not bending moment m_i(i ∈ [1,7] (sees document: Hu M.Visual Pattern Recognition by Moment Invariants[J].Information Theory Ire Transactions on,1962,8(2):179-187.)；

Described Hu not bending moment is defined as follows:

For image f that size is M × N (x, y), f (x, two dimension (p+q) rank square y) is defined as:

$m_{pq}={\&Sigma;}_{x=0}^{M-1}{\&Sigma;}_{y=0}^{N-1}{x}^p{y}^{q}f(x,y)$

(p+q) rank central moment is defined as accordingly:

Wherein

By η_pqThe normalization central moment represented is defined as:

Wherein γ=(p+q)/2+1

The linear combination of Hu normalization central moment constructs has translation, flexible, 7 Hu not bending moments of invariable rotary

(6) one normal shape picture of the tongue image (S of artificial selection₀), calculate picture of the tongue image (S₀) 7 Hu not bending moment M_i(i ∈ [1,7]), calculate matching degreeDescribed normal shape picture of the tongue image (S₀) can be traditional Chinese medical science doctor The raw normal shape picture of the tongue image rule of thumb selected with relevant professional knowledge；

(7) each connected domain convex closure (Si is calculated successively according to following three formula₎Similarity score, select similarity The maximum of score, the connected domain convex closure (S that the maximum of this similarity score is corresponding_i) it is the candidate's tongue body region D selected:

$score=\left\{\begin{array}{ccc}1& ifarea<0.1andscale<0.6& or\\ & area\&Element;\&lsqb;0.1,0.5)andscale<0.3& or\\ & area>0.5andscale<0.6& \\ 2& ifarea<0.1andscale\&Element;\&lsqb;0.6,0.8\&rsqb;& or\\ & area<\&Element;\&lsqb;0.1,0.5)andscale\&Element;\&lsqb;0.3,0.6)& or\\ & area>0.5andscale>0.6& \\ 3& ifarea<0.1andscale>0.8& or\\ & area\&Element;\&lsqb;0.1,0.3)andscale>0.6& \\ 4& ifarea\&Element;\&lsqb;0.3,0.5)andscale>0.6& \end{array}\right.$

$score=\left\{\begin{array}{cc}score+2& ifdis<20\\ score+1& ifdis\&Element;\&lsqb;20,60)\\ score-1& ifdis\&Element;\&lsqb;150,200)\\ score-2& ifdis>200\end{array}\right.$

$score=\left\{\begin{array}{cc}score+3& ifmatch<0.1\\ score+2& ifmatch\&Element;\&lsqb;0.1,0.5)\\ score-1& ifmatch\&Element;\&lsqb;10,20)\\ score-2& ifmatch>20\end{array}\right.$

Wherein, area is by step 4) (2nd) ratio partly tried to achieve；Scale is step 4) (4th) part tried to achieve Length-width ratio；Dis is step 4) (3rd) Euclidean distance partly tried to achieve；Match is step 4) (6th) part tried to achieve Matching degree；

5) to step 4) candidate's tongue body region D of obtaining carries out textural characteristics judgement, and concrete grammar is as follows:

(1) level of candidate's tongue body region D, vertical, the associating of three kinds of gray level co-occurrence matrixes of angular shift amount are calculated respectively Probability density distribution；

(2) the joint probability density distribution setting gray level co-occurrence matrixes is designated as [P_mn] L × L, wherein L is gray scale span, m =[0, L-1], n=[0, L-1]；According to joint probability density distribution be calculated 6 textural characteristics (see document: HARALICK R M,SHANMUGAM K,DINSTEIN I.Textural features for image classification[J].Systems Man&Cybernetics IEEE Transactions on,2010,smc-3(6): 610 621.), described 6 textural characteristics include that angle second moment, contrast, entropy, unfavourable balance square, intermediate value are relevant with gray scale；

Wherein,

(3) to step 5) (1st) part obtain three kinds of gray level co-occurrence matrixes joint probability density distribution respectively according to Step 5) (2nd) method 6 textural characteristics of calculating partly, there are 18 texture characteristic amount f of candidate's tongue body region D_i(i ∈[0.17])；

(4) the picture of the tongue image gathered under artificial selection's some width standard photoenvironment, under each width standard photoenvironment The picture of the tongue image gathered, is partitioned into tongue body region SD therein by hand, obtains several tongue body regions SD, to each tongue body district Territory SD calculates its level, vertical, the joint probability density distribution of three kinds of gray level co-occurrence matrixes of angular shift amount respectively；To these three Joint probability density is distributed respectively according to step 5) (2nd) method calculating textural characteristics partly, each tongue body region SD is altogether Obtain 18 texture characteristic amounts；18 texture characteristic amounts calculated to these tongue body regions SD, seek each texture characteristic amount Meansigma methods F_i(i ∈ [0,17])；

(5) calculation procedure 5) (3rd) the texture characteristic amount f partly tried to achieve_i(i ∈ [0,17]) and step 5) (4th) part Meansigma methods F of the texture characteristic amount tried to achieve_iThe texture similarity of (i ∈ [0,17]), uses following equation to calculate, if result E is little In set threshold value T3, then be judged as picture of the tongue, the image A otherwise gathered is not available for analyze picture of the tongue:

$E={\&Sigma;}_{i=0}^{17}\frac{\left|F_i-f_i\right|}{\left|F_i\right|+\left|f_i\right|}$

The feature of Tongue image under open environment that the present invention is directed to carries out picture of the tongue target detection, first carries out image The pretreatment of color correction, reduces the impact brought because of external light source colour temperature；Then image is split, obtain multiple connection Region；And each connected region is carried out feature judgement, obtain candidate's tongue body region；Carry out finally by comparison domain textural characteristics Judge, it is judged that whether this candidate's tongue body region is picture of the tongue.The present invention is finally reached and the picture of the tongue in image is carried out target detection Purpose, i.e. judges either with or without picture of the tongue in photo current, if it has, picture of the tongue target is at which.

Color correction process uses the gray world algorithm improved, and sets corresponding for the distinctive color characteristic of picture of the tongue image Parameter value；Picture of the tongue partitioning portion uses the segmentation of maximum between-cluster variance, hue threshold and the segmentation of RGB tri-colouring component difference to combine Method carry out image segmentation；Provincial characteristics judgment part judges by contrasting the shape facility in each region, obtains tongue body Region；The textural characteristics of picture of the tongue judges that then using gray level co-occurrence matrixes to be characterized detects, and is carried out the tongue body region obtained Judge comparison, be finally reached the purpose of picture of the tongue target detection.

Relative to prior art, the invention have the benefit that

1, the gray world algorithm that the present invention improves according to picture of the tongue characteristic use carries out color correction to image, decrease because of Gather the impact that environmental colors colour cast is brought.

2, the present invention uses the segmentation of maximum between-cluster variance, hue threshold and RGB tri-colouring component difference to split the side combined Method carries out image segmentation；Provincial characteristics judgement is carried out according to picture of the tongue shape, position；Textural characteristics is used to detect；These sides The picture of the tongue feature that method is both under open environment carries out processing, analyzing, and these methods complement each other, and being used in combination with can More accurately identify picture of the tongue target, there is the advantage that recognition accuracy is high.

3, the method operand that the present invention uses is little, program realizes simple, it is not necessary to training mass data, algorithm complex Low, processing speed is fast, the highest to the degree of dependence of data set.

The feature of picture of the tongue image under open environment that the present invention is directed to divides multiple step to carry out processing, analyzing, and reaches picture of the tongue mesh The purpose of mark detection.The image segmentation of the present invention, provincial characteristics judge, textural characteristics judges the mutually the most auxiliary phase of several step Becoming, image segmentation utilizes the color characteristic of image to carry out coarse sizing, and provincial characteristics judges to utilize the shape of image, space characteristics to enter Row fine screening, textural characteristics is judged to utilize the textural characteristics of image finally to judge, is entered by the different characteristic combining image Row contrast, the accuracy rate of identification is higher, fast operation, it is achieved simple, achieves new technique effect.Relative to immediate Prior art, has obvious distinguishing characteristics, and has obvious advantage: the method for the present invention need not hough space and throws Ticket, algorithm complex is low, and operand is little, it is not required that carries out the training mass data having supervision, therefore realizes relatively simple, right The degree of dependence of data set is the highest, more superior in accuracy rate with processing speed, has more practicality.

The result obtained by the present invention, is conducive to improving follow-up inspection of the tongue and analyzes the serious forgiveness of system, robustness and system Analysis speed, also allow for doctor of traditional Chinese medicine and observe picture of the tongue intuitively, accurately, easily, improve the speed of diagnosis.The present invention's is direct Purpose is not to obtain disease or the diagnostic result of health status.Even if obtaining result by the present invention, figure also can only be obtained Whether there is picture of the tongue, and the conclusion of the position of picture of the tongue in Xiang, diagnostic result can not be immediately arrived at, need follow-up inspection of the tongue to divide Analysis system or the further of doctor of traditional Chinese medicine are analyzed and judge.Method the most disclosed by the invention, its direct purpose is not diagnosis, The present invention is not belonging to the diagnostic method of disease.

Accompanying drawing explanation

Fig. 1 is the image gathered under open environment；

Fig. 2 is step 3) image segmentation result；

Fig. 3 is step 4) screen the candidate's tongue body region obtained.

Detailed description of the invention

With detailed description of the invention, technical solution of the present invention is described in further detail below in conjunction with the accompanying drawings:

The embodiment of the present invention comprises the steps:

1) the image A that input gathers under open environment, as shown in Figure 1；

2) the image A gathered is carried out color correction, obtain correction chart as B；

In step 2) in, the concrete grammar that the described image A to gathering carries out color correction is as follows:

(1) under standard photoenvironment, gather picture of the tongue image S1, calculate tri-Color Channel averages of RGB of picture of the tongue image S1 Respectively with overall average K of picture of the tongue image S1_sRatio cc_r、α_g、α_b:

${\mathrm{\&alpha;}}_r=\frac{R_{avgs}}{K_s}$

${\mathrm{\&alpha;}}_g=\frac{G_{avgs}}{K_s}$

${\mathrm{\&alpha;}}_b=\frac{B_{avgs}}{K_s}$

Wherein, overall average K of picture of the tongue image S1_s=(R_avgs+G_avgs+B_avgs)/3；R_avgs、G_avgs、B_avgsIt is respectively mark The average of tri-Color Channels of RGB of picture of the tongue image S1 is gathered under quasi-photoenvironment；It can be D65 that described standard illumination gathers environment Light source, colour temperature is 6500K；

In the present embodiment, the value of parameters is as follows: α_r=1.09, α_g=0.95, α_b=0.94.

(2) adjust the average of tri-Color Channels of RGB of image A as the following formula, obtain correction chart as B；

K=(R_avg+G_avg+B_avg)/3

$R_d=\frac{K\&times;{\mathrm{\&alpha;}}_r}{R_{avg}}{R}_s$

$G_d=\frac{K\&times;{\mathrm{\&alpha;}}_g}{G_{avg}}{G}_s$

$B_d=\frac{K\&times;{\mathrm{\&alpha;}}_b}{B_{avg}}{B}_s$

Wherein, K is the overall average of image A, R_avg、G_avg、B_avgRespectively tri-Color Channels of RGB of image A is equal Value；R_d、G_d、B_dFor correction chart as the value of tri-Color Channels of RGB of each pixel of B, R_s、G_s、B_sFor each pixel of image A The value of tri-Color Channels of RGB, α_r、α_g、α_bBy step 2) (1st) ratio partly tried to achieve；

3) to step 2) correction chart that obtains carries out image segmentation as B, and concrete grammar is as follows:

(1) correction image B is transformed into gray space image fB1, according to maximum variance between clusters to gray space image FB1 carries out Threshold segmentation, obtains splitting image B1 ', and uses morphology operations to smooth connected domain segmentation image B1 ', obtains Image B1；

In step 3) in (1st) part, described correction image B is transformed into gray space image fB1, according between maximum kind Variance method carries out Threshold segmentation to gray space image fB1, obtains splitting image B1 ', and segmentation image B1 ' is used morphology Computing smooths connected domain, obtains specifically comprising the following steps that of image B1

A) correction image B is transformed into gray space image fB1；

B) for gray space image fB1, if span G=[0, L-1] of gray scale G of gray space image fB1, respectively The probability that gray value occurs is P_i, threshold value is T, and threshold value T is divided into f after gray space image fB1 is carried out binaryzation₀And f₁: f₀= [0, T], f₁=[T+1, L-1], f₀And f₁Probability be respectivelyAnd α₁=1-α₀, average gray value is respectivelyWithThen f₀And f₁Maximum between-cluster variance be: g²(T)=α₀(μ₀- μ)²+α₁(μ₁-μ)²=α₀α₁(μ₀-μ₁)², whereinObtain threshold value T when g takes maximum, to gray space figure As fB1 carries out Threshold segmentation, RGB value f of image B1 ' pixel must be split_{X, y}(r, g, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& iffB1(x,y)>T\\ (0,0,0)& else\end{array}\right.$

Wherein, (x, y) represents the value of gray space image fB1 pixel to fB1, and T is threshold value；

In the present embodiment, T=90.

C) use the closed operation in morphology operations to smooth connected domain segmentation image B1 ', depend on according to following two formula Secondary calculating, obtains image B1；Pixel value g1 in image B1 (x, y) be:

G1 (x, y)=erode (dilate (f1 (x, y), element))

G1 (x, y)=bitwise_not (g1 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B1 ', element is defined as the structural elements in morphology operations to f1 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations； Bitwise_not is defined as the inversion operation of each pixel to image.

In the present embodiment, element is defined as the oval structure of [11 × 11].

(2) correction image B is transformed into hsv color spatial image fB2, the H passage of image fB2 is carried out Threshold segmentation, Obtain splitting image B2 ', and use morphology operations to smooth connected domain segmentation image B2 ', obtain image B2；

In step 3) in (2nd) part, described correction image B is transformed into hsv color spatial image fB2, to image fB2 H passage carry out Threshold segmentation, obtain split image B2 ', and to segmentation image B2 ' use morphology operations smooth connected domain, Obtain specifically comprising the following steps that of image B2

A) correction image B is transformed into hsv color spatial image fB2；

B) use following formula that image fB2 is carried out hue threshold segmentation, obtain splitting RGB value f of image B2 ' pixel_{X, y} (r, g, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& if{h}_{x,y}\&Element;\&lsqb;T_1,T_2\&rsqb;\\ (0,0,0)& else\end{array}\right.$

Wherein, h_{X, y}Represent H passage pixel value in image fB2, T₁And T₂Represent the threshold value set；

In the present embodiment, T₁=7, T₂=29.

C) use the closed operation in morphology operations to smooth connected domain segmentation image B2 ', depend on according to following two formula Secondary calculating, obtains image B2；Pixel value g2 in image B2 (x, y) be:

G2 (x, y)=erode (dilate (f2 (x, y), element))

G2 (x, y)=bitwise_not (g2 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B2 ', element is defined as the structural elements in morphology operations to f2 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations； Bitwise_not is defined as the inversion operation of each pixel to image.

In the present embodiment, element is defined as the oval structure of [11 × 11]

(3) use RGB trichroism component variance method that as B, correction chart is carried out Threshold segmentation, obtain splitting image B3 ', and right Segmentation image B3 ' uses morphology operations to smooth connected domain, obtains image B3.

In step 3) in (3rd) part, described employing RGB trichroism component variance method carries out Threshold segmentation to correction chart as B, Obtain splitting image B3 ', and use morphology operations to smooth connected domain segmentation image B3 ', obtain image B3 concrete step Rapid as follows:

A) for correction chart as B, it is assumed that its size is m × n, the RGB value of each pixel in correction image B is carried out Normalization operates, and its span is [0,1], uses following formula to correction chart as each pixel in B calculates RGB tri-colouring component (m, n), and splits as B variance gate to correction chart, obtains splitting RGB value f of image B3 ' pixel_{M, n}(r, g, b):

Gate (m, n)=(r_{M, n}-g_{M, n})+(b_{M, n}-g_{M, n})x6+(r_{M, n}+g_{M, n}+b_{M, n})/3

$f_{m,n}(r,g,b)=\left\{\begin{array}{cc}(1,1,1)& ifgate(m,n)<0.627\\ (0,0,0)& else\end{array}\right.$

Wherein, r_{M, n}Represent that correction chart is as pixel (m, the value of R passage n), g in B_{M, n}Represent that correction chart is as picture in B Vegetarian refreshments (m, the value of G passage n), b_{M, n}Represent that correction chart is as pixel (m, the value of channel B n) in B；

B) use the closed operation in morphology operations to smooth connected domain segmentation image B3 ', calculate according to formula below, To image B3；Pixel value g3 in image B3 (x, y) be:

G3 (x, y)=erode (dilate (f3 (x, y), element))

Wherein, (x, y) for the pixel value in segmentation image B3 ', element is defined as the structural elements in morphology operations to f3 Element；Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations.

In the present embodiment, element is defined as the oval structure of [11 × 11]

(4) image B1, image B2, tri-images of image B3 are carried out logic "and" operation, obtain image C, such as Fig. 2 institute Show.

4) to step 3) the image C that obtains carries out provincial characteristics judgement, and concrete grammar is as follows:

(1) for each connected domain in image C, the convex closure (S of this connected domain is calculated_i)；

(2) each convex closure (S is calculated_i) areaArea area with image C_CRatio:Delete the area ratio connected domain less than 0.02；

(5) each connected domain convex closure (S is calculated_i) barycenter (C_i) and image C center (C₀) Euclidean distance:

$dis=\sqrt{{(C_{ix}-C_{0x})}^2+{(C_{iy}-c_{0y})}^2};$

Wherein, C_ix、C_iyRepresent convex closure (S respectively_i) abscissa of barycenter and vertical coordinate, C_0xAnd C_0yRepresent respectively in image C The abscissa of the heart and vertical coordinate；

(4) each connected domain convex closure (S is calculated_i) length-width ratio scale=w/h of minimum enclosed rectangle；

Wherein w represents the width of minimum enclosed rectangle, and h represents the length of minimum enclosed rectangle；

(5) each connected domain convex closure (S is calculated_i) 7 Hu not bending moment m_i(i ∈ [1,7])；

Described Hu not bending moment is defined as follows:

For image f that size is M × N (x, y), f (x, two dimension (p+q) rank square y) is defined as:

$m_{pq}={\&Sigma;}_{x=0}^{M-1}{\&Sigma;}_{y=0}^{N-1}{x}^P{y}^{q}f(x,y)$

(p+q) rank central moment is defined as accordingly:

Wherein

By η_pqThe normalization central moment represented is defined as:

Wherein γ=(p+q)/2+1

The linear combination of Hu normalization central moment constructs has translation, flexible, 7 Hu not bending moments of invariable rotary:

(6) one normal shape picture of the tongue image (S of artificial selection₀), calculate picture of the tongue image (S₀) 7 Hu not bending moment M_i(i ∈ [1,7]), calculate matching degreeDescribed normal shape picture of the tongue image (S₀) can be traditional Chinese medical science doctor The raw normal shape picture of the tongue image rule of thumb selected with relevant professional knowledge；

In the present embodiment, M_iThe value of (i ∈ [1,7]) be respectively as follows: [0.167538,0.00155933,0.00032405, 6.71027×10^-6,-3.12169×10^-10,-2.63866×10^-7,-2.15107×10^-11]

(7) each connected domain convex closure (S is calculated successively according to following three formula_i) similarity score, select similarity The maximum of score, the connected domain convex closure (S that the maximum of this similarity score is corresponding_i) it is the candidate's tongue body region D selected, As shown in Figure 3:

$score=\left\{\begin{array}{ccc}1& ifarea<0.1andscale<0.6& or\\ & area\&Element;\&lsqb;0.1,0.5)andscale<0.3& or\\ & area>0.5andscale<0.6& \\ 2& ifarea<0.1andscale\&Element;\&lsqb;0.6,0.8\&rsqb;& or\\ & area<\&Element;\&lsqb;0.1,0.5)andscale\&Element;\&lsqb;0.3,0.6)& or\\ & area>0.5andscale>0.6& \\ 3& ifarea<0.1andscale>0.8& or\\ & area\&Element;\&lsqb;0.1,0.3)andscale>0.6& \\ 4& ifarea\&Element;\&lsqb;0.3,0.5)andscale>0.6& \end{array}\right.$

$score=\left\{\begin{array}{cc}score+2& ifdis<20\\ score+1& ifdis\&Element;\&lsqb;20,60)\\ score-1& ifdis\&Element;\&lsqb;150,200)\\ score-2& ifdis>200\end{array}\right.$

$score=\left\{\begin{array}{cc}score+3& ifmatch<0.1\\ score+2& ifmatch\&Element;\&lsqb;0.1,0.5)\\ score-1& ifmatch\&Element;\&lsqb;10,20)\\ score-2& ifmatch>20\end{array}\right.$

Wherein, area is step 4) (2nd) ratio partly tried to achieve；Scale is step 4) (4th) part tried to achieve Length-width ratio；Dis is step 4) (3rd) Euclidean distance partly tried to achieve；Match is step 4) (6th) part tried to achieve Degree of joining；

5) to step 4) candidate's tongue body region D of obtaining carries out textural characteristics judgement, and concrete grammar is as follows:

(1) level of candidate's tongue body region D, vertical, the associating of three kinds of gray level co-occurrence matrixes of angular shift amount are calculated respectively Probability density distribution；

(2) the joint probability density distribution setting gray level co-occurrence matrixes is designated as [P_mn]_L×L, wherein L is gray scale span, m =[0, L-1], n=[0, L-1]；It is calculated 6 textural characteristics, described 6 textural characteristics according to joint probability density distribution Relevant with gray scale including angle second moment, contrast, entropy, unfavourable balance square, intermediate value；

Wherein,

(3) to step 5) (1st) part obtain three kinds of gray level co-occurrence matrixes joint probability density distribution respectively according to Step 5) (2nd) method 6 textural characteristics of calculating partly, there are 18 texture characteristic amount f of candidate's tongue body region D_i(i ∈ [0,17]；

(4) the picture of the tongue image gathered under artificial selection's some width standard photoenvironment, under each width standard photoenvironment The picture of the tongue image gathered, is partitioned into tongue body region SD therein by hand, obtains several tongue body regions SD；To each tongue body district Territory SD calculates its level, vertical, the joint probability density distribution of three kinds of gray level co-occurrence matrixes of angular shift amount respectively；To these three Joint probability density is distributed respectively according to step 5) (2nd) method calculating textural characteristics partly, each tongue body region SD is altogether Obtain 18 texture characteristic amounts；18 texture characteristic amounts calculated to these tongue body regions SD, seek each texture characteristic amount Meansigma methods F_i(i ∈ [0,17]；

(5) calculation procedure 5) (3rd) the texture characteristic amount f partly tried to achieve_i(i ∈ [0,17]) and step 5) (4th) part Texture characteristic amount meansigma methods F tried to achieve_iThe texture similarity of (i ∈ [0,17]), uses following equation to calculate, if result E is less than Set threshold value T3, then be judged as picture of the tongue, the image A otherwise gathered is not available for analyze picture of the tongue:

$E={\&Sigma;}_{i=0}^{17}\frac{\left|F_i-f_i\right|}{\left|F_i\right|+\left|f_i\right|}$

In the present embodiment, standard picture of the tongue characteristic vector meansigma methods F_i(value of i ∈ [0,17] is respectively as follows:

[0.00280036,59.4779,0.428998,2.9444,136.397,939.903,0.00197886, 68.4362,0.348336,3.0673,136.616,1336.41,0.00292386,38.6503,0.442026,2.9221, 136.406,901.906], the value of threshold value T3 is 4.7.

Claims (10)

1. Tongue object detection method under an open environment, it is characterised in that comprise the following steps:

1) the image A that input gathers under open environment；

2) the image A gathered is carried out color correction, obtain correction chart as B；

3) to step 2) correction chart that obtains carries out image segmentation as B, obtains image C；

4) to step 3) the image C that obtains carries out provincial characteristics judgement, obtains candidate's tongue body region D；

5) to step 4) candidate's tongue body region D of obtaining carries out textural characteristics judgement.

2. Tongue object detection method under a kind of open environment, it is characterised in that in step 2) in, The concrete grammar that the described image A to gathering carries out color correction is as follows:

(1) under standard photoenvironment, gather picture of the tongue image S1, calculate tri-Color Channel averages of RGB of picture of the tongue image S1 respectively Overall average K with picture of the tongue image S1_sRatio cc_r、α_g、α_b:

${\mathrm{\&alpha;}}_r=\frac{R_{avgs}}{K_s}$

${\mathrm{\&alpha;}}_g=\frac{G_{avgs}}{K_s}$

${\mathrm{\&alpha;}}_b=\frac{B_{avgs}}{K_s}$

Wherein, overall average K of picture of the tongue image S1_s=(R_avgs+G_avgs+B_avgs)/3；R_avgs、G_avgs、B_avgsIt is respectively standard illumination The average of tri-Color Channels of RGB of picture of the tongue image S1 is gathered under environment；

(2) adjust the average of tri-Color Channels of RGB of image A as the following formula, obtain correction chart as B；

K=(R_avg+G_avg+B_avg)/3

$R_d=\frac{K\&times;{\mathrm{\&alpha;}}_r}{R_{avg}}{R}_s$

$G_d=\frac{K\&times;{\mathrm{\&alpha;}}_g}{G_{avg}}{G}_s$

$B_d=\frac{K\&times;{\mathrm{\&alpha;}}_b}{B_{avg}}{B}_s$

Wherein, K is the overall average of image A, R_avg、G_avg、B_avgIt is respectively the average of tri-Color Channels of RGB of image A；R_d、 G_d、B_dFor correction chart as the value of tri-Color Channels of RGB of each pixel of B, R_s、G_s、B_sRGB for each pixel of image A The value of three Color Channels, α_r、α_g、α_bBy step 2) (1st) ratio partly tried to achieve.

3. Tongue object detection method under a kind of open environment, it is characterised in that in step 2) the (1), in part, it is D65 light source that described standard illumination gathers environment, and colour temperature is 6500K.

4. Tongue object detection method under a kind of open environment, it is characterised in that in step 3) in, Described to step 2) correction chart that the obtains concrete grammar that carries out image segmentation as B is as follows:

(1) correction image B is transformed into gray space image fB1, according to maximum variance between clusters, gray space image fB1 is entered Row threshold division, obtains splitting image B1 ', and uses morphology operations to smooth connected domain segmentation image B1 ', obtain image B1；

(2) correction image B is transformed into hsv color spatial image fB2, the H passage of image fB2 is carried out Threshold segmentation, obtains Segmentation image B2 ', and use morphology operations to smooth connected domain segmentation image B2 ', obtain image B2；

(3) use RGB trichroism component variance method that as B, correction chart is carried out Threshold segmentation, obtain splitting image B3 ', and to segmentation Image B3 ' uses morphology operations to smooth connected domain, obtains image B3；

(4) image B1, image B2, tri-images of image B3 are carried out logic "and" operation, obtain image C.

5. Tongue object detection method under a kind of open environment, it is characterised in that in step 3) the (1), in part, described correction image B is transformed into gray space image fB1, according to maximum variance between clusters to gray space figure As fB1 carries out Threshold segmentation, obtain splitting image B1 ', and use morphology operations to smooth connected domain segmentation image B1 ', To specifically comprising the following steps that of image B1

A) correction image B is transformed into gray space image fB1；

B) for gray space image fB1, if span G=[0, L-1] of gray scale G of gray space image fB1, each gray scale The probability that value occurs is P_i, threshold value is T, and threshold value T is divided into f after gray space image fB1 is carried out binaryzation₀And f₁: f₀=[0, T],f₁=[T+1, L-1], f₀And f₁Probability be respectivelyAnd α₁=1-α₀, average gray value is respectivelyWithThen f₀And f₁Maximum between-cluster variance be: g²(T)=α₀(μ₀- μ)²+α₁(μ₁-μ)²=α₀α₁(μ₀-μ₁)², wherein μ=Σ iP_i, obtain threshold value T when g takes maximum, to gray space image FB1 carries out Threshold segmentation, must split RGB value f of image B1 ' pixel_{X, y}(r, g, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& iffB1(x,y)>T\\ (0,0,0)& else\end{array}\right.$

Wherein, (x, y) represents the value of gray space image fB1 pixel to fB1, and T is threshold value；

C) use the closed operation in morphology operations to smooth connected domain segmentation image B1 ', count successively according to following two formula Calculate, obtain image B1；Pixel value g1 in image B1 (x, y) be:

G1 (x, y)=erode (dilate (f1 (x, y), element))

G1 (x, y)=bitwise_not (g1 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B1 ', element is defined as the structural element in morphology operations to f1； Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations；bitwise_ Not is defined as the inversion operation of each pixel to image.

6. Tongue object detection method under a kind of open environment, it is characterised in that in step 3) the (2) in part, described correction image B is transformed into hsv color spatial image fB2, the H passage of image fB2 is carried out threshold value and divides Cut, obtain splitting image B2 ', and use morphology operations to smooth connected domain segmentation image B2 ', obtain the concrete step of image B2 Rapid as follows:

A) correction image B is transformed into hsv color spatial image fB2；

B) use following formula that image fB2 is carried out hue threshold segmentation, obtain splitting RGB value f of image B2 ' pixel_{X, y}(r, G, b):

$f_{x,y}(r,g,b)=\left\{\begin{array}{cc}(255,255,255)& if{h}_{x,y}\&Element;\&lsqb;T_1,T_2\&rsqb;\\ (0,0,0)& else\end{array}\right.$

Wherein, h_{X, y}Represent H passage pixel value in image fB2, T₁And T₂Represent the threshold value set；

C) use the closed operation in morphology operations to smooth connected domain segmentation image B2 ', count successively according to following two formula Calculate, obtain image B2；Pixel value g2 in image B2 (x, y) be:

G2 (x, y)=erode (dilate (f2 (x, y), element))

G2 (x, y)=bitwise_not (g2 (x, y))

Wherein, (x, y) for the pixel value in segmentation image B2 ', element is defined as the structural element in morphology operations to f2； Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations；bitwise_ Not is defined as the inversion operation of each pixel to image.

7. Tongue object detection method under a kind of open environment, it is characterised in that in step 3) the (3) in part, described employing RGB trichroism component variance method carries out Threshold segmentation to correction chart as B, obtains splitting image B3 ', and To segmentation image B3 ' use morphology operations smooth connected domain, obtain image B3 specifically comprise the following steps that

A) for correction chart as B, it is assumed that its size is m × n, the RGB value of each pixel in correction image B is carried out normalizing Changing operation, its span is [0,1], uses following formula to correction chart as each pixel in B calculates the trichroism component variance of RGB (m, n), and splits as B gate to correction chart, obtains splitting RGB value f of image B3 ' pixel_{M, n}(r, g, b):

Gate (m, n)=(r_{M, n}-g_{M, n})+(b_{M, n}-g_{M, n})×6+(r_{M, n}+g_{M, n}+b_{M, n})/3

$f_{m,n}(r,g,b)=\left\{\begin{array}{cc}(1,1,1)& ifgate(m,n)<0.627\\ (0,0,0)& else\end{array}\right.$

Wherein, r_{M, n}Represent that correction chart is as pixel (m, the value of R passage n), g in B_{M, n}Represent that correction chart is as pixel in B (m, the value of G passage n), b_{M, n}Represent that correction chart is as pixel (m, the value of channel B n) in B；

B) use the closed operation in morphology operations to smooth connected domain segmentation image B3 ', calculate according to formula below, obtain figure As B3；Pixel value g3 in image B3 (x, y) be:

G3 (x, y)=erode (dilate (f3 (x, y), element))

Wherein, (x, y) for the pixel value in segmentation image B3 ', element is defined as the structural element in morphology operations to f3； Dilate is defined as the expansive working in morphology operations；Erode is defined as the etching operation in morphology operations.

8. Tongue object detection method under a kind of open environment, it is characterised in that in step 4) in, Described to step 3) to carry out the concrete grammar of provincial characteristics judgement as follows for the image C that obtains:

(1) for each connected domain in image C, the convex closure (S of this connected domain is calculated_i)；

(2) each convex closure (S is calculated_i) areaArea area with image C_CRatio:Delete the area ratio connected domain less than 0.02；

(3) each connected domain convex closure (S is calculated_i) barycenter (C_i) and image C center (C₀) Euclidean distance:

$dis=\sqrt{{(C_{ix}-C_{0x})}^2+{(C_{iy}-C_{0y})}^2};$

Wherein, C_ix、C_iyRepresent convex closure (S respectively_i) abscissa of barycenter and vertical coordinate, C_0xAnd C_0yRepresent image C center respectively Abscissa and vertical coordinate；

(4) each connected domain convex closure (S is calculated_i) length-width ratio scale=w/h of minimum enclosed rectangle；

Wherein w represents the width of minimum enclosed rectangle, and h represents the length of minimum enclosed rectangle；

(5) each connected domain convex closure (S is calculated_i) 7 Hu not bending moment m_i(i ∈ [1,7])；

Described Hu not bending moment is defined as follows:

For image f that size is M × N (x, y), f (x, two dimension (p+q) rank square y) is defined as:

$m_{pq}={\mathrm{\&Sigma;}}_{x=0}^{M-1}{\mathrm{\&Sigma;}}_{y=0}^{N-1}{x}^p{y}^{q}f(x,y)$

(p+q) rank central moment is defined as accordingly:

Wherein

By η_pqThe normalization central moment represented is defined as:

Wherein γ=(p+q)/2+1

The linear combination of Hu normalization central moment constructs has translation, flexible, 7 Hu not bending moments of invariable rotary

(6) one normal shape picture of the tongue image (S of artificial selection₀), calculate picture of the tongue image (S₀) 7 Hu not bending moment M_i(i ∈ [1, 7]), matching degree is calculated

(7) each connected domain convex closure (S is calculated successively according to following three formula_i) similarity score, select similarity score Maximum, the connected domain convex closure (S that the maximum of this similarity score is corresponding_i) it is the candidate's tongue body region D selected:

$score=\left\{\begin{array}{ccc}1& ifarea<0.1andscale<0.6& or\\ & area\&Element;\&lsqb;0.1,0.5)andscale<0.3& or\\ & area>0.5andscale<0.6& \\ 2& ifarea<0.1andscale\&Element;\&lsqb;0.6,0.8\&rsqb;& or\\ & area<\&Element;\&lsqb;0.1,0.5)andscale\&Element;\&lsqb;0.3,0.6)& or\\ & area>0.5andscale>0.6& \\ 3& ifarea<0.1andscale>0.8& or\\ & area\&Element;\&lsqb;0.1,0.3)andscale>0.6& \\ 4& ifarea\&Element;\&lsqb;0.3,0.5)andscale>0.6& \end{array}\right.$

$score=\left\{\begin{array}{cc}score+2& ifdis<20\\ score+1& ifdis\&Element;\&lsqb;20,60)\\ score-1& ifdis\&Element;\&lsqb;150,200)\\ score-2& ifdis>200\end{array}\right.$

$score=\left\{\begin{array}{cc}score+3& ifmatch<0.1\\ score+2& ifmatch\&Element;\&lsqb;0.1,0.5)\\ score-1& ifmatch\&Element;\&lsqb;10,20)\\ score-2& ifmatch>20\end{array}\right.$

Wherein, area is by step 4) (2nd) ratio partly tried to achieve；Scale is step 4) (4th) length partly tried to achieve Wide ratio；Dis is step 4) (3rd) Euclidean distance partly tried to achieve；Match is step 4) (6th) coupling partly tried to achieve Degree.

9. Tongue object detection method under a kind of open environment, it is characterised in that in step 4) the (6) in part, described normal shape picture of the tongue image (S₀) it is that doctor of traditional Chinese medicine is rule of thumb normal with what relevant professional knowledge selected Shape picture of the tongue image.

10. Tongue object detection method under a kind of open environment, it is characterised in that in step 5) In, described to step 4) to carry out the concrete grammar of textural characteristics judgement as follows for candidate's tongue body region D of obtaining:

(1) level of candidate's tongue body region D, vertical, the joint probability of three kinds of gray level co-occurrence matrixes of angular shift amount are calculated respectively Density Distribution；

(2) the joint probability density distribution setting gray level co-occurrence matrixes is designated as [P_mn]_L×L, wherein L is gray scale span, m=[0, L-1], n=[0, L-1]；Being calculated 6 textural characteristics according to joint probability density distribution, described 6 textural characteristics include angle Second moment, contrast, entropy, unfavourable balance square, intermediate value are relevant with gray scale；

Wherein,

(3) to step 5) joint probability density of three kinds of gray level co-occurrence matrixes that obtains of (1st) part is distributed respectively according to step 5) method of (2nd) part calculates 6 textural characteristics, there are 18 texture characteristic amount f of candidate's tongue body region D_i(i ∈ [0, 17])；

(4) the picture of the tongue image gathered under artificial selection's some width standard photoenvironment, gathers under each width standard photoenvironment Picture of the tongue image, be by hand partitioned into tongue body region SD therein, obtain several tongue body regions SD, to each tongue body region SD Calculate its level, vertical, the joint probability density distribution of three kinds of gray level co-occurrence matrixes of angular shift amount respectively；These three is combined Probability density distribution is respectively according to step 5) (2nd) method calculating textural characteristics partly, each tongue body region SD there are 18 texture characteristic amounts；18 texture characteristic amounts calculated to these tongue body regions SD, seek the flat of each texture characteristic amount Average f_i(i ∈ [0,17])；

(5) calculation procedure 5) (3rd) the texture characteristic amount f partly tried to achieve_i(i ∈ [0,17]) and step 5) (4th) part tries to achieve Meansigma methods F of texture characteristic amount_iThe texture similarity of (i ∈ [0,17]), uses following equation to calculate, if result E is less than setting Threshold value T3, then be judged as picture of the tongue, the image A otherwise gathered is not available for analyze picture of the tongue:

$E={\mathrm{\&Sigma;}}_{i=0}^{17}\frac{|F_i-f_i|}{\left|F_i\right|+\left|f_i\right|}.$