CN109003279A - Fundus retina blood vessel segmentation method and system based on K-Means clustering labeling and naive Bayes model - Google Patents

Fundus retina blood vessel segmentation method and system based on K-Means clustering labeling and naive Bayes model Download PDF

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CN109003279A
CN109003279A CN201810733535.8A CN201810733535A CN109003279A CN 109003279 A CN109003279 A CN 109003279A CN 201810733535 A CN201810733535 A CN 201810733535A CN 109003279 A CN109003279 A CN 109003279A
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陈大力
王孝阳
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Northeastern University China
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Abstract

The invention provides a fundus retina blood vessel segmentation method and system based on a K-Means clustering labeling and naive Bayes model. The method comprises the following steps: randomly extracting color fundus images in the data set, and constructing a training set and a testing set; extracting a gray scale image of a G channel of the color fundus image as a feature extraction object; extracting the characteristics of the gray level image, and expressing each pixel in the image by using a multi-dimensional characteristic vector; clustering and labeling the feature vectors of each image in the training set after feature extraction by using a K-Means clustering algorithm; training a naive Bayes model based on training set data of K-Means clustering labels; and (5) segmenting the blood vessels of each image in the test set by using a trained naive Bayes model. The invention takes the clustering result as the mark with supervision training, and trains the naive Bayes classification model by using the mark to segment the retinal vessel, so that the whole process does not need to be manually marked, time and labor are saved, and the processing efficiency of the machine learning model is greatly improved.

Description

A kind of eye ground based on K-Means cluster mark and model-naive Bayesian Blood vessel segmentation method and system
Technical field
The present invention relates to field of medical image processing, specifically, more particularly to a kind of based on K-Means cluster mark With the eye ground blood vessel segmentation method of model-naive Bayesian.
Background technique
Retinal vessel is the important component of systemic microcirculation system, the variation and diabetes, height of morphosis The coincident with severity degree of condition of the cardiovascular diseases such as blood pressure, coronary sclerosis and cerebrovascular sclerosis is closely related.Therefore, retina The research of image blood vessel segmentation technology has a very important significance clinical medicine application.
Current eye ground blood vessel method mainly has two general orientation: rule-based blood vessel segmentation method and based on learn The blood vessel segmentation method of habit.What rule-based blood vessel segmentation method mainly utilized is image processing techniques, according to vessel properties Some rules are designed to find blood vessel: different scale lower linear filter is devised according to the piecewise linearity of blood vessel and connectivity, By combining filter result and the thresholding vessel segmentation that handles to the end under different scale；It is transversal due to blood vessel Face at handstand Gaussian Profile, so dimensional Gaussian matched filtering method using characterization different directions 12 Gaussian templates as Matched filter is filtered image, then carries out thresholding to response results, chooses and responds maximum matched filtering knot Fruit exports as blood vessel, finally extracts retinal vascular images.Filtering method based on Hessian matrix utilizes Hessian The characteristic value of matrix enhances all blood vessel structures including vascular bifurcation, and inhibits non-vascular structure.These image procossings Method requires thresholding processing, and needs to select different threshold values for different retinal fundus images, this is not The process of one automation.
Blood vessel segmentation problem is considered as to the classification problem of Pixel-level based on the dividing method of study, has label by one group Training dataset training obtain classifier.Wherein, feature mainly includes that grey scale pixel value, boundary characteristic and Gabor wavelet become It changes, classifier mainly includes multilayer neural network, Bayes classifier, random forest and support vector machines.Due to introducing Differentiate the powerful mechanism of inquiry learning, the segmentation performance of these methods is often better than rule-based dividing method.Based on study Method can divide automatically and accuracy rate is higher, and main flow is pre-processing image data, image characteristics extraction and model instruction Practice.Difficult point is that retinal fundus images feature extraction and Pixel-level label data obtain.Having the machine learning techniques of supervision needs The artificial labeled data of a large amount of expert is wanted, and the retinal vessel in eye fundus image is in the tree-shaped and the tiny blood of tip of diverging The presence of pipe makes label task more difficult.
In computer-aided medical science image analysis, can from a large amount of medical image of infection from hospital as training set, but If often unpractical it is required that medical expert is identified the blood vessel in these images.If only used a small amount of Have label example, then the learning system trained using them is often difficult have strong generalization ability；On the other hand, such as Fruit has label example without being then to data resource using a large amount of " inexpensively " unmarked examples using only a small amount of " expensive " Greatly waste.
Summary of the invention
The a large amount of artificial labeled data of expert is needed according to the machine learning techniques for having supervision set forth above, it is time-consuming and laborious The technical issues of, and a kind of eye ground blood vessel segmentation based on K-Means cluster mark and model-naive Bayesian is provided Method.The present invention mainly using the result of K-Means cluster mark as the label of data, then goes to train classification using it again Model is split, to improve work efficiency, segmentation effect is more preferable, saves manpower and material resources.The technology that the present invention uses Means are as follows:
A kind of eye ground blood vessel segmentation method based on K-Means cluster mark and model-naive Bayesian, including Following steps:
The Pixel-level label of training set image is constructed, specifically: each image in the training set after feature extraction is made Feature vector is subjected to cluster mark with K-Means clustering algorithm；
Training set data training model-naive Bayesian based on K-Means cluster mark；
With the blood vessel of each image in trained model-naive Bayesian segmentation test set.
Further, also there are following steps before the Pixel-level label of the step building training set image:
The colored eye fundus image in data set is randomly selected, training set and test set are constructed；
Object of the grayscale image in the channel G of colored eye fundus image as feature extraction is extracted, and to retina region of interest Domain carries out expansion process；
Feature extraction is carried out to the grayscale image after expansion process, by each pixel in training set and test set image It is indicated with the feature vector of ten dimensions, specifically:
The contrast for enhancing the grayscale image by the adaptive histogram equalization method of contrast-limited, by this gray scale It is worth as a kind of feature for distinguishing blood vessel and background, gray scale described in technical treatment is enhanced by the blood vessel designed based on vessel properties Figure,
The grayscale image is filtered as follows respectively:
By Gabor wavelet conversion process, four-dimensional Gabor characteristic is obtained；
Retinal images are filtered by linear detector, obtain the response results of linearity test as one-dimensional Linearity test feature；
Retinal images are filtered by the filter of gaussian shape, chooses and responds maximum filter result work For one-dimensional Gauss matching characteristic；
Frangi filtering characteristics based on Hessian matrix are filtered retinal images, choose peak response As one-dimensional Frangi filtering characteristics；
Retinal images are filtered based on B-COSFIRE filtering characteristics, by symmetrical B-COSFIRE filter It sums to obtain final one-dimensional B-COSFIRE filtering characteristics with asymmetric B-COSFIRE filter response results；
The one-dimensional weber Expressive Features that operator solves any pixel point in retinal images are described by weber；
It is described as follows with also having after the blood vessel of each image in trained model-naive Bayesian segmentation test set Step:
Judge whether blood vessel segmentation method is effective by preset evaluation index.
Further, the step randomly selects the colored eye fundus image in data set, constructs in training set and test set, Data set uses disclosed DRIVE data set, and the training set is identical with the picture number of test set, and each image corresponding 2 The Mask image in the effective information region of the result of a expert's manual segmentation and 1 display eye fundus image.
Further, Gabor wavelet conversion process is carried out in the following way:
Gabor kernel function is as follows:
Wherein, x'=xcos θ+ysin θ, y'=-xsin θ+ycos θ,
(u0,v0) indicate the temporal frequency of sinusoidal plane wave, σxAnd σyIndicate the variance of the oval Gauss on the direction x and y, Which determine the size of the filter zone of action, i.e. scale, θ is rotation angle,
One group of direction Gabor wavelet different with scale, which is obtained, by change parameter σ and θ compares institute under fixed size There is the response on direction, using peak response as the characteristic image under the scale, according to the scale of setting, under all scales As a result it is used as Gabor characteristic.
Further, the linear detector is by under the window of W*W, length of rotation be the detection line of l obtain it is N number of not Equidirectional filter is filtered grayscale image with filter, and when the direction of detection line is consistent with vessel directions, response is most Greatly, compare it is angled under response retain maximum value as the testing result under the windowBy the length for changing detection line Degree adapts to the blood vessel under different scale, and the response results summation under different scale is averagely obtained to the response results of linearity test again As linearity test feature.
Further, the filter graph picture filtering processing of gaussian shape is carried out in the following way:
The Gaussian kernel function of handstand is as follows:
WhereinThe mean normalization of filter to 0, L is indicated along y-axis The neighborhood length of smooth noise, t are constants, setting x'=xcos θ+ysin θ, y'=-xsin θ+ycos θ, θ=(0:12) * π/ 12 change to detect the blood vessel of different directions, and the peak response on N number of direction is chosen under a certain scale as under the scale Filter result compares the response under different scale, chooses and responds maximum filter result as Gauss matching characteristic.
Further, the Frangi filtering processing based on Hessian matrix is carried out in the following way:
For two-dimensional input image, obtain constructing each pixel (x, y) with the second order local derviation convolution of gaussian kernel function Hessian matrix,
The result of Frangi filtering is calculated by such as minor function:
Wherein, the characteristic value of Hessian matrix is λ1And λ2, and | λ1| < | λ2|, Rb=λ2/λ1,
β controls the parameter of whole smoothness for adjusting linear and block-like difference, c statement, and more each pixel exists Response under each scale chooses peak response as Frangi filtering characteristics.
Further, the Difference of Gaussian filter that B-COSFIRE filter is collinearly arranged by one group, i.e. DoG filter structure At B-COSFIRE filter response is obtained by calculating the geometric mean of all DoG filter responses, and the DoG filter response is logical The convolution for crossing DoG filter and image obtains,
For extracting continuous blood vessel structure feature, DoG filter exists the COSFIRE model of symmetrical structure in pairs, they Center be located at the two sides of B-COSFIRE filter center in the opposite direction；The COSFIRE model of unsymmetric structure is for detecting Tail vein, the center of DoG filter are located at the side of B-COSFIRE filter center, obscure to DoG filter response Operation, the fuzzy operation are the maximum value of DoG filter weight threshold response, and weight is DoG filter response and Gaussian function Final B- is made and obtained to symmetrical B-COSFIRE filter and asymmetric B-COSFIRE filter response result by the product of coefficient COSFIRE filtering characteristics.
Further, pass through the weber Expressive Features of any pixel point in following equations retinal images:
Wherein, p is any pixel point in image I, and N (p) indicates the neighborhood territory pixel set of pixel p, and I (z) indicates target picture The gray value of any pixel of N (p) in the neighborhood of plain p.
It is further, described to judge whether blood vessel segmentation method is effective by preset evaluation index, specifically:
Wherein, the puncta vasculosa of each image is distinguished by four kinds of different colors, the puncta vasculosa true positives TP correctly divided, The non-vascular point false positive FP of segmentation errors, the puncta vasculosa true negative TN of erroneous segmentation, the non-vascular point false negative correctly divided FN, Acc indicate that the correct pixel of segmentation accounts for the ratio of whole image pixel summation, i.e. accuracy, and Se indicates that segmentation is correct Puncta vasculosa account for the ratio of goldstandard puncta vasculosa summation, i.e. sensitivity, Sp indicates that dividing correct background dot accounts for goldstandard background The ratio of point summation, i.e., specific, Ppv indicates to divide the ratio that correct puncta vasculosa accounts for the puncta vasculosa being partitioned into, i.e. precision is pre- Survey ratio.
The present invention also provides a kind of eye ground blood vessels based on K-Means cluster mark and model-naive Bayesian Segmenting system, comprising:
Sample sampling unit constructs training set and test for randomly selecting colored eye fundus image from DRIVE data set Collection；
Pretreatment unit, for extract the grayscale image in the channel G in colored eye fundus image and to retina sense therein it is emerging Interesting region carries out expansion process；
Feature extraction unit carries out feature extraction for the grayscale image to the channel G after expansion process, by training set and survey Each pixel in examination collection image is indicated with the feature vector of ten dimensions comprising:
Module, Gauss matching characteristic extraction module, Frangi is filtered in Gabor wavelet processing module, linear detector Filtering characteristics extraction module, B-COSFIRE filtering characteristics extraction module, weber Expressive Features extraction module；
Cluster mark unit: for feature vector to be carried out cluster mark by K-Means clustering algorithm；
Machine learning model training unit: for the simple pattra leaves of training set data training by K-Means cluster mark This model；
Image processing unit: for being split by blood vessel of the parted pattern to each image in test set；
Segmentation effect judges unit: judging for the validity to blood vessel segmentation method.
Compared with the prior art, the invention has the following advantages that
The present invention, using the result of cluster as the label of Training, then utilizes these label instructions by clustering technique To practice Naive Bayes Classification Model and carries out retinal vessel segmentation, whole process does not need artificially to participate in label, and it is time saving and energy saving, greatly Amplitude improves the treatment effeciency of machine learning model, and relative to the segmentation result of cluster, the present invention is divided in retinal images Accuracy rate is improved in treatment process.
The present invention can be widely popularized in field of medical image processing based on the above reasons.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is this hair Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with It obtains other drawings based on these drawings.
Fig. 1 is the method for the present invention flow chart.
Fig. 2 is apparatus of the present invention module map.
Fig. 3 is the schematic diagram of original eye ground image and Mask image in the present invention.
Fig. 4 is the Analysis of Contrast schematic diagram of RGB triple channel grayscale image in the present invention, wherein (a) is red channel, (b) it is green channel, (c) is blue channel.
Fig. 5 is retina area-of-interest expansion process schematic diagram in green channel grayscale image of the present invention.
Fig. 6 is that the present invention successively obtains obtaining characteristic pattern schematic diagram after each feature extraction, wherein (a) is original color Image (b) is gray feature, is (c) Gabor transformation, be (d) linearity test (e) is Gauss matched filtering, (f) is Frangi Filtering (g) filters for B-COSFIRE, (h) describes for weber.
Fig. 7 is the cluster result schematic diagram that the present invention uses K-Means clustering algorithm in training set image.
Fig. 8 is the segmentation result schematic diagram that the trained parted pattern of the present invention divides test set.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work It encloses.
As shown in Figure 1, the present invention provides a kind of eyeground based on K-Means cluster mark and model-naive Bayesian to regard Retinal vasculature dividing method, includes the following steps:
S1, colored eye fundus image in data set is randomly selected, the present embodiment uses disclosed DRIVE data set, DRIVE is the colored eyeground figure that Niemeijer team established in 2004 according to the work of Dutch screening for diabetic retinopathy Library, image are to shoot to obtain from 453 25~90 years old Different Individuals.The present embodiment extracts totally 40 width image in database, Wherein 7 width have early diabetic retinopathy, and 33 width are the eyeground figure of not diabetic retinopathy, each image Pixel be 584 × 565.
It is divided into training set and test set, each 20 width image of subset, the result of the corresponding 2 expert's manual segmentations of each image The mask image in the effective information region of eye fundus image is shown with 1.As shown in figure 3, being partial colour retinal fundus images With corresponding Mask image.The library is the most frequently used database for measuring Segmentation Method of Retinal Blood Vessels performance quality.
The grayscale image contrast of the RGB triple channel of S2, the colored eye fundus image of analysis, the grayscale image medium vessels and back in the channel G The contrast highest of scape, as shown in figure 4, object of the grayscale image in the channel G of the colored eye fundus image of extraction as feature extraction, is The influence for reducing region contour edge noise, carries out expansion process as shown in Figure 5 to retina area-of-interest.
S3, the contrast for enhancing the grayscale image by the adaptive histogram equalization method of contrast-limited, by this Gray value is enhanced described in technical treatment as a kind of feature for distinguishing blood vessel and background by the blood vessel designed based on vessel properties Grayscale image highlights the blood vessel structure in eye fundus image, while inhibiting insignificant part, reinforces image interpretation and identification.Point For based on airspace and based on the big algorithm of frequency domain two, the former directly calculates image grayscale, the latter is based on image transform domain to change Coefficient is changed to be modified.
As shown in fig. 6, the grayscale image is filtered as follows respectively:
Gabor wavelet transformation, the blood vessel curvature in retinal images is small, width gradual change and direction is arbitrary, and Gabor wavelet transformation is sensitive to image border, the characteristic with good scale selection and direction selection, so using Gabor Image is transformed into Frequency domain from time domain by wavelet transformation.
Gabor kernel function is the Gauss function modulated by multiple SIN function:
Wherein, x'=xcos θ+ysin θ, y'=-xsin θ+ycos θ,
(u0,v0) indicate the temporal frequency of sinusoidal plane wave, σxAnd σyIndicate the variance of the oval Gauss on the direction x and y, Which determine the sizes of the filter zone of action, it is seen that parallel and inhibition zone striped quantity is more, commonly referred to as scale, and θ is rotation Gyration.
Change the available one group of direction the major parameter σ and θ Gabor wavelet different with scale.Under fixed size, than Response on more all directions, using peak response as the characteristic image under the scale.In an experiment setting scale be [2,3,4, 5], the Gabor characteristic of 4 dimensions can be obtained in this way.
Linear detector is the filter of an engineer, be mainly based upon blood vessel piecewise linearity and connectivity this What priori knowledge was suggested, by fixed size mode development to multi-scalability.Under the window of W*W, length is the detection of l Line with 15 ° in 0:165 rotation obtain the filters of 12 different directions original image be filtered, when detection line direction and When vessel directions are consistent, response is maximum, compare institute it is angled under response reservation maximum value as the testing result under the windowIn the length for changing detection line to adapt to the blood vessel under different scale, finally the response results under different scale are summed The response results of linearity test are averagely obtained again as linearity test feature.
Gauss matched filtering feature.Retinal vessel cross section intensity profile is in the Gaussian Profile stood upside down, it is possible to benefit It is used to detect blood vessel come ' matching ' with the filter of gaussian shape.The Gaussian kernel function of handstand is defined as:
WhereinBy the mean normalization of filter to 0.L is smooth along y-axis The neighborhood length of noise, theoretically L is bigger, and retinal images smooth effect can be made more excellent, while can also reduce retina Picture noise.T is constant, is usually arranged as 3, because 99% area is located in [- 3 σ, 3 σ] range under Gaussian curve.When setting X'=xcos θ+ysin θ, y'=-xsin θ+ycos θ, θ=(0:12) * π/12 variation are set to detect the blood vessel of different directions.? The peak response on 12 directions is chosen under a certain scale as the filter result under the scale.In experiment, L=9, ruler are set Degree variation σ=1:0.3:2, compares the response under different scale, chooses and responds maximum filter result as Gauss matching characteristic.
Frangi filtering characteristics.The vasculature part on eyeground is the structure of a tubulose, and the response that Gauss second order is led compares Greatly；The background parts on eyeground are evenly distributed, and the response that Gauss second order is led is smaller.And Hessian matrix is a Second Order Partial Matrix is led, and for two dimensional image, the spy at each pixel there are two Hessian matrix exgenvalues, at puncta vasculosa Value indicative small one and large one, characteristic value at intersecting blood vessels point is both very big, the both very little of the characteristic value at background dot.
For two-dimensional input image, obtain constructing each pixel (x, y) with the second order local derviation convolution of gaussian kernel function Hessian matrix,
The result of Frangi filtering is calculated by such as minor function:
Wherein, the characteristic value of Hessian matrix is λ1And λ2, and | λ1| < | λ2|, Rb=λ2/λ1,
β controls the parameter of whole smoothness for adjusting linear and block-like difference, c statement, and the present embodiment chooses β and is The range scale of 0.5, c 5, Gauss function is σ ∈ [1,3], and response of more each pixel under each scale is chosen Peak response is as Frangi filtering characteristics.
B-COSFIRE filtering characteristics.
The Difference of Gaussian filter that B-COSFIRE filter is collinearly arranged by one group, i.e. DoG filter are constituted, B- COSFIRE filter response is obtained by calculating the geometric mean of all DoG filter responses.DoG filter expression is as follows:
The DoG filter response of image obtains c by the convolution of DoG filter and imageσ(x, y)=| I*DoG |.Parameter CollectionDescribe the parameter of n DoG filter, σiIt is the standard deviation of each DoG filter.(ρi,φi) be Polar coordinate position indicates position of the center with respect to COSFIRE filter center of each DoG filter.
For extracting continuous blood vessel structure feature, DoG filter exists the COSFIRE model of symmetrical structure in pairs, they Center be located at the two sides of B-COSFIRE filter center in the opposite direction；
For the COSFIRE model of unsymmetric structure for detecting tail vein, the center of DoG filter is located at B-COSFIRE The side of filter center,
In order to improve the fault-tolerance of each position, fuzzy operation is carried out to DoG filter response, the fuzzy operation is The maximum value of DoG filter weight threshold response, weight are the response of DoG filter and Gaussian function Gσ' (x', y') coefficient multiplies Product, wherein standard deviation sigma ' be distance ρiLinear function:
σ '=σ0+αρi
σ0And ρiIt is all constant.Then to φiShift operation is carried out after the DoG filter blurs operation of opposite direction, is moved Bit vector is (Δ xi,Δyi), Δ xi=-ρicosφi,Δyi=-ρisinφi。
Each tuple (σ in final parameter collection Si,ρi,φi) corresponding DoG filter fuzzy displacement response are as follows:
So the response of B-COSFIRE filter is that the corresponding all fuzzy weightings for shifting DoG filter responses of parameter set S are several What mean value, calculation formula are as follows:
Wherein | |tIt indicates to maximum response threshold valueization processing,
When changing φ, the arrangement mode of DoG filter, which can change, is equivalent to rotation B-COSFIRE filter, calculates figure Each of picture filter result of the pixel on 12 direction ψ, is maximized the B-COSFIRE feature as the pixel, Correlation formula is as follows:
Finally symmetrical B-COSFIRE filter and asymmetric B-COSFIRE filter response results are made and obtained final B-COSFIRE filtering characteristics.
Parameter setting about filter is as follows:
Weber description operator is as a kind of strong local texture description method, for any pixel point p in image I, Differential excitation is gray scale difference value of the pixel relative to its neighborhood territory pixel, then asks the summation of difference and the ratio of central pixel point Then value solves arc tangent and obtains weber Expressive Features of the gradient as pixel p of differential excition,
Wherein, p is any pixel point in image I, and N (p) indicates the neighborhood territory pixel set of pixel p, and I (z) indicates target picture The gray value of any pixel of N (p) in the neighborhood of plain p.
S4,10 dimensions of each pixel as shown in fig. 7, after feature extraction, in training set and test set image Feature vector indicates.For each image in training set, all feature vectors are polymerized to 2 classes-using K-Means clustering algorithm Blood vessel cluster (being labeled as 1) and background cluster (being labeled as 0), such training set image is just provided with Pixel-level label.
S5, the training set data training model-naive Bayesian based on K-Means cluster mark, for there is a small amount of mistake mark The data of label, model-naive Bayesian can approximation obtain the corresponding prior probability of true tag data and conditional probability distribution.
Based on 2016RaMATLAB platform, K-Means is clustered and model-naive Bayesian uses the statistics and machine of MATLAB Function idx=kmeans (X, k) and Mdl=fitcnb (X, Y), X in device learning tool case are training datasets, and k is cluster Number of clusters, idx are the labels of the X obtained by clustering algorithm, and Y is corresponding cluster labels, and Mdl is the simple shellfish that training obtains This model of leaf.
S6, the blood vessel for dividing each image in test set with trained model-naive Bayesian.As shown in Fig. 8, In, green indicates the puncta vasculosa correctly divided, and red indicates the non-vascular point of segmentation errors, and blue is the blood vessel of erroneous segmentation Point；Black is the non-vascular point correctly divided.
S7, in order to judge whether blood vessel segmentation method effective, need an effective Performance Evaluating Indexes.By blood vessel segmentation As a result compared with the goldstandard that expert demarcates manually, the segmentation result of pixel has 4 kinds of situations, true positives (true Positive, TP) it indicates to divide correct puncta vasculosa；The blood vessel of false positive (false positive, FP) expression segmentation errors Point；True negative (true negative, TN) indicates to divide correct background dot；False negative (false negative, FN) indicates The background dot of segmentation errors.
Acc indicates to divide the ratio that correct pixel accounts for whole image pixel summation, i.e. accuracy, Se expression segmentation Correct puncta vasculosa accounts for the ratio of goldstandard puncta vasculosa summation, i.e. sensitivity, and Sp indicates that dividing correct background dot accounts for goldstandard The ratio of background dot summation, i.e., specific, Ppv indicates to divide the ratio that correct puncta vasculosa accounts for the puncta vasculosa being partitioned into, i.e., smart Spend predicted ratio.
Following table gives the performance comparison using K-Mean clustering algorithm and context of methods, and the accuracy rate of two methods is about 96%, and in retinal images medium vessels ratio very little, so performance of the Se and PPv value more representative of a method.The side this paper The Se value of method shows that more puncta vasculosas can be partitioned into more greatly, and 80% is all correct puncta vasculosa, so context of methods Effect is promoted relative to K-Means cluster really.
Fig. 8
As shown in Fig. 2, the present invention also provides a kind of eyeground based on K-Means cluster mark and model-naive Bayesian Retinal vessel segmenting system, comprising:
Sample sampling unit constructs training set and test for randomly selecting colored eye fundus image from DRIVE data set Collection；
Pretreatment unit, for extract the grayscale image in the channel G in colored eye fundus image and to retina sense therein it is emerging Interesting region carries out expansion process；
Feature extraction unit carries out feature extraction for the grayscale image to the channel G after expansion process, by training set and survey Each pixel in examination collection image is indicated with the feature vector of ten dimensions comprising:
Module, Gauss matching characteristic extraction module, Frangi is filtered in Gabor wavelet processing module, linear detector Filtering characteristics extraction module, B-COSFIRE filtering characteristics extraction module, weber Expressive Features extraction module；
Cluster mark unit: for feature vector to be carried out cluster mark by K-Means clustering algorithm；
Machine learning model training unit: for the simple pattra leaves of training set data training by K-Means cluster mark This model；
Image processing unit: for being split by blood vessel of the parted pattern to each image in test set；
Segmentation effect judges unit: judging for the validity to blood vessel segmentation method.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution The range of scheme.

Claims (10)

1. a kind of eye ground blood vessel segmentation method based on K-Means cluster mark and model-naive Bayesian, feature It is, includes the following steps:
The Pixel-level label of training set image is constructed, specifically: K- is used to each image in the training set after feature extraction Feature vector is carried out cluster mark by Means clustering algorithm；
Training set data training model-naive Bayesian based on K-Means cluster mark；
With the blood vessel of each image in trained model-naive Bayesian segmentation test set.
2. the method according to claim 1, wherein before the Pixel-level label of the building training set image also With following steps:
The colored eye fundus image in data set is randomly selected, training set and test set are constructed；
Extract object of the grayscale image in the channel G of colored eye fundus image as feature extraction, and to retina area-of-interest into Row expansion process；
Feature extraction is carried out to the grayscale image after expansion process, by each pixel in training set and test set image with ten The feature vector expression of dimension, specifically:
The contrast for enhancing the grayscale image by the adaptive histogram equalization method of contrast-limited makees this gray value For a kind of feature for distinguishing blood vessel and background, grayscale image described in technical treatment is enhanced by the blood vessel designed based on vessel properties,
The grayscale image is filtered as follows respectively:
By Gabor wavelet conversion process, four-dimensional Gabor characteristic is obtained；
Retinal images are filtered by linear detector, obtain the response results of linearity test as one-dimensional line Property detection feature；
Retinal images are filtered by the filter of gaussian shape, chooses and responds maximum filter result as one The Gauss matching characteristic of dimension；
Frangi filtering characteristics based on Hessian matrix are filtered retinal images, choose peak response conduct One-dimensional Frangi filtering characteristics；
Retinal images are filtered based on B-COSFIRE filtering characteristics, by symmetrical B-COSFIRE filter and non-right B-COSFIRE filter response results are claimed to sum to obtain final one-dimensional B-COSFIRE filtering characteristics；
The one-dimensional weber Expressive Features that operator solves any pixel point in retinal images are described by weber；
The blood vessel with each image in trained model-naive Bayesian segmentation test set also has following steps later:
Judge whether blood vessel segmentation method is effective by preset evaluation index.
3. according to the method described in claim 2, it is characterized in that, the colored eye fundus image randomly selected in data set, It constructs in training set and test set, data set uses disclosed DRIVE data set, the picture number of the training set and test set It is identical, and the Mask in the effective information region of the result and 1 display eye fundus image of the corresponding 2 expert's manual segmentations of each image Image.
4. according to the method described in claim 2, it is characterized in that, carrying out Gabor wavelet conversion process in the following way:
Gabor kernel function is as follows:
Wherein, x'=xcos θ+ysin θ, y'=-xsin θ+ycos θ,
(u0,v0) indicate the temporal frequency of sinusoidal plane wave, σxAnd σyIt indicates the variance of the oval Gauss on the direction x and y, determines The size of the filter zone of action, i.e. scale, θ are rotation angle,
One group of direction Gabor wavelet different with scale, under fixed size, more all sides are obtained by changing parameter σ and θ Upward response, using peak response as the characteristic image under the scale, according to the scale of setting, the result under all scales As Gabor characteristic.
5. according to the method described in claim 4, it is characterized in that, the linear detector is by rotating under the window of W*W Length is that the detection line of l obtains the filter of N number of different directions, grayscale image is filtered with filter, when the side of detection line To it is consistent with vessel directions when, response is maximum, compare institute it is angled under response reservation maximum value as the detection under the window As a resultLength by changing detection line adapts to the blood vessel under different scale, again by the response results summation under different scale The response results of linearity test are averagely obtained as linearity test feature；
The filter graph picture filtering processing of gaussian shape is carried out in the following way:
The Gaussian kernel function of handstand is as follows:
WhereinThe mean normalization of filter to 0, L is indicated smoothly to make an uproar along y-axis The neighborhood length of sound, t are constants, setting x'=xcos θ+ysin θ, y'=-xsin θ+ycos θ, θ=(0:N) * π/N variation with The blood vessel for detecting different directions, chooses peak response on N number of direction as the filter result under the scale under a certain scale, The response under different scale is compared, chooses and responds maximum filter result as Gauss matching characteristic.
6. according to the method described in claim 5, it is characterized in that, being carried out in the following way based on Hessian matrix Frangi filtering processing:
For two-dimensional input image, obtain constructing each pixel (x, y) with the second order local derviation convolution of gaussian kernel function Hessian matrix,
The result of Frangi filtering is calculated by such as minor function:
Wherein, the characteristic value of Hessian matrix is λ1And λ2, and | λ1| < | λ2|,
β controls the parameter of whole smoothness for adjusting linear and block-like difference, c statement, and more each pixel is each Response under scale chooses peak response as Frangi filtering characteristics.
7. according to the method described in claim 6, it is characterized in that, the Gauss that B-COSFIRE filter is collinearly arranged by one group Difference filter, i.e. DoG filter are constituted, and B-COSFIRE filter response is equal by the geometry for calculating all DoG filter responses Value obtains, and the DoG filter response is obtained by the convolution of DoG filter and image,
For extracting continuous blood vessel structure feature, DoG filter exists the COSFIRE model of symmetrical structure in pairs, in them The heart is located at the two sides of B-COSFIRE filter center in the opposite direction；The COSFIRE model of unsymmetric structure is for detecting end Blood vessel, the center of DoG filter are located at the side of B-COSFIRE filter center, carry out fuzzy operation to DoG filter response, The fuzzy operation is the maximum value of DoG filter weight threshold response, and weight is DoG filter response and Gaussian function coefficient Final B- is made and obtained to symmetrical B-COSFIRE filter and asymmetric B-COSFIRE filter response result by product COSFIRE filtering characteristics.
8. the method according to the description of claim 7 is characterized in that passing through any pixel in following equations retinal images The weber Expressive Features of point:
Wherein, p is any pixel point in image I, and N (p) indicates the neighborhood territory pixel set of pixel p, and I (z) indicates object pixel p's The gray value of any pixel of N (p) in neighborhood.
9. according to the method described in claim 2, it is characterized in that, described judge blood vessel segmentation side by preset evaluation index Whether method is effective, specifically:
Wherein, the puncta vasculosa of each image passes through four kinds of different colors differentiations, the puncta vasculosa true positives TP correctly divided, segmentation The non-vascular point false positive FP of mistake, the puncta vasculosa true negative TN of erroneous segmentation, the non-vascular point false negative FN correctly divided, Acc indicates to divide the ratio that correct pixel accounts for whole image pixel summation, i.e. accuracy, the correct blood of Se expression segmentation Pipe point accounts for the ratio of goldstandard puncta vasculosa summation, i.e. sensitivity, and it is total that Sp indicates that the correct background dot of segmentation accounts for goldstandard background dot The ratio of sum, i.e., specific, Ppv indicates to divide the ratio that correct puncta vasculosa accounts for the puncta vasculosa being partitioned into, i.e. accuracy prediction ratio Value.
10. a kind of eye ground blood vessel segmentation system based on K-Means cluster mark and model-naive Bayesian, feature It is, comprising:
Sample sampling unit constructs training set and test set for randomly selecting colored eye fundus image from DRIVE data set；
Pretreatment unit, for extracting the grayscale image in the channel G in colored eye fundus image and to retina region of interest therein Domain carries out expansion process；
Feature extraction unit carries out feature extraction for the grayscale image to the channel G after expansion process, by training set and test set Each pixel in image is indicated with the feature vector of ten dimensions comprising:
Module, Gauss matching characteristic extraction module, Frangi filtering is filtered in Gabor wavelet processing module, linear detector Characteristic extracting module, B-COSFIRE filtering characteristics extraction module, weber Expressive Features extraction module；
Cluster mark unit: for feature vector to be carried out cluster mark by K-Means clustering algorithm；
Machine learning model training unit: for the training set data training naive Bayesian mould by K-Means cluster mark Type；
Image processing unit: for being split by blood vessel of the parted pattern to each image in test set；
Segmentation effect judges unit: judging for the validity to blood vessel segmentation method.
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