CN106919950A - Probability density weights the brain MR image segmentation of geodesic distance - Google Patents

Abstract

The invention discloses the brain MR image segmentation that a kind of probability density weights geodesic distance, belong to technical field of image processing.Including：Some width simulation brain database images are read in, statistics with histogram is carried out, the interval sample value of most integrated distribution is drawn；Each pixel on pending image using the sample value for obtaining as priori to selection carries out Multilayer networks；Super-pixel segmentation is carried out to pending image based on probability density function；Super-pixel after segmentation is scanned, filter out non-compliant super-pixel and enter line splitting, it is two classes to be gathered all pixels in super-pixel again with FCM algorithms, connected region is found according to classification results, and using the pixel in each connected region as a new class, update super-pixel classification results matrix；Clustered with the basis of super-pixel of the FCM algorithms after all renewals, obtained pending image brain tissue segmentation result.The present invention improves the degree of accuracy of super-pixel segmentation and brain tissue's segmentation.

Description

Probability density weights the brain MR image segmentation of geodesic distance

Technical field

The present invention relates to technical field of image processing, a kind of brain MR figures of probability density weighting geodesic distance are particularly related to As dividing method.

Background technology

Image segmentation is one of most classical research topic in the fields such as image procossing, graphical analysis and computer vision, It is one of maximum difficult point, image Segmentation Technology plays pivotal player in many medical image applications, is also each in image The further basis analyzed of the pathology of tissue and organ is planted, by using image segmentation, region interested in image is carried Take out, be that clinical diagnosis and treatment etc. provide foundation, and brain is the vitals of human body, therefore study dividing for brain area Cut diagnosis equal important in inhibiting of the technology for brain three-dimensional reconstruction, the research of neural circuitry and clinical brain diseases.

Super-pixel segmentation is a kind of image over-segmentation algorithm, can be as the pretreatment work in some image applications, example Such as segmentation, conspicuousness detection, recognition of face.Super-pixel can greatly reduce successive image treatment with the redundancy in capture images The complexity of task.A kind of existing effective superpixel segmentation method is based on geodesic distance (Geodesic Distance) Super-pixel, with geodesic distance rather than Euclidean distance measure pixel between similarity, for natural image split Effect is pretty good, but for brain MR (Magnetic Resonance, abbreviation MR) image, the method can not be separated often exactly One tiny brain tissue's region super-pixel block.

Fuzzy C-Means Cluster Algorithm (Fuzzy C-Means, abbreviation FCM) is the fuzzy clustering image being most widely used Partitioning algorithm.Relative to other dividing methods, FCM can retain more information of initial pictures.However, traditional FCM is calculated Method fail in image segmentation consider each point gray feature and its neighborhood territory pixel correlation degree, result in the algorithm for Noise and the uneven comparing of gray scale are sensitive, regarding to the issue above, it has been proposed that many improved FCM algorithms, although improved Method is improved to some extent at the aspect such as anti-noise or efficiency, but due to the high complexity of brain image, can not still obtain Gratifying segmentation result, therefore actual requirement can not be met using traditional single method segmentation.

The content of the invention

The present invention provides the brain MR image segmentation that a kind of probability density weights geodesic distance, which raises super-pixel Segmentation and the degree of accuracy of brain tissue's segmentation.

In order to solve the above technical problems, present invention offer technical scheme is as follows：

A kind of probability density weights the brain MR image segmentation of geodesic distance, including：

Step 1：Read in some width simulation brain database images, statistics with histogram carried out to it, draw white matter, grey matter or The interval sample value of cerebrospinal fluid most integrated distribution；

Step 2：Piece image is randomly selected as pending image from the simulation brain database images, using obtaining The interval sample value of the most integrated distribution to carry out probability to each pixel on pending image as priori close Degree estimation, obtains probability density function；

Step 3：Super-pixel segmentation is carried out to pending image based on the probability density function for obtaining, and is recorded super Pixel classifications matrix of consequence；

Step 4：Super-pixel after segmentation is scanned, is filtered out according to super-pixel color standard difference non-compliant Super-pixel enters line splitting, and during division, it is two classes to be gathered all pixels in super-pixel again with FCM algorithms, is tied according to classification afterwards Fruit finds connected region, and using the pixel in each connected region as a new class, updates the super-pixel classification results square Battle array；

Step 5：According to the super-pixel classification results matrix after updating, the super-pixel base with FCM algorithms after all renewals Clustered on plinth, obtained pending image brain tissue segmentation result.

The invention has the advantages that：

Probability density of the invention weights the brain MR image segmentation of geodesic distance, first draws statistics with histogram Sample value Multilayer networks are carried out to each pixel on image as priori value, then weighted with based on probability density The super-pixel method of geodesic distance carries out super-pixel segmentation to image, then scans super-pixel and filters out non-compliant super picture Element enters line splitting, and merokinesis optimization is carried out using super-pixel internal feature, and super-pixel classification results square is updated after the completion of division Battle array, is clustered again on the basis of being finally based on the super-pixel split with FCM algorithms, completes the segmentation of brain image.The method At least have the following advantages that：(1) design the new weights influence factor to define geodesic distance, incorporated probability density function, make Contrast more obvious between brain different tissues, gradient calculation is more reasonable；(2) process of local segmentation post processing is increased, it is right Super-pixel carries out merokinesis, and pixel is more accurately sorted out, and further increases the degree of accuracy of super-pixel segmentation, that is, use Most simple most traditional FCM carries out last cluster, as a result still fine；(3) by based on probability density weighting geodesic distance The methods such as super-pixel segmentation technology, FCM are connected, and brain tissue's segmentation result is obtained on the basis of super-pixel.

Brief description of the drawings

Fig. 1 is the schematic flow sheet of the brain MR image segmentation of probability density weighting geodesic distance of the invention；

Fig. 2 is the principle schematic of the brain MR image segmentation of probability density weighting geodesic distance of the invention；

Fig. 3 be probability density of the invention weighting geodesic distance brain MR image segmentation in the flow of step 3 show It is intended to；

Fig. 4 is that the idiographic flow of the brain MR image segmentation of probability density weighting geodesic distance of the invention is illustrated Figure；

Fig. 5 (a)：It is pending image of the invention：Synthesis brain MR example images；

Fig. 5 (b)：It is the super-pixel segmentation figure for synthesizing brain MR example images corresponding to Fig. 5 (a) of the invention；

Fig. 5 (c)：It is according to regions such as the brain MR example images of present invention realization final white matter, grey matter, cerebrospinal fluid Segmentation result figure；

Fig. 6 (a)：It is pending image of the invention：True brain MR example images；

Fig. 6 (b)：It is the super-pixel segmentation figure corresponding to the true brain MR example images of Fig. 6 (a) of the invention；

Fig. 6 (c)：It is according to regions such as the brain MR example images of present invention realization final white matter, grey matter, cerebrospinal fluid Segmentation result figure.

Specific embodiment

To make the technical problem to be solved in the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and tool Body embodiment is described in detail.

The present invention provides the brain MR image segmentation that a kind of probability density weights geodesic distance, as shown in figures 1 to 6, bag Include：

Step 1：Read in some width simulation brain database images, statistics with histogram carried out to it, draw white matter, grey matter or The interval sample value of cerebrospinal fluid most integrated distribution；

In this step, the simulation brain database images of reading are the synthesis brain MR image of bmp forms, read in its gray scale Value.

Step 2：Piece image is randomly selected as pending image from simulation brain database images, using obtaining most The interval sample value of integrated distribution carries out Multilayer networks as priori to each pixel on pending image, obtains To probability density function；

Step 3：Super-pixel segmentation is carried out to pending image based on the probability density function for obtaining, and records super-pixel Classification results matrix；

Step 4：Super-pixel after segmentation is scanned, is filtered out according to super-pixel color standard difference non-compliant Super-pixel enters line splitting, and during division, it is two classes to be gathered all pixels in super-pixel again with FCM algorithms, is tied according to classification afterwards Fruit finds connected region, and using the pixel in each connected region as a new class, updates super-pixel classification results matrix；

Step 5：According to the super-pixel classification results matrix after updating, the super-pixel base with FCM algorithms after all renewals Clustered on plinth, obtained pending image brain tissue segmentation result.

Probability density of the invention weights the brain MR image segmentation of geodesic distance, first draws statistics with histogram Sample value Multilayer networks are carried out to each pixel on image as priori value, then weighted with based on probability density The super-pixel method of geodesic distance carries out super-pixel segmentation to image, then scans super-pixel and filters out non-compliant super picture Element enters line splitting, and merokinesis optimization is carried out using super-pixel internal feature, and super-pixel classification results square is updated after the completion of division Battle array, is clustered again on the basis of being finally based on the super-pixel split with FCM algorithms, completes the segmentation of brain image.The method At least have the following advantages that：(1) design the new weights influence factor to define geodesic distance, incorporated probability density function, make Contrast more obvious between brain different tissues, gradient calculation is more reasonable；(2) process of local segmentation post processing is increased, it is right Super-pixel carries out merokinesis, and pixel is more accurately sorted out, and further increases the degree of accuracy of super-pixel segmentation, that is, use Most simple most traditional FCM carries out last cluster, as a result still fine；(3) by based on probability density weighting geodesic distance The methods such as super-pixel segmentation technology, FCM are connected, and brain tissue's segmentation result is obtained on the basis of super-pixel.

Preferably, step 1 is further：

Some width simulation brain MR (being obtained from BrainWeb databases) images are read in, K-means algorithms are first used it Preliminary classification is carried out, N number of interval is divided into after then gray value is normalized, to the gray value of its white matter, grey matter or cerebrospinal fluid Scope is counted, and count white matter, grey matter or cerebrospinal fluid most integrated distribution K is interval, in this K interval, each M gray value is chosen as sample in interval, and the numerical value of the sample is the interval sample value of most integrated distribution.In this step, N, K, m are the integer more than 0.

In the present invention, due to having coincidence between brain image grey matter, white matter, the grey scale pixel value of cerebrospinal fluid different tissues Place, between them the definite division border of neither one, and the classification that each pixel belongs to is fuzzy, directly to it Segmentation is difficult.So, the present invention carries out statistics with histogram first, the sample value that will be obtained as priori, for Multilayer networks are carried out to each pixel on image in next step.

Further, step 2 includes：

Step 21：Calculate K probability Estimation model of each pixel in pending image respectively according to formula (1)：

Wherein, x is the gray value of each pixel in image, x_iIt is the individual interval of kth (k=1,2 ..., K) as priori M gray value sample of value, h is bandwidth (control parameter)；

Step 22：The K probability Estimation model that will be obtained calculates mixing probability density function by formula (2), obtains every The Multilayer networks value of individual pixel, i.e. each pixel belong to the possibility of white matter, grey matter or cerebrospinal fluid：

Wherein, p (k) is factor of influence of each probability Estimation model to data point, between 0~1；

Step 23：Mixing probability density function to obtaining is normalized, and obtains probability density function：

Wherein, wmax and wmin are the maximum and minimum value of P (x).

In the present invention, probability density function is incorporated, can made between the brain different tissues such as grey matter, white matter, cerebrospinal fluid Contrast it is more obvious, and make our follow-up gradient calculations more accurate.

As a modification of the present invention, as shown in figure 3, step 3 includes：

Step 31：Initialization seed point, is first uniformly distributed n/2 seed point on image, then uses self adaptation hexagon (Adaptive Hexagonal) method inserts other seed points, and the complexity of each seed point hexagon is calculated with formula (4), looks for To the hexagon that complexity is maximum, it is divided into six small hexagons of overlap, is inserted in the maximum small hexagon of complexity One new seed point, iteration successively, until n cluster centre of sampling, complexity is defined as：

Wherein, H_iIt is seed point s_iHexagonal area, N is the pixel quantity of image I, and M is to meet condition in braces Pixel p quantity, α is control parameter, It is the ladder of pixel p on image Degree, G_σIt is the Gaussian function with standard deviation sigma, ω is a regulation parameter, is preventedIt is zero situation；

Step 32：Seed point is upset, each seed point is moved to the minimum gradient locations in its local 3*3 region, and remember Its X, Y-coordinate information are recorded as new seed point；

Step 33：The seed point sensitising gradient based on probability density is calculated with formula (5)：

PSSG(s_i, G (t))=| | S_p(s_i,G(t))|| (5)

Wherein,S_pIt is the P calculated with sobel operators on geodetic path_wThe gradient of (x)；

Step 34：Calculated using FMM (fast marching method) algorithm and geodesic distance weighted based on probability density, Row bound of going forward side by side spreads, and produces a series of pixel composition super-pixel block with like attribute；

In this step, A：Geodesic distance is calculated by the following method：

From seed point s_iOne to any one pixel p probability density weighting geodesic distance is described as：From seed point s_iOpen Begin to reach pixel p along a most short path, every is multiplied by a weighting function W (s on path_i, G (t)) minimum arc Integration long, it is defined as：

Wherein, G (t) is from seed point s_iTo a geodetic path between pixel, t is continually changing, and takes 0~1 Between value, weight W is set to the gradient that a pixel belongs to white matter, grey matter or cerebrospinal fluid possibility, for defining from kind Sub- point s_iDistance increment onto certain pixel path G：

Geodesic distance is calculated by the expanding policy of the Fast Marching Method (FMM) with appropriate velocity field iterative diffusion , velocity function is defined based on formula (7), its computing formula is：

B：Boundary diffusion, produces super-pixel block：

In diffusion process, the new speed of each pixel is no longer static, depends on the seed point nearest from it (with it there is most short probability density to weight the pixel of geodesic distance), since given seed point, is counted with formula (5) The gradient of its neighborhood territory pixel point is calculated, is extended along the pixel with maximal rate formula (8), the pixel color value after extension To be replaced by the color value of seed point, the probability seed point sensitising gradient of next pixel can constantly pass through diffusing through for FMM Journey is calculated with formula (5) with the value of current neighborhood territory pixel point and updated, and is spread forward every time from the minimum (speed of seed point geodesic distance Degree function is maximum) pixel, finished until all of pixel all spreads, obtain the classification results matrix of super-pixel.

Step 35：Position and the color of seed point are updated with publicity (9)；

Wherein, S^lIt is l-th super-pixel, s_lIt is super-pixel S^lSeed point, x_l', c_l' respectively be update after seed point position Put and color value,Measurement pixel is subordinate to the weighting function of seed point degree；

Step 36：Repeat step 33, step 34, step 35, algorithm are intended to optimize an energy function, are defined as formula (10), when the change of energy function in subsequent iteration twice just stops less than a specific threshold, preliminary super-pixel Segmentation is completed,

In the present invention, for the high complexity of brain image, design the new weights influence factor to define geodesic distance, make With new gradient calculation method, a more obvious border is had at the fuzzy region edge of brain MR image, can be with calibrated True Ground Split goes out each tiny brain tissue's region super-pixel block.

Further, step 4 includes：

Step 41：Super-pixel after scanning segmentation, if the color standard difference of super-pixel is more than certain threshold value T_c, then to full The super-pixel of sufficient this condition enters line splitting, and its calculating is defined as：

C(s_l)=λ Std_l＞ T_c (11)

Wherein, Std_lIt is the standard deviation of pixel color in each super-pixel, T_cIt is threshold value, λ is control parameter；

Step 42：It is two classes, local FCM to be gathered all pixels in the super-pixel of each needs division again with FCM algorithms Object function be：

Wherein, C is the number of cluster, Q_nIt is the number of pixel in the super-pixel of Current Scan, μ_ijIt is j-th pixel category In the fuzzy membership function of ith cluster, constraint μ is met_ij∈[0,1],M is acted on fuzzy membership Weighting function, v_iIt is ith cluster center, x_jIt is the color value of pixel in the super-pixel of Current Scan；

Step 43：Connected region is found according to the super-pixel classification results for obtaining, and the pixel in each connected region Used as a new class, i.e., one new super-pixel block updates the matrix of consequence of super-pixel classification；

Step 44：To needing the super-pixel repeat step 42, step 43 of division until all super pictures for meeting splitting condition Element all divisions are completed.

In the present invention, local segmentation post processing is carried out after the completion of primary segmentation again, office is carried out using super-pixel internal feature Optimization is split in part, pixel is more accurately sorted out, and improves the segmentation accuracy of super-pixel, allows the division of super-pixel more Brain different tissues are separated by further cluster exactly.

Preferably, step 5 is further：

Using super-pixel (rather than pixel) as the input of FCM algorithms, gathered on the basis of the super-pixel split Class, completes the segmentation of brain image, and the FCM object functions based on super-pixel are：

Wherein, C is the number of cluster, and Q is the number of super-pixel in image, μ_ijIt is that j-th super-pixel belongs to ith cluster Fuzzy membership function, meet constraint μ_ij∈[0,1],M is to act on the weighting function on fuzzy membership, v_iIt is ith cluster center, ξ j are super-pixel S_jColor average.

In the present invention, the super-pixel segmentation technology based on probability density weighting geodesic distance, FCM algorithms are connected, Brain tissue's segmentation result is obtained on the basis of super-pixel, segmentation efficiency and the degree of accuracy is improve.

Used as a modification of the present invention, the image of reading can also be the true brain MR image of dcm forms, true brain The processing mode of portion's MR images is similar with the processing mode of synthesis brain MR image, and difference is that synthesis brain MR image reads in Be gray value, and it is the density value limited by window width and window level that true brain MR image reads in.

Processing result image is analyzed below, is such as directed to Fig. 5 (a), the picture that Fig. 6 (a) is provided, using side above Method is processed, and shown in result such as Fig. 5 (c), Fig. 6 (c), Fig. 5 (b), Fig. 6 (b) are super-pixel segmentation figure.

In Fig. 5 (b) and Fig. 6 (b), it can be seen that super-pixel segmentation of the present invention is accurate, border from visual effect Laminating edge, can accurately separate each tiny region, be subsequently to separate white matter (WM), grey matter (GM), spinal fluid (CSF) the cluster work of brain tissue such as provides solid foundation.Even our effect is clustered with most simple most traditional FCM Fruit is still fine.

In Fig. 5 (c) and Fig. 6 (c), it can be seen that the present invention can exactly be partitioned into brain tissue's image, maximum journey Remain the raw information of image degree.

The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art For, on the premise of principle of the present invention is not departed from, some improvements and modifications can also be made, these improvements and modifications Should be regarded as protection scope of the present invention.

Claims (6)

1. a kind of probability density weights the brain MR image segmentation of geodesic distance, it is characterised in that including：

Step 1：Some width simulation brain database images are read in, statistics with histogram is carried out to it, draw white matter, grey matter or brain ridge The interval sample value of liquid most integrated distribution；

Step 2：Piece image is randomly selected as pending image from the simulation brain database images, using the institute for obtaining State the interval sample value of most integrated distribution probability density is carried out to each pixel on pending image as priori and estimate Meter, obtains probability density function；

Step 3：Super-pixel segmentation is carried out to pending image based on the probability density function for obtaining, and records super-pixel Classification results matrix；

Step 4：Super-pixel after segmentation is scanned, non-compliant super picture is filtered out according to super-pixel color standard difference Element enters line splitting, and during division, it is two classes to be gathered all pixels in super-pixel again with FCM algorithms, is sought according to classification results afterwards Connected region is looked for, and using the pixel in each connected region as a new class, updates the super-pixel classification results matrix；

Step 5：According to the super-pixel classification results matrix after updating, on the basis of super-pixel of the FCM algorithms after all renewals Clustered, obtained pending image brain tissue segmentation result.

2. probability density according to claim 1 weights the brain MR image segmentation of geodesic distance, it is characterised in that The step 1 is further：

Some width simulation brain MR images are read in, preliminary classification first is carried out with K-means algorithms to it, then by gray value normalizing Be divided into N number of interval after change, the intensity value ranges to its white matter, grey matter or cerebrospinal fluid are counted, count white matter, grey matter or K of person's cerebrospinal fluid most integrated distribution is interval, and in this K interval, m gray value is chosen as sample in each interval.

3. probability density according to claim 2 weights the brain MR image segmentation of geodesic distance, it is characterised in that The step 2 includes：

Step 21：Calculate K probability Estimation model of each pixel in pending image respectively according to formula (1)：

$p\left(x\right|k)=\frac{1}{mh}\underset{i=1}{\overset{m}{\Σ}}{e}^{-{\left(\frac{x-xi}{h}\right)}^2}---\left(1\right)$

Wherein, x is the gray value of each pixel in image, x_iIt is the individual interval m as priori value of kth (k=1,2 ..., K) Individual gray value sample, h is bandwidth (control parameter)；

Step 22：The K probability Estimation model that will be obtained calculates mixing probability density function by formula (2), obtains each picture The Multilayer networks value of vegetarian refreshments, i.e. each pixel belong to the possibility of white matter, grey matter or cerebrospinal fluid：

$P\left(x\right)=\underset{k=1}{\overset{K}{\Σ}}p\left(k\right)p\left(x\right|k),\underset{k=1}{\overset{K}{\Σ}}p\left(k\right)=1,p\left(k\right)\∈\[0,1\]---\left(2\right)$

Wherein, p (k) is factor of influence of each probability Estimation model to data point, between 0~1；

Step 23：Mixing probability density function to obtaining is normalized, and obtains probability density function：

$P_w\left(x\right)=\frac{P\left(x\right)-wmin}{wmax-wmin}---\left(3\right)$

Wherein, wmax and wmin are the maximum and minimum value of P (x).

4. probability density according to claim 3 weights the brain MR image segmentation of geodesic distance, it is characterised in that The step 3 includes：

Step 31：Initialization seed point, is first uniformly distributed n/2 seed point on image, is then inserted with self adaptation hexagon method Enter other seed points, the complexity of each seed point hexagon is calculated with formula (4), find the maximum hexagon of complexity, It is divided into six small hexagons of overlap, inserts a new seed point in the maximum small hexagon of complexity, successively iteration, Until n cluster centre of sampling, complexity is defined as：

Wherein, H_iIt is seed point s_iHexagonal area, N is the pixel quantity of image I, and M is the picture that condition is met in braces The quantity of vegetarian refreshments p, α is control parameter, It is the gradient of pixel p on image, G_σ It is the Gaussian function with standard deviation sigma, ω is a regulation parameter, is preventedIt is zero situation；

Step 32：Seed point is upset, each seed point is moved to the minimum gradient locations in its local 3*3 region, and record it X, Y-coordinate information are used as new seed point；

Step 33：The seed point sensitising gradient based on probability density is calculated with formula (5)：

PSSG(s_i, G (t))=| | S_p(s_i,G(t))|| (5)

Wherein,S_pIt is the P calculated with sobel operators on geodetic path_wThe gradient of (x)；

Step 34：Calculated using FMM algorithms and geodesic distance is weighted based on probability density, row bound of going forward side by side diffusion is produced a series of Pixel composition super-pixel block with like attribute；

In this step, A：Geodesic distance is calculated by the following method：

From seed point s_iOne to any one pixel p probability density weighting geodesic distance is described as：From seed point s_iStart edge A most short path and reach pixel p, every is multiplied by a weighting function W (s on path_i, G (t)) minimum arc length product Point, it is defined as：

$PBD(s_i,p)=min\left\{{\∫}_0^{1}W\right(s_i,G\left(t\right))\·|\left|\stackrel{\·}{G}\left(t\right)\right|\left|dt\right\}---\left(6\right)$

Wherein, G (t) is from seed point s_iTo a geodetic path between pixel, t is continually changing, takes between 0~1 Value, weight W is set to the gradient that a pixel belongs to white matter, grey matter or cerebrospinal fluid possibility, for defining from seed point s_i Distance increment onto certain pixel path G：

$W(s_i,G(t\left)\right)=e^{\frac{PSSG(s_i,G(t\left)\right)}{\mathrm{\σ}}}---\left(7\right)$

Geodesic distance is calculated by the expanding policy of the Fast Marching Method FMM with appropriate velocity field iterative diffusion, base Velocity function is defined in formula (7), its computing formula is：

$V\left(p\right)=e^{-\frac{PSSG(s_i,G(t\left)\right)}{\mathrm{\σ}}}---\left(8\right)$

B：Boundary diffusion, produces super-pixel block：

In diffusion process, the new speed of each pixel is no longer static, depends on the seed point nearest from it, i.e., with There is most short probability density to weight the pixel of geodesic distance for it, since given seed point, calculate it with formula (5) adjacent The gradient of domain pixel, extends along the pixel with maximal rate formula (8), and the pixel color value after extension will be by planting The color value substitution of son point, the probability seed point sensitising gradient of next pixel can be used constantly by the diffusion process of FMM works as The value of preceding neighborhood territory pixel point is calculated with formula (5) and updated, and is spread forward every time from the minimum i.e. speed letter of seed point geodesic distance The maximum pixel of number, finishes until all of pixel all spreads, and obtains super-pixel classification results matrix；

Step 35：Position and the color of seed point are updated with publicity (9)；

${x_l}^{\′}=\frac{{\∫}_{s_l}{\mathrm{PW}}_{sl}\·\frac{PBD\left(s_l,p\right)}{\left|\right|x-x_l\left|\right|}xdx}{{\∫}_{s_l}{\mathrm{PW}}_{sl}\·\frac{PBD\left(s_l,p\right)}{\left|\right|x-x_l\left|\right|}dx},{c_l}^{\′}=\frac{{\∫}_{s_l}{\mathrm{PW}}_{sl}\·\frac{PBD\left(s_l,p\right)}{\left|\right|c-c_l\left|\right|}cdc}{{\∫}_{s_l}{\mathrm{PW}}_{sl}\·\frac{PBD\left(s_l,p\right)}{\left|\right|c-c_l\left|\right|}dc}---\left(9\right)$

Wherein, S_lIt is l-th super-pixel, s_lIt is super-pixel S_lSeed point, x_l', c_l' respectively be update after seed point position and Color value,Measurement pixel is subordinate to the weighting function of seed point degree；

Step 36：Repeat step 33, step 34, step 35, algorithm are intended to optimize an energy function, are defined as formula (10), When the change of energy function in subsequent iteration twice just stops less than a specific threshold, preliminary super-pixel segmentation Complete,

$E_{new}=\underset{l}{\Σ}{\∫}_{I\left(p\right)}{\mathrm{PW}}_{s_l}\·{\mathrm{PBD}}^2(s_l,p)dp---\left(10\right).$

5. probability density according to claim 4 weights the brain MR image segmentation of geodesic distance, it is characterised in that The step 4 includes：

Step 41：Super-pixel after scanning segmentation, if the color standard difference of super-pixel is more than certain threshold value T_c, then to meeting this The super-pixel of condition enters line splitting, and its calculating is defined as：

C(s_l)=λ Std_l＞ T_c (11)

Wherein, Std_lIt is the standard deviation of pixel color in each super-pixel, T_cIt is threshold value, λ is control parameter；

Step 42：It is two classes, the mesh of local FCM to be gathered all pixels in the super-pixel of each needs division again with FCM algorithms Scalar functions are：

$E=\underset{i=1}{\overset{C}{\Σ}}\underset{j=1}{\overset{Q_n}{\Σ}}{{\mathrm{\μ}}_{ij}}^m\left|\right|x_j-v_i|{|}^2---\left(12\right)$

Wherein, C is the number of cluster, Q_nIt is the number of pixel in the super-pixel of Current Scan, μ_ijIt is that j-th pixel belongs to The i fuzzy membership function of cluster, meets constraint μ_ij∈[0,1],M is to act on the power on fuzzy membership Weight function, v_iIt is ith cluster center, x_jIt is the color value of pixel in the super-pixel of Current Scan；

Step 43：Find connected region according to the super-pixel classification results that obtain, and using the pixel in each connected region as A new class, i.e., one new super-pixel block updates the super-pixel classification results matrix；

Step 44：To need division super-pixel repeat step 42, step 43 until all super-pixel for meeting splitting condition all Division is completed.

6. probability density according to claim 5 weights the brain MR image segmentation of geodesic distance, it is characterised in that The step 5 is further：

Using super-pixel as the input of FCM algorithms, clustered on the basis of the super-pixel split, completed brain image Split, the FCM object functions based on super-pixel are：

$E=\underset{i=1}{\overset{C}{\Σ}}\underset{j=1}{\overset{Q}{\Σ}}{{\mathrm{\μ}}_{ij}}^m\left|\right|{\mathrm{\ξ}}_j-v_i|{|}^2---\left(13\right)$

Wherein, C is the number of cluster, and Q is the number of super-pixel in image, μ_ijIt is mould that j-th super-pixel belongs to ith cluster Paste membership function, meets constraint μ_ij∈[0,1],M is to act on the weighting function on fuzzy membership, v_iIt is Ith cluster center, ξ_jIt is super-pixel S_jColor average.