CN108171703A - A kind of method that tracheae tree is automatically extracted from chest CT image - Google Patents
A kind of method that tracheae tree is automatically extracted from chest CT image Download PDFInfo
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Abstract
The invention belongs to the technical field of image processing based on medical image more particularly to a kind of methods that tracheae tree is automatically extracted from chest CT image.Main tracheae and main bronchus are obtained from chest CT image;According to 3D Region growing segmentations mode and main tracheae and main bronchus information have been obtained, has established adaptive threshold 3D Region growing segmentations model and adaptive threshold leak model;Using adaptive threshold 3D Region growing segmentations model and adaptive threshold leak model, the second class tracheae branch of chest CT image is extracted;According to the average information of the second class tracheae branch of extraction, the parameter of adaptive threshold 3D region growings model and adaptive threshold leakage model is adjusted, then extracts the third class tracheae branch of the chest CT image;Based on tracheae tree topology extraction tip tracheae branch has been obtained, the tracheae tree of the chest CT image is obtained.Method provided by the invention improves the tracheae segmentation precision that tracheae tree is extracted from CT images, while reduces extraction time.
Description
Technical field
The invention belongs to the technical field of image processing based on medical image more particularly to one kind from chest CT image from
The method of dynamic extraction tracheae tree.
Background technology
Accurate lung qi pipe tree construction is obtained from CT images to be of great significance in medical field and computer application circle.Face
Bed doctor can be to the common respiratory disease such as chronic obstructive pulmonary disease, bronchiectasis by tracheae parameter and rating information
Pathological analysis and follow-up study is unfolded;Patient can also look into without intrusive virtual bronchoscopy with this;In addition, gas
The one-to-one correspondence of the substates lung structure such as the Guan Shuyu lobes of the lung, lung section anatomically is also the segmentation of dependency structure image and analysis
Important foundation.Therefore, the lung qi pipe segmentation based on CT images is always the hot spot of researcher's concern.
In chest CT image, lung qi pipe totally in tree, is split into left and right main bronchus, then by main tracheae
Leaf, section, sub- segmental bronchus are split into, then is split into rudimentary bronchus and tip bronchus, is stretched into pulmonary parenchyma.Tracheae branch is certainly
Substantially in club shaped structure after main bronchus division, tracheae chamber is in low gray value region, by the tracheal wall of higher gray value surround with
It is isolated with pulmonary parenchyma.
Due to topological features special possessed by lung qi Guan Shu and gray scale textural characteristics, with tracheae tree tissue by
Grade division, tube chamber attenuates, tube wall is thinning, and on CT images, the gray value of tracheal wall continuously decreases, conventionally used for pulmonary parenchyma, liver
Some dividing methods of portion, brain are not ideally suited for the segmentation of lung qi pipe.Tracheorrhaphy based on conventional threshold values growth
It cuts and easily crosses tracheal wall and diffuse into pulmonary parenchyma, form large-scale over-segmentation, i.e., " leak " phenomenon, it is also difficult to effectively identification
Subtle or lesion branch, misses a large amount of detailed information, this is difficult to obtain accurate lung qi Guan Shu.
In current published tracheae tree processing method, patent CN201210423958.2 is increased using multiple dimensioned gray reconstruction
Strong lumen area;On this basis, patent CN201510009239.X utilize multiple dimensioned tubular structure feature extraction tracheae tree, two
Kind method is both needed to the consumption plenty of time;Patent CN201110405950.9 extracts the following bronchus of section grade, institute using fixed threshold
Design leakage processing model is also only for Duan Ji branches, it is difficult to deeper bronchus and subtle tracheae is obtained, more without needle
Processing method is specifically leaked to these branch designs;Patent CN201510224781.7 is rebuild subtle and last using energy function
Tip tracheae, but there is still a need for fixed thresholds to judge tracheae degree of membership and leakage.The above method need stringent artificial parameter setting or
It needs to carry out CT images complicated pretreatment enhancing operation or needs to extract the auxiliary tracheae segmentation such as tubulose structure feature, greatly
Processing time is improved greatly, also constrains tracheae segmentation precision, it is difficult to the segmentation task of CT images under a variety of image-forming conditions is completed,
Seriously constrain reliability and applicability in clinical practice.
Invention content
(1) technical problems to be solved
In order to improve the tracheae segmentation precision that tracheae tree is extracted from CT images, while the extraction time of tracheae tree is reduced,
The present invention provides a kind of method that tracheae tree is automatically extracted from chest CT image.
(2) technical solution
To achieve the above object, the main technical schemes that the present invention uses include:
A kind of method that tracheae tree is automatically extracted from chest CT image, including:
101st, for chest CT image, the main tracheae of tracheae tree and main branch gas are obtained based on 3D Region growing segmentation modes
Pipe;
102nd, based on described in the 3D Region growing segmentations mode, the average information of the main tracheae obtained and acquisition
The average information of main bronchus is established to extract the adaptive threshold 3D region growings model of the second class tracheae branch and adaptive
Answer sub-threshold leakage model;
103rd, according to the adaptive threshold 3D region growings model and the adaptive threshold leak model, described in extraction
Second class tracheae branch of chest CT image;
104th, according to the average information of the second class tracheae branch of extraction, the adaptive threshold 3D regions life is adjusted
The parameter of long model and adaptive threshold leakage model;
105th, it according to the adaptive threshold 3D region growings model of adjustment and the adaptive threshold leak model, carries
Take the third class tracheae branch of the chest CT image;
106th, tracheae tree is formed according to the main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch
Topological structure, extract the tip tracheae branch of the tracheae tree, obtain the tracheae tree of the chest CT image.
Further, described 101 include:
1011st, the chest CT image is pre-processed;
1012nd, image binaryzation processing is carried out to the pretreated chest CT image, obtains the chest after binaryzation
The foreground area of portion's CT images and background area;
1013rd, centered on the center of chest CT image described after binaryzation, the corresponding area of the foreground area is selected
A maximum prospect connected domain;
1014th, judge whether the prospect connected domain of selection meets the first preset condition, if satisfied, then by the prospect
Connected domain divides initial connected domain as main tracheae, and the prospect connected domain central point is as initial seed point s0;
1015th, the initial seed point s is handled based on the 3D Region growing segmentations mode0, the main gas of acquisition tracheae tree
Pipe.
Further, described 1015 include:
S01, by the initial seed point s0Segmentation queue is added in, based on the 3D Region growing segmentations mode, to chest bottom
Direction carries out 3D Region growing segmentations;
S02, in the ith iteration of the 3D Region growing segmentations mode, take out it is described segmentation queue in the first kind
Sub- pointThe pixel average gray value of the prospect connected domain is not more than default first gray threshold TtraAnd unlabelled institute
It states26 neighborhood territory pixel point w1,w2,...,wmIt marks as main tracheae;
S03 and using the 26 neighborhood territory pixel point as new seed pointAdd in the segmentation queue;After
It is described to divide removing for queue in continuous processing ith iterationExcept remaining seed pointUntil the ith changes
Whole seed points in generation are all handled through the 3D Region growing segmentations mode;
S04, iterations add 1 and repeat the iterative process of ith, and the main tracheae label described in the CT images is most lower
When side is split into two connected domains, the main tracheae of the tracheae tree is obtained, wherein, the main tracheae is upper close to the one end in oral cavity
Side, one end far from the oral cavity are downside, and the main tracheae lower side is the distalmost end of the main tracheae far from the oral cavity.
Further, it described 101 further includes;
The main bronchus includes left principal bronchus and right principal bronchus, is obtained based on the 3D Region growing segmentations mode
The step of left principal bronchus, includes:
M01, using the central point of the connected domain on the left of the main tracheae anatomy as initial kind of left principal bronchus
Sub- point Sleft;
M02, the pixel average gray value μ according to the main tracheae region of acquisitioniniAnd standard deviation sigmainiDetermine institute
State the second gray threshold T of left principal bronchus segmentationbro, Tbro=μini+2σini;
M03, by initial seed point SleftSegmentation queue is added in, based on the 3D Region growing segmentations mode, and with reference to institute
State whole seed points in the processing segmentation queue of the second gray threshold, the left principal bronchus label division described in the CT images
During for two connected domains, the left principal bronchus segmentation terminates, and obtains the left principal bronchus;
And the step of based on the 3D Region growing segmentations mode obtaining the right principal bronchus, includes:
N01, using the central point of the connected domain on the right side of the main tracheae anatomy as initial kind of right principal bronchus
Sub- point Sright;
N02, the pixel average gray value μ according to the right main tracheae region of acquisitioniniAnd standard deviation sigmainiIt determines
Second gray threshold T of main bronchus segmentationbro, Tbro=μini+2σini;
N03, by initial seed point SrightSegmentation queue is added in, based on the 3D Region growing segmentations mode, and with reference to institute
State whole seed points in the processing segmentation queue of the second gray threshold, the right principal bronchus label division described in the CT images
During for two connected domains, the right principal bronchus segmentation terminates, and obtains the right principal bronchus.
Further, described 102 include:Establish the adaptive threshold 3D region growing models:
It specifically includes:
1021:Obtain initial segmentation seed point set;
1022:Obtain gray threshold;
1023:Tracheae degree of membership rule is determined according to the gray threshold, including:
(1) first is subordinate to rule:The gray value I of seed point wwNo more than third gray threshold T;
(2) second are subordinate to rule:The gray value I of the seed point wwNo more than the 4th gray threshold, the 4th gray scale
Threshold value is the gray value T for the seed point v for generating the seed point wv;
(3) third is subordinate to rule:The pixel that tracheae is marked as in the 6 neighborhood territory pixel points of the seed point w is no less than 4
It is a, i.e.,:Wherein L represents dividing mark,Represent the 6 of the seed point w
The set of neighborhood territory pixel point;
(4) the 4th are subordinate to rule:The 6 neighborhood territory pixel point gray values of the seed point w and seed point w and average value it is little
In the third gray threshold T, and in the 6 neighborhood territory pixel points of the seed point w, gray value is not more than the third gray threshold
The pixel of T is no less than 4, i.e.,:
1024:Obtain growing strategy:In ith iteration, segmentation first seed point of queue is taken outBy described inSymbol
It is regular and unlabelled described to close the tracheae degree of membership26 neighborhood territory pixel point w1,w2,...,wmLabeled as tracheae, and will
The 26 neighborhood territory pixel point w1,w2,...,wmAs new seed pointAdd in the segmentation queue;It continues with
Remaining seed point in the segmentation queueUntilIt is all finished by growth, into i+1 time repeatedly
Generation;This step is repeated until meeting termination rules.
Further, the initial segmentation seed point set includes:The left principal bronchus is divided in iteration ends
Whole seed points and the right principal bronchus in queue divide whole seed points in queue in iteration ends.
Further, it described 102 further includes:Establish the adaptive threshold leak model;
It specifically includes:
1025:Leakage detection method:Record during 3D Region growing segmentations in described 1024 what iteration every time was divided
New seed points C1,C2,...,CNIf not leaked during iterative segmentation, with the adaptive leak threshold of this iterative segmentation
Calculate the required adaptive leak threshold of leak judgement method;
Segmentation leakage phenomenon includes:Drastically leakage, progressive leakage and end leakage;
The adaptive leak threshold includes:
(1) for the first leak threshold E of drastically leakage phenomenon setting1:First leak threshold is set as
In M-R to the M times iterative process, adjacent secondary iteration generates the maximum value of new seed points difference, i.e.,:
(2) for the second leak threshold E of the progressive leakage phenomenon setting2:Second leak threshold is set as
In M-R to the M times iterative process, the maximum value of new seed points is generated, i.e.,:
(3) for the third leak threshold E of end leakage setting3With the 4th leak threshold E4:Iv-th iteration is grown
E3And E4It is respectively set as:With
The Iter1For the first iteration threshold, the Iter1=R/2, wherein R be adaptive scale, R=(xmax-xmin+
ymax-ymin)/2, the xmin, ymin, xmax, ymax, the last layer of cleavage layer is marked in length and width two-dimensional directional for the main tracheae
Maximum and min coordinates;
The Iter2For secondary iteration threshold value, the Iter2=R/4;
1026:Leak judgement rule is determined, if the 3D Region growing segmentations of current region have completed n-th and changed
Generation, and last leakage phenomenon is happened at the M times iteration, then the leakage rule is:
Further, which is characterized in that described 102 further include:
1027:Determine leakage processing method, including:
10271:When iv-th iteration segmentation leaks, N-Iter is calculated2With the maximum value N of M+1max;If the N
Secondary iteration is exactly the most last iteration of the main bronchus growth segmentation, then Nmax=N;
10272:Remove N described in segmentation queuemaxThe tracheae label of whole seed points caused by after secondary iteration;
10273:To the NmaxThe seed point set of secondary iterationUsing the adaptive threshold
3D region growings model carries out simulation growth segmentation, and the maximum iteration of simulation growth cutting procedure is set as Iter1;
10274:Single trachea-seed point v in described 10273iFive leak threshold E of simulation growth regulation5By viSurrounding is marked
The neighborhood territory pixel points for being denoted as tracheae determine, count the seed point vi6 neighborhood territory pixel points and 18 neighborhood territory pixels point in gas
Pipe reference numerals, and weight W and obtain E5:
10275:Record the seed point viThe new seed points c caused by each iteration in simulation growth segmentation1,
c2,...,cR/2If adjacent time the new seed points difference is not less than E5, then fromThe middle removal viIf adjacent time described
New seed points difference is less than E5Then retain the vi;
10276:Described 10272 are repeated, after growth segmentation is all simulated, after screeningAs it is described from
Adapt to the initial segmentation seed point set of threshold value 3D region growing model next iterations.
Further, the termination rules are:
When adaptive threshold 3D region growings model growth cutting procedure is examined by the adaptive threshold leak model
It measures when there is no new seed point that can be marked as tracheae in growth cutting procedure when leaking or in described 1024, institute
Adaptive threshold 3D region growing models are stated to terminate.
Further, described 103 include:
1031:The adaptive gray threshold of left bronchus tree is worth to according to the average gray of the left principal bronchus
Tleft, and by TleftThe tracheae degree of membership rule is determined as the third gray threshold, and according to the third gray threshold;
1032:Extract seed point set S caused by the most last iteration of the left principal bronchus growth segmentationleftIf
The seed point set that the fixed adaptive threshold 3D region growings model carries out simulation growth segmentation is Spre=Sleft;
1033:The S is screened using the leakage processing method of the adaptive threshold leak modelpre, obtain initial segmentation
Seed point set S 'pre, wherein, the weight W is set as R;
1034:The S ' is divided using adaptive threshold 3D region growings model growthpre;
1035:Growth using the leak judgement method detection described 1034 of the adaptive threshold leak model was divided
If the growth cutting procedure leaks, the seed of the simulation growth segmentation is obtained using the leakage processing method for journey
Point set Spre, perform 1033;
If the growth cutting procedure does not leak, 1034 are continued to execute, until the adaptive threshold 3D regions are given birth to
Model terminates, and completes to extract the second Lei Zuo tracheaes branch from the CT images;
The mode of the second Lei Zuo tracheaes branch is obtained based on 1031-1035 described above, by the TleftIt replaces with
Tright, the SleftReplace with Sright, complete to extract the second Lei You tracheaes branch from the CT images, with described in realization
The extraction of second class tracheae branch.
Further, described 105 include:
1051:Extraction has grown the whole edge pixel points of the second class tracheae branch divided and finished as seed point
Set Slocal, while by the SlocalIt is set as the seed point set S of the simulation growth segmentationpre;
1052:By the 1051 seed point set SpreThe gray value of each seed point itself be set as the seed point
The local gray level threshold value of segmentation, and determine tracheae degree of membership rule according to the local gray level threshold value;
1053:The S is screened using the leakage processing methodpre, obtain initial segmentation seed point set S 'pre;
1054:Using S ' described in the adaptive threshold 3D region growing model treatments of adjustmentpre;
1055:Described 1054 growth is detected using the leak judgement method of the adaptive threshold leak model of adjustment
Cutting procedure if the growth cutting procedure leaks, is obtained using the leakage processing method of the leak model of adjustment
The seed point set S of the simulation growth segmentationpre, perform described 1053;If the growth cutting procedure does not leak, after
It is continuous to perform described 1054, until the adaptive threshold 3D region growings model of the adjustment terminates, complete to third class tracheorrhaphy
The extraction of branch.
Further, described 106 include:
1061:Tracheae tree is formed according to the main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch
Topological structure, obtain the initial seed point of the whole tip tracheae dendritic growths segmentations of the tracheae tree and the initial seed point
Initial growth direction, including:
10611:Calculate the source distance field and distance from boundary field of the tracheae tree;
10612:Extract all pixel S with local maximum of the tracheae tree source distance fieldend={ s1,
s2,...,snAs the tip tracheae dendritic growth segmentation initial seed point;
10613:To any initial seed point v0∈Send, find the seed point v026 neighborhood territory pixel points in have part
The pixel u of maximum boundary distance field then sets the seed point v0Initial growth direction vectorFor:
1062:By the seed point v0Add in segmentation queue;
1063:Determine the direction of growth of the tip tracheae branch:To by direction of growth vectorIt obtains
The seed point v obtainedi26 neighborhood territory pixel point w,It is the seed
Point viThe direction vector of the seed point w is grown into, it is describedWill simultaneously with it is describedWithKeep close, the growth side
It is to term restriction:
1064:Determine tip tracheae branch degree of membership rule:
If the seed point viThe 26 neighborhood territory pixel points for meeting 1063 condition beDue to the end
The diameter of tip tracheae branch is very small, and the pixel of the tip tracheorrhaphy bronchial lumen is more most than the tip tracheae branch
Several 26 neighborhood territory pixel point gray values are low, and appointing in 1 26 neighborhood territory pixel point for meeting condition of selection is only needed in comparing every time
Neighborhood territory pixel point, specific rules are set as:
1065:In ith iteration, first seed point v of queue is taken outi, search the vi26 neighborhood territory pixel points in institute
There is unlabelled pixelSelection meets the neighborhood territory pixel point of the tip tracheae branch degree of membership ruleBy described inAs new seed point vi+1Add in segmentation queue;
1066:Repeat described 1065;If described 1065 have not had the generation of new seed point or described 1062 initial seed point
The R times iteration is completed, stops the growth to the initial seed point;
1067:To the SendIn all initial seed points carry out growth segmentation using described 1065, complete to the end
The extraction of tip tracheae branch.
(3) advantageous effect
The beneficial effects of the invention are as follows:
A kind of method that tracheae tree is automatically extracted from chest CT image of the present invention, including:For chest CT image, it is based on
3D Region growing segmentation modes obtain the main tracheae and main bronchus of tracheae tree;Based on 3D Region growing segmentations mode, obtain
The average information of the average information of main tracheae and the main bronchus obtained is established to extract the adaptive of the second class tracheae branch
Threshold value 3D region growings model and adaptive threshold leak model;According to adaptive threshold 3D region growings model and adaptive thresholding
It is worth leak model, extracts the second class tracheae branch of chest CT image;According to the average information of the second class tracheae branch of extraction,
Adjust the parameter of adaptive threshold 3D region growings model and adaptive threshold leakage model;According to the adaptive threshold 3D of adjustment
Region growing model and adaptive threshold leak model extract the third class tracheae branch of chest CT image;According to main tracheae, master
The topological structure of bronchus, the second class tracheae branch and third class tracheae branch composition tracheae tree extracts the tip gas of tracheae tree
Pipe branch obtains the tracheae tree of the chest CT image.Method provided by the invention improves the extraction tracheae tree from CT images
Tracheae segmentation precision, while reduce the extraction time of tracheae tree.
Description of the drawings
Fig. 1 is to the 2D schematic diagrames of tracheae branch taxonomy to be extracted in the embodiment of the present invention;
Fig. 2 is the flow chart that the embodiment of the present invention automatically extracts tracheae tree method from chest CT image;
Fig. 3 is the main trachea-seed point schematic diagram of selection of the embodiment of the present invention;
Fig. 4 is the schematic diagram of the different leakage phenomenons of the embodiment of the present invention;
Fig. 5 is the process schematic that tracheae tree is automatically extracted in the slave chest CT image of the embodiment of the present invention;
Fig. 6 is the extraction result schematic diagram for CT image window tracheae trees in Fig. 1;
Fig. 7 is that method using the present invention extracts result schematic diagram to the 3D tracheaes tree of the chest CT image of different condition.
Specific embodiment
In order to preferably explain the present invention, in order to understand, below in conjunction with the accompanying drawings, by specific embodiment, to this hair
It is bright to be described in detail.
(scanning device, breathing phases, reconstruction parameter, noise grade), different patients and different tissues are imaged in different CT
Under the conditions of lesion, gray scale, form, the position difference of tracheae are very big.As shown in Figure 1, by tracheae tree tissue include branch according to
Feature in CT images is divided into four classes, including:
(1) as shown in Figure 1A, main tracheae and main bronchus, i.e. first kind tracheae branch:The tracheae chamber of low gray value is high
The tube wall of gray value is closely surrounded and is separated with pulmonary parenchyma;
(2) as shown in Figure 1B, the second class tracheae branch includes leaf, section and the sub- other tracheae branch of section grade:Main bronchus
Pulmonary parenchyma is stretched by division, tube chamber and tube wall grey-scale contrast are relatively high;But the factors such as imaging and lesion make localized branches
Variation takes place in place's intensity profile, and tracheae is lower with peripheral region contrast;
(3) as shown in Figure 1 C, third class tracheae branch, i.e., rudimentary tracheae branch:Second class tracheae forms grade by division
Not lower tracheae branch, the intensity profile of these branches and first and second class branch gap bigger, tube chamber gray scale is more between each branch
It is uneven;
(4) as shown in figure iD, tip tracheae branch, i.e. the 4th class tracheae branch:Third class tracheae is expanded to by division
The tracheae tree tip that peripheral lung is formed;Diameter is no more than 2 pixels on CT images;Due to lesion or local volumetric effect shadow
It rings, the raising of tube chamber gray scale is apparent with second and third class tube chamber gray difference.
Under normal conditions, for tracheae tree after dividing step by step, tracheal wall is thinning dimmed on CT images, it is difficult to distinguish its institute
The tracheae chamber of encirclement and external pulmonary parenchyma.Tracheae tree gassy in air-breathing place CT images and turgor, tube chamber gray scale are very low,
Tube wall gray scale is higher, and rudimentary tracheae is also more easy to identify;And expiration phase CT images are due to air exclusion in tracheae tree, tube chamber receipts
Contracting, rudimentary tracheae are almost closed, and tracheae tree intensity profile and air-breathing place difference are larger.Therefore, according to man-machine interactively or system
One preset threshold value can greatly improve complexity and the processing time of tracheae extraction;Improperly artificial setting may both cause big
The mistake segmentation (leakage) of range, it is also possible to leak in order to avoid accidentally dividing and divide a large amount of branches.
In view of the above-mentioned problems, the present invention determines sequential extraction process according to the tracheae branch classification concluded, according to image
Self information is obtained according to extracted tracheae branching characteristic and growth cutting procedure comprising adaptive in different extraction steps
The model of parameter and rule obtains complete tracheae tree.
Embodiment 1
The technical issues of the present embodiment solves is to provide a kind of method that tracheae tree is automatically extracted in CT images from chest, profit
The required man-machine interactively of the prior art and parameter setting are avoided with adaptive threshold, to adapt to a variety of image-forming conditions, lesion feelings
The tracheae tree segmentation task of condition.As shown in Figure 1, the present invention provides a kind of sides that tracheae is automatically extracted from chest CT image
Method, technical solution are as follows:
101st, for chest CT image, the first kind tracheae branch of tracheae tree is obtained based on 3D Region growing segmentation modes,
I.e. main tracheae and main bronchus, wherein, main bronchus includes left principal bronchus and right principal bronchus;
102nd, based in 3D Region growing segmentations mode, the average information of main tracheae and the main bronchus of acquisition that obtain
Between information, establish to extract the adaptive threshold 3D region growings model of the second class tracheae branch and adaptive threshold tunnelling ray
Type;
103rd, according to adaptive threshold 3D region growings model and adaptive threshold leak model, chest CT image is extracted
Second class tracheae branch, the second class tracheae branch include leaf, section and the sub- other tracheae branch of section grade;
104th, according to the average information of the second class tracheae branch of extraction, adjustment adaptive threshold 3D region growings model and
Adaptive threshold reveals the parameter of model;
105th, according to the adaptive threshold 3D region growings model and adaptive threshold leak model of adjustment, chest CT is extracted
The third class tracheae branch of image;
106th, opening up for tracheae tree is formed according to main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch
Structure is flutterred, extracts the tip tracheae branch of tracheae tree, obtains the tracheae tree of the chest CT image.
Embodiment 2
Main tracheae and main bronchus are surrounded and detached with pulmonary parenchyma by more complete highlighted tracheal walls, are extracted from CT images
Main tracheae and main bronchus do not have to consider leakage situation, and difficulty is relatively low, therefore use common threshold value 3D Region growing segmentation sides
Method can effectively obtain main tracheae and main bronchus.
In the present embodiment, first kind tracheae branch is obtained from chest CT image, including:
First, main tracheae is obtained from chest CT image:
1011st, chest CT image is read in, for the influence that CT image noises is avoided to divide subsequent growth, to chest CT image
Carry out the pre-treatment job that three dimension scale is the Gaussian smoothing of σ=0.5mm;
1012nd, one layer of CT image of pretreatment potruncus CT images is obtained from chest top to chest bottom direction, and to the tomographic image
Image binaryzation processing is carried out, obtains foreground area and the background area of binaryzation potruncus CT images, wherein, image binaryzation
Including any binarization method, as shown in figure 3, the low dark gray values region of image is set to foreground area, highlighted gray value is put
For background area;
1013rd, since the initial layers position of chest scan can reach oral cavity, after obtaining CT images and binaryzation by top, meeting
Generate the very irregular shape prospect connected domain (oral cavity and nasal cavity are shown in Fig. 3 A) of large area or far from before the two dimension synusia center
Scape connected domain (bottleneck throat or apex pulmonis are shown in Fig. 3 B);These are not the present embodiment area of interest, therefore, are selected in the present embodiment
It selects centered on the center of binaryzation potruncus CT images, setting image length and width each 1/4 is the window of the length of side, in this window,
Select area the maximum in multiple prospect connected domains (see Fig. 3 C);
1014th, judge whether the prospect connected domain of selection meets the first preset condition, if satisfied, then making prospect connected domain
Divide initial connected domain for main tracheae, prospect connected domain central point is as initial seed point s0, wherein, the first preset condition is is somebody's turn to do
The perimeter l and area a of prospect connected domain meet condition:l2/ 4 π a >=0.6 (see Fig. 3 D);
1015th, initial seed point s is handled based on 3D Region growing segmentation modes0, the main tracheae of tracheae tree is obtained, it is specific to walk
Suddenly it is (attached to be not shown in figure):
S01, by initial seed point s0Segmentation queue is added in, based on 3D Region growing segmentation modes, is carried out to chest bottom direction
3D Region growing segmentations calculate the pixel average gray value μ of initial connected domaininiAnd standard deviation sigmaini, 3D Region growing segmentations adopt
It is with the first gray threshold:Ttra=μini+2σini;
S02, in the ith iteration of 3D Region growing segmentation modes, take out segmentation queue in first seed pointIt will
The pixel average gray value of prospect connected domain is not more than default first gray threshold TtraAnd unlabelled seed point26 neighborhoods
Pixel w1,w2,...,wmIt marks as main tracheae;
S03 and using 26 neighborhood territory pixel points as new seed pointAdd in segmentation queue;Continue with this
Divide removing for queue in ith iterationExcept remaining seed pointUntil whole seeds of the ith iteration
Point is all handled through 3D Region growing segmentation modes;
S04, iterations add 1 and repeat the iterative process of ith, when tracheae label lower side main in CT images is split into
During two connected domains, main tracheae growth segmentation finishes, and obtains the main tracheae of tracheae tree.Wherein, main tracheae is close to the one end in oral cavity
For upside, one end far from oral cavity is downside, and main tracheae lower side is distalmost end of the main tracheae far from oral cavity.
2nd, main bronchus is obtained from chest CT image:
Main bronchus includes left principal bronchus and right principal bronchus, and left main branch gas is obtained based on 3D Region growing segmentation modes
The step of pipe, includes (attached to be not shown in figure):
M01, using the central point of the connected domain on the left of main tracheae anatomy as the initial seed point S of left principal bronchusleft;
M02, the pixel average gray value μ according to the main tracheae region of acquisitioniniAnd standard deviation sigmainiDetermine left main branch
Second gray threshold T of tracheae segmentationbro, Tbro=μini+2σini;
M03, by initial seed point SleftSegmentation queue is added in, based on 3D Region growing segmentation modes, and combines the second ash
Whole seed points in threshold process segmentation queue are spent, when left principal bronchus label is split into two connected domains in CT images,
Left principal bronchus segmentation terminates, and obtains left principal bronchus;
The step of obtaining right principal bronchus based on 3D Region growing segmentation modes includes (attached to be not shown in figure):
N01, using the central point of the connected domain on the right side of main tracheae anatomy as the initial seed point S of right principal bronchusright;
N02, the pixel average gray value μ according to the right main tracheae region of acquisitioniniAnd standard deviation sigmainiDetermine main branch
Second gray threshold T of tracheae segmentationbro, Tbro=μini+2σini;
N03, by initial seed point SrightSegmentation queue is added in, based on 3D Region growing segmentation modes, and combines the second ash
Whole seed points in threshold process segmentation queue are spent, when right principal bronchus label is split into two connected domains in CT images,
Right principal bronchus segmentation terminates, and obtains right principal bronchus.
Embodiment 3
In the present embodiment, according to the main tracheae and the tracheae intensity profile of main bronchus, space scale, segmentation obtained
The average informations such as procedural information establish adaptive threshold 3D region growings model and adaptive threshold leak model.
Adaptive threshold 3D region growing models are established to be as follows:
1021:Obtain initial segmentation seed point set;Initial segmentation seed point set includes:Left principal bronchus is at iteration end
Whole seed points and right principal bronchus when only in segmentation queue divide whole seed points in queue in iteration ends.
1022:Obtain gray threshold;The present embodiment sets certain gray threshold to determine tracheae to the segmentation of tracheae tree
The upper limit of segmentation, while gray value more than the upper limit but is also divided into tracheae by the pixel that trachea area closely surrounds, so as to
Extract the raised tracheae branch of tube chamber gray scale in Fig. 1 C, Fig. 1 D.
1023:Tracheae degree of membership rule is determined according to gray threshold, including:
(1) first is subordinate to rule:The gray value I of seed point wwNo more than third gray threshold T;
(2) second are subordinate to rule:The gray value I of seed point wwNo more than the 4th gray threshold, the 4th gray threshold is production
The gray value T of the seed point v of raw seed point wv;
(3) third is subordinate to rule:The pixel that tracheae is marked as in the 6 neighborhood territory pixel points of seed point w is no less than 4,
I.e.:Wherein L represents dividing mark,Represent 6 neighborhoods of seed point w
The set of pixel;
(4) the 4th are subordinate to rule:The 6 neighborhood territory pixel point gray values of seed point w and seed point w and average value no more than the
Three gray threshold T, and in the 6 neighborhood territory pixel points of seed point w, pixel of the gray value no more than third gray threshold T is no less than 4
It is a, i.e.,:
1024:Obtain growing strategy:In ith iteration, segmentation first seed point of queue is taken outIt willMeet gas
Pipe degree of membership is regular and unlabelled26 neighborhood territory pixel point w1,w2,...,wmLabeled as tracheae, and by 26 neighborhood territory pixel points
w1,w2,...,wmAs new seed pointAdd in segmentation queue;Continue with remaining seed in segmentation queue
PointUntilIt is all finished by growth, into i+1 time iteration;This step is repeated until meeting
Termination rules;
Termination rules are:When adaptive threshold 3D region growings model grows cutting procedure by adaptive threshold leak model
It detects when there is no new seed point that can be marked as tracheae in growth cutting procedure when leaking or in 1024, it is adaptive
Threshold value 3D region growing models is answered to terminate.
Because the factors such as imaging and lesion influence, difference etc. in the tracheae gray feature, same CT images between different CT images
Grade, the tracheae gray feature of different location are different, and the adaptive threshold 3D region growing models of the present embodiment can basis
The gray feature of first kind tracheae calculates the threshold value of the second class tracheae segmentation, and third class is voluntarily determined according to each tracheae branch location
The threshold value of branch's local segmentation.
In tracheae tree cutting procedure, by tracheae chamber cross thinning dimmed tracheal wall enter pulmonary parenchyma mistake segmentation be referred to as let out
Leakage.Control leakage is to extract the significant challenge that tracheae tree faces, and segmentation leakage can cause extraction result form, textural anomaly, shadow
Ring the clinical analysis of tracheae tree and other application expansion.Different CT images are because of factors such as imaging, lesions, in any branch, any
Position can may all leak.Artificial given threshold condition excessively can strictly omit a large amount of tracheae branches;Excessively it is loose then without
Method effectively avoids leakage, and artificial threshold value can not be widely used in the CT images of different condition.The present embodiment is using adaptive
Sub-threshold leakage model is answered, the adaptive threshold and rule needed according to cutting procedure information acquisition model is to avoid leakage, it is ensured that
It can continue to obtain more tracheae branches, and expand the scope of application of method on this basis.
In the present embodiment, the step of establishing adaptive threshold leak model is specific as follows:
1025:If the 3D Region growing segmentations of current region have completed iv-th iteration, and last leakage phenomenon occurs
In the M times iteration, leakage detection method is:The novel species that iteration is divided every time during 3D Region growing segmentations in record 1024
Son points C1,C2,...,CNIf not leaked during iterative segmentation, calculated with the adaptive leak threshold of this iterative segmentation
The required adaptive leak threshold of leak judgement method;
The present embodiment will divide leakage phenomenon conclusion and be divided into three classes:
(1) it drastically leaks:As shown in Figure 4 A, the unexpected hair of new seed points caused by certain iteration in cutting procedure is grown
It is raw to increase severely, indicate that the secondary growth has passed over tracheal wall and diffuses into pulmonary parenchyma;
(2) progressive leakage:As shown in Figure 4 B, seed points are gradually increased and are run up to very big caused by each iteration
Value, different from the first kind, adjacent generation seed points difference keeps lower value, but a large amount of lung parenchyma tissues are accidentally divided into gas at this time
Pipe;
(3) end leaks:When region growing is close to tracheae end, new seed points are gradually decrease to 0 under normal circumstances,
But as shown in Figure 4 C, if seed points become larger again since small value;Region growing terminates after iteration several times, but at this time
In part, tracheae terminal region leaks.
Adaptive leak threshold includes:
(1) for the first leak threshold E of drastically leakage phenomenon setting1:First leak threshold is set as M-R to M
In secondary iterative process, adjacent secondary iteration generates the maximum value of new seed points difference, i.e.,:
(2) for the second leak threshold E of progressive leakage phenomenon setting2:Second leak threshold is set as M-R to M
In secondary iterative process, the maximum value of new seed points is generated, i.e.,:
(3) for the third leak threshold E of end leakage setting3With the 4th leak threshold E4:The E of iv-th iteration growth3
ForE4It is set as
Iter1For the first iteration threshold, Iter1=R/2, wherein R be adaptive scale, R=(xmax-xmin+ymax-ymin)/
2, xmin, ymin, xmax, ymax, the last layer of cleavage layer is marked in the maximum and min coordinates of length and width two-dimensional directional for main tracheae;
Here the last layer of main tracheae label cleavage layer is meant:When main tracheae divides, left and right main bronchus is generated, for master
Main tracheae is exactly last layer for bronchus;
Iter2For secondary iteration threshold value, Iter2=R/4;
1026:Determine leak judgement rule, if the 3D Region growing segmentations of current region have completed iv-th iteration, and
Last leakage phenomenon is happened at the M times iteration, then leaks rule and be:
First leakage rule:Current iteration generates new seed points difference more than the first adaptive leakage threshold with preceding an iteration
Value, and current iteration and the difference of last effective iterations are more than the first adaptive iteration threshold value;
Second leakage rule:Current iteration generates new seed points more than the second adaptive leak threshold;
Third leakage rule:It is more than the adaptive leak threshold of third or current iteration that current iteration, which generates new seed points,
The whole new seeds divided in preceding first adaptive iteration threshold number are counted more than the 4th adaptive leak threshold;
The formula for leaking rule is as follows:
1027:Determine leakage processing method, including:
10271:When iv-th iteration segmentation leaks, N-Iter is calculated2With the maximum value N of M+1max;If n-th changes
In generation, exactly main bronchus grew the most last iteration divided, then Nmax=N;
10272:Remove N in segmentation queuemaxThe tracheae label of whole seed points caused by after secondary iteration;
10273:To NmaxThe seed point set of secondary iterationIt is given birth to using adaptive threshold 3D regions
Long model carries out simulation growth segmentation, and the maximum iteration of simulation growth cutting procedure is set as Iter1;
10274:Single trachea-seed point v in 10273iFive leak threshold E of simulation growth regulation5By viSurrounding is marked as
The neighborhood territory pixel points of tracheae determine, count seed point vi6 neighborhood territory pixel points and 18 neighborhood territory pixels point in tracheae reference numerals,
And it weights W and obtains E5:
Seed point viThe labeled neighborhood point of surrounding is more, it was demonstrated that seed point viThe space scale of the tracheae branch is bigger;
10275:Record seed point viThe new seed points c caused by each iteration in simulation growth segmentation1,c2,...,
cR/2If adjacent secondary new seed points difference is not less than E5, then fromMiddle removal viIf adjacent secondary new seed points difference is less than E5Then
Retain vi;
10276:10272 are repeated, after growth segmentation is all simulated, after screeningAs adaptive threshold
The initial segmentation seed point set of 3D region growing model next iterations.
Embodiment 4
Bronchial tree middle period, section, the sub- other bronchus intensity profile of section grade are more uniform, using based on global threshold from
Threshold value 3D region growings model extraction the second class tracheae branch is adapted to, leakage phenomenon may occur for localized region using adaptive
Sub-threshold leakage model is handled.The present embodiment separately extracts left and right bronchial tree, including:
1031:According to the average gray value μ of left (right side) main bronchusleft, σleft(μright, σright) obtain left (right side) branch gas
The adaptive gray threshold T of Guan Shuleft=μleft+2σleft(Tright=μright+2σright), and by Tleft(Tright) as third
Gray threshold, and determine tracheae degree of membership rule according to third gray threshold;
1032:Seed point set S caused by the most last iteration of left (right side) main bronchus growth segmentation of extractionleft
(Sright), it sets adaptive threshold 3D region growings model and carries out the seed point set of simulation growth segmentation as Spre=Sleft(Spre
=Sright);
1033:S is screened using the leakage processing method of adaptive threshold leak modelpre, obtain initial segmentation seed point set
Close S 'pre, wherein, the second class tracheae branch W is needed to take higher value in the present embodiment, so weight W is set as R,;
1034:Segmentation S ' is grown using adaptive threshold 3D region growings modelpre;
1035:Using the growth cutting procedure of the leak judgement method detection 1034 of adaptive threshold leak model, if raw
Long cutting procedure leaks, and utilizes the seed point set S for leaking processing method acquisition simulation growth segmentationpre, perform 1033;
If growth cutting procedure does not leak, 1034 are continued to execute, until adaptive threshold 3D regions life model terminates,
Left (right side) the tracheae branch of the second class is extracted in completion from CT images;To realize the extraction of the second class tracheae branch.
Embodiment 5
Third class tracheae branch has been fully inserted into pulmonary parenchyma, although with using the adaptive threshold based on global threshold
3D region growings model cannot obtain these branches, but these branches are connected with tracheae tree, each branch internal, adjacent pixel
The gray scale of point is still close to each other, therefore, is grown since extracted tracheae tree table face, according to the similarity of adjacent pixel gray scale
It can be found that potential trachea area.The intensity profile of third class tracheae branch is more complicated, is also easier to leak, be still to herein
Using adaptive threshold leak model.
Extraction third class tracheae branch is as follows:
1051:Extraction has grown the whole edge pixel points of the second class tracheae branch divided and finished as seed point set
Slocal, while by SlocalIt is set as the seed point set S of simulation growth segmentationpre;
1052:By the seed point set S in 1051preThe gray value of each seed point itself be set as the seed point point
The local gray level threshold value cut, and local gray level threshold value determines tracheae degree of membership rule according to this;
1053:S is screened using the leakage processing method of adaptive threshold leak modelpre, obtain initial segmentation seed point set
Close S 'pre, wherein, smaller value should be taken for the weight W of third class tracheae branch in the present embodiment, so weight W is set aslx、ly、lzIt is chest CT image single pixel in three directions
On size;
1054:Using the adaptive threshold 3D region growing model treatments S ' of adjustmentpre;
1055:Growth using the leak judgement method detection 1054 of the adaptive threshold leak model of adjustment was divided
Journey if growth cutting procedure leaks, is simulated using the leakage processing method of the adaptive threshold leak model of adjustment
Grow the seed point set S of segmentationpre, perform 1053;If growth cutting procedure does not leak, 1054 are continued to execute, until adjusting
Whole adaptive threshold 3D region growings model terminates, and completes the extraction to third class tracheae branch.
Embodiment 6
Tip tracheae branch can not effectively be extracted using adaptive threshold 3D region growing models, using tracheae
Tree topology determines the direction in space of each tip tracheae branch, under the constraint of this direction in space, is compared according to pixel grey scale
Degree gradually extracts tip tracheae branch.
Tracheae topological structure refers to comprising the system including tracheae center path, branch's grade, branch's endpoint, branch 3D directions
Row morphology, anatomy and geometry structure.Although the intensity profile of tip tracheae branch and other class tracheae intensity profile are poor
It is different larger, but its tube chamber is still more slightly lower than tube wall gray value;Tip tracheae branch approximation is in rodlike, and same branch's starting terminates,
Or the sub- grade bronchus after division is close with the parent bronchus direction before division;Such tracheal diameter is no more than 2 pixels.Cause
This, continues search in CT image relays along tracheae branch direction has been extracted, searches for the trachea area for meeting above-mentioned gray feature, can be with
Obtain including the tracheae tree of more final words.
Do not need to obtain complete topological structure in practical application, one embodiment of the invention only need tracheae branch endpoint and
Center path point passes through source distance field DFS (english abbreviation of Distance Field of Source) and distance from boundary field DFB
(english abbreviation of Distance Field of Boundary) can quick obtaining information needed.
In the present embodiment, tracheae is formed according to main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch
The topological structure of tree extracts the tip tracheae branch of tracheae tree, is as follows:
1061:Opening up for tracheae tree is formed according to main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch
Structure is flutterred, obtains the initial life of the initial seed point and the initial seed point of the whole tip tracheae dendritic growth segmentations of tracheae tree
Length direction, including:
10611:Calculate the DFS and DFB of tracheae tree;
10612:Extract all pixel S with local maximum of tracheae tree DFSend={ s1,s2,...,snConduct
The initial seed point of tip tracheae dendritic growth segmentation,
10613:To any initial seed point v0∈Send, find seed point v026 neighborhood territory pixel points in have local maxima
The pixel u of DFB, then set seed point v0Initial growth direction vectorFor:
1062:By seed point v0Add in growth segmentation queue;
1063:Determine the direction of growth of tip tracheae branch:
Direction condition is divided in the growth of tip tracheae branch:
A. to the required direction of growth, if its have on three-dimensional either direction with corresponding dimension target direction in the straight angle or
The required direction of growth is vertical with target direction, then the required direction of growth does not meet the growth segmentation direction item of tip tracheae branch
Part;
B. target direction includes the seed point initial segmentation direction of tip tracheae branch and the preceding an iteration direction of growth;
Specific explanations are:To by direction of growth vectorThe seed point v of acquisitioni26 neighborhood territory pixel points
W,It is seed point viThe direction vector of seed point w is grown into,
Will simultaneously withWithKeep close, direction of growth term restriction is:
1064:Determine tip tracheae branch degree of membership rule:
Tip tracheae branch degree of membership rule is (attached to be not shown in figure):
A. it presses tip segmentation direction condition and obtains the required growth unlabelled 26 neighborhood point set of seed point;
B. to each point in step set A, condition need to further be met:Gray value is not more than the neighborhood of a point in its 26 neighborhood
Points not more than 3;
C. using through the pixel with minimum gradation value that step B is constrained as new tip trachea-seed point;
Tip tracheae branch degree of membership rule is specially:If seed point viThe 26 neighborhood territory pixel points for meeting 1063 conditions beSince the diameter of tip tracheae branch is very small, the pixel of tip tracheorrhaphy bronchial lumen is than tip tracheae
Most of 26 neighborhood territory pixel point gray values of branch are low, and 1 26 neighborhood territory pixel point for meeting condition of selection are only needed in comparing every time
In any neighborhood territory pixel point, specific rules formula is set as:
1065:In ith iteration, first seed point v of queue is taken outi, search vi26 neighborhood territory pixel points in it is all not
The pixel of labelSelection meets the neighborhood territory pixel point of tip tracheae branch degree of membership ruleIt willMake
For new seed point vi+1Add in segmentation queue;
1066:Repeat 1065;If 1065 surpass without the generation of new seed point or 1062 initial seed point iteration growth number
Cross third iteration threshold (third iteration threshold Iter3=R), i.e., 1062 initial seed point has completed the R times iteration, stopping pair
The growth of initial seed point;
1067:To SendIn all initial seed points carry out growth segmentation using 1065, complete to tip tracheae branch
Extraction.
In embodiments of the present invention, after whole tracheae branch extractions, it can obtain required tracheae tree.Especially need to refer to
Go out, part third class tracheae branch influences because the factors such as lesion or local volumetric effect occur, and also shows tip on CT images
Therefore tracheae bifurcation state, can perform 105 and 106 again after performing 106, further improve segmentation precision.
As shown in figure 5, the process schematic of tracheae tree is automatically extracted from chest CT image for one embodiment of the invention, such as
It is main tracheae and main bronchus shown in Fig. 5 A;As shown in Figure 5 B, newly-increased tissue part is through adaptive threshold 3D region growing moulds
The bronchial tree for including the second class tracheae branches of level branches such as leaf, section, sub- section of type segmentation;As shown in Figure 5 C, it compares
The tissue part that Fig. 5 B are increased newly is the third class tracheae branch that the segmentation of adaptive threshold 3D region growings model obtains;Such as Fig. 5 D institutes
Show, the tissue part that Fig. 5 C are increased newly that compares is the tip tracheae branch extracted based on tracheae tree topology.
Fig. 6 lists the extraction result to CT image windows tracheae tree in Fig. 1.1 region of Fig. 6 A is the first kind in Figure 1A
The segmentation result of tracheae branch;2 regions of Fig. 6 B and Fig. 6 C are the segmentation results of the second class tracheae branch in Figure 1B and Fig. 1 C, by
Fig. 6 B through the present embodiment adaptive threshold leak model as it can be seen that handle, the adaptive threshold 3D region growings based on global threshold
The tracheae branch of model extraction is not bled into pulmonary parenchyma at the tube wall place of obscuring;3 regions of Fig. 6 C are third class tracheae branches
Segmentation result, can be effective using the adaptive threshold 3D region growings model based on local threshold and adaptive threshold leak model
Obtain such branch;4 regions of Fig. 6 D are the segmentation results of tip tracheae branch, and the present embodiment has divided tracheae branch from one section
End start, complete the extraction to narrow tip branch.
Fig. 7 is that method using the present invention extracts result schematic diagram to the 3D tracheaes tree of the chest CT image of different condition.
The relevant information of each group CT images is as follows:
It is to be appreciated that it is described above to what specific embodiments of the present invention carried out simply to illustrate that the skill of the present invention
Art route and feature, its object is to allow those skilled in the art that can understand present disclosure and implement according to this, but
The present invention is not limited to above-mentioned particular implementations.Every various change made within the scope of the claims is repaiied
Decorations should all be covered within the scope of the present invention.
Claims (12)
- A kind of 1. method that tracheae tree is automatically extracted from chest CT image, which is characterized in that including:101st, for chest CT image, the main tracheae and main bronchus of tracheae tree are obtained based on 3D Region growing segmentation modes;102nd, based on the 3D Region growing segmentations mode, the average information of the main tracheae obtained and the main branch of acquisition The average information of tracheae establishes adaptive threshold 3D region growings model and adaptive thresholding for extracting the second class tracheae branch It is worth leak model;103rd, according to the adaptive threshold 3D region growings model and the adaptive threshold leak model, the chest is extracted Second class tracheae branch of CT images;104th, according to the average information of the second class tracheae branch of extraction, the adaptive threshold 3D region growing moulds are adjusted The parameter of type and adaptive threshold leakage model;105th, according to the adaptive threshold 3D region growings model of adjustment and the adaptive threshold leak model, institute is extracted State the third class tracheae branch of chest CT image;106th, opening up for tracheae tree is formed according to the main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch Structure is flutterred, extracts the tip tracheae branch of the tracheae tree, obtains the tracheae tree of the chest CT image.
- 2. according to the method described in claim 1, it is characterized in that, described 101 include:1011st, the chest CT image is pre-processed;1012nd, image binaryzation processing is carried out to the pretreated chest CT image, obtains the chest CT after binaryzation The foreground area of image and background area;1013rd, centered on the center of chest CT image described after binaryzation, select the corresponding area of the foreground area maximum A prospect connected domain;1014th, judge whether the prospect connected domain of selection meets the first preset condition, if satisfied, then connecting the prospect Initial connected domain is divided in domain as main tracheae, and the prospect connected domain central point is as initial seed point s0;1015th, the initial seed point s is handled based on the 3D Region growing segmentations mode0, the main tracheae of acquisition tracheae tree.
- 3. according to the method described in claim 2, it is characterized in that, described 1015 include:S01, by the initial seed point s0Add in segmentation queue, based on the 3D Region growing segmentations mode, to chest bottom direction into Row 3D Region growing segmentations;S02, in the ith iteration of the 3D Region growing segmentations mode, take out it is described segmentation queue in first seed pointThe pixel average gray value of the prospect connected domain is not more than default first gray threshold TtraIt is and unlabelled described's 26 neighborhood territory pixel point w1,w2,...,wmIt marks as main tracheae;S03 and using the 26 neighborhood territory pixel point as new seed pointAdd in the segmentation queue;It continues with It is described to divide removing for queue in the ith iterationExcept remaining seed pointIt is complete until the ith iteration Portion's seed point is all handled through the 3D Region growing segmentations mode;S04, iterations add 1 and repeat the iterative process of ith, the main tracheae label lower side point described in the CT images When splitting for two connected domains, the main tracheae of the tracheae tree is obtained, wherein, the main tracheae is upside close to the one end in oral cavity, One end far from the oral cavity is downside, and the main tracheae lower side is the distalmost end of the main tracheae far from the oral cavity.
- 4. according to the method described in claim 3, it is characterized in that, described 101 further include;The main bronchus includes left principal bronchus and right principal bronchus, is obtained based on the 3D Region growing segmentations mode described The step of left principal bronchus, includes:M01, using the central point of the connected domain on the left of the main tracheae anatomy as the initial seed point of left principal bronchus Sleft;M02, the pixel average gray value μ according to the main tracheae region of acquisitioniniAnd standard deviation sigmainiDetermine the left side Second gray threshold T of main bronchus segmentationbro, Tbro=μini+2σini;M03, by initial seed point SleftSegmentation queue is added in, based on the 3D Region growing segmentations mode, and with reference to described second Whole seed points in gray threshold processing segmentation queue, the left principal bronchus label described in the CT images are split into two During connected domain, the left principal bronchus segmentation terminates, and obtains the left principal bronchus;And the step of based on the 3D Region growing segmentations mode obtaining the right principal bronchus, includes:N01, using the central point of the connected domain on the right side of the main tracheae anatomy as the initial seed point of right principal bronchus Sright;N02, the pixel average gray value μ according to the right main tracheae region of acquisitioniniAnd standard deviation sigmainiDetermine main branch Second gray threshold T of tracheae segmentationbro, Tbro=μini+2σini;N03, by initial seed point SrightSegmentation queue is added in, based on the 3D Region growing segmentations mode, and with reference to described the Whole seed points in the processing segmentation queue of two gray thresholds, the right principal bronchus label described in the CT images are split into two During a connected domain, the right principal bronchus segmentation terminates, and obtains the right principal bronchus.
- 5. according to the method described in claim 4, it is characterized in that, described 102 include:Establish the adaptive threshold 3D regions Growth model specifically includes:1021:Obtain initial segmentation seed point set;1022:Obtain gray threshold;1023:Tracheae degree of membership rule is determined according to the gray threshold, including:(1) first is subordinate to rule:The gray value I of seed point wwNo more than third gray threshold T;(2) second are subordinate to rule:The gray value I of the seed point wwNo more than the 4th gray threshold, the 4th gray threshold is Generate the gray value T of the seed point v of the seed point wv;(3) third is subordinate to rule:The pixel that tracheae is marked as in the 6 neighborhood territory pixel points of the seed point w is no less than 4, I.e.:Wherein L represents dividing mark,Represent the 6 of the seed point w The set of neighborhood territory pixel point;(4) the 4th are subordinate to rule:The 6 neighborhood territory pixel point gray values of the seed point w and seed point w and average value be not more than institute Third gray threshold T is stated, and in the 6 neighborhood territory pixel points of the seed point w, gray value is no more than the third gray threshold T's The pixel is no less than 4, i.e.,:1024:Obtain growing strategy:In ith iteration, segmentation first seed point of queue is taken outBy described inMeet institute It is regular and unlabelled described to state tracheae degree of membership26 neighborhood territory pixel point w1,w2,...,wmLabeled as tracheae, and by described in 26 neighborhood territory pixel point w1,w2,...,wmAs new seed pointAdd in the segmentation queue;It continues with described Divide remaining seed point in queueUntilIt is all finished by growth, into i+1 time iteration;Weight This step is performed again until meeting termination rules.
- 6. according to the method described in claim 5, it is characterized in that, the initial segmentation seed point set includes:The left master Bronchus divides whole seed points in queue in iteration ends and the right principal bronchus divides queue in iteration ends In whole seed points.
- 7. according to the method described in claim 5, it is characterized in that, described 102 further include:Establish the adaptive threshold leakage Model specifically includes:1025:Leakage detection method:Record the novel species that iteration is divided every time during 3D Region growing segmentations in described 1024 Son points C1,C2,...,CNIf not leaked during iterative segmentation, calculated with the adaptive leak threshold of this iterative segmentation The required adaptive leak threshold of leak judgement method;Segmentation leakage phenomenon includes:Drastically leakage, progressive leakage and end leakage;The adaptive leak threshold includes:(1) for the first leak threshold E of drastically leakage phenomenon setting1:First leak threshold is set as M-R and arrives In the M times iterative process, adjacent secondary iteration generates the maximum value of new seed points difference, i.e.,:(2) for the second leak threshold E of the progressive leakage phenomenon setting2:Second leak threshold is set as M-R and arrives In the M times iterative process, the maximum value of new seed points is generated, i.e.,:(3) for the third leak threshold E of end leakage setting3With the 4th leak threshold E4:The E of iv-th iteration growth3 And E4It is respectively set as:WithThe Iter1For the first iteration threshold, the Iter1=R/2, wherein R be adaptive scale, R=(xmax-xmin+ymax- ymin)/2, the xmin, ymin, xmax, ymax, for the main tracheae mark the last layer of cleavage layer in length and width two-dimensional directional most Greatly and min coordinates;The Iter2For secondary iteration threshold value, the Iter2=R/4;1026:Determine leak judgement rule, if the 3D Region growing segmentations of current region have completed iv-th iteration, and Last leakage phenomenon is happened at the M times iteration, then the leakage rule is:
- 8. the method according to the description of claim 7 is characterized in that described 102 further include:1027:Determine leakage processing method, including:10271:When iv-th iteration segmentation leaks, N-Iter is calculated2With the maximum value N of M+1max;If the n-th changes In generation, exactly the main bronchus grew the most last iteration divided, then Nmax=N;10272:Remove N described in segmentation queuemaxThe tracheae label of whole seed points caused by after secondary iteration;10273:To the NmaxThe seed point set of secondary iterationUsing the adaptive threshold 3D areas Domain growth model carries out simulation growth segmentation, and the maximum iteration of simulation growth cutting procedure is set as Iter1;10274:Single trachea-seed point v in described 10273iFive leak threshold E of simulation growth regulation5By viSurrounding is marked as The neighborhood territory pixel points of tracheae determine, count the seed point vi6 neighborhood territory pixel points and 18 neighborhood territory pixels point in tracheae mark Numeration, and weight W and obtain E5:10275:Record the seed point viThe new seed points c caused by each iteration in simulation growth segmentation1,c2,..., cR/2If adjacent time the new seed points difference is not less than E5, then fromThe middle removal viIf the adjacent new seed point Number difference is less than E5Then retain the vi;10276:Described 10272 are repeated, after growth segmentation is all simulated, after screeningAs described adaptive The initial segmentation seed point set of threshold value 3D region growing model next iterations.
- 9. according to claim 5-8 any one of them methods, which is characterized in that the termination rules are:When adaptive threshold 3D region growings model growth cutting procedure is detected by the adaptive threshold leak model When there is no new seed point that can be marked as tracheae when leaking or in described 1024 in growth cutting procedure, it is described oneself Threshold value 3D region growing models are adapted to terminate.
- 10. according to the method described in claim 9, it is characterized in that, described 103 include:1031:The adaptive gray threshold T of left bronchus tree is worth to according to the average gray of the left principal bronchusleft, and will TleftThe tracheae degree of membership rule is determined as the third gray threshold, and according to the third gray threshold;1032:Extract seed point set S caused by the most last iteration of the left principal bronchus growth segmentationleft, described in setting The seed point set that adaptive threshold 3D region growings model carries out simulation growth segmentation is Spre=Sleft;1033:The S is screened using the leakage processing method of the adaptive threshold leak modelpre, obtain initial segmentation seed Point set Sp′re, wherein, the weight W is set as R;1034:The S is divided using adaptive threshold 3D region growings model growthp′re;1035:Described 1034 growth cutting procedure is detected using the leak judgement method of the adaptive threshold leak model, If the growth cutting procedure leaks, the seed point set of the simulation growth segmentation is obtained using the leakage processing method Close Spre, perform 1033;If the growth cutting procedure does not leak, 1034 are continued to execute, until model is given birth in the adaptive threshold 3D regions It terminates, completes to extract the second Lei Zuo tracheaes branch from the CT images;The mode of the second Lei Zuo tracheaes branch is obtained based on 1031-1035 described above, by the TleftReplace with Tright, The SleftReplace with Sright, complete to extract the second Lei You tracheaes branch from the CT images, to realize described second The extraction of class tracheae branch.
- 11. according to the method described in claim 10, it is characterized in that, described 105 include:1051:Extraction has grown the whole edge pixel points of the second class tracheae branch divided and finished as seed point set Slocal, while by the SlocalIt is set as the seed point set S of the simulation growth segmentationpre;1052:By the 1051 seed point set SpreThe gray value of each seed point itself be set as seed point segmentation Local gray level threshold value, and according to the local gray level threshold value determine tracheae degree of membership rule;1053:The S is screened using the leakage processing methodpre, obtain initial segmentation seed point set Sp′re;1054:Using S described in the adaptive threshold 3D region growing model treatments of adjustmentp′re;1055:Described 1054 growth segmentation is detected using the leak judgement method of the adaptive threshold leak model of adjustment Process, if the growth cutting procedure leaks, using described in the leakage processing method acquisition of the leak model of adjustment The seed point set S of simulation growth segmentationpre, perform described 1053;If the growth cutting procedure does not leak, continue to hold Row described 1054 until the adaptive threshold 3D region growings model of the adjustment terminates, is completed to third class tracheae branch Extraction.
- 12. according to the method for claim 11, which is characterized in that described 106 include:1061:Opening up for tracheae tree is formed according to the main tracheae, main bronchus, the second class tracheae branch and third class tracheae branch Flutter structure, obtain the whole tip tracheae dendritic growths segmentations of the tracheae tree initial seed point and the initial seed point just The beginning direction of growth, including:10611:Calculate the source distance field and distance from boundary field of the tracheae tree;10612:Extract all pixel S with local maximum of the tracheae tree source distance fieldend={ s1,s2,..., snAs the tip tracheae dendritic growth segmentation initial seed point;10613:To any initial seed point v0∈Send, find the seed point v026 neighborhood territory pixel points in have local maxima The pixel u of distance from boundary field then sets the seed point v0Initial growth direction vectorFor:1062:By the seed point v0Add in segmentation queue;1063:Determine the direction of growth of the tip tracheae branch:To by direction of growth vectorThe kind of acquisition Sub- point vi26 neighborhood territory pixel point w,It is the seed point viIt is raw The direction vector of the seed point w is grown to, it is describedWill simultaneously with it is describedWithKeep close, the direction of growth condition It is defined to:1064:Determine tip tracheae branch degree of membership rule:If the seed point viThe 26 neighborhood territory pixel points for meeting 1063 condition beDue to the tip gas The diameter of pipe branch is very small, and the pixel of the tip tracheorrhaphy bronchial lumen is more most of by 26 than the tip tracheae branch Neighborhood territory pixel point gray value is low, and any neighbour in 1 26 neighborhood territory pixel point for meeting condition of selection is only needed in comparing every time Domain pixel, specific rules are set as:1065:In ith iteration, first seed point v of queue is taken outi, search the vi26 neighborhood territory pixel points in it is all not The pixel of labelSelection meets the neighborhood territory pixel point of the tip tracheae branch degree of membership ruleBy institute It statesAs new seed point vi+1Add in segmentation queue;1066:Repeat described 1065;If described 1065 have completed without the generation of new seed point or described 1062 initial seed point The R times iteration stops the growth to the initial seed point;1067:To the SendIn all initial seed points carry out growth segmentation using described 1065, complete to the tip gas The extraction of pipe branch.
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