CN105574882B

CN105574882B - Lung segmentation extracting method based on chest cross section CT images and system

Info

Publication number: CN105574882B
Application number: CN201511023356.8A
Authority: CN
Inventors: 刘记奎; 李烨; 蔡云鹏; 尹丽妍
Original assignee: Shenzhen Institute of Advanced Technology of CAS
Current assignee: Zhuhai Institute Of Advanced Technology Chinese Academy Of Sciences Co ltd
Priority date: 2015-12-30
Filing date: 2015-12-30
Publication date: 2018-08-10
Anticipated expiration: 2035-12-30
Also published as: CN105574882A

Abstract

The present invention provides a kind of lung segmentation extracting method and system based on chest cross section CT images, the method includes：Obtain the CT images in chest cross section；The CT images are pre-processed；To pretreated CT images into row threshold division；Lung extracted region is carried out to the CT images after Threshold segmentation.The present invention can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation, avoid the problem that causing to fail to pinpoint a disease in diagnosis during follow-up diagnosis due to the edge missing in lung region and the missing in region.

Description

Lung segmentation extracting method based on chest cross section CT images and system

Technical field

The present invention is about medical image technical field, especially with regard to the CT image processing techniques in chest cross section, Concretely it is a kind of lung segmentation extracting method and system based on chest cross section CT images.

Background technology

Currently, lung cancer has become one of most common malignant tumour in countries in the world.Although the clinic based on lung cancer is multidisciplinary Complex treatment technology achieves significant progress, but 5 years survival rates of most of patients with lung cancer are still less than 15%, main cause It is that 80% patient is already belonging to advanced lung cancer when medical, loses the best period of operative treatment.Therefore lung cancer morning how is improved Phase diagnosis is particularly important.

With the development of computer technology and medical image technology, the computer-aided diagnosis based on medical image (Computer Aided Diagnosis, CAD) has the correct diagnosis for improving doctor (especially basic hospital doctor) Greatly help.The scientific research institution for being engaged in the research of Lung neoplasm CAD system is in the majority with Japan with the U.S..In Lung neoplasm CAD system Pulmonary parenchyma is extracted the stage, and Hu et al. extracts pulmonary parenchyma using based on threshold value and region growing method, is then based on Dynamic Programming calculation Method detaches pulmo, the smooth lung wall of open and close operator based on mathematical morphology.The Ukil lung region segmentations for extracting escape pipe tree Method improves the segmentation accuracy at hilus pulumonis position.Araiato and Sensakovic has studied lung region segmentation as computer The importance of assistant diagnosis system, research have shown that incorrect lung region segmentation method will cause the tubercle of 5%-17% to lose It loses.

Currently, the whole world shares 8 by United States food and drag administration (Food and Drug Administration, FDA) certification the lung cancer CAD system based on CT images, one of them is R2Technology companies ImageChecker CT LN-1000 systems, the system obtained FDA certifications in 2004.ImageChecker CT LN- 1000 systems provide the CT images to thickness between 0.5mm-3min and carry out real-time, full automatic nodule detection function, can examine The tubercle of survey is solid type tubercle of the diameter in 4min or more.Further include Pulmo package, the GE public affairs that PHILIPS Co. releases Take charge of the GE Rapid Screen Digital Lung VCAR etc. released.

The automatic segmentation of lung areas is a necessary processing procedure of any lung's computer-aided diagnosis system, especially Lung neoplasm CAD system.Currently, most of the dividing method of lung areas is that had on gray value with background area based on lung region Larger difference, these algorithms include mainly：Threshold segmentation, region growing and unicom label etc., finally pass through form student movement again It calculates removal lung areas isolated island and fills up in lung areas and edge cavity.

Since CT image backgrounds are very complicated, the CT images with lesion are more complicated, increase segmentation difficulty.Therefore, if Only the lung areas of extraction is handled by morphology operations, it is easy to cause the edge missing in lung region and lacking for region It loses, and absent region is often lesion region, to cause to fail to pinpoint a disease in diagnosis during follow-up diagnosis.

Therefore, a kind of new scheme how is researched and developed out, to avoid due to the edge in lung region missing and region The problem of lacking and causing to fail to pinpoint a disease in diagnosis during follow-up diagnosis is urgent technical problem to be solved in the field.

Invention content

In order to overcome above-mentioned technical problem of the existing technology, the present invention provides one kind to be schemed based on chest cross section CT The lung segmentation extracting method and system of picture are pre-processed by the CT images to chest cross section, then carry out threshold value Segmentation and lung extracted region can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation.

It is an object of the invention to provide a kind of lung segmentation extracting method based on chest cross section CT images, institutes The method of stating includes：Obtain the CT images in chest cross section；The CT images are pre-processed；To pretreated CT images Into row threshold division；Lung extracted region is carried out to the CT images after Threshold segmentation.

In a preferred embodiment of the invention, denoising method is combined to described using medium filtering and Wavelet Denoising Method CT images are pre-processed.

In a preferred embodiment of the invention, include into row threshold division to pretreated CT images：From pretreatment The pixel that gray value is less than 0 is determined in CT images afterwards；Pixel by gray value less than 0 is set to 0, obtains the first image；Really Determine the grey level histogram of described first image；It determines the trough between two wave crests in the grey level histogram, is considered as segmentation threshold；Root Preliminary binary segmentation is carried out to first image according to the segmentation threshold, obtains the second image；To second figure As being negated, third image is obtained；Processing is removed to the third image using morphology opening operation, obtains binary map Picture；Region labeling is carried out according to strategy from left to right, from top to bottom to the connected region in the bianry image, obtains threshold CT images after value segmentation.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation includes：From threshold The connected region after region labeling is extracted in CT images after value segmentation；Judge region labeling for 2 connected region it is most left Whether end pixel column is more than 20；When being judged as YES, judge region labeling for where the right end pixel of 2 connected region Whether row are less than 290 row；When being judged as YES, judge whether region labeling is more than 2000 for the area of 2 connected region；When sentencing When breaking to be, the connected region that the region labeling is 2 is left side lung region.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation further includes：From Region labeling be not 1 and 2 connected region in select the connected region that area is more than 2000, as the right lung region.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation includes：From threshold The connected region after region labeling is extracted in CT images after value segmentation；Judge region labeling for 2 connected region it is most left Whether end pixel column is more than 20；When being judged as YES, judge whether region labeling is more than for the area of 2 connected region 2000；When being judged as YES, judge whether region labeling is less than 290 row for the right end pixel column of 2 connected region；When When being judged as NO, the connected region that the region labeling is 2 is left and right adhesion of lung lung region.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation includes：From threshold The connected region after region labeling is extracted in CT images after value segmentation；Judge region labeling for 2 connected region it is most left Whether end pixel column is more than 20；When being judged as YES, judge region labeling for where the right end pixel of 2 connected region Whether row are less than 290 row；When being judged as NO, determine that the region labeling is 3 connected region and is according to the CT images No is left side lung region.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation includes：From threshold The connected region after region labeling is extracted in CT images after value segmentation；Judge region labeling for 2 connected region it is most right Whether end pixel column is less than 290 row；When being judged as YES, judge region labeling for 2 connected region left end pixel Whether column is more than 20；When being judged as NO, determine that the region labeling is 3 connected region and is according to the CT images No is left side lung region.

In a preferred embodiment of the invention, determine that the region labeling is 3 connected region according to the CT images Whether domain is that left side lung region includes：Judge whether region labeling is less than 2 for 2 connected region left end pixel column；When When being judged as YES, judge whether region labeling is less than 290 for the right end pixel column of 3 connected region；When being judged as YES When, judge whether region labeling is more than 2000 for the area of 3 connected region；When being judged as YES, the region labeling is 3 Connected region is left side lung region.

In a preferred embodiment of the invention, determine that the region labeling is 3 connected region according to the CT images Whether domain is that left side lung further includes：It is not that area is selected in 1,2,3 and 4 connected region more than 2000 from region labeling Connected region, as the right lung region.

In a preferred embodiment of the invention, carrying out lung extracted region to the CT images after Threshold segmentation includes：From threshold The connected region after region labeling is extracted in CT images after value segmentation；Regional ensemble is determined from the connected region, The regional ensemble includes multiple regions, and each region is satisfied by left end pixel column less than 2 and area is more than 100000；The maximum region of region labeling, referred to as start region are determined from the regional ensemble；From the connected region Suspicious region is filtered out, the region labeling of the suspicious region is more than the region labeling of the start region；From the doubtful area Determine that target area, the area of the target area are more than 2000 in domain, the target area is lung region.

It is an object of the invention to provide a kind of lung segmentation extraction system based on chest cross section CT images, The system includes CT image acquiring devices, the CT images for obtaining chest cross section；Pretreatment unit, for described CT images pre-processed；Threshold segmentation device, for pretreated CT images into row threshold division；Lung extracted region Device, for carrying out lung extracted region to the CT images after Threshold segmentation.

In a preferred embodiment of the invention, the Threshold segmentation device includes：Determining module is used for from pretreatment The pixel that gray value is less than 0 is determined in CT images afterwards；First image determining module, the pixel for gray value to be less than to 0 It is set to 0, obtains the first image；Histogram determining module, the grey level histogram for determining described first image；Segmentation threshold is true Cover half block is considered as segmentation threshold for determining the trough in the grey level histogram between two wave crests；Second image determining module, For carrying out preliminary binary segmentation to first image according to the segmentation threshold, the second image is obtained；Third image Determining module obtains third image for being negated to second image；Bianry image determining module, for using Morphology opening operation is removed processing to the third image, obtains bianry image；Region labeling module, for described Connected region in bianry image carries out region labeling according to strategy from left to right, from top to bottom, after obtaining Threshold segmentation CT images.

In a preferred embodiment of the invention, the lung region extracting device includes：Connected region extraction module is used In the connected region after extracting region labeling in the CT images after Threshold segmentation；First judgment module, for judging region Whether the left end pixel column of the connected region marked as 2 is more than 20；Second judgment module, for sentencing when described first When disconnected module is judged as YES, judge whether region labeling is less than 290 row for the right end pixel column of 2 connected region；The Three judgment modules, for when second judgment module is judged as YES, judge region labeling for 2 connected region area Whether 2000 are more than；First lung's determining module, for when the third judgment module is judged as YES, determining the region Connected region marked as 2 is left side lung region.

The Threshold segmentation device further includes：Second lung's determining module, for not being 1 and 2 company from region labeling The connected region that area is more than 2000, as the right lung region are selected in logical region.

In a preferred embodiment of the invention, the Threshold segmentation device further includes：4th judgment module, for working as When first judgment module is judged as YES, judge whether region labeling is more than 2000 for the area of 2 connected region；5th Judgment module, for when the 4th judgment module is judged as YES, judge region labeling for 2 connected region right end picture Whether plain column is less than 290 row；Third lung determining module, for when the 4th judgment module is judged as NO, determining It is left and right adhesion of lung lung region to go out the connected region that the region labeling is 2.

In a preferred embodiment of the invention, the lung region extracting device includes：6th judgment module, for sentencing Whether the right end pixel column for the connected region that disconnected region labeling is 2 is less than 290 row；7th judgment module, for working as institute When stating the 6th judgment module and being judged as YES, judge whether region labeling is more than for the left end pixel column of 2 connected region 20；When the 7th judgment module is judged as NO, the lung region extracting device further includes：4th lung's determining module, For determining whether the connected region that the region labeling is 3 is left side lung region according to the CT images.

In a preferred embodiment of the invention, when second judgment module is judged as NO, the lung region carries The device is taken to further include：4th lung's determining module, for determining that the region labeling is 3 connected region according to the CT images Whether domain is left side lung region.

In a preferred embodiment of the invention, the 4th lung's determining module includes：First judging unit, is used for Judge whether region labeling is less than 2 for 2 connected region left end pixel column；Second judgment unit, for when described the When one judging unit is judged as YES, judge whether region labeling is less than 290 for the right end pixel column of 3 connected region； Third judging unit, for when the second judgment unit is judged as YES, judge region labeling for 3 connected region area Whether 2000 are more than；First lung's determination unit, for when the third judging unit is judged as YES, determining the region Connected region marked as 3 is left side lung region.

In a preferred embodiment of the invention, the 4th lung's determining module further includes：Second determination unit, For not being the connected region for selecting area in 1,2,3 and 4 connected region and being more than 2000, as the right lung from region labeling Region.In a preferred embodiment of the invention, the lung region extracting device includes：Area is expected and determining module, is used for Determine that regional ensemble, the regional ensemble include multiple regions from the connected region, each region is satisfied by most left End pixel column is less than 2 and area is more than 100000；Start region determining module, for being determined from the regional ensemble The maximum region of region labeling, referred to as start region；Suspicious region determining module, it is doubtful for being filtered out from the connected region Like region, the region labeling of the suspicious region is more than the region labeling of the start region；Target area determining module, is used for Determine that target area, the area of the target area are more than 2000 from the suspicious region, the target area is lung Region.

The beneficial effects of the present invention are provide a kind of lung segmentation extracting method based on chest cross section CT images And system, it is pre-processed by the CT images to chest cross section, then into row threshold division and lung extracted region, energy Enough accurate segmentations realized to lung region, ensure the integrality of pulmonary parenchyma region segmentation, and the edge due to lung region is avoided to lack And region missing and the problem of cause to fail to pinpoint a disease in diagnosis during follow-up diagnosis.

For the above and other objects, features and advantages of the present invention can be clearer and more comprehensible, preferred embodiment cited below particularly, And coordinate institute's accompanying drawings, it is described in detail below.

Description of the drawings

In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with Obtain other attached drawings according to these attached drawings.

Fig. 1 is a kind of stream of the lung segmentation extracting method based on chest cross section CT images provided in an embodiment of the present invention Cheng Tu；

Fig. 2 is the particular flow sheet of the step S103 in Fig. 1；

Fig. 3 is the flow chart of the embodiment one of the step S104 in Fig. 1；

Fig. 4 is the flow chart of the embodiment two of the step S104 in Fig. 1；

Fig. 5 is the flow chart of the embodiment three of the step S104 in Fig. 1；

Fig. 6 is the flow chart of the embodiment four of the step S104 in Fig. 1；

Fig. 7 is the flow chart of the embodiment five of the step S104 in Fig. 1；

Fig. 8 is the flow chart of the embodiment one of the step S704 in Fig. 7；

Fig. 9 is the flow chart of the embodiment two of the step S704 in Fig. 7；

Figure 10 is the flow chart of the embodiment six of the step S104 in Fig. 1；

Figure 11 is a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention Structure diagram；

Figure 12 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of Threshold segmentation device；

Figure 13 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment one of lung region extracting device；

Figure 14 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment two of lung region extracting device；

Figure 15 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment three of lung region extracting device；

Figure 16 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment four of lung region extracting device；

Figure 17 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment five of lung region extracting device；

Figure 18 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment one of 4th lung's determining module；

Figure 19 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment two of 4th lung's determining module；

Figure 20 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment six of lung region extracting device；

Figure 21 is the schematic diagram one of connected region label in specific embodiment provided by the invention；

Figure 22 is the schematic diagram two of connected region label in specific embodiment provided by the invention；

Figure 23 is the flow diagram of lung extracted region in specific embodiment provided by the invention；

Figure 24 is the schematic diagram in the lung region extracted in embodiment one provided by the invention；

Figure 25 is the schematic diagram in pulmonary parenchyma region in embodiment one provided by the invention；

Figure 26 is the schematic diagram in the lung region extracted in embodiment two provided by the invention；

Figure 27 is the schematic diagram in pulmonary parenchyma region in embodiment two provided by the invention；

Figure 28 is the schematic diagram in the lung region extracted in embodiment three provided by the invention；

Figure 29 is the schematic diagram in pulmonary parenchyma region in embodiment three provided by the invention.

Specific implementation mode

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.

In the lung cancer computer-aided diagnosis based on CT images, the correct of lung region is completely extracted particularly important, is The basis of Lung neoplasm extraction.The present invention is complicated for chest cross section CT image backgrounds in the prior art, correct complete extraction The extremely difficult problem of lung areas, it is proposed that a kind of lung segmentation extracting method based on chest cross section CT images and be System.

The Key Term of the present invention is introduced first below.

Morphology operations (Morphology operations) are for binary picture according to mathematical morphology The image processing method that the set enumeration tree of (Mathematical Morphology) grows up.At usual morphology image Reason shows as a kind of neighborhood operation form, a kind of specifically defined neighborhood referred to as " structural element " (Structure Element), its region corresponding with binary picture carries out specific logical operation on each pixel location, logical operation As a result it is the respective pixel of output image, includes mainly：Burn into expansion, opening operation and closed operation

Tubercular lesion, stand alone entity tubercle, chest is presented in Lung neoplasm (Lung nodule) on lung CT image Membranous type tubercle, adhesion vascular type tubercle, frosted glass tubercle and empty type tubercle.

Computer-aided diagnosis CAD refers to by iconography, Medical Image Processing and other possible physiology, lifes Change means are calculated in conjunction with the analysis of computer, and auxiliary finds lesion, improves the accuracy rate of diagnosis.

Fig. 1 is a kind of detailed process of the lung segmentation extracting method based on chest cross section CT images proposed by the present invention Figure, as shown in Figure 1, the method includes：

S101：Obtain the CT images in chest cross section.

In a particular embodiment, the CT images in chest cross section are generally DICOM format.

S102：The CT images are pre-processed；

In a particular embodiment, medium filtering can be used to scheme the CT with the denoising method of combining of Wavelet Denoising Method As carrying out pretreatment removal noise.

S103：To pretreated CT images into row threshold division.Fig. 2 is the particular flow sheet of step S103.

S104：Lung extracted region is carried out to the CT images after Threshold segmentation.In a particular embodiment, the present invention can lead to It crosses preset template and lung extracted region is carried out to CT images.

Fig. 2 is the particular flow sheet of step S103, and as shown in Figure 2, which specifically includes：

S201：The pixel that gray value is less than 0 is determined from pretreated CT images；

S202：Pixel by gray value less than 0 is set to 0, obtains the first image.

Since the intensity value ranges of DICOM images are [- 1024,1024], and human region includes the gray value in lung region Both greater than 0, therefore first by fixed threshold 0, the gray value less than 0 is set to 0.

S203：Determine the grey level histogram of described first image；

S204：It determines the trough between two wave crests in the grey level histogram, is considered as segmentation threshold；

S205：Preliminary binary segmentation is carried out to first image according to the segmentation threshold, obtains the second image；

In a particular embodiment, Ostu algorithms estimation segmentation threshold, primary segmentation lung areas (experience also can be used 500) threshold value is.

S206：Second image is negated, third image is obtained；

S207：Processing is removed to the third image using morphology opening operation, obtains bianry image；

S208：Region is carried out according to strategy from left to right, from top to bottom to the connected region in the bianry image Label obtains the CT images after Threshold segmentation.

The connected region that area is smaller in bianry image is removed using morphology opening operation, region mark then is carried out to image Number (region refers to connected region).In a particular embodiment, opening operation is carried out to third image, structural element is radius For 2 circle, purpose removal isolates " island " and obtains bianry image.Figure 21, Figure 22 are respectively specific embodiment provided by the invention The signal of middle connected region label.

In the other embodiment of the present invention, pulmonary parenchyma region segmentation can also pass through the general of double gauss mixed model Rate Density Distribution realizes Threshold segmentation, is then removed by mathematical morphological operation isolated island and defect repairing.

Fig. 3 is the flow chart of the embodiment one of step S104, from the figure 3, it may be seen that in embodiment one, by setting in advance Fixed template extracts lung region, which specifically includes：

S301：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, extraction The connected region that the connected region gone out is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, extraction The connected region gone out is the connected region that region labeling is followed successively by 1 to 5.

S302：Judge whether region labeling is more than 20, namely corresponding rope for the left end pixel column of 2 connected region Draw and whether is more than 10000；

S303：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 2 connected region Whether 290 row namely manipulative indexing are less than 150000；

S304：When being judged as YES, judge region labeling for 2 connected region area (pixel number for including) whether More than 2000；

S305：When being judged as YES, the connected region that the region labeling is 2 is left side lung region.It is to see herein The person of examining is reference, and inconsistent in anatomy.

In embodiment one, judge whether marked as 2 connected region be left lung, that is, judges whether the connected region is full Sufficient condition 1 (judging whether 2 region left end pixel columns were more than for 20 (manipulative indexing is more than 10000)), condition 2 (judge 2nd area Whether domain right end pixel column is less than 290 row (manipulative indexing be less than 150000)), condition 3 (judge that the area in region 2 (wraps The pixel number contained) whether it is more than 2000).All meet and if only if three conditions, it can be determined that 2 regions are left lung region.

Fig. 4 is the flow chart of the embodiment two of step S104, as shown in Figure 4, in embodiment two, by setting in advance Fixed template extracts lung region, which specifically includes：

S401：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, extraction The connected region that the connected region gone out is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, extraction The connected region gone out is the connected region that region labeling is followed successively by 1 to 5.

S402：Judge whether region labeling is more than 20, namely corresponding rope for the left end pixel column of 2 connected region Draw and whether is more than 10000；

S403：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 2 connected region Whether 290 row namely manipulative indexing are less than 150000；

S404：When being judged as YES, judge region labeling for 2 connected region area (pixel number for including) whether More than 2000；

S405：When being judged as YES, the connected region that the region labeling is 2 is left side lung region.It is to see herein The person of examining is reference, and inconsistent in anatomy.

S406：It is not the connected region for selecting area in 1 and 2 connected region and being more than 2000 from region labeling, as The right lung region.

In embodiment two, first determines whether marked as 2 connected region be left lung, that is, judge that the connected region is It is no to meet condition 1, condition 2, condition 3.All meet and if only if three conditions, it can be determined that the region marked as 2 is the areas Zuo Fei Domain, meanwhile, marked as void area of 1 region between human body and CT, then search searching area is big since marked as 3 regions In 2000 connected region, if there is right lung is regarded as, otherwise fault image only finds a lung region.

Fig. 5 is the flow chart of the embodiment three of step S104, as shown in Figure 5, in embodiment three, by setting in advance Fixed template extracts lung region, which specifically includes：

S501：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, extraction The connected region that the connected region gone out is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, extraction The connected region gone out is the connected region that region labeling is followed successively by 1 to 5.

S502：Judge whether region labeling is more than 20, namely corresponding rope for the left end pixel column of 2 connected region Draw and whether is more than 10000.The step judges whether the connected region marked as 2 meets condition 1.

S503：When being judged as YES, judge region labeling for 2 connected region area (pixel number for including) whether More than 2000, which judges whether the connected region marked as 2 meets condition 3.

S504：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 2 connected region Whether 290 row namely manipulative indexing are less than 150000, which judges whether the connected region marked as 2 meets condition 2.

S505：When being judged as NO, the connected region that the region labeling is 2 is left and right adhesion of lung lung region.

In embodiment three, first determine whether the connected region marked as 2 meets condition 1, condition 3, condition 2.When And if only if when the right end pixel column that condition 2 is unsatisfactory for i.e. 2 regions is not less than 290, it can be determined that the region marked as 2 is The lung region of left and right adhesion of lung.

Fig. 6 is the flow chart of the embodiment four of step S104, it will be appreciated from fig. 6 that in embodiment four, by setting in advance Fixed template extracts lung region, which specifically includes：

S601：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, extraction The connected region that the connected region gone out is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, extraction The connected region gone out is the connected region that region labeling is followed successively by 1 to 5.

S602：Judge whether region labeling is more than 20, namely corresponding rope for the left end pixel column of 2 connected region Draw and whether is more than 10000.The step judges whether the connected region marked as 2 meets condition 1.

S603：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 2 connected region Whether 290 row namely manipulative indexing are less than 150000, which judges whether the connected region marked as 2 meets condition 2.

S604：When being judged as NO, according to the CT images determine connected region that the region labeling is 3 whether be Left side lung region.

In embodiment four, first determine whether the connected region marked as 2 meets condition 1, condition 2.And if only if When condition 1 meets condition 2 and is unsatisfactory for, determine whether the connected region that the region labeling is 3 is left according to the CT images Side lung region.

Fig. 7 is the flow chart of the embodiment five of step S104, as shown in Figure 7, in embodiment five, by setting in advance Fixed template extracts lung region, which specifically includes：

S701：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, extraction The connected region that the connected region gone out is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, extraction The connected region gone out is the connected region that region labeling is followed successively by 1 to 5.

S702：Judge region labeling for 2 connected region right end pixel column whether be less than 290 row namely it is right It should index and whether be less than 150000, which judges whether the connected region marked as 2 meets condition 2.

S703：When being judged as YES, judge whether region labeling is more than for the left end pixel column of 2 connected region 20 namely manipulative indexing whether be more than 10000.The step judges whether the connected region marked as 2 meets condition 1.

S704：When being judged as NO, according to the CT images determine connected region that the region labeling is 3 whether be Left side lung region.

In embodiment five, first determine whether the connected region marked as 2 meets condition 1, condition 2.And if only if When condition 1 is unsatisfactory for condition 2 and meets, determine whether the connected region that the region labeling is 3 is left according to the CT images Side lung region.

Fig. 8 is the flow chart of the embodiment one of the step S704 in step S604, Fig. 7 in Fig. 6, as shown in Figure 8, In embodiment one, determine whether the connected region that the region labeling is 3 is left side lung region tool according to the CT images Body includes：

S801：Judge whether region labeling is less than 2 namely manipulative indexing for 2 connected region left end pixel column Whether 1000 are less than；

S802：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 3 connected region Whether 290 row namely manipulative indexing are less than 150000；

S803：When being judged as YES, judge region labeling for 3 connected region area (pixel number for including) whether More than 2000；

S804：When being judged as YES, the connected region that the region labeling is 3 is left side lung region.

In this embodiment, judge whether marked as 3 connected region be left lung, that is, judge whether the connected region is full Whether sufficient left end pixel column is less than 2, judges whether the connected region right end pixel column marked as 3 is less than 290 Row (manipulative indexing is less than 150000) judge whether the area (pixel number for including) of the connected region marked as 3 is more than 2000).All meet and if only if three conditions, it can be determined that 3 regions are left lung region.

Fig. 9 is the flow chart of the embodiment two of the step S704 in step S604, Fig. 7 in Fig. 6, as shown in Figure 9, In the embodiment, determine whether the connected region that the region labeling is 3 is left side lung region tool according to the CT images Body includes：

S901：Judge whether region labeling is less than 2 namely manipulative indexing for 2 connected region left end pixel column Whether 1000 are less than；

S902：When being judged as YES, judge whether region labeling is less than for the right end pixel column of 3 connected region Whether 290 row namely manipulative indexing are less than 150000；

S903：When being judged as YES, judge region labeling for 3 connected region area (pixel number for including) whether More than 2000；

S904：When being judged as YES, the connected region that the region labeling is 3 is left side lung region.

S905：It is not the connected region for selecting area in 1 and 2 connected region and being more than 2000 from region labeling, as The right lung region.

In this embodiment, judge whether marked as 3 connected region be left lung, that is, judge whether the connected region is full Whether sufficient left end pixel column is less than 2, judges whether the connected region right end pixel column marked as 3 is less than 290 Row (manipulative indexing is less than 150000) judge whether the area (pixel number for including) of the connected region marked as 3 is more than 2000).All meet and if only if three conditions, it can be determined that marked as 3 region be left lung region, region marked as 1 and Void area of the region between human body and CT marked as 2, then search searching area is more than since the region marked as 4 2000 connected region, if there is right lung is regarded as, otherwise fault image only finds a lung region.

Figure 10 is the flow chart of the embodiment six of step S104, as shown in Figure 10, in embodiment six, above-mentioned implementation The condition of mode one to embodiment five is unsatisfactory for, and is extracted to lung region by preset template, step tool Body includes：

S1001：Connected region after extracting region labeling in the CT images after Threshold segmentation.By taking Figure 22 as an example, carry The connected region that the connected region of taking-up is the connected region that region labeling is 1 and region labeling is 2.By taking Figure 21 as an example, carry The connected region of taking-up is the connected region that region labeling is followed successively by 1 to 5.

S1002：Determine that regional ensemble, the regional ensemble include multiple regions from the connected region, each Region is satisfied by left end pixel column less than 2 and area is more than 100000.

In a particular embodiment, judge whether the left end pixel column of each connected region is less than 2 successively, when When being judged as YES, continue to judge whether the area of the connected region is more than 100000, when being judged as YES, the connected region As the region in regional ensemble.

S1003：The maximum region of region labeling, referred to as start region are determined from the regional ensemble；

S1004：Suspicious region is filtered out from the connected region, the region labeling of the suspicious region is more than described open The region labeling in beginning region.

S1005：Determine that target area, the area of the target area are described more than 2000 from the suspicious region Target area is lung region.

In embodiment six, first determine whether that the void area label between human body and CT, grade traverse all connected regions successively Domain searches out the last one and meets region left end pixel column less than 2 (manipulative indexing is less than 1000), and region area is big In 100000 region, which is denoted as start by the region labeling. All connected regions are begun stepping through from the regions start+1, its area is arranged from big to small, all areas is chosen and is more than 2000 Block combination is considered as lung region.

As described above, a kind of lung segmentation extracting method based on chest cross section CT images as proposed by the present invention, Pre-processed by the CT images to chest cross section, then into row threshold division, finally according to preset template into Row lung extracted region can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation.

Figure 11 is a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention Structure diagram, as shown in Figure 11, the system includes：

CT image acquiring devices 100, the CT images for obtaining chest cross section.

Pretreatment unit 200, for being pre-processed to the CT images；

Threshold segmentation device 300, for pretreated CT images into row threshold division.Figure 12 is Threshold segmentation device Concrete structure block diagram.

Lung region extracting device 400, for carrying out lung extracted region to the CT images after Threshold segmentation.Specifically implementing In mode, the present invention can carry out lung extracted region by preset template to CT images.

Figure 12 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of Threshold segmentation device, as shown in Figure 12, Threshold segmentation device 300 specifically include：

Determining module 301, the pixel for being less than 0 for determining gray value from pretreated CT images；

First image determining module 302 is set to 0 for the pixel by gray value less than 0, obtains the first image.

Histogram determining module 303, the grey level histogram for determining described first image；

Segmentation threshold determining module 304 is considered as segmentation threshold for determining the trough in the grey level histogram between two wave crests Value；

Second image determining module 305, for carrying out preliminary two to first image according to the segmentation threshold Value segmentation, obtains the second image；

Third image determining module 306 obtains third image for being negated to second image；

Bianry image determining module 307 is obtained for being removed processing to the third image using morphology opening operation To bianry image；

Region labeling module 308 is used for the connected region in the bianry image according to from left to right, from top to bottom Strategy carry out region labeling, obtain the CT images after Threshold segmentation.

Figure 13 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment one of lung's extraction element, as shown in Figure 13, in embodiment one, lung region extracting device 400 specifically include：

Connected region extraction module 401, for the connection after extracting region labeling in the CT images after Threshold segmentation Region.By taking Figure 22 as an example, the connected region extracted is the connected region that region labeling is 1 and the connection that region labeling is 2 Region.By taking Figure 21 as an example, the connected region extracted is the connected region that region labeling is followed successively by 1 to 5.

First judgment module 402, for judging whether region labeling is big for the left end pixel column of 2 connected region Whether it is more than 10000 in 20 namely manipulative indexing；

Second judgment module 403, for when first judgment module is judged as YES, judge region labeling for 2 Whether the right end pixel column of connected region is less than 290 row namely whether manipulative indexing is less than 150000；

Third judgment module 404, for when second judgment module is judged as YES, judging region labeling for 2 Whether the area (pixel number for including) of connected region is more than 2000；

First lung's determining module 405, for when the third judgment module is judged as YES, the region labeling to be 2 Connected region be left side lung region.Be with observer herein it is reference, and it is inconsistent in anatomy.

Figure 14 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment two of lung's extraction element, as shown in Figure 14, in embodiment two, Threshold segmentation device is specific Including：

Second judgment module 403, for when first judgment module is judged as YES, judging region labeling for 2 Whether the right end pixel column of connected region is less than 290 row namely whether manipulative indexing is less than 150000；

Second lung's determining module 406, for not being to select area in 1 and 2 connected region to be more than from region labeling 2000 connected region, as the right lung region.

Figure 15 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment three of lung's extraction element, as shown in Figure 15, in embodiment three, Threshold segmentation device is specific Including：

First judgment module 402, for judging whether region labeling is big for the left end pixel column of 2 connected region Whether it is more than 10000 in 20 namely manipulative indexing.The step judges whether the connected region marked as 2 meets condition 1.

4th judgment module 407, for when first judgment module is judged as YES, judging region labeling for 2 Whether the area (pixel number for including) of connected region is more than 2000, which judges whether the connected region marked as 2 is full Sufficient condition 3.

5th judgment module 408, for when the 4th judgment module is judged as YES, judge region labeling for 2 company Whether the right end pixel column in logical region is less than 290 row namely whether manipulative indexing is less than 150000, which judges mark Number whether meet condition 2 for 2 connected region.

Third lung determining module 409, for when the 4th judgment module is judged as NO, determining the region mark Number it is left and right adhesion of lung lung region for 2 connected region.

Figure 16 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment four of lung's extraction element, as shown in Figure 16, in embodiment four, lung region extracting device tool Body includes：

Second judgment module 403, for when first judgment module is judged as YES, judging region labeling for 2 Whether the right end pixel column of connected region is less than 290 row namely whether manipulative indexing is less than 150000, which judges Whether the connected region marked as 2 meets condition 2.

When second judgment module is judged as NO, the lung region extracting device further includes：4th lung determines Module 410, for determining whether the connected region that the region labeling is 3 is left side lung region according to the CT images.

Figure 17 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment five of lung's extraction element, as shown in Figure 17, in embodiment five, lung region extracting device tool Body includes：

6th judgment module 411, for judging whether region labeling is small for the right end pixel column of 2 connected region Whether it is less than 150000 in 290 row namely manipulative indexing, which judges whether the connected region marked as 2 meets condition 2.

7th judgment module 412, for when the 6th judgment module is judged as YES, judge region labeling for 2 company The left end pixel column in logical region whether be more than 20 namely manipulative indexing whether be more than 10000.The step judge marked as Whether 2 connected region meets condition 1.

When the 7th judgment module is judged as NO, the lung region extracting device further includes：4th lung determines Module 410, for determining whether the connected region that the region labeling is 3 is left side lung region according to the CT images.

Figure 18 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment one of 4th lung's determining module, as shown in Figure 18, in embodiment one, the 4th lung determines Module 410 specifically includes：

First judging unit 4101, for judging whether region labeling is small for 2 connected region left end pixel column Whether it is less than 1000 in 2 namely manipulative indexing；

Second judgment unit 4102, for when first judging unit is judged as YES, judge region labeling for 3 company Whether the right end pixel column in logical region is less than 290 row namely whether manipulative indexing is less than 150000；

Third judging unit 4103, for when the second judgment unit is judged as YES, judge region labeling for 3 company Whether the area (pixel number for including) in logical region is more than 2000；

First lung's determination unit 4104, for when the third judging unit is judged as YES, the region labeling to be 3 Connected region be left side lung region.

Figure 19 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment two of 4th lung's determining module, it appears from figure 19 that in this embodiment, the 4th lung determines Module 410 specifically includes：

4th lung's determining module 4101, for judging that region labeling is for 2 connected region left end pixel column It is no be less than 2 namely manipulative indexing whether be less than 1000；

Second determination unit 4105, for not being to select area in 1 and 2 connected region to be more than from region labeling 2000 connected region, as the right lung region.

Figure 20 is in a kind of lung segmentation extraction system based on chest cross section CT images provided in an embodiment of the present invention The structure diagram of the embodiment six of lung's extraction element, as shown in Figure 20, in embodiment six, the above embodiment one to The condition of embodiment five is unsatisfactory for, and the lung region extracting device 400 specifically includes：

Start region determining module 401, for the connection after extracting region labeling in the CT images after Threshold segmentation Region.By taking Figure 22 as an example, the connected region extracted is the connected region that region labeling is 1 and the connection that region labeling is 2 Region.By taking Figure 21 as an example, the connected region extracted is the connected region that region labeling is followed successively by 1 to 5.

Area is expected and determining module 413, for determining regional ensemble, the regional ensemble packet from the connected region Multiple regions are included, each region is satisfied by left end pixel column less than 2 and area is more than 100000.

Start region determining module 414, for determining the maximum region of region labeling from the regional ensemble, referred to as Start region；

Suspicious region determining module 415, for filtering out suspicious region from the connected region, the suspicious region Region labeling is more than the region labeling of the start region.

Target area determining module 416, for determining target area from the suspicious region, the target area Area is more than 2000, and the target area is lung region.

As described above, a kind of lung segmentation extraction system based on chest cross section CT images as proposed by the present invention, Pre-processed by the CT images to chest cross section, then into row threshold division, finally according to preset template into Row lung extracted region can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation.

With reference to specific embodiment, technical scheme of the present invention is discussed in detail.In this specific embodiment, the program Including following：

1, gathered data：Lung CT image data are obtained, if the image is I.

2, it pre-processes.

Denoising method is combined to image I denoisings using medium filtering and Wavelet Denoising Method.

3, Threshold segmentation.

(1), the pixel value by Hu values in image I less than 0 is classified as 0, obtains I_；

(2), preliminary binary segmentation is carried out to image I_ using automatic threshold segmentation method (Ostu algorithms), then to binary map As negating, B_I_ is obtained；

(3), opening operation is carried out to B_I_, structural element is the circle that radius is 2.Purpose removal isolates " island " and obtains two-value Image _ B_I_；

(4), right _ B_I_ carries out region labeling, and label sequence is from left to right, from top to bottom；

4, lung region is extracted by the template of setting.Figure 23 is the flow of lung extracted region in the specific embodiment Schematic diagram, in the figure, block (2,1) indicate that the 1st point in block 2 of index, block (t) indicate block t, len (block (t)) indicate that the area (number of the point in the area region indicates) of block t, L==t indicate to find the region marked as t.

The juche idea extracted to lung region is：First determine whether left lung (including left and right adhesion of lung) region labeling；Its It is secondary judge pulmo whether adhesion, then determine pulmo zone number；Finally when above-mentioned steps all cannot judge lung region： The first step first determines the number (remaining area is voxel areas) of non-voxel block, and second step is again to the face of the block of voxel areas Product (number of pixel) carries out descending sort, and block of the front two area more than 2000 is chosen as lung region according to sequence, Otherwise judge that this layer of CT images are free of lung areas, be specifically described as：

A, judge 2 connected region of label whether be left lung (be herein with observer for reference, differ with anatomy It causes).Condition 1 judges whether 2 region left end pixel columns were more than for 20 (manipulative indexing is more than 10000)；Condition 2 judges 2 Whether region right end pixel column is less than 290 row (manipulative indexing is less than 150000)；Condition 3, the area for judging region two Whether (pixel number for including) is more than 2000.If three conditions all meet, it can be determined that 2 regions are lung areas and 1 Void area of the region between human body and CT, then the unicom region that area is more than 2000 is found in search since 3 regions, if In the presence of right lung is regarded as, otherwise fault image only finds a lung region.

If B, in A conditionals 1, condition 2, condition 3, when only condition 2 is unsatisfactory for, i.e. where the right end pixel in 2 regions Row are more than 290.It may determine that 2 regions are the lung region of left and right adhesion of lung at this time.

C, if A conditionals 1 are unsatisfactory for 2 either condition of condition, judge whether 3 regions are left lung.Setting judges item Part：Condition 1 judges whether 2 region left end pixel columns were less than for 2 (manipulative indexing is less than 1000)；Condition 2 judges 3 regions Right end pixel column whether be less than 290 (manipulative indexings be less than 150000)；Condition 3 judges whether the area in 3 regions is big In 2000.If three conditions all meet, it can be determined that 3 regions are lung region and 1 region and 2 regions between human body and CT Void area, then the unicom region that area is more than 2000 is found in search since 4 regions, if there is being regarded as the right side Lung, otherwise fault image only find a lung region.

If D, A, B, C are unsatisfactory for, the void area label between human body and CT is first determined whether：All companies are traversed successively Logical region searches for the last one and meets region left end pixel column less than 2 (manipulative indexing is less than 1000), and region area More than 100000, it is considered as void area of the region between human body and CT if met, which is denoted as start.From The regions start+1 begin stepping through all connected regions, its area is arranged from big to small, choose the area that all areas are more than 2000 Block combination is considered as lung region.

If E, above-mentioned condition is all unsatisfactory for being considered as the fault image without lung region.

5, lung region segmentation result.

Figure 24 is the schematic diagram in the lung region extracted in embodiment one, and Figure 25 is the signal in pulmonary parenchyma region in embodiment one Figure, Figure 26 are the schematic diagram in the lung region extracted in embodiment two, and Figure 27 is the schematic diagram in pulmonary parenchyma region in embodiment two, figure 28 be the schematic diagram in the lung region extracted in embodiment three, and Figure 29 is the schematic diagram in pulmonary parenchyma region in embodiment three.By Figure 24 Compared to Figure 29 it is found that in LIDC databases, compared with the goldstandard of expert's calibration, lung region that the present invention extracts it is accurate Rate is more than 96%.

In conclusion a kind of lung segmentation extracting method based on chest cross section CT images proposed by the present invention and being System can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation, avoid the edge due to lung region Missing and the missing in region and the problem of cause to fail to pinpoint a disease in diagnosis during follow-up diagnosis.

The main protection point of this patent is lung extracted region process, including five steps of A, B, C, D, E in specific embodiment. The juche idea of lung extracted region process is：First determine whether left lung (including left and right adhesion of lung) region labeling；Secondly judge left and right Lung whether adhesion, then determine pulmo zone number；Finally when above-mentioned steps all cannot judge lung region：The first step is first true The number (remaining area is voxel areas) of fixed non-voxel block, second step is again to the area (pixel of the block of voxel areas Number) carry out descending sort, according to sequence choose front two area more than 2000 block be used as lung region, otherwise judge be somebody's turn to do Layer CT images are free of lung areas.

The beneficial effects of the present invention are：

1, CT imaging techniques are taken full advantage of to the precancerous diagnostic value of lung, auxiliary doctor improves the correct of Lung neoplasm Diagnosis, and the diagnosis efficiency of doctor is improved, alleviate labour fatigue；

2, the precancerous lesser tubercle of lung can make patient obtain the survival rate of the longer time limit as can timely treated.This Patent is in computer-aided diagnosis it is possible to prevente effectively from the missing inspection of Lung neoplasm, timely diagnoses and reducing the same of patient's slight illness When, also reduce the medical treatment cost of patient.

3, it can realize the accurate segmentation to lung region, ensure the integrality of pulmonary parenchyma region segmentation.

One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, Ke Yitong It crosses computer program and is completed to instruct relevant hardware, the program can be stored in general computer read/write memory medium In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..

Those skilled in the art will also be appreciated that the various functions that the embodiment of the present invention is listed are by hardware or soft Part depends on the design requirement of specific application and whole system to realize.Those skilled in the art can be specific for each Using, the function that the realization of various methods can be used described, but this realization is understood not to protect beyond the embodiment of the present invention The range of shield.

Specific embodiment is applied in the present invention, and principle and implementation of the present invention are described, above example Explanation be merely used to help understand the present invention method and its core concept；Meanwhile for those of ordinary skill in the art, According to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion in this specification Appearance should not be construed as limiting the invention.

Claims

1. a kind of lung segmentation extracting method based on chest cross section CT images, characterized in that the method includes：

Obtain the CT images in chest cross section；

The CT images are pre-processed；

To pretreated CT images into row threshold division, which includes：Gray scale is determined from pretreated CT images Pixel of the value less than 0；Pixel by gray value less than 0 is set to 0, obtains the first image；Determine that the gray scale of described first image is straight Fang Tu；It determines the trough between two wave crests in the grey level histogram, is considered as segmentation threshold；According to the segmentation threshold to described The first image carry out preliminary binary segmentation, obtain the second image；Second image is negated, third figure is obtained Picture；Processing is removed to the third image using morphology opening operation, obtains bianry image；To in the bianry image Connected region according to from left to right, from top to bottom strategy carry out region labeling, obtain the CT images after Threshold segmentation, it is described CT images after Threshold segmentation include the connected region after multiple regions label, region labeling 1,2 ..., n, and the n is The number of connected region；

Lung extracted region is carried out to the CT images after Threshold segmentation, which includes：It is extracted from the CT images after Threshold segmentation Go out the connected region after region labeling；Determine that regional ensemble, the regional ensemble include multiple from the connected region Region, each region is satisfied by left end pixel column less than 2 and area is more than 100000；It is determined from the regional ensemble Go out the maximum region of region labeling, referred to as start region；Suspicious region, the suspicious region are filtered out from the connected region Region labeling be more than the start region region labeling；Target area, the target are determined from the suspicious region The area in region is more than 2000, and the target area is lung region.

2. according to the method described in claim 1, it is characterized in that, denoising method pair is combined using medium filtering and Wavelet Denoising Method The CT images are pre-processed.

3. according to the method described in claim 1, it is characterized in that, to after Threshold segmentation CT images carry out lung extracted region packet It includes：

Connected region after extracting region labeling in the CT images after Threshold segmentation；

Judge whether region labeling is more than 20 for the left end pixel column of 2 connected region；

When being judged as YES, judge whether region labeling is less than 290 row for the right end pixel column of 2 connected region；

When being judged as YES, judge whether region labeling is more than 2000 for the area of 2 connected region；

When being judged as YES, the connected region that the region labeling is 2 is left side lung region.

4. according to the method described in claim 3, it is characterized in that, to after Threshold segmentation CT images carry out lung extracted region also wrap It includes：

It is not the connected region for selecting area in 1 and 2 connected region and being more than 2000, as the right lung area from region labeling Domain.

5. according to the method described in claim 1, it is characterized in that, to after Threshold segmentation CT images carry out lung extracted region packet It includes：

When being judged as NO, the connected region that the region labeling is 2 is left and right adhesion of lung lung region.

6. according to the method described in claim 1, it is characterized in that, to after Threshold segmentation CT images carry out lung extracted region packet It includes：

When being judged as NO, determine whether the connected region that the region labeling is 3 is left side lung area according to the CT images Domain.

7. according to the method described in claim 1, it is characterized in that, to after Threshold segmentation CT images carry out lung extracted region packet It includes：

Judge whether region labeling is less than 290 row for the right end pixel column of 2 connected region；

When being judged as YES, judge whether region labeling is more than 20 for the left end pixel column of 2 connected region；

8. the method described according to claim 6 or 7, characterized in that determine that the region labeling is 3 according to the CT images Connected region whether be that left side lung region includes：

Judge whether region labeling is less than 2 for 2 connected region left end pixel column；

When being judged as YES, judge whether region labeling is less than 290 for the right end pixel column of 3 connected region；

When being judged as YES, judge whether region labeling is more than 2000 for the area of 3 connected region；

When being judged as YES, the connected region that the region labeling is 3 is left side lung region.

9. according to the method described in claim 8, it is characterized in that, determine that the region labeling is 3 according to the CT images Whether connected region is that left side lung further includes：

It is not the connected region for selecting area in 1,2,3 and 4 connected region and being more than 2000, as the right lung from region labeling Region.

10. a kind of lung segmentation extraction system based on chest cross section CT images, characterized in that the system includes：

CT image acquiring devices, the CT images for obtaining chest cross section；

Pretreatment unit, for being pre-processed to the CT images；

Threshold segmentation device, for, into row threshold division, the Threshold segmentation device to include determining to pretreated CT images Module, the pixel for being less than 0 for determining gray value from pretreated CT images；First image determining module, being used for will Pixel of the gray value less than 0 is set to 0, obtains the first image；Histogram determining module, the gray scale for determining described first image Histogram；Segmentation threshold determining module is considered as segmentation threshold for determining the trough in the grey level histogram between two wave crests； Second image determining module is obtained for carrying out preliminary binary segmentation to first image according to the segmentation threshold Second image；Third image determining module obtains third image for being negated to second image；Bianry image Determining module obtains bianry image for being removed processing to the third image using morphology opening operation；Region labeling Module, for carrying out region labeling according to strategy from left to right, from top to bottom to the connected region in the bianry image, The CT images after Threshold segmentation are obtained, the CT images after the Threshold segmentation include the connected region after multiple regions label, Region labeling be 1,2 ..., n, the n be connected region number；

Lung region extracting device, for carrying out lung extracted region, the lung region extracting device to the CT images after Threshold segmentation Including area is estimated and determining module, for determining that regional ensemble, the regional ensemble include more from the connected region A region, each region is satisfied by left end pixel column less than 2 and area is more than 100000；Start region determining module, For determining the maximum region of region labeling, referred to as start region from the regional ensemble；Suspicious region determining module is used In filtering out suspicious region from the connected region, the region labeling of the suspicious region is more than the region of the start region Label；Target area determining module, for determining that target area, the area of the target area are big from the suspicious region In 2000, the target area is lung region.

11. system according to claim 10, characterized in that combine denoising method using medium filtering and Wavelet Denoising Method The CT images are pre-processed.

12. system according to claim 10, characterized in that the lung region extracting device includes：

Connected region extraction module, for the connected region after extracting region labeling in the CT images after Threshold segmentation；

First judgment module, for judging whether region labeling is more than 20 for the left end pixel column of 2 connected region；

Second judgment module, for when first judgment module is judged as YES, judge region labeling for 2 connected region Right end pixel column whether be less than 290 row；

Third judgment module, for when second judgment module is judged as YES, judge region labeling for 2 connected region Area whether be more than 2000；

First lung's determining module, for when the third judgment module is judged as YES, determining that the region labeling is 2 Connected region is left side lung region.

13. system according to claim 12, characterized in that the Threshold segmentation device further includes：

Second lung's determining module, for not being the company for selecting area in 1 and 2 connected region and being more than 2000 from region labeling Logical region, as the right lung region.

14. system according to claim 12, characterized in that the Threshold segmentation device further includes：

4th judgment module, for when first judgment module is judged as YES, judge region labeling for 2 connected region Area whether be more than 2000；

5th judgment module, for when the 4th judgment module is judged as YES, judge region labeling for 2 connected region Whether right end pixel column is less than 290 row；

Third lung determining module, for when the 4th judgment module is judged as NO, determining that the region labeling is 2 Connected region is left and right adhesion of lung lung region.

15. system according to claim 10, characterized in that the lung region extracting device includes：

6th judgment module, for judging whether region labeling is less than 290 for the right end pixel column of 2 connected region Row；

7th judgment module, for when the 6th judgment module is judged as YES, judge region labeling for 2 connected region Whether left end pixel column is more than 20；

When the 7th judgment module is judged as NO, the lung region extracting device further includes：4th lung's determining module, For determining whether the connected region that the region labeling is 3 is left side lung region according to the CT images.

16. system according to claim 12, characterized in that described when second judgment module is judged as NO Lung region extracting device further includes：

4th lung's determining module, for according to the CT images determine connected region that the region labeling is 3 whether be Left side lung region.

17. system according to claim 15 or 16, characterized in that the 4th lung's determining module includes：

First judging unit, for judging whether region labeling is less than 2 for 2 connected region left end pixel column；

Second judgment unit, for when first judging unit is judged as YES, judge region labeling for 3 connected region Whether right end pixel column is less than 290；

Third judging unit, for when the second judgment unit is judged as YES, judge region labeling for 3 connected region Whether area is more than 2000；

First lung's determination unit, for when the third judging unit is judged as YES, determining that the region labeling is 3 Connected region is left side lung region.

18. system according to claim 17, characterized in that the 4th lung's determining module further includes：

Second determination unit, for not being to select area more than 2000 in 1,2,3 and 4 connected region from region labeling Connected region, as the right lung region.