CN107507197A - A kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks - Google Patents
A kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks Download PDFInfo
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Abstract
The present invention proposes a kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks, the present invention is pre-processed using clustering algorithm to lung CT image, obtain the pulmonary parenchyma region of CT images and the data set in non-pulmonary parenchyma region, the data set of known lung CT image is divided into training set and checking collects, using the data set of unknown lung CT image as test set；Convolutional neural networks model is established, convolutional neural networks model is trained using training set and checking collection data, the convolutional neural networks model after being trained；By in the convolutional neural networks model after test set input training, obtain CT image pulmonary parenchymas region, realize and pulmonary parenchyma extracted region function is carried out to the lung CT image of unknown patient, and lower basis is built to next stage automatic searching lung cancer region, contribute to the lung cancer of next stage to extract, classify.
Description
Technical field
The invention belongs to technical field of medical image processing, and in particular to one kind is based on clustering algorithm and convolutional neural networks
Pulmonary parenchyma extracting method.
Background technology
In histology, lung tissue is divided into pulmonary parenchyma and interstitial lung two parts, and pulmonary parenchyma is the bronchial each fraction of intrapulmonary
Branch and its eventually a large amount of alveolar structures at end, interstitial lung is connective tissue and blood vessel, lymphatic vessel, nerve etc..In order to assess and study
Pulmonary volume, lung cancer, often doctor need the first step to know pulmonary parenchyma situation, the accurate segmentation of pulmonary parenchyma is as further study of lung
The tissue and lesion of interior each organ dysfunction, are played a very important role to doctor.
During the research and application of image, people are often only interested in some parts in image.These portions
Divide and be frequently referred to target, they generally correspond to specific region with unique properties in image.In order to distinguish and analyze target, need
These region disconnectings are extracted, further utilized on this basis.Image segmentation is exactly to divide the image into their own characteristics
Region and extract the technology and process of interesting target.Classical image partition method such as threshold method, region increase, edge
Detection, cluster and nerual network technique etc..
Threshold segmentation is most ancient cutting techniques, and most simple and practical.In many cases, objective area in image
Its gray value has differences between background area in other words different zones, can now enter the homogeneity of gray scale as foundation
Row segmentation, it is generally affected by noise larger.
Edge detection algorithm is relatively adapted to the segmentation of edge gray value transition simple image more less than more significant and noise.
It is more complicated and image compared with very noisy be present for edge, then face the contradiction of noise immunity and accuracy of detection.If improve inspection
Precision is surveyed, then pseudo-edge caused by noise can cause irrational profile：If improving noise immunity, profile missing inspection and position can be produced
Put deviation.
Cluster analysis is an important branch of non-supervisory pattern-recognition in pattern-recognition.According to the internal junction of data acquisition system
Structure is divided into different classifications so that the feature of sample sample point that is as similar as possible, and belonging to a different category in same class
Difference it is as big as possible.
The basic thought of dividing method based on neutral net is to obtain linear decision letter by training multi-layer perception (MLP)
Number, is then classified to reach the purpose of segmentation with decision function to pixel.This method needs substantial amounts of training data.God
The connection of flood tide through network be present, be readily incorporated spatial information, can preferably solve noise and problem of non-uniform in image.Choosing
It is this method subject matter to be solved to select which kind of network structure.
In summary, the dividing method having pointed out at present is mostly directed to particular problem, and has his own strong points, not a kind of logical
Use standard.Split for pulmonary parenchyma, the present invention proposes a kind of convolutional neural networks using cluster generation data training, non-threshold
The automatic segmentation pulmonary parenchyma region of value, it is efficiently and accurate.
The content of the invention
In view of the shortcomings of the prior art, the present invention proposes that a kind of pulmonary parenchyma based on clustering algorithm and convolutional neural networks carries
Take method.
A kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks, comprises the following steps：
Step 1：Lung CT image is pre-processed using clustering algorithm, obtains the pulmonary parenchyma region of CT images and non-lung
The data set of parenchyma section, the data set of known lung CT image is divided into training set and checking collects, by unknown lung
The data set of CT images is as test set；
Step 1.1：Lung CT image is standardized, the lung CT image after standardization is split, point
It is segmented into the image fritter that size is A；
Step 1.2：Use CT value average value and CT value minimum value of the Kmeans algorithms respectively to size for A image fritter
Clustered, it is low-density tissue and the class of high density tissue two to be clustered；
Step 1.3：By to the poly- of the CT value minimum values of the cluster result of the CT value average values of image fritter and image fritter
Class result carries out cross inspection, removes the background area of CT images；
Step 1.4：Extract the pulmonary parenchyma region in the cluster result of the CT value average values of image fritter and image fritter
The common factor in the pulmonary parenchyma region of the cluster result of CT value minimum values；
Step 1.5：The common factor in the pulmonary parenchyma region to being obtained in step 1.4 does largest connected gymnastics and made, and obtains CT images
Pulmonary parenchyma region and non-pulmonary parenchyma region data set, the data set of known lung CT image is divided into and training set and tested
Card collection, using the data set of lung CT image to be divided as test set.
Step 2：Convolutional neural networks model is established, convolutional neural networks model is entered using training set and checking collection data
Row training, the convolutional neural networks model after being trained；
Step 2.1：The image fritter that the size that training set and checking are concentrated is A is extended, is expanded to size
For B image fritter；
Step 2.2：Convolutional neural networks model is established, the image fritter after extension is inputted into convolutional neural networks model,
The weight and deviation of each layer of training convolutional neural networks model；
Step 2.3：Checking is collected into input convolutional neural networks model to be classified, is lost by run time and checking collects
Classification accuracy determine optimize training parameter, the convolutional neural networks model after being trained.
Step 3：By in the convolutional neural networks model after test set input training, CT image pulmonary parenchymas region is obtained.
The convolutional neural networks model structure is：First layer is image fritter input layer, and the second layer is convolutional layer, the 3rd
Layer is maximum pond layer, and the 4th layer is full articulamentum；
The convolutional layer includes convolutional layer ReLU layers and Norm layers；
The full articulamentum includes full articulamentum ReLU layers, forgets layer, full articulamentum grader and Softmax functions at random
Layer.
The criteria for classifying of the size A is：The size divided is to include the lung in CT images in A image fritter
Portion organizes, and the image fritter automatic sliced time of each CT images is within 50MS.
The figure that the size of the image fritter that the size of pulmonary parenchyma is A and non-pulmonary parenchyma is A is concentrated in the training set and checking
As the quantity of fritter respectively accounts for 50%.
It is described by the cluster knot of the cluster result of the CT value average values of image fritter and the CT value minimum values of image fritter
Fruit carries out cross inspection, and the detailed process for removing the background area of CT images is as follows：
Examine whether four radial directions of the image fritter of each low-density tissue have the image fritter of high density tissue,
If in the presence of the image fritter of the low-density tissue is doubtful pulmonary parenchyma region, and otherwise the image fritter of the low-density tissue is
Background area.
The image fritter that the size that training set and checking are concentrated is A is extended, and it is B's to be expanded to size
The detailed process of image fritter is as follows：
By size to expand to image fritter of the size for B on the position centered on A image fritter in former CT images.
Described training parameter includes：Learning rate, convolution kernel size, convolution kernel number, Norm layers standardization port number
Mesh, full connection first layer output number, Dropout layers forgetting rate, pond channel type, Batch numbers, Epochs numerical value.
Beneficial effects of the present invention：
The present invention proposes a kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks, and the present invention passes through nothing
Supervised learning algorithm, i.e. clustering algorithm, to the automatic division pulmonary parenchyma region of CT picture of patient and non-pulmonary parenchyma region, reach automatic
Generate the training set needed for training convolutional neural networks, checking collects function；Pass through supervised learning algorithm, i.e. convolutional Neural net
Network, for pulmonary parenchyma territorial classification, optimal convolutional neural networks model is designed, reach the high pulmonary parenchyma territorial classification of accuracy rate
Effect；Realize and pulmonary parenchyma extracted region function is carried out to the lung CT image of unknown patient, and to next stage automatic searching
Lower basis is built in lung cancer region, contributes to the lung cancer of next stage to extract, classify.
Brief description of the drawings
Fig. 1 is the pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks in the specific embodiment of the invention
Flow chart；
Fig. 2 is the process schematic split in the specific embodiment of the invention to the lung CT image after standardization；
Wherein, (a) is the lung CT image that cut size is 64*64；
(b) it is lung CT image that cut size is 32*32；
(c) it is lung CT image that cut size is 16*16；
(d) it is lung CT image that cut size is 8*8；
(e) it is lung CT image that cut size is 4*4；
(f) it is lung CT image that cut size is 2*2；
Fig. 3 is that the process in the pulmonary parenchyma region and non-pulmonary parenchyma region that obtain CT images in the specific embodiment of the invention is shown
It is intended to；
Wherein, (a) is to use what Kmeans algorithms were clustered to size for the CT value average values of A image fritter respectively
As a result；
(b) it is the low-density class cross assay of CT value average values；
(c) it is to use the result that Kmeans algorithms are clustered to size for the CT value minimum values of A image fritter respectively；
(d) it is the low-density class cross assay of CT value minimum values；
(e) it is the pulmonary parenchyma region in the cluster result of the CT value average values of extraction image fritter and the CT values of image fritter
The schematic diagram of the common factor in the pulmonary parenchyma region of the cluster result of minimum value；
(f) schematic diagram in the pulmonary parenchyma region for CT images and non-pulmonary parenchyma region；
Fig. 4 be in the specific embodiment of the invention by the image fritter that size is A be extended for size be B image it is small
The schematic diagram of block；
Wherein, (a) is the image fritter that size is A；
(b) it is image fritter that size is B；
Fig. 5 is convolutional neural networks model schematic in the specific embodiment of the invention；
Wherein, (a) is image fritter input layer；
(b) it is convolutional layer；
(c) it is maximum pond layer；
(d) it is full articulamentum；
Fig. 6 is to the schematic diagram for the pulmonary parenchyma region progress three-dimensional modeling being partitioned into the specific embodiment of the invention；
Wherein, (a) is the classification three-dimensional reconstruction result of patients with chronic obstructive pulmonary diseases；
(b) it is the classification three-dimensional reconstruction result of the patients with lung cancer under common CT scan image datas；
(c) the classification three-dimensional reconstruction result of the patients with lung cancer under scanning pattern is imaged for the general tumour of PET/CT equipment.
Embodiment
The specific embodiment of the invention is described in detail below in conjunction with the accompanying drawings.
A kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks, as shown in figure 1, including following step
Suddenly：
Step 1：Lung CT image is pre-processed using clustering algorithm, obtains the pulmonary parenchyma region of CT images and non-lung
The data set of parenchyma section, the data set of known lung CT image is divided into training set and checking collects, by unknown lung
The data set of CT images is as test set.
Step 1.1：Lung CT image is standardized, the lung CT image after standardization is split, point
It is segmented into the image fritter that size is A.
In present embodiment, the size A criteria for classifying is：The size divided is to include CT in A image fritter
Lung tissue in image, and the image fritter automatic sliced time of each CT images is within 50MS.
The process schematic that lung CT image after standardization is split is as shown in Fig. 2 to the lung CT after standardization
The elapsed time and segmentation feature that image is split are as shown in table 1.
The elapsed time and segmentation feature that table 1 is split to the lung CT image after standardization
In present embodiment, the assessment with reference to the characteristics of the elapsed time in table 1 and segmentation, it can be seen that with fritter
Size reduction, time loss exponentially increase, and the difference of feature is increasing between each fritter, and therefore, the present invention is last
Select optimal sizes of the 8*8 as image fritter A.
Step 1.2：Use CT value average value and CT value minimum value of the Kmeans algorithms respectively to size for A image fritter
Clustered, it is low-density tissue and the class of high density tissue two to be clustered.
In present embodiment, Kmeans algorithms are used to gather respectively to size for the CT value average values of A image fritter
Shown in the result of class such as Fig. 3 (a), Kmeans algorithms are used to gather respectively to size for the CT value minimum values of A image fritter
Shown in the result of class such as Fig. 3 (c).
Step 1.3：By to the poly- of the CT value minimum values of the cluster result of the CT value average values of image fritter and image fritter
Class result carries out cross inspection, removes the background area of CT images.
In present embodiment, it is described by the CT values of the cluster result of the CT value average values of image fritter and image fritter most
The cluster result of small value carries out cross inspection, and the detailed process for removing the background area of CT images is as follows：
Examine whether four radial directions of the image fritter of each low-density tissue have the image fritter of high density tissue,
If in the presence of the image fritter of the low-density tissue is doubtful pulmonary parenchyma region, and otherwise the image fritter of the low-density tissue is
Background area.
The low-density class cross assay of CT value average values, as shown in Fig. 3 (b), the low-density class ten of CT value minimum values
Word assay, as shown in Fig. 3 (d).
Step 1.4：Extract the pulmonary parenchyma region in the cluster result of the CT value average values of image fritter and image fritter
The common factor in the pulmonary parenchyma region of the cluster result of CT value minimum values, as shown in Fig. 3 (e).
Step 1.5：The common factor in the pulmonary parenchyma region to being obtained in step 1.4 does largest connected gymnastics and made, and obtains CT images
Pulmonary parenchyma region and non-pulmonary parenchyma region data set, as shown in Fig. 3 (f), by the data set of known lung CT image draw
It is divided into training set and checking collects, using the data set of lung CT image to be divided as test set.
In present embodiment, the image fritter and non-pulmonary parenchyma that the size of pulmonary parenchyma is A are concentrated in the training set and checking
Size be A the quantity of image fritter respectively account for 50%.The data set of known lung CT image is divided into training set and tested
The ratio setting of card collection is 7: 1.
Found from Fig. 3, use Kmeans algorithms to be clustered respectively to size for the CT value minimum values of A image fritter
Result pulmonary parenchyma region be more than use Kmeans algorithms to gather respectively to size for the CT value average values of A image fritter
The pulmonary parenchyma region of the result of class, i.e. CT values minimum value clustering method beyond real pulmonary parenchyma edge, and remove in human body
In terms of following ambient noise, CT value minimum value clustering method effects are better than CT value average value clustering methods.Therefore the present invention takes
The intersection area of the two, you can, again can be by CT values most to retain the accurate pulmonary parenchyma region of CT value average value clustering methods
Small value clustering method control ambient noise.After above step, still there is the ambient noise below few body not go
Remove, so as to be operated using connected component, be completely removed background, obtain more accurate pulmonary parenchyma region.
Step 2：Convolutional neural networks model is established, convolutional neural networks model is entered using training set and checking collection data
Row training, the convolutional neural networks model after being trained.
Step 2.1：The image fritter that the size that training set and checking are concentrated is A is extended, is expanded to size
For B image fritter.
In present embodiment, as shown in figure 4, by size on the position centered on A image fritter in former CT images
The image fritter that size is B is expanded to, can avoid rolling up undersized fritter because convolution kernel size is big in convolution process
Product DeGrain.
In present embodiment, size B is 32*32.
Step 2.2：Convolutional neural networks model is established, the image fritter after extension is inputted into convolutional neural networks model,
The weight and deviation of each layer of training convolutional neural networks model.
In present embodiment, convolutional neural networks model structure use simplification after AlexNet structures as shown in figure 5,
Including：First layer is image fritter input layer, and the second layer is convolutional layer, and third layer is maximum pond layer, and the 4th layer is to connect entirely
Connect layer.
The convolutional layer includes convolutional layer ReLU layers and Norm layers；
The full articulamentum includes full articulamentum ReLU layers, forgets layer, full articulamentum grader and Softmax functions at random
Layer.
In present embodiment, class categories quantity is considered to greatest extent and only includes 2 classes, rather than the one of AlexNet
Thousand kinds of classifications, therefore the convolutional neural networks model only remains a convolution nuclear volume in AlexNet 5 convolutional layers and is
6 convolutional layer, and ReLU active coatings and Norm normalization layers are added, stochastic gradient descent can be accelerated and prevent over-fitting；Pond
Change layer and have selected maximum pond rather than average value pond, network complexity can be reduced；It is special that full articulamentum includes 120 distributions
Sign, and then adds ReLU active coatings and Dropout forgets layer, stochastic gradient can be accelerated to restrain, and prevent over-fitting；Quan Lian
The second layer for connecing layer is a 2 classification grader, act as classify pulmonary parenchyma region and non-pulmonary parenchyma region；Finally use
For Softmax functions as output layer, its probability distribution exported is approximate to represent output distribution.
Step 2.3：Checking is collected into input convolutional neural networks model to be classified, is lost by run time and checking collects
Classification accuracy determine optimize training parameter, the convolutional neural networks model after being trained.
In present embodiment, training parameter includes：Learning rate, convolution kernel size, convolution kernel number, the standardization of Norm layers
Number of active lanes, full connection first layer output number, Dropout layers forgetting rate, pond channel type, Batch numbers, Epochs numbers
Value.
In present embodiment, convolutional neural networks model optimization parameter and experimental result are as shown in table 2：
The convolutional neural networks model optimization parameter of table 2 and experimental result
It can be seen from table 2, the first row is as reference standard, it is seen that the size of convolution kernel increases to 10 from 5 × 5 in convolutional layer
× 10, checking accuracy rate is not lifted, and simply corresponding time loss improves 27%；When Epochs value is arranged to 50 to 80
When, checking accuracy rate is not lifted and time loss sharply rises to three times；When learning rate is arranged to 0.0001, time loss
To be double, but its rate of accuracy reached can receive to sacrifice time cost within the specific limits to lift accuracy rate to 99.17%；
Make our accuracys close to 100% when learning rate is set to 0.00001, but the 85% of time cost.To sum up institute
State, present invention determine that optimized parameter as shown in last column in table 2.
The training parameter of optimization is：Learning rate is 0.0001, convolution kernel size is 5*5, convolution kernel number is 6,
Norm layers standardization number of active lanes is 3, full connection first layer output number is 120, Dropout layer forgetting rates be 0.5 (50%),
Pond layer Max types, Batch numbers are 128, Epochs numerical value is 50.
Step 3：By in the convolutional neural networks model after test set input training, CT image pulmonary parenchymas region is obtained.
In present embodiment, in the convolutional neural networks model after test set input is trained, and the lung to being partitioned into is real
Matter region carries out three-dimensional modeling, tests the accuracy rate and versatility of the convolutional neural networks model.
In convolutional neural networks model after test set input is trained, and the pulmonary parenchyma region to being partitioned into carries out three-dimensional
Modeling, the goldstandard for carrying out pulmonary parenchyma region to lung CT image to be divided according to doctor delimited, then existed according to test set
Classification results in neutral net, the progress of the image of goldstandard and classification results is overlapping, calculate accuracy rate and (wear this similitude system
Number), sensitivity, specificity etc., as shown in fig. 6, wherein, classification three-dimensional reconstruction result such as Fig. 6 of patients with chronic obstructive pulmonary diseases
(a) shown in, shown in classification three-dimensional reconstruction result such as Fig. 6 (b) of the patients with lung cancer under common CT scan image datas, PET/CT is set
Shown in classification three-dimensional reconstruction result such as Fig. 6 (c) of patients with lung cancer under standby general tumour imaging scanning pattern.
Claims (9)
1. a kind of pulmonary parenchyma extracting method based on clustering algorithm and convolutional neural networks, it is characterised in that comprise the following steps：
Step 1：Lung CT image is pre-processed using clustering algorithm, obtains the pulmonary parenchyma region of CT images and non-pulmonary parenchyma
The data set in region, the data set of known lung CT image is divided into training set and checking collects, by unknown lung CT figure
The data set of picture is as test set；
Step 2：Convolutional neural networks model is established, convolutional neural networks model is instructed using training set and checking collection data
Practice, the convolutional neural networks model after being trained；
Step 3：By in the convolutional neural networks model after test set input training, CT image pulmonary parenchymas region is obtained.
2. the pulmonary parenchyma extracting method according to claim 1 based on clustering algorithm and convolutional neural networks, its feature exist
In the step 1 comprises the following steps：
Step 1.1：Lung CT image is standardized, the lung CT image after standardization is split, is divided into
Size is A image fritter；
Step 1.2：Kmeans algorithms are used to be carried out respectively to size for the CT values average value and CT values minimum value of A image fritter
Cluster, it is low-density tissue and the class of high density tissue two to be clustered；
Step 1.3：By to the cluster knot of the cluster result of the CT value average values of image fritter and the CT value minimum values of image fritter
Fruit carries out cross inspection, removes the background area of CT images；
Step 1.4：Extract the pulmonary parenchyma region in the cluster result of the CT value average values of image fritter and the CT values of image fritter
The common factor in the pulmonary parenchyma region of the cluster result of minimum value；
Step 1.5：The common factor in the pulmonary parenchyma region to being obtained in step 1.4 does largest connected gymnastics and made, and obtains the lung of CT images
Parenchyma section and the data set in non-pulmonary parenchyma region, training set and checking are divided into by the data set of known lung CT image
Collection, using the data set of lung CT image to be divided as test set.
3. the pulmonary parenchyma extracting method according to claim 1 based on clustering algorithm and convolutional neural networks, its feature exist
In the step 2 comprises the following steps：
Step 2.1：The image fritter that the size that training set and checking are concentrated is A is extended, it is B's to be expanded to size
Image fritter；
Step 2.2：Convolutional neural networks model is established, the image fritter after extension is inputted into convolutional neural networks model, training
The weight and deviation of each layer of convolutional neural networks model；
Step 2.3：Checking is collected into input convolutional neural networks model to be classified, point of collection is lost and verified by run time
Class accuracy rate determines the training parameter optimized, the convolutional neural networks model after being trained.
4. the pulmonary parenchyma extracting method according to claim 1 based on clustering algorithm and convolutional neural networks, its feature exist
In the convolutional neural networks model structure is：First layer is image fritter input layer, and the second layer is convolutional layer, and third layer is
Maximum pond layer, the 4th layer is full articulamentum；
The convolutional layer includes convolutional layer ReLU layers and Norm layers；
The full articulamentum includes full articulamentum ReLU layers, forgets layer, full articulamentum grader and Softmax function layers at random.
5. the pulmonary parenchyma extracting method according to claim 2 based on clustering algorithm and convolutional neural networks, its feature exist
In the criteria for classifying of the size A is：The size divided is to include lung's group in CT images in A image fritter
Knit, and the image fritter automatic sliced time of each CT images is within 50MS.
6. the pulmonary parenchyma extracting method according to claim 2 based on clustering algorithm and convolutional neural networks, its feature exist
The image fritter that the size of the image fritter that the size of pulmonary parenchyma is A and non-pulmonary parenchyma is A is concentrated in, the training set and checking
Quantity respectively account for 50%.
7. the pulmonary parenchyma extracting method according to claim 2 based on clustering algorithm and convolutional neural networks, its feature exist
In described by the cluster result of the CT value average values of image fritter and the progress of the cluster result of the CT value minimum values of image fritter
Cross is examined, and the detailed process for removing the background area of CT images is as follows：
Examine whether four radial directions of the image fritter of each low-density tissue have the image fritter of high density tissue, if depositing
Then the image fritter of the low-density tissue is doubtful pulmonary parenchyma region, and otherwise the image fritter of the low-density tissue is background
Region.
8. the pulmonary parenchyma extracting method according to claim 3 based on clustering algorithm and convolutional neural networks, its feature exist
In the image fritter that the size for concentrating training set and checking is A is extended, and is expanded to the image that size is B
The detailed process of fritter is as follows：
By size to expand to image fritter of the size for B on the position centered on A image fritter in former CT images.
9. the pulmonary parenchyma extracting method according to claim 3 based on clustering algorithm and convolutional neural networks, its feature exist
In described training parameter includes：It is learning rate, convolution kernel size, convolution kernel number, Norm layers standardization number of active lanes, complete
Connect first layer output number, Dropout layers forgetting rate, pond channel type, Batch numbers, Epochs numerical value.
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