CN107369189A - The medical image super resolution ratio reconstruction method of feature based loss - Google Patents
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Abstract
The invention discloses the medical image super resolution ratio reconstruction method of feature based loss, including image switching network fw, described image switching network fwIt is the full Connection Neural Network of feedforward, the full Connection Neural Network of the feedforward is by each neuron in network, it is divided into different groups by the priority of receive information, each group is regarded as an Internet, neuron in each layer receives the number output of preceding layer neuron as the input of oneself, the output of oneself is input to next layer again, the information in whole network is to propagate in one direction；Image switching network fwThe low resolution medical image for H/4 × W/4 sizes that feedforward neural network is sent is received, the low resolution medical image of H/4 × W/4 sizes is converted into the high-definition picture of H × W sizes.
Description
Technical field
The present invention relates to a kind of super-resolution rebuilding, and in particular to the medical image super-resolution rebuilding of feature based loss
Method.
Background technology
In recent years, super-resolution rebuilding mainly has two class methods：One kind is the method based on reconstruction；Another kind of is based on
The method of habit.Method based on reconstruction, it is by being modeled to the acquisition process of low-resolution image, utilizing regularization method
The prior-constrained of high-definition picture is constructed, estimates that high-definition picture, reconstruction are lost during degrading by low-resolution image
High-frequency signal, the problem of most problem is converted into cost function optimization under constraints at last；Another kind of is based on study
Super-resolution rebuilding, the basic thought of this method is by learning to obtain reflecting between high-definition picture and ground resolution image
Relation is penetrated, in this kind of method, the process of study is crucial, is learnt using rarefaction representation scheduling theory, and study terminates can
Low resolution is instructed to be rebuild with the priori independent of people.
Lifted along with the raising of the level of informatization and graphics processor computing capability so that the acquisition of view data and place
Reason also becomes easy, and the super resolution ratio reconstruction method based on study receives extensive concern.Yang etc. is to low resolution and high score
The image library that resolution image block is formed carries out rarefaction representation, and finds low resolution and high-resolution by the method for joint training
Corresponding excessively complete dictionary, the contact between foundation between image block；Rueda, Wang et al. are using based on rarefaction representation
Method produces high-resolution brain MR image from low resolution brain MR image, and Huang Haofeng, white Fu also uses same method
Rebuild for different types of medical image；Done et al. establishes the convolutional Neural net of an only hidden layer
Network SRCNN, network is considered as one and mapped end to end, one end is that the low-resolution image other end is high-definition picture, is obtained
Obtained preferable natural image super-resolution rebuilding effect；Bahrami et al. devises one five layers of Three dimensional convolution nerve net
Network, class 7T brain images are reconstructed from 3T brain images；Oktay et al. is based on residual error network from two-dimensional cardiac MR image sequence weights
Build out high-resolution 3-D view；Other medical image super-resolution methods, as Burgos goes out a kind of topography's similitude
Method go out CT images from MR image reconstructions；Bahrami proposes a kind of method for combining typical association analysis to be schemed using 3TMR
Method as reconstructing 7TMR images.
Super-resolution rebuilding problem is that solve the problems, such as to rebuild high-definition picture by low-resolution image, wherein low resolution
The acquisition methods of rate image are usually to carry out down-sampling to high-definition picture, by the high-definition picture of down-sampling as weight
The data source built, original high-resolution image is as the target rebuild.Sample rate is more than the sampling for obtaining the data signal originally
Rate is referred to as up-sampling, and the main purpose of up-sampling is enlarged drawing, so as to be shown on the display device of higher resolution.
SRCNN is also required to image using the up-sampling of image as the image procossing before training convolutional neural networks in test phase
Carry out same up-sampling processing.The low-resolution image for so in advance obtaining down-sampling carries out up-sampling and is used further to convolutional Neural
The training of network is cumbersome, and the process of up-sampling is to be dissolved into convolutional neural networks, so as to independent of fixed upper
Sample interpolation function, the process of up-sampling is also served as into the part that network can learn so that the network trained has more
Universal applicability.
The up-sampling of image is realized in convolutional neural networks, conventional method is exactly to set transposition convolutional layer or sub-pixel
Convolutional layer.Transposition convolution is also named deconvolution, but in using transposition convolution process, it may occur that convolution is uneven to cause image some
The problem of color of position is more deeper than other positions, there is the artifact similar to checker-wise, such artifact is in convolution kernel
Size is more obvious when can not be divided exactly by step-length；Sub-pixel convolution convolution nuclear energy is divided exactly by step-length, but although this method has
Help, but still easily produce the artifact similar to checker-wise.
The content of the invention
When the technical problems to be solved by the invention are traditional super-resolution rebuildings, it can produce similar to checker-wise
Artifact solves traditional Super-resolution reconstruction, and it is an object of the present invention to provide the medical image super resolution ratio reconstruction method of feature based loss
When building, the problem of artifact similar to checker-wise can be produced.
The present invention is achieved through the following technical solutions：
The medical image super resolution ratio reconstruction method of feature based loss, including
The medical image super resolution ratio reconstruction method of feature based loss, it is characterised in that:Including image switching network fw,
Described image switching network fwIt is the full Connection Neural Network of feedforward, the feedforward neural network presses each neuron in network
The priority of receive information is divided into different groups, and each group is regarded as an Internet, and the neuron in each layer receives preceding layer
The number output of neuron is input to next layer as the input of oneself, then by the output of oneself, and the information in whole network is court
Propagate in one direction；Image switching network fwThe low resolution medical image of H/4 × W/4 sizes is handled, by H/4 × W/4 sizes
Low resolution medical image be converted into the high-definition pictures of H × W sizes, the H and W are natural number.Feedforward neural network
Each neuron in network is divided into different groups by the priority of receive information, each group can be regarded as an Internet, often
Neuron in one layer receives the number output of preceding layer neuron as the input of oneself, then the output of oneself is input to next
Layer.Information in whole network is propagated in one direction.Feedforward network is considered as one and passes through simple non-linear functions
Multiple combination, realize the input space to output space complex mappings.Image switching network is a full connection feed forward neural
Network, full connection can make network use the image of any size in test phase, and the input for not requiring network is fixed size.
The function of realization is that the low resolution medical image of size is converted into the high-definition picture of size.
Described image switching network fwIncluding two amplification convolutional layers, by amplifying convolutional layer twice, image is just enlarged into
Input 4 times of size.Convolutional layer uses the size for first adjusting network middle level, such as：Inserted using closest interpolation method or bilinearity
Value method, carry out convolution operation again afterwards.
In network fwMiddle setting residual block, the input using the output of preceding layer as residual block, input are passed through convolutional layer, swashed
Result after layer living is along with output of the input as residual block, and in the structure of network, network exceedes certain depth, it may appear that
Gradient disperse, even if using batch normalization operation, also result in very high training error, the method for solving this problem be
The residual block of addition input and the quick connection of the output of active coating in network.
The residual error number of blocks is 5.Residual block use standard feedforward convolutional neural networks, and add once skip it is several layers of
Connection, skip generation residual block every time, skip part result of calculation be added to input in be used as output result, include five
Such residual block, further, the preferred scheme as the present invention.
Also include up-sampling layer.Set up-sampling layer position when, according to typically first amplify after training network thinking,
Up-sampling layer is placed on to the leading portion of network.
The up-sampling layer is arranged on image switching network fwBack segment.Up-sampling layer is placed on to the leading portion of network.Meeting
The amount of calculation of Internet below is caused to increase, so reasonable manner is that up-sampling layer is placed on to the back segment of network so that on
Simply small-sized image is handled before sample level, layer is to the last up-sampled and is amplified again, reduce amount of calculation.
Also include loss network φ, described image switching network fwHigh-definition picture is sent to loss network φ, loss
Network φ inputs include original image and resolution chart, are damaged by the good image classification network calculations feature of training in advance.
Described image sorter network is VGG16 networks, performs 3 × 3 or 2 × 2 convolution, in image classification task, one
After individual network training is good, the different layers in network differ to the level of abstraction of image different characteristic.In order to increase image
The output of switching network and the similarity degree of true picture spatially, introduce the good volume for image classification of a training in advance
Product neutral net VGG16, it has highly uniform framework, from start to finish only performs 3 × 3 and 2 × 2 convolution.In order to more
Network internal graphical representation process is visually known, using the expression information of image in itself, using random noise as initial solution, instead
Neural network characteristics are involved in expression, the different layers of network are visualized.
The weight lost in network φ is fixed value in the training process.Image switching network fwIn weight in training
It is activity value.Weight represents different layer parameters in network, and these parameters are being randomly generated at the beginning, then by training,
Constantly amendment so that these parameters can complete task of instructing low-resolution image to reconstruct full resolution pricture.
The present invention compared with prior art, has the following advantages and advantages：
1st, the medical image super resolution ratio reconstruction method of feature based loss of the present invention, the full connection convolutional Neural of feedforward is used
Network carries out 4 times of magnetic resonance brain medical image super-resolution rebuildings, efficiently convenient；
2nd, the medical image super resolution ratio reconstruction method of feature based loss of the present invention, can be applied to other medical images,
Such as CT scan CT；The super-resolution rebuilding of human body different parts imaging is can be used for, applicable surface is extensive；
3rd, the medical image super resolution ratio reconstruction method of feature based loss of the present invention,.
Brief description of the drawings
Accompanying drawing described herein is used for providing further understanding the embodiment of the present invention, forms one of the application
Point, do not form the restriction to the embodiment of the present invention.In the accompanying drawings：
Fig. 1 is the artifact of checker-wise pattern of the present invention；
Fig. 2 is schematic structural view of the invention；
Fig. 3 is feature of present invention costing bio disturbance flow chart；
Fig. 4 is residual error block structural diagram of the present invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, with reference to embodiment and accompanying drawing, to this
Invention is described in further detail, and exemplary embodiment of the invention and its explanation are only used for explaining the present invention, do not make
For limitation of the invention.
As Figure 1-4：
Embodiment 1
The medical image super resolution ratio reconstruction method of feature based loss, including image switching network fw, described image turn
Switching network fwIt is the full Connection Neural Network of feedforward, the feedforward neural network is by each neuron in network, by receive information
Successively it is divided into different groups, each group is regarded as an Internet, and the neuron in each layer receives the number of preceding layer neuron
Output is input to next layer as the input of oneself, then by the output of oneself, and the information in whole network is to pass in one direction
Broadcast；Image switching network fwThe low resolution medical image of H/4 × W/4 sizes is handled, by the low resolution of H/4 × W/4 sizes
Medical image is converted into the high-definition picture of H × W sizes, and the H and W are natural number.Described image switching network fwIncluding
Two amplification convolutional layers, by amplifying convolutional layer twice, image is just enlarged into 4 times of input size.In network fwMiddle setting is residual
Poor block, the input using the output of preceding layer as residual block, inputs and passes through convolutional layer, the result after active coating is made along with input
For the output of residual block.The residual error number of blocks is 5.Also include up-sampling layer.The up-sampling layer is arranged on image transition net
Network fwBack segment.Solve the problems, such as that convolutional neural networks processing image easily produces chessboard artifact.
Embodiment 2
The medical image super resolution ratio reconstruction method of feature based loss, including image switching network fw, described image turn
Switching network fwIt is the full Connection Neural Network of feedforward, the feedforward neural network is by each neuron in network, by receive information
Successively it is divided into different groups, each group is regarded as an Internet, and the neuron in each layer receives the number of preceding layer neuron
Output is input to next layer as the input of oneself, then by the output of oneself, and the information in whole network is to pass in one direction
Broadcast；Image switching network fwThe low resolution medical image of H/4 × W/4 sizes is handled, by the low resolution of H/4 × W/4 sizes
Medical image is converted into the high-definition picture of H × W sizes, and the H and W are natural number.Described image switching network fwIncluding
Two amplification convolutional layers, by amplifying convolutional layer twice, image is just enlarged into 4 times of input size.In network fwMiddle setting is residual
Poor block, the input using the output of preceding layer as residual block, inputs and passes through convolutional layer, the result after active coating is along with input
Output as residual block.The residual error number of blocks is 5.Also include up-sampling layer.The up-sampling layer is arranged on image conversion
Network fwBack segment.Also include loss network φ, described image switching network fwSend high-definition picture to lose network φ,
Loss network φ inputs include original image and high resolution graphics, pass through the good image classification network calculations feature of training in advance
Damage.Described image sorter network is VGG16 networks, performs 3 × 3 or 2 × 2 convolution.Weight in loss network φ is being trained
During be fixed value, image switching network fwIn weight be activity value in training.
By analyzing VGG16 network structures, when image passes through VGG16 lower levels, operation saves image main contents letter
Breath, and image absolute position feature is weakened, using this characteristic, by the output of super-resolution rebuilding convolutional neural networks
Input in VGG16 networks, use again with target image yWith MSEs of the y in VGG16 lower levels as loss function, phase
Hope them have similar character representation in VGG16, increase output and the similitude of target image spatially of network, do not make list
One compares pixel-by-pixel, and such loss function is defined as characteristic loss, computational methods such as formula (1):
In formula, the good VGG16 networks of φ expressions training in advance, l ∈ { Relu1_1, Relu1_2 ..., Relu5_3 }, this
Test l=Relu2_2,, Hl, Wl, ClIt is illustrated respectively in a certain active coating output in VGG16
Length and width and port number.
In having the machine learning task of supervision, the image of high quality, target letter can be generated by optimization object function
Number is for estimating the difference degree between the predicted value of model and actual value, and it is a nonnegative number function, optimization aim letter
Number is exactly to find parameter w to lose J minimums.In the application of super-resolution rebuilding, the loss function that is defined using formula (3)
Training convolutional neural networks export by networkVery close target image y, but be not to allow them to accomplish completely to match,
Simply visually it can less be distinguished with y so that reconstruct the visual signature that the image come more conforms to human eye.In the training of network
During, φ weight is all fixed in network, Super-resolution reconstruction establishing network fwIn weight w be not in the training process
Disconnected study amendment.
In order to prevent over-fitting, total variation regularization terms are added by loss function, constrain the ginseng to be optimized
W is counted, thus the object function of super-resolution rebuilding can be expressed as formula (2):
λ=10 in formula-3Regularization factors are represented, because image is discrete distribution, are substituted using finite-difference approximation, it is right
In image x ∈ RH×W, limited R difference is defined as formula (3):
In formula, xi,jThe pixel value of (i, j) is put in expression in image x.
Devising the full connection convolutional neural networks of a feedforward realizes single width medical image super-resolution rebuilding, from experiment
As a result it can be seen that solving the problems, such as that convolutional neural networks processing image easily produces chessboard artifact；In order to obtain preferably
Image perception effect, without using the single mean square error loss function based on pixel, but by the good image of training in advance
Sorter network VGG16, from its lower level extraction feature calculation mean square error as loss function, from experimental result it can be seen that.
Most of image is resumed, and some are smoothed similar to the details of noise, is more conformed to the visual perception of human eye, is visually tested
The validity of method is demonstrate,proved
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., all should include
Within protection scope of the present invention.
Claims (10)
1. the medical image super resolution ratio reconstruction method of feature based loss, it is characterised in that:Including image switching network fw, institute
State image switching network fwIt is the full Connection Neural Network of feedforward, the feedforward neural network is by each neuron in network, by connecing
The priority of breath of collecting mail is divided into different groups, and each group is regarded as an Internet, and the neuron in each layer receives preceding layer god
Number output through member is input to next layer as the input of oneself, then by the output of oneself, and the information in whole network is towards one
Propagate in individual direction；
Image switching network fwThe low resolution medical image of H/4 × W/4 sizes is handled, by the low resolution of H/4 × W/4 sizes
Medical image is converted into the high-definition picture of H × W sizes, and the H and W are natural number.
2. the medical image super resolution ratio reconstruction method of feature based loss according to claim 1, it is characterised in that:Institute
State image switching network fwIncluding two amplification convolutional layers, by amplifying convolutional layer twice, image is just enlarged into the 4 of input size
Times.
3. the medical image super resolution ratio reconstruction method of feature based loss according to claim 1, it is characterised in that:
Network fwMiddle setting residual block, the input using the output of preceding layer as residual block, inputs and passes through convolutional layer, the knot after active coating
Fruit is along with output of the input as residual block.
4. the medical image super resolution ratio reconstruction method of feature based loss according to claim 3, it is characterised in that:Institute
Residual error number of blocks is stated as 5.
5. the medical image super resolution ratio reconstruction method of feature based loss according to claim 1, it is characterised in that:Also
Including up-sampling layer.
6. the medical image super resolution ratio reconstruction method of feature based loss according to claim 5, it is characterised in that:Institute
State up-sampling layer and be arranged on image switching network fwBack segment.
7. the medical image super resolution ratio reconstruction method of feature based loss according to claim 1, it is characterised in that:Also
Including losing network φ, described image switching network fwHigh-definition picture is sent to loss network φ, the φ inputs of loss network
End includes original image and high resolution graphics, is damaged by the good image classification network calculations feature of training in advance.
8. the medical image super resolution ratio reconstruction method of feature based loss according to claim 7, it is characterised in that:Institute
It is VGG16 networks to state image classification network, and network performs 3 × 3 or 2 × 2 convolution.
9. the medical image super resolution ratio reconstruction method of feature based loss according to claim 7, it is characterised in that:Damage
The weight lost in network φ is fixed value in the training process.
10. the medical image super resolution ratio reconstruction method of feature based loss according to claim 7, it is characterised in that:
Image switching network fwIn weight be activity value in training.
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