CN113139925B - Pneumonia image processing method, system and storage medium - Google Patents
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Abstract
The invention discloses a method, a system and a storage medium for processing a pneumonia image, which are used for carrying out filtering reconstruction and feature enhancement on the pneumonia image to be processed, then fusing the pneumonia image with an original pneumonia image, retaining the features of the original pneumonia image and the image with the enhanced features, and facilitating subsequent neural network learning. Through verification of an InceptionV3 network, the method disclosed by the invention is used for processing the pneumonia image, and compared with an unprocessed pneumonia image and a pneumonia image processed only by using a Retinex algorithm, the accuracy and the specificity of the obtained pneumonia image are improved.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method, a system, and a storage medium for processing a pneumonia image.
Background
Under the condition of new coronaries, researchers in the field in China propose different methods for quickly identifying X-ray images of pneumonia. However, since the deep learning training is performed by long-term dependence on the features extracted by the convolutional neural network, a plurality of students lock the eyes on the convolutional neural network optimization. The pretreatment of the X-ray image still stays in the simple denoising, enhancement and other operations.
Although the X-ray images obtained by the traditional denoising, filtering, histogram equalization and other methods are characterized in human vision, the symptoms are easier to judge, but the original tiny characteristics are not easy to learn by the neural network due to the fact that the normal lung images and the diseased lung images are not obviously distinguished.
Disclosure of Invention
The embodiment of the invention provides a pneumonia image processing method, a pneumonia image processing system and a storage medium, which are used for solving the problems that a traditional preprocessing method in the prior art causes tiny characteristic loss and is unfavorable for neural network learning.
In one aspect, an embodiment of the present invention provides a pneumonia image processing method, including:
carrying out filtering reconstruction on the pneumonia image to be processed to obtain a reconstructed image;
performing feature enhancement on the reconstructed image to obtain an enhanced image;
and carrying out feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image.
In one possible implementation manner, filtering and reconstructing the pneumonia image to be processed to obtain a reconstructed image may include: constructing an edge centering matrix; and carrying out convolution processing on the pneumonia image to be processed by using the edge centering matrix to obtain a reconstructed image.
In one possible implementation, the edge centering matrix may be a third order matrix with a center value of 0.00 and edge data of 0.125.
In one possible implementation, feature enhancing the reconstructed image to obtain an enhanced image may include: and carrying out feature enhancement on the reconstructed appearance by adopting a single-scale Retinex algorithm to obtain an enhanced image.
In one possible implementation, the feature enhancement of the reconstructed image using the single-scale Retinex algorithm to obtain the enhanced image may include: decomposing the reconstructed image into an incident image and a reflected image; and reducing the influence of the incident image on the reconstructed image, obtaining the reflection attribute of the reconstructed image, and obtaining the enhanced image.
In one possible implementation manner, feature fusion is performed on the pneumonia image to be processed and the enhanced image, so as to obtain a fused image, which may include: the weight ratio of the pneumonia image to be processed and the enhanced image is set to be 0.50, and the threshold value is set to be 0.00; and carrying out feature fusion on the pneumonia image to be processed and the enhanced image by adopting the set weight proportion and the set threshold value to obtain a fusion image.
In another aspect, an embodiment of the present invention provides a pneumonia image processing system, including:
the reconstruction module is used for carrying out filtering reconstruction on the pneumonia image to be processed to obtain a reconstructed image;
the enhancement module is used for carrying out feature enhancement on the reconstructed image to obtain an enhanced image;
and the fusion module is used for carrying out feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image.
In another aspect, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores computer instructions executable by the at least one processor to cause the at least one processor to perform the method described above.
In another aspect, an embodiment of the present invention provides a computer readable storage medium having stored therein a plurality of computer instructions for causing a computer to perform the above-described method.
In another aspect, embodiments of the present invention provide a computer program product, which when executed by a processor implements the above method.
The pneumonia image processing method, the pneumonia image processing system and the storage medium have the following advantages:
after the filtering reconstruction and the characteristic enhancement are carried out on the pneumonia image to be processed, the pneumonia image to be processed is fused with the original pneumonia image, so that the characteristics of the original pneumonia image and the image with the enhanced characteristics are reserved, and the subsequent neural network learning is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a pneumonia image processing method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram showing the image processing effect of the pneumonia image processing method according to the present invention;
FIG. 3 is a flowchart of a pneumonia image processing method according to a second embodiment of the present invention;
FIG. 4 is a flowchart of a pneumonia image processing method according to a third embodiment of the present invention;
FIG. 5 is a flowchart of a pneumonia image processing method according to a fourth embodiment of the present invention;
fig. 6 is a flowchart of a pneumonia image processing method according to a fifth embodiment of the present invention;
fig. 7 is a functional block diagram of a pneumonia image processing system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, different methods are proposed for rapid identification of the pneumonic X-ray images by domestic researchers, and the methods are concentrated on convolutional neural networks. Before image learning by using the convolutional neural network, the image needs to be preprocessed, and the existing preprocessing method is simpler, so that some details are lost in the processing process, and the learning accuracy of the convolutional neural network is reduced.
Aiming at the problems in the prior art, the invention provides a pneumonia image processing method, a system and a storage medium, which are used for carrying out filtering reconstruction and feature enhancement on a pneumonia image to be processed, then fusing the pneumonia image with an original pneumonia image, retaining the features of the original pneumonia image and the image with the enhanced features, and facilitating subsequent neural network learning. Through verification of an acceptance V3 network, the method disclosed by the invention is adopted to process the pneumonia image, and compared with an unprocessed pneumonia image and a pneumonia image processed only by using a Retinex algorithm, the obtained pneumonia image has improved accuracy and specificity.
Fig. 1 is a flowchart of a pneumonia image processing method according to a first embodiment of the present invention. The embodiment of the invention provides a pneumonia image processing method, which comprises the following steps:
s100, filtering and reconstructing the pneumonia image to be processed to obtain a reconstructed image.
Illustratively, the pneumonia image to be processed is an X-ray image. The purpose of the filtering reconstruction of the pneumonia image is to keep the detailed characteristics of the pneumonia image and inhibit noise in the pneumonia image. Common filtering reconstruction methods include: linear filtering mainly comprises mean filtering and gaussian filtering, and nonlinear filtering mainly comprises median filtering and bilateral filtering. The mean value filtering is to replace the value of a certain pixel in the original image with the mean value of all pixels in a region, and the filtering reconstruction method adopted by the embodiment of the invention is the mean value filtering method.
S101, carrying out feature enhancement on the reconstructed image to obtain an enhanced image.
Illustratively, there are two purposes of feature enhancement of an image: firstly, the visual effect of the image is improved, the definition of the image is improved, secondly, aiming at the application occasion of the given image, the interesting features are highlighted, the uninteresting features are restrained, the difference between the features of different objects in the image is enlarged, and the special analysis requirements are met. The current image feature enhancement method comprises the following steps: a spatial domain based approach and a frequency domain based approach. The spatial domain-based method is to directly process pixels of an image, and the frequency domain-based method is to correct transformation coefficients of the image in a certain transformation domain of the image, and then inversely transform the image to an original spatial domain to obtain an enhanced image.
S102, carrying out feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image.
Illustratively, after the two processes of S100 and S101, some details of the obtained enhanced image are inevitably lost, and these details are helpful to learning of the convolutional neural network, so that simply using the enhanced image may adversely affect the learning of the convolutional neural network. And the enhanced image and the pneumonia image to be processed are subjected to feature fusion, so that the detail features in the original pneumonia image can be reserved, the detail features of the enhanced image can be reserved, and convenience is brought to subsequent convolutional neural network learning, as shown in fig. 2.
After the method is adopted, the pneumonia image processing method provided by the embodiment of the invention has the following beneficial effects:
1. compared with the method that a professional acquires X-rays to judge pneumonia, the method saves more time and increases efficiency from registering to taking an X-ray image.
2. Compared with the unprocessed images, the method has the advantages that the accuracy is more accurate, and the probability of misdiagnosis is reduced to a greater extent.
Fig. 3 is a flowchart of a pneumonia image processing method according to a second embodiment of the present invention. In one possible embodiment, S100, performing filtering reconstruction on the pneumonia image to be processed to obtain a reconstructed image may include: s300, constructing an edge centering matrix; s301, carrying out convolution processing on the pneumonia image to be processed by using the edge centering matrix to obtain a reconstructed image.
Illustratively, the edge centering matrix is a third order matrix with a center value of 0.00 and edge data of 0.125, and the matrix is as follows:
after the edge centering matrix is adopted to carry out convolution processing on the pneumonia image to be processed, the average value of 8 pixels at the edge of a certain pixel in the pneumonia image to be processed is used for replacing the value of the pixel.
Fig. 4 is a flowchart of a pneumonia image processing method according to a third embodiment of the present invention. In one possible embodiment, S101, performing feature enhancement on the reconstructed image to obtain an enhanced image may include: and S400, performing feature enhancement on the reconstructed image by adopting a single-scale Retinex algorithm to obtain an enhanced image.
For example, retinex is a common image enhancement method, which was proposed by edwin.h.land in 1963. The basic theory of Retinex theory is: the color of the object is determined by the object's ability to reflect long (red), medium (green) and short (blue) light, rather than by the absolute value of the intensity of the reflected light, and is not affected by illumination non-uniformity, with uniformity, i.e., retinex, being based on uniformity of color perception. Unlike conventional linear and nonlinear methods, which can only enhance a certain class of characteristics of an image, retinex can achieve a balance in three aspects of dynamic range compression, edge enhancement and color constancy, so that various different types of images can be adaptively enhanced. Over 40 years, researchers have developed the Retinex algorithm by mimicking the human visual system, from a single-scale Retinex algorithm, improved to a multi-scale weighted average MSR algorithm, and developed a color recovery multi-scale MSRCR algorithm.
Fig. 5 is a flowchart of a pneumonia image processing method according to a fourth embodiment of the present invention. In one possible embodiment, S400, performing feature enhancement on the reconstructed image by using a single-scale Retinex algorithm to obtain an enhanced image may include: s500, decomposing the reconstructed image into an incident image and a reflected image; s501, reducing the influence of the incident image on the reconstructed image, obtaining the reflection attribute of the reconstructed image, and obtaining the enhanced image.
Illustratively, a single-scale Retinex algorithm (Single Scale Retinex, SSR) is used in embodiments of the present invention, which is a theoretical algorithm that builds an image seen by the human eye as light rays reflect off an object. Assuming that one image is S (x, y), it can be decomposed into two different images: a reflected image R (x, y) and an incident image L (x, y), also called luminance image:
S(x,y)＝R(x,y)*L(x,y)
the theory of the Retinex algorithm is to enhance the image by reducing the effect of the incident image L (x, y) on the original image S (x, y) to obtain the reflective properties of the object in the image. The formula from which the SSR algorithm can be derived is:
r(x,y)＝logS(x,y)-logL(x,y)
take L (x, y) =f (x, y) ×s (x, y), whereAs a center surround function, c is a gaussian surround scale, and λ is an automatically determined scale. Lambda is given a value satisfying ≡ ≡f (x, y) dxdy=1. Taking c=300, the image is processed.
Fig. 6 is a flowchart of a pneumonia image processing method according to a fifth embodiment of the present invention. In one possible embodiment, step S102, performing feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fused image may include: s600, setting the weight ratio of the pneumonia image to be processed and the enhanced image to be 0.50, and setting the threshold to be 0.00; s601, carrying out feature fusion on the pneumonia image to be processed and the enhanced image by adopting the set weight proportion and the set threshold value, and obtaining a fusion image.
By way of example, with the above parameter settings, features in the pneumonia image to be processed and the enhanced image can be retained to the greatest extent, so as to facilitate the use of the fused image as convolutional neural network learning.
Performance verification
In order to prove that the method proposed by the invention is indeed effective, experimental verification is carried out below.
Experiments were performed under the Windows 10 professional edition:
a processor: intel (R) Core (TM) i5-8500 [email protected]
Memory: 8.00GB
System type: 64-bit operating system, x 64-based processor
A frame: tensorflow framework of Windows edition
Prior to the start of the experiment, a data set of Chest-X-Ray Image pneumonia images, including plain lung images and pneumonia images, was collected as disclosed in 2018 Kerman et al. Dividing images in a data set into a training set and a testing set according to a certain proportion, wherein the number of the images in the two sets is as follows:
ChestX-Ray Image dataset distribution
At the beginning of the experiment, adopting the acceptance V3 of Google open source as a test network, inputting the images in the training set into the test network, using two 3×3 networks to replace 5×5 networks in the test network, using one 1×3 network and one 3×1 network to replace 3×3 networks, and greatly enhancing the network nonlinearity under the condition that the receptive field is kept unchanged. The network structure is as follows:
inception V3 network architecture
In the training process, in order to prevent images in a training set from randomly affecting a training result, the stability of the training result is ensured, the last 20 layers of networks of the acceptance V3 network are trained for 3 times at the same time under the same CPU environment, and the average value of the 3 times of training is used as a final training result. After the network training is completed, the data in the test set are respectively processed as follows: the method is kept unchanged, only a single-scale Retinex algorithm is adopted for image enhancement, the method provided by the invention is adopted for processing, the three processed images are respectively input into a trained test network, and the results of three aspects of accuracy, specificity and sensitivity are output:
average value of acceptance V3 network test results
Among the three indexes, the sensitivity is the accurate prediction proportion in the cases with pneumonia, and the specificity is the accurate prediction proportion in the cases without pneumonia. From the above test results, it can be seen that compared with the original image and the image processed only by the SSR algorithm, the method of the invention has a large improvement in accuracy and specificity, while the sensitivity is reduced by a small extent. Illustrating that the method of the present invention does indeed provide certain benefits.
The invention also provides a pneumonia image processing system, comprising:
the reconstruction module 700 is configured to perform filtering reconstruction on the pneumonia image to be processed, so as to obtain a reconstructed image;
the enhancement module 701 is configured to perform feature enhancement on the reconstructed image to obtain an enhanced image;
and the fusion module 702 is used for carrying out feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image.
The invention also provides an electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores computer instructions executable by the at least one processor to cause the at least one processor to perform the method described above.
The present invention also provides a computer readable storage medium having stored therein a plurality of computer instructions for causing a computer to perform the above-described method.
The invention also provides a computer program product which, when executed by a processor, implements the method described above.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. A pneumonia image processing method, characterized by comprising:
carrying out filtering reconstruction on the pneumonia image to be processed to obtain a reconstructed image;
performing feature enhancement on the reconstructed image to obtain an enhanced image;
performing feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image;
the filtering reconstruction of the pneumonia image to be processed to obtain a reconstructed image comprises the following steps:
constructing an edge centering matrix;
carrying out convolution processing on the pneumonia image to be processed by using the edge centering matrix to obtain the reconstructed image;
the step of performing feature enhancement on the reconstructed image to obtain an enhanced image comprises the following steps:
performing feature enhancement on the reconstructed occurrence by adopting a single-scale Retinex algorithm to obtain the enhanced image;
the feature enhancement is performed on the reconstructed image by adopting a single-scale Retinex algorithm to obtain the enhanced image, and the method comprises the following steps:
decomposing the reconstructed image into an incident image and a reflected image;
reducing the influence of the incident image on the reconstructed image, and obtaining the reflection attribute of the reconstructed image to obtain the enhanced image;
the step of performing feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fused image, including:
setting the weight ratio of the pneumonia image to be processed and the enhanced image to be 0.50, and setting the threshold to be 0.00;
and carrying out feature fusion on the pneumonia image to be processed and the enhanced image by adopting the set weight proportion and the set threshold value to obtain the fusion image.
2. The method according to claim 1, wherein the edge centering matrix is a third-order matrix, the center value is 0.00, and the edge data are all 0.125.
3. A pneumonia image processing system, comprising:
the reconstruction module is used for carrying out filtering reconstruction on the pneumonia image to be processed to obtain a reconstructed image;
the enhancement module is used for carrying out feature enhancement on the reconstructed image to obtain an enhanced image;
the fusion module is used for carrying out feature fusion on the pneumonia image to be processed and the enhanced image to obtain a fusion image;
the filtered reconstruction includes:
constructing an edge centering matrix;
carrying out convolution processing on the pneumonia image to be processed by using the edge centering matrix to obtain the reconstructed image;
the feature enhancement adopts a single-scale Retinex algorithm, which comprises the following steps:
decomposing the reconstructed image into an incident image and a reflected image;
reducing the influence of the incident image on the reconstructed image, and obtaining the reflection attribute of the reconstructed image to obtain the enhanced image;
the feature fusion includes:
setting the weight ratio of the pneumonia image to be processed and the enhanced image to be 0.50, and setting the threshold to be 0.00;
and carrying out feature fusion on the pneumonia image to be processed and the enhanced image by adopting the set weight proportion and the set threshold value to obtain the fusion image.
4. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores computer instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 1-2.
5. A computer readable storage medium having stored therein a plurality of computer instructions for causing a computer to perform the method of any of claims 1-2.
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