CN112837324A - Automatic tumor image region segmentation system and method based on improved level set - Google Patents
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Abstract
The invention belongs to the technical field of image region segmentation, and discloses an automatic tumor image region segmentation system and method based on an improved level set, wherein the automatic tumor image region segmentation system based on the improved level set comprises the following steps: the tumor image processing system comprises a tumor image acquisition module, an image preprocessing module, a central processing module, an image region segmentation module, an image feature extraction module, an image feature fusion module, an image data analysis module, a cloud storage module and a display module. The image feature extraction module is used for setting the neural network model into a multi-branch neural network structure, so that two-dimensional tumor features and three-dimensional tumor features of the tumor image can be captured simultaneously; the image data analysis module adopts a random forest algorithm to generate a plurality of decision trees, and the classification results obtained by the two-dimensional SVM algorithm are reclassified according to the decision trees, so that the classification accuracy is improved, and the method has great practical value in clinical diagnosis and treatment.
Description
Technical Field
The invention belongs to the technical field of image region segmentation, and particularly relates to an automatic tumor image region segmentation system and method based on an improved level set.
Background
At present, in the process of diagnosis and treatment of tumors, compared with some clinical analysis modes, the medical imaging technology can quantify tumor heterogeneity by extracting massive features from tumor images, so that quantitative analysis of tumor image data is achieved noninvasively. With the continuous increase of the data volume of clinical images, the development of a data-driven segmentation model is of great significance for the diagnosis and treatment of tumors. There are two main categories of traditional tumor diagnosis methods: 1. the radiologist judges the type and the malignancy degree of the tumor according to personal experience by checking the nuclear magnetic resonance image. 2. A small amount of tumor tissue is extracted through a puncture surgery, and then diagnosis is made by using various pathological analysis methods, gene detection and other means. However, there is great uncertainty about the way in which a radiologist makes a decision by viewing a medical image. Since doctors need to read a large number of images every day, the judgment result is easily interfered by external and internal factors, and a high misjudgment rate is caused to a great extent; the method of extracting a small amount of tumor tissues through the puncture operation to carry out pathological examination can obtain diagnosis with high accuracy, but the puncture operation inevitably causes certain trauma to the patient, which can increase the pain of the patient, and compared with the imaging examination, the method has long time required by the puncture examination and cannot be popularized on a large scale.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) there is great uncertainty in the way radiologists make their decisions by looking at medical images. Since doctors need to read a large number of images every day, the judgment result is easily interfered by external and internal factors, and a high misjudgment rate is caused to a great extent.
(2) The method of extracting a small amount of tumor tissues through the puncture operation to carry out pathological examination can obtain diagnosis with high accuracy, but the puncture operation inevitably causes certain trauma to the patient, which can increase the pain of the patient, and compared with the imaging examination, the method has long time required by the puncture examination and cannot be popularized on a large scale.
Disclosure of Invention
In view of the problems of the prior art, the present invention provides an automatic tumor image region segmentation system and method based on an improved level set.
The invention is realized by an automatic tumor image region segmentation method based on an improved level set, which comprises the following steps:
acquiring original tumor image data to be segmented by using an image acquisition device through a tumor image acquisition module;
step two, carrying out denoising, cutting and enhancing processing on the acquired original tumor image data to be segmented by an image preprocessing module by utilizing an image preprocessing program;
the denoising, cutting and enhancing treatment of the acquired original tumor image data to be segmented comprises the following steps:
(2.1) carrying out multi-scale decomposition on the acquired original tumor image data by utilizing an isotropic thermal diffusion equation;
(2.2) determining a denoising intensity coefficient, and denoising and controlling denoising intensity of the tumor image data in each scale by utilizing a penalty weighted least squares algorithm based on the determined denoising intensity coefficient;
(2.3) smoothing, enhancing and compensating the denoised residual edge; sequentially traversing the scales, and fusing the compensated tumor image data of all scales to obtain complete enhanced tumor image data;
(2.4) dividing a focus area and a background area of the enhanced tumor image, and cutting an edge background area;
thirdly, controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set by using a central processing module and a central processor;
fourthly, performing region division on the processed tumor image by using a region division program through an image region division module, and dividing the tumor image into corresponding target regions;
fifthly, extracting high-dimensional quantitative features of each target area of the tumor image by using a feature extraction program and a neural network model through an image feature extraction module;
step six, fusing the extracted high-dimensional quantitative characteristics of each target area by using a characteristic fusion program through an image characteristic fusion module to obtain tumor image fusion characteristics;
analyzing the tumor state by using an image data analysis module according to the extracted high-dimensional quantitative characteristics of each target region of the tumor image by using a data analysis program;
the analyzing the tumor state comprises:
(a) obtaining a first classification probability according to tumor image fusion characteristics through a tumor data analysis module, and taking the first classification probability as a first classification dimension;
(b) generating a plurality of decision trees by using a data analysis program based on a random forest algorithm, obtaining a second classification probability according to the decision trees, and taking the second classification probability as a second classification dimension;
(c) taking the first classification dimension and the second classification dimension as a first two-dimensional feature, obtaining a third classification probability based on a two-dimensional SVM algorithm, and taking the third classification probability as a first classification result;
step eight, storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result by using a cloud database server through a cloud storage module;
and step nine, displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result by using a display through a display module.
Further, in the first step, the tumor image includes a two-dimensional tumor image and a three-dimensional tumor image.
Further, in step four, the method for performing region segmentation on the processed tumor image by the image region segmentation module includes:
(1) constructing a hypergraph according to the processed tumor images, and determining the tumor images to be an initial zero level set;
(2) performing a modified level set approach on the initial zero level set to determine a tumor region;
(3) performing edge smoothing on the tumor region according to a morphological operation.
Further, the performing the improved level set method on the initial zero level set to determine a tumor region comprises:
1) adding the gradient field information of the preprocessed tumor image to be segmented into the initial zero level set to construct a new evolution equation;
2) performing downsampling on a coarse tumor region in the tumor image at a resolution that is the same as a resolution of the original tumor image to obtain an initial zero level set;
3) and carrying out multiple iterations on the initial zero level set on the evolution equation according to a finite difference method to determine a tumor region.
Further, in step five, the method for extracting high-dimensional quantitative features of each target region of the tumor image includes:
(I) dividing the target area into a set of element graphs with preset sizes by an image feature extraction module;
(II) respectively acquiring feature maps of corresponding dimensions of corresponding target regions by using a feature extraction program based on the convolution layers in the neural network model according to the element map set;
(III) acquiring a feature subset from the feature map corresponding to each dimension of the target region through a pooling layer of the neural network model, and using the feature subset as a high-dimensional quantitative feature of the tumor image.
Further, in the sixth step, the method for fusing the extracted high-dimensional quantitative features of each target region includes:
and inputting the high-dimensional quantitative characteristics of each target area into a full-connection network layer as an input value, and carrying out nonlinear combination on the high-dimensional quantitative characteristics of each target area through a multilayer full-connection network and setting an activation function of a full-connection output node as a nonlinear function to obtain tumor image fusion characteristics.
Further, each of the decision trees includes a number of the features that are randomly selected.
Another object of the present invention is to provide an improved level set-based automatic tumor image region segmentation system for implementing the improved level set-based automatic tumor image region segmentation method, which includes:
the tumor image processing system comprises a tumor image acquisition module, an image preprocessing module, a central processing module, an image region segmentation module, an image feature extraction module, an image feature fusion module, an image data analysis module, a cloud storage module and a display module.
The tumor image acquisition module is connected with the central control module and used for acquiring original tumor image data to be segmented through the image acquisition device; the tumor image comprises a two-dimensional tumor image and a three-dimensional tumor image;
the image preprocessing module is connected with the central control module and is used for carrying out denoising, cutting and enhancing processing on the acquired original tumor image data to be segmented through an image preprocessing program;
the central processing module is connected with the tumor image acquisition module, the image preprocessing module, the image region segmentation module, the image feature extraction module, the image feature fusion module, the image data analysis module, the cloud storage module and the display module and is used for controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set through a central processing unit;
the image area segmentation module is connected with the central control module and is used for carrying out area segmentation on the processed tumor image through an area segmentation program and segmenting the tumor image into corresponding target areas;
the image feature extraction module is connected with the central control module and used for extracting high-dimensional quantitative features of each target area of the tumor image by utilizing a neural network model through a feature extraction program;
the image feature fusion module is connected with the central control module and used for fusing the extracted high-dimensional quantitative features of each target area through a feature fusion program to obtain tumor image fusion features;
the data analysis module is connected with the central control module and is used for analyzing the tumor state according to the extracted high-dimensional quantitative characteristics of each target area of the tumor image through a data analysis program;
the cloud storage module is connected with the central control module and used for storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result through the cloud database server;
and the display module is connected with the central control module and used for displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result through the display.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for automatic segmentation of tumor image regions based on an improved level set when executed on an electronic device.
It is another object of the present invention to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the method for automatic segmentation of tumor image regions based on an improved level set.
By combining all the technical schemes, the invention has the advantages and positive effects that: according to the automatic tumor image region segmentation system based on the improved level set, the neural network model is set into the multi-branch neural network structure corresponding to the dimensionality of the tumor image through the image feature extraction module, the multi-branch neural network structure is used for extracting the tumor image features of the tumor image with the corresponding dimensionality, and the two-dimensional tumor features and the three-dimensional tumor features of the tumor image can be captured simultaneously; and finally, classifying the classification results obtained by the two classes of algorithms by using a two-dimensional SVM algorithm to obtain a third classification probability, so that the classification accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of an automatic tumor image region segmentation method based on an improved level set according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for performing region segmentation on a processed tumor image by an image region segmentation module according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for extracting high-dimensional quantitative features of each target region of a tumor image according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for analyzing a tumor status by a tumor data analysis module according to an embodiment of the present invention.
FIG. 5 is a block diagram of an embodiment of an automatic tumor image region segmentation system based on an improved level set;
in the figure: 1. a tumor image acquisition module; 2. an image preprocessing module; 3. a central processing module; 4. an image area segmentation module; 5. an image feature extraction module; 6. an image feature fusion module; 7. an image data analysis module; 8. a cloud storage module; 9. and a display module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems of the prior art, the present invention provides an automatic tumor image region segmentation system and method based on an improved level set, which will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides an automatic tumor image region segmentation method based on an improved level set, which includes the following steps:
s101, acquiring original tumor image data to be segmented by using an image acquisition device through a tumor image acquisition module; the tumor image includes a two-dimensional tumor image and a three-dimensional tumor image.
S102, denoising, cutting and enhancing the acquired original tumor image data to be segmented by using an image preprocessing program through an image preprocessing module.
And S103, controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set by using a central processing module and a central processor.
And S104, performing region division on the processed tumor image by using a region division program through an image region division module, and dividing the tumor image into corresponding target regions.
And S105, extracting the high-dimensional quantitative features of each target area of the tumor image by using a feature extraction program and a neural network model through an image feature extraction module.
And S106, fusing the extracted high-dimensional quantitative characteristics of each target area by using a characteristic fusion program through an image characteristic fusion module to obtain tumor image fusion characteristics.
And S107, analyzing the tumor state by using the image data analysis module and a data analysis program according to the extracted high-dimensional quantitative characteristics of each target area of the tumor image.
And S108, storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result by using the cloud database server through the cloud storage module.
And S109, displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result by using a display through a display module.
In step S102, the denoising, clipping, and enhancing processing performed on the acquired original tumor image data to be segmented provided by the embodiment of the present invention includes:
(2.1) carrying out multi-scale decomposition on the acquired original tumor image data by utilizing an isotropic thermal diffusion equation;
(2.2) determining a denoising intensity coefficient, and denoising and controlling denoising intensity of the tumor image data in each scale by utilizing a penalty weighted least squares algorithm based on the determined denoising intensity coefficient;
(2.3) smoothing, enhancing and compensating the denoised residual edge; sequentially traversing the scales, and fusing the compensated tumor image data of all scales to obtain complete enhanced tumor image data;
and (2.4) dividing the focus area and the background area of the enhanced tumor image, and cutting the edge background area.
As shown in fig. 2, in step S104, the method for performing region segmentation on the processed tumor image by the image region segmentation module according to the embodiment of the present invention includes:
s201, constructing a hypergraph according to the processed tumor images, and determining the tumor images to be an initial zero level set.
S202, performing a modified level set method on the initial zero level set to determine a tumor region.
And S203, performing edge smoothing processing on the tumor region according to morphological operation.
The embodiment of the invention provides a method for performing improved level set on an initial zero level set so as to determine a tumor region, which comprises the following steps:
1) and adding the gradient field information of the preprocessed tumor image to be segmented into the initial zero level set to construct a new evolution equation.
2) Performing downsampling on a coarse tumor region in the tumor image at a resolution that is the same as a resolution of the original tumor image to obtain an initial zero level set.
3) And carrying out multiple iterations on the initial zero level set on the evolution equation according to a finite difference method to determine a tumor region.
As shown in fig. 3, in step S105, the method for extracting high-dimensional quantitative features of each target region of a tumor image according to an embodiment of the present invention includes:
s301, the target area is divided into a set of element images with preset sizes through an image feature extraction module.
S302, respectively obtaining feature maps corresponding to the dimensions of the target area by utilizing a feature extraction program based on the convolution layer in the neural network model according to the element map set.
And S303, acquiring a feature subset from the feature map corresponding to each dimension of the target region through a pooling layer of the neural network model, and taking the feature subset as a high-dimensional quantitative feature of the tumor image.
In step S106, the method for fusing the extracted high-dimensional quantitative features of each target region provided in the embodiment of the present invention includes:
and inputting the high-dimensional quantitative characteristics of each target area into a full-connection network layer as an input value, and carrying out nonlinear combination on the high-dimensional quantitative characteristics of each target area through a multilayer full-connection network and setting an activation function of a full-connection output node as a nonlinear function to obtain tumor image fusion characteristics.
As shown in fig. 4, in step S107, the method for analyzing a tumor state by a tumor data analysis module according to an embodiment of the present invention includes:
s401, obtaining a first classification probability according to tumor image fusion characteristics through a tumor data analysis module, and taking the first classification probability as a first classification dimension.
S402, generating a plurality of decision trees based on a random forest algorithm by using a data analysis program, obtaining a second classification probability according to the decision trees, and taking the second classification probability as a second classification dimensionality.
S403, taking the first classification dimension and the second classification dimension as a first two-dimensional feature, obtaining a third classification probability based on a two-dimensional SVM algorithm, and taking the third classification probability as a first classification result.
Each decision tree provided by the embodiment of the invention comprises a plurality of randomly selected features.
As shown in fig. 5, an embodiment of the present invention provides an automatic tumor image region segmentation system based on an improved level set, which includes: the system comprises a tumor image acquisition module 1, an image preprocessing module 2, a central processing module 3, an image region segmentation module 4, an image feature extraction module 5, an image feature fusion module 6, an image data analysis module 7, a cloud storage module 8 and a display module 9.
The tumor image acquisition module 1 is connected with the central control module 3 and used for acquiring original tumor image data to be segmented through an image acquisition device; the tumor image comprises a two-dimensional tumor image and a three-dimensional tumor image;
the image preprocessing module 2 is connected with the central control module 3 and is used for carrying out denoising, cutting and enhancing processing on the acquired original tumor image data to be segmented through an image preprocessing program;
the central processing module 3 is connected with the tumor image acquisition module 1, the image preprocessing module 2, the image region segmentation module 4, the image feature extraction module 5, the image feature fusion module 6, the image data analysis module 7, the cloud storage module 8 and the display module 9, and is used for controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set through a central processing unit;
the image area segmentation module 4 is connected with the central control module 3 and is used for carrying out area division on the processed tumor image through an area segmentation program and segmenting the tumor image into corresponding target areas;
the image feature extraction module 5 is connected with the central control module 3 and used for extracting high-dimensional quantitative features of each target area of the tumor image by utilizing a neural network model through a feature extraction program;
the image feature fusion module 6 is connected with the central control module 3 and is used for fusing the extracted high-dimensional quantitative features of each target area through a feature fusion program to obtain tumor image fusion features;
the data analysis module 7 is connected with the central control module 3 and is used for analyzing the tumor state according to the extracted high-dimensional quantitative characteristics of each target area of the tumor image through a data analysis program;
the cloud storage module 8 is connected with the central control module 3 and used for storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result through a cloud database server;
and the display module 9 is connected with the central control module 3 and is used for displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result through a display.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An improved level set based automatic tumor image region segmentation method, comprising the following steps:
acquiring original tumor image data to be segmented by using an image acquisition device through a tumor image acquisition module;
step two, carrying out denoising, cutting and enhancing processing on the acquired original tumor image data to be segmented by an image preprocessing module by utilizing an image preprocessing program;
the denoising, cutting and enhancing treatment of the acquired original tumor image data to be segmented comprises the following steps:
(2.1) carrying out multi-scale decomposition on the acquired original tumor image data by utilizing an isotropic thermal diffusion equation;
(2.2) determining a denoising intensity coefficient, and denoising and controlling denoising intensity of the tumor image data in each scale by utilizing a penalty weighted least squares algorithm based on the determined denoising intensity coefficient;
(2.3) smoothing, enhancing and compensating the denoised residual edge; sequentially traversing the scales, and fusing the compensated tumor image data of all scales to obtain complete enhanced tumor image data;
(2.4) dividing a focus area and a background area of the enhanced tumor image, and cutting an edge background area;
thirdly, controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set by using a central processing module and a central processor;
fourthly, performing region division on the processed tumor image by using a region division program through an image region division module, and dividing the tumor image into corresponding target regions;
fifthly, extracting high-dimensional quantitative features of each target area of the tumor image by using a feature extraction program and a neural network model through an image feature extraction module;
step six, fusing the extracted high-dimensional quantitative characteristics of each target area by using a characteristic fusion program through an image characteristic fusion module to obtain tumor image fusion characteristics;
analyzing the tumor state by using an image data analysis module according to the extracted high-dimensional quantitative characteristics of each target region of the tumor image by using a data analysis program;
the analyzing the tumor state comprises:
(a) obtaining a first classification probability according to tumor image fusion characteristics through a tumor data analysis module, and taking the first classification probability as a first classification dimension;
(b) generating a plurality of decision trees by using a data analysis program based on a random forest algorithm, obtaining a second classification probability according to the decision trees, and taking the second classification probability as a second classification dimension;
(c) taking the first classification dimension and the second classification dimension as a first two-dimensional feature, obtaining a third classification probability based on a two-dimensional SVM algorithm, and taking the third classification probability as a first classification result;
step eight, storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result by using a cloud database server through a cloud storage module;
and step nine, displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result by using a display through a display module.
2. The method according to claim 1, wherein in step one, the tumor image comprises a two-dimensional tumor image and a three-dimensional tumor image.
3. The method according to claim 1, wherein in step four, the method for performing region segmentation on the processed tumor image by the image region segmentation module comprises:
(1) constructing a hypergraph according to the processed tumor images, and determining the tumor images to be an initial zero level set;
(2) performing a modified level set approach on the initial zero level set to determine a tumor region;
(3) performing edge smoothing on the tumor region according to a morphological operation.
4. The method of claim 3, wherein the performing the modified level set method on the initial zero level set to determine the tumor region comprises:
1) adding the gradient field information of the preprocessed tumor image to be segmented into the initial zero level set to construct a new evolution equation;
2) performing downsampling on a coarse tumor region in the tumor image at a resolution that is the same as a resolution of the original tumor image to obtain an initial zero level set;
3) and carrying out multiple iterations on the initial zero level set on the evolution equation according to a finite difference method to determine a tumor region.
5. The method according to claim 1, wherein in step five, the method for extracting high-dimensional quantitative features of each target region of the tumor image comprises:
(I) dividing the target area into a set of element graphs with preset sizes by an image feature extraction module;
(II) respectively acquiring feature maps of corresponding dimensions of corresponding target regions by using a feature extraction program based on the convolution layers in the neural network model according to the element map set;
(III) acquiring a feature subset from the feature map corresponding to each dimension of the target region through a pooling layer of the neural network model, and using the feature subset as a high-dimensional quantitative feature of the tumor image.
6. The method for automatic segmentation of tumor image regions based on improved level set according to claim 1, wherein in step six, the method for fusing the extracted high-dimensional quantitative features of each target region comprises:
and inputting the high-dimensional quantitative characteristics of each target area into a full-connection network layer as an input value, and carrying out nonlinear combination on the high-dimensional quantitative characteristics of each target area through a multilayer full-connection network and setting an activation function of a full-connection output node as a nonlinear function to obtain tumor image fusion characteristics.
7. The method according to claim 1, wherein each of the decision trees comprises a plurality of randomly selected features.
8. An improved level set based automatic tumor image region segmentation system, comprising:
the system comprises a tumor image acquisition module, an image preprocessing module, a central processing module, an image region segmentation module, an image feature extraction module, an image feature fusion module, an image data analysis module, a cloud storage module and a display module;
the tumor image acquisition module is connected with the central control module and used for acquiring original tumor image data to be segmented through the image acquisition device; the tumor image comprises a two-dimensional tumor image and a three-dimensional tumor image;
the image preprocessing module is connected with the central control module and is used for carrying out denoising, cutting and enhancing processing on the acquired original tumor image data to be segmented through an image preprocessing program;
the central processing module is connected with the tumor image acquisition module, the image preprocessing module, the image region segmentation module, the image feature extraction module, the image feature fusion module, the image data analysis module, the cloud storage module and the display module and is used for controlling the work of each module of the automatic tumor image region segmentation system based on the improved level set through a central processing unit;
the image area segmentation module is connected with the central control module and is used for carrying out area segmentation on the processed tumor image through an area segmentation program and segmenting the tumor image into corresponding target areas;
the image feature extraction module is connected with the central control module and used for extracting high-dimensional quantitative features of each target area of the tumor image by utilizing a neural network model through a feature extraction program;
the image feature fusion module is connected with the central control module and used for fusing the extracted high-dimensional quantitative features of each target area through a feature fusion program to obtain tumor image fusion features;
the data analysis module is connected with the central control module and is used for analyzing the tumor state according to the extracted high-dimensional quantitative characteristics of each target area of the tumor image through a data analysis program;
the cloud storage module is connected with the central control module and used for storing the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the tumor state analysis result through the cloud database server;
and the display module is connected with the central control module and used for displaying the acquired original tumor image data to be segmented, the target area, the high-dimensional quantitative characteristics and the real-time data of the tumor state analysis result through the display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the method of automatic tumor image region segmentation based on an improved level set according to any one of claims 2 to 8 when executed on an electronic device.
10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the method for automatic segmentation of tumor image regions based on improved level set according to any one of claims 2 to 8.
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