CN113269196B - Method for realizing hyperspectral medical component analysis of graph convolution neural network - Google Patents
Method for realizing hyperspectral medical component analysis of graph convolution neural network Download PDFInfo
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Abstract
The invention discloses a method for realizing hyperspectral medicine component analysis of a graph convolution neural network, which is characterized in that on one hand, medicine hyperspectral image data are processed into graph data, so that the number of pixels is greatly reduced, and the data volume is effectively reduced; on the other hand, the characteristic information of the medicine is extracted by the graph convolution neural network model, the spatial relationship between the visual characteristic and the medicine component in the hyperspectral image of the medicine is effectively learned, the expression capability of the classification characteristic of the medicine component is improved, the component and attribute precision of the detected medicine is improved, and the nondestructive and rapid detection and analysis of the medicine component and the quality can be realized.
Description
Technical Field
The invention relates to the field of hyperspectral intelligent detection and analysis of high-end medicines, in particular to a method for realizing hyperspectral medicine component analysis of a graph convolution neural network.
Background
The medical safety is a big matter related to the health and economic development of people, has become a civil and public safety problem concerned by people all the time, and has great significance for maintaining the national stability and the social harmony and stability. The existing medicine component quality detection methods, such as a chemical detection method, a spectrophotometry method and the like, can only be suitable for sampling detection, are destructive and cannot meet the requirements of nondestructive detection of medicine quality. In recent years, the near infrared spectrum detection technology is widely applied in the field of drug analysis, and the spectrum information of the near infrared spectrum detection technology is a fingerprint-like feature with strong robustness and can be used for metering and classifying different drug components. The spectral detection method is recorded in the 'Chinese pharmacopoeia' 2015 edition as the guarantee for testing the quality of medicines, but the spectral detection method can only detect the quantitative information of the components of a tested sample at a light source irradiation point, and cannot analyze the whole components of medicines. Therefore, there is a need to develop a novel, versatile and reliable method for mass spectrometric detection and analysis of pharmaceutical ingredients.
The hyperspectral imaging technology can simultaneously acquire the spectral information and the spatial information of the detected medicine, the acquired data information amount is very rich, the integral property of the detected medicine can be accurately reflected, and the nondestructive testing analysis requirement of the integral component of the current medicine is well met. At present, the hyperspectral imaging technology is combined with a chemometrics related algorithm, and related researches such as identification of medicinal materials and tablets, uniformity distribution detection of active ingredients and auxiliary materials in solid tablets, composition and distribution condition monitoring of a drug-carrying film and the like are carried out in the pharmaceutical field, so that the hyperspectral imaging technology can be used as a high-efficiency nondestructive quality detection means in the pharmaceutical field. However, due to the fact that medicines are various in types and complex in components, and the amount of hyperspectral data is huge, effective characteristic information of the medicines is difficult to extract by a chemometric method, and the component and attribute prediction accuracy of the detected medicines is not high. Deep learning is good at exploring complex relationships in multidimensional data and is one of the best methods for processing and analyzing mass data at present. The graph neural network is a kind of neural network for processing graph domain information, and has been widely paid attention to medical fields such as brain science, medical diagnosis, drug discovery and research due to strong explanatory property on biomolecular structures and functional relationships between molecules. The graph neural network has good learning ability on the spatial characteristics of a topological data structure, but is difficult to be directly used in component analysis of medical hyperspectral images. Therefore, the visual information of the hyperspectral image of the medicine is urgently needed to be deeply explored aiming at the difficult problems of various medicine types and complex medicine component analysis, and the accuracy of the medicine component analysis is improved by combining the spatial characteristics of the medicine to be detected.
Disclosure of Invention
In view of the above, the invention provides a method for implementing a graph convolution neural network hyperspectral medical component analysis, which effectively implements lossless medical component analysis and rapid quality detection by learning spectral information characteristics and effective component spatial distribution characteristics of a drug in a hyperspectral medical image.
In one aspect, the invention provides a method for realizing hyperspectral medical component analysis of a graph convolution neural network
The method comprises the following steps:
step 1, acquiring a medical hyperspectral image, and constructing a medical hyperspectral data set, wherein the medical hyperspectral data set comprises a training set and a testing set;
step 2, segmenting the medical hyperspectral images in the training set by utilizing a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels, wherein the mutually non-overlapping superpixels form a medical hyperspectral superpixel set;
step 3, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data;
step 4, constructing a region adjacency graph by taking each super pixel as a graph node and the nearest neighbor super pixel as an edge, and obtaining an adjacency weight matrix of graph data;
and 6, repeating the steps 2 to 4 on the medical hyperspectral images in the test set to obtain a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to the medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in the step 5 to obtain a medicine component analysis result.
Further, the step 1 specifically includes the following steps:
step 1.1, preparing a drug sample: seven drug samples of cefprozil tablets, oxytetracycline tablets, chlorphenamine maleate tablets, furosemide tablets, aspirin enteric-coated tablets, pelteubicin tablets and callicarpa nudiflora dispersible tablets;
step 1.2, acquiring a medicine hyperspectral image and constructing a medicine hyperspectral data set: acquiring a medical hyperspectral image of a drug sample by adopting a hyperspectral sorter, performing reflectivity correction on the acquired medical hyperspectral image, and taking the corrected image as aA sample of a medical hyperspectral dataset;
step 1.3, medical hyperspectral data setRandom partitioning into training setsAnd test set, ,,,Is composed ofTo middleiThe image of one of the samples is taken,is composed ofTo middleiThe label of the drug component corresponding to each sample,for training setTo middleiThe image of one of the samples is taken,for training setTo middleiThe label of the drug component corresponding to each sample,to test the setTo middleiThe image of one of the samples is taken,to test the setTo middleiThe medicine component labels corresponding to the samples, d represents a medicine hyperspectral datasetTotal number of samples in (1), s represents the training setTotal number of samples in (1), m represents the test setTotal number of samples in (1).
Further, a K-fold cross validation method is adopted to verify the hyperspectral data set of the traditional Chinese medicine in the step 1.3Training setAnd test setThe division of (2).
Further, the step 2 is embodied as: performing hyperspectral image of medicine in the training set by SLIC algorithmDividing, calculating the space distance and the spectrum distance between pixel points, updating the super-pixel clustering center and the boundary range in an iterative mode, stopping iteration when the error between a new clustering center and an old clustering center is smaller than a preset threshold value, and thus obtaining super-pixels which are not overlapped with each other, wherein the super-pixels which are not overlapped with each other form a medical hyperspectral super-pixel set,Is as followsiN is the number of super pixels which are not overlapped with each other.
Further, the step 3 is embodied as: subjecting each super pixel obtained in step 2Obtaining each super pixelPixel mean ofCentroid pixelPosition ofCircumference length, ofArea ofAzimuth of areaAnd centroid pixelDistances from each super pixel region boundary to east, south, west, north, south, north and west 8 directionsThereby obtaining a feature matrixX,Wherein N is the number of superpixels, M is the feature dimension,representing a set of real numbers.
Further, the specific implementation of the adjacent weight matrix in step 4 includes the following steps:
step 4.1, according to the medical hyperspectral superpixel set V obtained in the step 2, superpixels in the medical hyperspectral superpixel setForming individual graph nodes, and selecting super-pixel by adopting K nearest neighbor algorithmConstructing edges by the nearest K super pixel points so as to form a region adjacency graph G;
step 4.2, according to each super-pixel area in the medical hyperspectral super-pixel set V obtained in the step 2, counting adjacent super-pixels of each super-pixel area to obtain an adjacent super-pixel set;
Step 4.3, obtaining the super pixel according to the step 3Pixel mean ofBetween each super pixelPixel mean distance of;
Step 4.4, obtaining the super pixel according to the step 3Centroid pixel ofPosition ofCalculating the distance of each superpixel interstitial center coordinate;
Step 4.5, obtaining the pixel mean distance between the super pixels according to the step 4.3And the super-pixel interstitial-to-heart coordinate distance obtained in step 4.4Calculating to obtain an adjacent weight matrix A,。
further, the specific implementation of step 5 includes the following steps:
step 5.1, initializing model parameters of graph convolution neural network model by using Xavier method;
Step 5.2, calculating a degree matrix D of each graph node according to the region adjacency graph G constructed in the step 4,;
step 5.3, calculating the characteristic H of each layer of the graph convolution neural network GCN in the graph convolution neural network model according to the following formula:
wherein,,Wfor the matrix of weight parameters that can be learned,is an activation function, andlwhen the value is not less than 0, the reaction time is not less than 0,,Xis a feature matrix;
step 5.4, in the training phase, the adjustment is carried out through graph convolution and micro-poolingWTo reduce the error on a continuous basis to optimize the output, the loss function is calculated by:
wherein,is a training sampleThe real label of (a) is,sin order to train the number of samples,Lis a loss function;
step 5.5, according to the loss functionLThe gradient of the whole graph convolution neural network model is adjusted through back propagationUsing the obtained parameter as the network initialization parameter in step 5.1, continuously iterating step 5.1 to step 5.5 until the graph volumeThe analytical precision of the product neural network model to the medicine components tends to be stable.
Further, the pixel mean distance between each superpixel in step 4.3Calculated from the following formula:
in the formula,is shown asiThe pixel mean of the individual super-pixels,is shown asjPixel mean of individual superpixels.
Further, each superpixel interstitial-to-heart coordinate distance in step 4.4Calculated from the following formula:
in the formula,is shown asiThe center of mass of each super-pixel,is shown asjThe center of mass of each super-pixel,is shown asiThe abscissa of the centroid of an individual super-pixel,is shown asiThe ordinate of the individual superpixel centroid,is shown asjThe abscissa of the centroid of an individual super-pixel,is shown asjThe ordinate of the individual superpixel centroid.
Further, the adjacency weight matrix a in step 4.5 is calculated by the following formula:
therefore, the method for realizing the hyperspectral medical component analysis of the atlas neural network comprises the steps of firstly, obtaining a medical hyperspectral image, and constructing a medical hyperspectral data set comprising a training set and a testing set; secondly, segmenting the medical hyperspectral images in the training set by utilizing a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels; then, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data; then, each super pixel is taken as a graph node, the nearest neighbor super pixel is taken as an edge, a region adjacency graph is constructed, and an adjacency weight matrix of graph data is obtained; thirdly, inputting the feature matrix, the adjacent weight matrix and the medical hyperspectral component labels corresponding to the medical hyperspectral images in the training set into a atlas neural network for training to obtain model parameters of the atlas neural network; and finally, steps 2 to 4, obtaining a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in step 5 to obtain a medicine component analysis result. Compared with the prior art, on one hand, the medical hyperspectral image data is processed into image data, so that the number of pixels is greatly reduced, and the data volume is effectively reduced; on the other hand, the characteristic information of the medicine is extracted by the graph convolution neural network model, the spatial relationship between the visual characteristic and the medicine component in the medicine hyperspectral image is effectively learned, the expression capability of the medicine component classification characteristic is improved, the component and attribute precision of the detected medicine is improved, the problems of various medicine types, complex composition components, different physical characteristics and the like are solved, and the nondestructive medicine component analysis and the rapid quality detection are realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an implementation method for hyperspectral pharmaceutical composition analysis by a graph convolution neural network according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for implementing a hyperspectral pharmaceutical composition analysis by a convolutional neural network according to a second embodiment of the present invention;
FIG. 3 is a flowchart illustrating an embodiment of a process for obtaining an adjacency weight matrix;
FIG. 4 is a block diagram of a schematic structural framework of a convolutional neural network model according to an embodiment of the present invention;
FIG. 5 is a sample schematic diagram of a portion of a hyperspectral pharmaceutical composition analysis dataset according to an embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a flowchart of an implementation method for hyperspectral pharmaceutical composition analysis by a graph-convolution neural network according to an embodiment of the present invention. As shown in FIG. 1, the method for realizing the hyperspectral medical component analysis of the atlas neural network is realized by the following steps:
step 1, acquiring a medical hyperspectral image, and constructing a medical hyperspectral data set, wherein the medical hyperspectral data set comprises a training set and a testing set;
step 2, segmenting the medical hyperspectral images in the training set by using a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels, wherein the mutually non-overlapping superpixels form a medical hyperspectral superpixel set;
step 3, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data;
step 4, constructing a region adjacency graph by taking each super pixel as a graph node and the nearest neighbor super pixel as an edge, and obtaining an adjacency weight matrix of graph data;
and 6, repeating the steps 2 to 4 on the medical hyperspectral images in the test set to obtain a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to the medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in the step 5 to obtain a medicine component analysis result.
Firstly, acquiring a medical hyperspectral image, and constructing a medical hyperspectral data set comprising a training set and a testing set; secondly, segmenting the medical hyperspectral images in the training set by utilizing a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels; then, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data; then, each super pixel is taken as a graph node, the nearest neighbor super pixel is taken as an edge, a region adjacency graph is constructed, and an adjacency weight matrix of graph data is obtained; thirdly, inputting the feature matrix, the adjacent weight matrix and the medical hyperspectral component labels corresponding to the medical hyperspectral images in the training set into a atlas neural network for training to obtain model parameters of the atlas neural network; and finally, repeating the steps 2 to 4 on the medical hyperspectral images in the test set to obtain a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to the medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in the step 5 to obtain a medicine component analysis result. Compared with the prior art, the method can accurately analyze different components of the medicine sample in the medical hyperspectral image, solves the problems of various medicine types, complex composition, different physical characteristics and the like, and realizes nondestructive medicine component analysis and rapid quality detection.
Referring to fig. 2 to 4, fig. 2 is a flowchart of an implementation method of hyperspectral pharmaceutical composition analysis by a graph convolution neural network according to a second embodiment of the present invention; FIG. 3 is a flowchart of an acquisition process of an adjacent weight matrix in the embodiment of the present invention, and FIG. 4 is a schematic structural framework diagram of a graph convolution neural network model in the embodiment of the present invention.
A method for realizing hyperspectral medical component analysis of a graph convolution neural network comprises the following steps:
1.1, preparing a plurality of different drug samples;
it should be noted that, in this example, experiments were performed on seven kinds of drug samples, including cefprozil tablets, oxytetracycline tablets, chlorphenamine maleate tablets, furosemide tablets, aspirin enteric tablets, peruviol tablets, and callicarpa nudiflora dispersible tablets, but the number and kind of the drugs are not limited thereto. Fig. 5 is a sample diagram of a part of a hyperspectral pharmaceutical ingredient analysis dataset of a cefprozil tablet, a chlorphenamine maleate tablet and a callicarpa nudiflora dispersible tablet, specifically, in fig. 5, (a) shows a sample diagram of a callicarpa nudiflora dispersible tablet, (b) shows a sample diagram of a cefprozil tablet, and (c) shows a sample diagram of a chlorphenamine maleate tablet.
Step 1.2, acquiring a medicine hyperspectral image and constructing a medicine hyperspectral data set: acquiring a medical hyperspectral image of a medicine sample by adopting a hyperspectral sorter, performing reflectivity correction on the acquired medical hyperspectral image, and taking the corrected image as a sample of a medical hyperspectral data set;
in the process, the high spectrum sorter preferably adopts Sichuan Lianghe spectrum high spectrum sorter (V10E, N25E-SWIR), and the spectral ranges are respectively 400-1000nm and 1000-2500 nm;
step 1.3, medical hyperspectral data setRandom partitioning into training setsAnd test set, , ,,Is composed ofTo middleiThe image of one of the samples is taken,is composed ofTo middleiThe label of the drug component corresponding to each sample,for training setTo middleiThe image of one of the samples is taken,for training setTo middleiThe label of the drug component corresponding to each sample,to test the setTo middleiThe image of one of the samples is taken,to test the setTo middleiThe medicine component labels corresponding to the samples, d represents a medicine hyperspectral datasetTotal number of samples in (1), s represents the training setTotal number of samples in (1), m represents the test setTotal number of samples in (1);
step 2, segmenting the medical hyperspectral images in the training set by utilizing a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels, wherein the mutually non-overlapping superpixels form a medical hyperspectral superpixel set;
preferably, this step is embodied as: dividing medical hyperspectral images in the training set by adopting a Simple Linear Iterative Clustering (SLIC) algorithm, iteratively updating a superpixel Clustering center and a boundary range by calculating a spatial distance and a spectral distance between pixel points, stopping iteration when an error between a new Clustering center and an old Clustering center is smaller than a preset threshold value, thereby obtaining non-overlapping superpixels, wherein the non-overlapping superpixels form a medical hyperspectral superpixel set,Is as followsiN is the number of the super pixels which are not overlapped;
step 3, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data;
specifically, the steps are represented as: subjecting each super pixel obtained in step 2Obtaining each super pixelPixel mean ofCentroid pixelPosition ofCircumference length, ofArea ofAzimuth of areaAnd centroid pixelDistances from each super pixel region boundary to east, south, west, north, south, north and west 8 directionsThereby obtaining a feature matrixX,Wherein N is the number of superpixels, M is the feature dimension,representing a set of real numbers;
step 4, constructing a region adjacency graph by taking each super pixel as a graph node and the nearest neighbor super pixel as an edge, and obtaining an adjacency weight matrix of graph data; specifically, referring to fig. 3, this step is decomposed into the following processes:
step 4.1, according to the medical hyperspectral superpixel set V obtained in the step 2, superpixels in the medical hyperspectral superpixel setForming individual graph nodes, and selecting super-pixel by adopting K nearest neighbor algorithmMost recent K superimagesConstructing edges by using the prime points so as to form a region adjacency graph G, wherein the value of K is 8;
step 4.2, according to each super-pixel area in the medical hyperspectral super-pixel set V obtained in the step 2, counting adjacent super-pixels of each super-pixel area to obtain an adjacent super-pixel set;
Step 4.3, obtaining the super pixel according to the step 3Pixel mean ofCalculating the pixel mean distance between each super pixelMean distance of pixel between each superpixelCalculated from the following formula:
in the formula,is shown asiThe pixel mean of the individual super-pixels,is shown asjA pixel mean of the individual superpixels;
step 4.4, obtaining the super pixel according to the step 3Centroid pixel ofPosition ofCalculating the distance of each superpixel interstitial center coordinateEach super pixel interstitial-to-heart coordinate distanceCalculated from the following formula:
in the formula,is shown asiThe center of mass of each super-pixel,is shown asjThe center of mass of each super-pixel,is shown asiThe abscissa of the centroid of an individual super-pixel,is shown asiThe ordinate of the individual superpixel centroid,is shown asjThe abscissa of the centroid of an individual super-pixel,is shown asjA vertical coordinate of the individual superpixel centroids;
step 4.5, obtaining the pixel mean distance between the super pixels according to the step 4.3And the super-pixel interstitial-to-heart coordinate distance obtained in step 4.4Calculating to obtain an adjacent weight matrix A,the adjacency weight matrix A is calculated by the following formula:
and 6, repeating the steps 2 to 4 on the medical hyperspectral images in the test set to obtain a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to the medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in the step 5 to obtain a medicine component analysis result.
As a preferred embodiment of the invention, a K-fold cross-validation method is adopted to perform verification on the hyperspectral data set of the traditional Chinese medicine in the step 1.3Training setAnd test setWherein K is 10.
Meanwhile, in a further technical scheme, the step 5 of inputting the feature matrix, the adjacent weight matrix and the medical hyperspectral component labels corresponding to the medical hyperspectral images in the training set into a convolutional neural network for training to obtain the specific realization of the model parameters of the convolutional neural network comprises the following steps:
step 5.1, initializing model parameters of graph convolution neural network model by using Xavier methodIt should be noted that the Xavier method is an effective neural network parameter initialization method, and the purpose of the method is mainly to make the variance of each layer output of the neural network equal as much as possible;
step 5.2, calculating a degree matrix D of each graph node according to the region adjacency graph G constructed in the step 4,;
step 5.3, calculating the characteristic H of each layer of the graph convolution neural network GCN in the graph convolution neural network model according to the following formula:
wherein,,Wfor the matrix of weight parameters that can be learned,is an activation function, andlwhen the value is not less than 0, the reaction time is not less than 0,,Xis a feature matrix;
step 5.4, in the training phase, the adjustment is carried out through graph convolution and micro-poolingWTo reduce errors on a continuous basis, thereby optimizingThe output, the loss function is calculated by:
wherein,is a training sampleThe real label of (a) is,sin order to train the number of samples,Lis a loss function; wherein L is calculated using the following formula of the cross entropy loss function:
in the formula,for training samplesThe real components of the (c) are,for training samplesPredicted component, s is the number of samples.
The atlas neural network model in fig. 4 includes the atlas layer, the atlas pooling layer, and the output layer.
Step 5.5, according to the loss functionLThe gradient of the whole graph convolution neural network model is adjusted through back propagationUsing the initial parameter as the network initialization parameter in step 5.1, continuously iterating step 5.1 to step 5.5 until the graph convolution neural networkThe model tends to be stable to the analysis precision of the medicine components.
Compared with the prior art, the medical hyperspectral image data are processed into image data, so that the number of pixels is greatly reduced, and the data volume is effectively reduced; the characteristic information of the medicine is extracted by the graph convolution neural network, the spatial relationship between the visual characteristic and the medicine component in the hyperspectral image of the medicine is effectively learned, the expression capability of the classification characteristic of the medicine component is improved, the component and attribute precision of the detected medicine is improved, and the nondestructive and rapid detection and analysis of the medicine component and the quality can be realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A method for realizing hyperspectral medical component analysis of a graph convolution neural network is characterized by comprising the following steps:
step 1, acquiring a medical hyperspectral image, and constructing a medical hyperspectral data set, wherein the medical hyperspectral data set comprises a training set and a testing set;
step 2, segmenting the medical hyperspectral images in the training set by utilizing a superpixel segmentation algorithm to obtain mutually non-overlapping superpixels, wherein the mutually non-overlapping superpixels form a medical hyperspectral superpixel set, and the detailed expression is as follows: dividing the medical hyperspectral images in the training set by adopting an SLIC algorithm, iteratively updating a superpixel clustering center and a boundary range by calculating a spatial distance and a spectral distance between pixel points, stopping iteration when an error between a new clustering center and an old clustering center is smaller than a preset threshold value, thereby obtaining mutually non-overlapping superpixels, wherein the mutually non-overlapping superpixels form a medical hyperspectral pixel set,Is as followsiN is the number of the super pixels which are not overlapped;
step 3, respectively counting the pixel mean value, the centroid pixel position, the perimeter, the area and the region azimuth angle of each super pixel, and the characteristic parameters of the distance from the centroid pixel to the boundary of each super pixel region, and constructing a characteristic matrix of the graph data;
step 4, constructing a region adjacency graph by taking each super-pixel as a graph node and the nearest neighbor super-pixel as an edge, obtaining an adjacency weight matrix of graph data, and specifically, according to the medical hyperspectral super-pixel set V obtained in the step 2, performing hyperspectral super-pixel collection on the super-pixels in the medical hyperspectral-pixel setForming individual graph nodes, and selecting super-pixel by adopting K nearest neighbor algorithmConstructing edges by the nearest K super pixel points so as to form a region adjacency graph G;
step 5, inputting the feature matrix, the adjacent weight matrix and the medical hyperspectral component labels corresponding to the medical hyperspectral images in the training set into a atlas neural network for training to obtain model parameters of the atlas neural network;
and 6, repeating the steps 2 to 4 on the medical hyperspectral images in the test set to obtain a region adjacency graph needing to be subjected to medicine component analysis, obtaining a feature matrix and an adjacency weight matrix of the region adjacency graph needing to be subjected to the medicine component analysis, and inputting the feature matrix and the adjacency weight matrix obtained in the test set into a graph convolution neural network model initialized by the model parameters trained in the step 5 to obtain a medicine component analysis result.
2. The method for realizing the hyperspectral medical composition analysis of the convolutional neural network according to claim 1, wherein the step 1 specifically comprises the following steps:
1.1, preparing a plurality of different drug samples;
step 1.2, acquiring a medicine hyperspectral image and constructing a medicine hyperspectral data set: acquiring a medical hyperspectral image of a medicine sample by adopting a hyperspectral sorter, performing reflectivity correction on the acquired medical hyperspectral image, and taking the corrected image as a sample of a medical hyperspectral data set;
step 1.3, medical hyperspectral data setRandom partitioning into training setsAnd test set, ,,,Is composed ofTo middleiThe image of one of the samples is taken,is composed ofTo middleiThe label of the drug component corresponding to each sample,for training setTo middleiThe image of one of the samples is taken,for training setTo middleiThe label of the drug component corresponding to each sample,to test the setTo middleiThe image of one of the samples is taken,to test the setTo middleiThe medicine component labels corresponding to the samples, d represents a medicine hyperspectral datasetTotal number of samples in (1), s represents the training setTotal number of samples in (1), m represents the test setTotal number of samples in (1).
3. The method for realizing the hyperspectral medical component analysis of the convolutional neural network according to claim 2, wherein a K-fold cross-validation method is adopted to perform hyperspectral medical data collection on the medicine in the step 1.3Training setAnd test setThe division of (2).
4. The method for implementing hyperspectral medical composition analysis by a convolutional neural network according to claim 3, wherein the step 3 is embodied as: subjecting each super pixel obtained in step 2Obtaining each super pixelPixel mean ofCentroid pixelPosition ofCircumference length, ofArea ofAzimuth of areaAnd centroid pixelDistances from each super pixel region boundary to east, south, west, north, south, north and west 8 directionsThereby obtaining a feature matrixX,Wherein N is the number of superpixels, M is the feature dimension,representing a set of real numbers.
5. The method for realizing the hyperspectral medical component analysis of the convolutional neural network according to claim 4, wherein the concrete realization of the adjacent weight matrix in the step 4 comprises the following steps:
step 4.1, according to the medical hyperspectral superpixel set V obtained in the step 2, superpixels in the medical hyperspectral superpixel setForming individual graph nodes, and selecting super-pixel by adopting K nearest neighbor algorithmConstructing edges by the nearest K super pixel points so as to form a region adjacency graph G;
step 4.2, according to each super-pixel area in the medical hyperspectral super-pixel set V obtained in the step 2, counting adjacent super-pixels of each super-pixel area to obtain an adjacent super-pixel set;
Step 4.3, obtaining the super pixel according to the step 3Pixel mean ofCalculating the pixel mean distance between each super pixel;
Step 4.4, obtaining the super pixel according to the step 3Centroid pixel ofPosition ofCalculating the distance of each superpixel interstitial center coordinate;
6. the method for realizing hyperspectral medical composition analysis of the convolutional neural network according to claim 5, wherein the concrete implementation of the step 5 comprises the following steps:
step 5.1, initializing model parameters of graph convolution neural network model by using Xavier method;
Step 5.2, calculating a degree matrix D of each graph node according to the region adjacency graph G constructed in the step 4,;
step 5.3, calculating the characteristic H of each layer of the graph convolution neural network GCN in the graph convolution neural network model according to the following formula:
wherein,,Wfor the matrix of weight parameters that can be learned,is an activation function, andlwhen the value is not less than 0, the reaction time is not less than 0,,Xis a feature matrix;
step 5.4, in the training phase, the adjustment is carried out through graph convolution and micro-poolingWTo reduce the error on a continuous basis to optimize the output, the loss function is calculated by:
wherein,is a training sampleThe real label of (a) is,sin order to train the number of samples,Lis a loss function;
step 5.5, according to the loss functionLThe gradient of the whole graph convolution neural network model is adjusted through back propagationAnd taking the parameter as the network initialization parameter in the step 5.1, and continuously iterating the step 5.1 to the step 5.5 until the analysis precision of the graph convolution neural network model to the medicine components tends to be stable.
7. The method for implementing the hyperspectral medical composition analysis of the convolutional neural network of claim 5, wherein the pixel mean distance between each superpixel in the step 4.3Calculated from the following formula:
8. The method for performing hyperspectral medical composition analysis according to claim 7, wherein the centroid position between each superpixel in step 4.4Marking distanceCalculated from the following formula:
in the formula,is shown asiThe center of mass of each super-pixel,is shown asjThe center of mass of each super-pixel,is shown asiThe abscissa of the centroid of an individual super-pixel,is shown asiThe ordinate of the individual superpixel centroid,is shown asjThe abscissa of the centroid of an individual super-pixel,is shown asjThe ordinate of the individual superpixel centroid.
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