CN104361318A - Disease diagnosis auxiliary system and disease diagnosis auxiliary method both based on diffusion tensor imaging technology - Google Patents

Disease diagnosis auxiliary system and disease diagnosis auxiliary method both based on diffusion tensor imaging technology Download PDF

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CN104361318A
CN104361318A CN201410627555.9A CN201410627555A CN104361318A CN 104361318 A CN104361318 A CN 104361318A CN 201410627555 A CN201410627555 A CN 201410627555A CN 104361318 A CN104361318 A CN 104361318A
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tensor
image
dispersion
projection
disease
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CN104361318B (en
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王书强
胡勇
谈维棋
申妍燕
胡金星
尹凌
曾春霞
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中国科学院深圳先进技术研究院
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G06K9/6203Shifting or otherwise transforming the patterns to accommodate for positional errors
    • G06K9/6211Matching configurations of points or features, e.g. constellation matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/626Selecting classification rules
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/627Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
    • G06K9/6271Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns based on distances to prototypes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05Recognition of patterns in medical or anatomical images
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05Recognition of patterns in medical or anatomical images
    • G06K2209/053Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.

Abstract

The invention belongs to the technical field of medical-image auxiliary diagnosis, in particular to a disease diagnosis auxiliary system and a disease diagnosis auxiliary method both based on a diffusion tensor imaging technology. The disease diagnosis auxiliary system comprises an image preprocessing module, an expert knowledge base module, a tensor learning module and a diagnosis result output module. The image preprocessing module is used for collection, re-registration and feature extraction of diffusion tensor images. The expert knowledge base module is used for establishing an expert knowledge base of diseases related to brains and spinal cords. The tensor learning module is used for performing tensor classification model training, optimizing and testing on the diffusion tensor images by means of the expert knowledge base and a tensor learning algorithm. The diagnosis result output module is used for outputting diagnosis results. The disease diagnosis auxiliary system and the disease diagnosis auxiliary method have the advantages that original information of the images is fully mined, mode classification accuracy is improved, calculation quantity is decreased greatly, and real-time performance of disease diagnosis is guaranteed effectively.

Description

A kind of medical diagnosis on disease backup system based on diffusion tensor technology and method

Technical field

The invention belongs to medical image aided diagnosis technique field, particularly relate to a kind of medical diagnosis on disease backup system based on diffusion tensor technology and method.

Background technology

Diffusion tensor (Diffusion Tensor Imaging, DTI) technology is current unique AT white matter nerve fiber bundles living imaging method, is the special shape of Magnetic resonance imaging (MRI).Being different from Magnetic resonance imaging is follow the trail of the hydrogen atom in hydrone, and diffusion tensor is according to the drawing of hydrone moving direction.Diffusion tensor figure can disclose brain tumor how to affect the nerves cell connect, guide healthcare givers carry out operation on brain.It can also disclose the trickle abnormality change of the regarding brain such as same apoplexy, multiple sclerosis, schizophrenia, Dyslexia and spinal cord.Diffusion tensor data are second-order tensor structure in essence, and its each voxel contains the three-dimensional spatial information of hydrone disperse in white matter nerve fiber bundles.

The effective information judging human body diseases can be extracted from original dispersion tensor image by machine learning method, thus provide strong help for the disease that forecast analysis brain is relevant with spinal cord.But traditional machine learning method is all the algorithm based on vector pattern, such as support vector machine, linear discriminant analysis and neural network etc., some scalar indexs of these algorithms or just a process diffusion tensor data, and the structure space information of dispersion tensor image can not be made full use of; Before analyzing and processing, first tensor is expanded into vector.But this way can bring following problem: 1, destroy the structure of raw data and the inherent correlativity of tensor structured data.2, brain diffusion tensor data are after vectorization, the dimension of its statistical parameter (being typically covariance matrix) generated is very big, the structure of raw data can be destroyed, the inherent correlativity of obliterated data, thus cause high computation complexity and storage cost.

Summary of the invention

The invention provides a kind of medical diagnosis on disease backup system based on diffusion tensor technology and method, be intended to solve the structure space information that existing vector pattern learning algorithm can not make full use of dispersion tensor view data, and in the process of tensor data vector, the structure of raw data can be destroyed, the inherent correlativity of obliterated data, increases the technical matters of computation complexity and storage cost.

The present invention is achieved in that a kind of medical diagnosis on disease backup system based on diffusion tensor technology, comprises image pre-processing module, expertise library module, tensor study module and diagnostic result output module; Described image pre-processing module is used for the accurate and dispersion tensor image characteristics extraction of the collection of dispersion tensor image, dispersion tensor image reprovision; Described expertise library module is for setting up the expert knowledge library of brain and spinal cord relevant disease; Described tensor study module is used for utilizing expert knowledge library and tensor learning algorithm carry out the training of tensor disaggregated model and the optimization of tensor disaggregated model to dispersion tensor image and test; Described diagnostic result output module is for exporting diagnostic result.

The technical scheme that the embodiment of the present invention is taked also comprises: described image pre-processing module comprises image acquisition units, image registration unit and feature extraction unit;

Described image acquisition units is for gathering dispersion tensor image;

Described image registration unit for carrying out the dispersion tensor image registration based on tensor similarity, then carries out the dispersion tensor image registration based on scalar similarity;

Described feature extraction unit is used for the feature extraction and the dimensionality reduction that carry out dispersion tensor image based on the polyteny core principle component analysis method of tensor algebra.

The technical scheme that the embodiment of the present invention is taked also comprises: feature extraction and the dimensionality reduction of described dispersion tensor image are specially: first choose suitable kernel function and raw data is mapped to respective feature space; Again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional; According to minimum alternately square principle, calculate and solve new feature.

The technical scheme that the embodiment of the present invention is taked also comprises: described tensor study module comprises tensor disaggregated model training unit and unit optimized by tensor disaggregated model;

Described tensor disaggregated model training unit is used for utilizing expert knowledge library and optimum projection to support that tensor machine carries out the training of tensor disaggregated model to dispersion tensor image;

Described tensor disaggregated model is optimized unit and is used for carrying out the optimization of tensor disaggregated model and test.

The technical scheme that the embodiment of the present invention is taked also comprises: the training of described tensor disaggregated model is specially: support that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection, find the optimum projection of image on boundary direction, and calculating optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection, the projection vector of described calculating optimum specifically comprises: matrix S between compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved; Training sample is projected by v, solves quadratic programming problem, in like manner try to achieve the optimum projection vector on second boundary direction; With scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix between class and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.

The technical scheme that the embodiment of the present invention is taked also comprises: the training of described tensor disaggregated model also comprises: support that tensor machine is analyzed two kinds of projection scheme and is based on optimum projection: the training sample of each sub-classifier is to same direction projection, or each sub-classifier determines different projection vectors, then training sample projects respectively on respective projection vector direction.

Another technical scheme that the embodiment of the present invention is taked is: a kind of medical diagnosis on disease householder method based on diffusion tensor technology, comprising:

Step a: gather dispersion tensor image, and carry out dispersion tensor image reprovision standard and dispersion tensor image characteristics extraction;

Step b: the expert knowledge library setting up brain and spinal cord relevant disease;

Step c: utilize expert knowledge library and tensor learning algorithm carry out the training of tensor disaggregated model and the optimization of tensor disaggregated model to dispersion tensor image and test;

Steps d: export diagnostic result.

The technical scheme that the embodiment of the present invention is taked also comprises: in described step a, and described dispersion tensor image reprovision standard comprises the dispersion tensor image registration based on tensor similarity and the dispersion tensor image registration based on scalar similarity.

The technical scheme that the embodiment of the present invention is taked also comprises: in described step a, and described dispersion tensor image characteristics extraction is: the polyteny core principle component analysis method based on tensor algebra carries out feature extraction and the dimensionality reduction of dispersion tensor image; Specifically comprise: first choose suitable kernel function and raw data is mapped to respective feature space, again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional, according to minimum alternately square principle, calculate and solve new feature.

The technical scheme that the embodiment of the present invention is taked also comprises: in described step c, the training of described tensor disaggregated model is specially: support that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection, find the optimum projection of image on boundary direction, and calculating optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection, the projection vector of described calculating optimum specifically comprises: matrix S between compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved; Training sample is projected by v, solves quadratic programming problem, in like manner try to achieve the optimum projection vector on second boundary direction; With scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix between class and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.

The medical diagnosis on disease backup system based on diffusion tensor technology of the embodiment of the present invention and method, for the feature more responsive than half-tone information of directional information in dispersion tensor image, propose double tolerance method for registering, secondly, feature extraction and dimensionality reduction that a kind of polyteny core principle component analysis method realizes dispersion tensor image are proposed, finally, for the limitation of vector pattern learning algorithm in process tensor structured data, the structural risk minimization principle of combining classification device and existing tensor pattern learning algorithm, and based on optimum projection vector criterion, propose to support tensor machine based on optimum projection, realize the medical diagnosis on disease Real time identification based on tensor pattern algorithm and dispersion tensor image, can under the prerequisite not destroying initial data structure, the original information of abundant excavation image, improve pattern classification precision, greatly can reduce calculated amount and assess the cost, and the real-time of effectively having protected medical diagnosis on disease whole.The present invention is used in the diseases analysis and prediction aspect that modification between astrocytoma, spiritual demyelinating pathology, development of central nervous system exception, schizophrenia, depression, cervical spondylotic myelopathy etc. be associated with brain and spinal cord, and the present invention simultaneously also may be used for the brain of normal person and the prevention of spinal cord relevant disease and health care aspect.

Accompanying drawing explanation

Fig. 1 is the structural representation of the medical diagnosis on disease backup system based on diffusion tensor technology of the embodiment of the present invention;

Fig. 2 is the process flow diagram of the medical diagnosis on disease householder method based on diffusion tensor technology of the embodiment of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.

Referring to Fig. 1, is the structural representation of the medical diagnosis on disease backup system based on diffusion tensor technology of the embodiment of the present invention.The medical diagnosis on disease backup system based on diffusion tensor technology of the embodiment of the present invention comprises image pre-processing module, expertise library module, tensor study module and diagnostic result output module; Wherein, the collection of image pre-processing module for dispersion tensor image, the standard of the dispersion tensor image reprovision based on tensor pattern and the dispersion tensor image characteristics extraction based on polyteny core principle component analysis method; Expertise library module is for setting up the expert knowledge library of brain and spinal cord relevant disease; Tensor study module is used for utilizing expert knowledge library and tensor learning algorithm carry out the training of tensor disaggregated model and the optimization of tensor disaggregated model to dispersion tensor image and test; Diagnostic result output module is for exporting diagnostic result.

Particularly, image pre-processing module comprises image acquisition units, image registration unit and feature extraction unit,

Image acquisition units is for gathering dispersion tensor image;

Image registration unit is used for, for the feature more responsive than half-tone information of directional information in dispersion tensor image, first carrying out the dispersion tensor image registration based on tensor similarity, then carrying out the dispersion tensor image registration based on scalar similarity.

Feature extraction unit is used for the feature extraction and the dimensionality reduction that realize dispersion tensor image based on the polyteny core principle component analysis method of tensor algebra, be specially: first choose suitable kernel function and raw data is mapped to respective feature space, again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional, according to minimum alternately square principle, calculate and solve new feature.Wherein, principal component analysis (PCA) (Principal Component Analysis, PCA) method be by multiple variable by linear transformation to select a kind of Multielement statistical analysis method of less number significant variable, also known as principal component analysis.

Tensor study module comprises tensor disaggregated model training unit and unit optimized by tensor disaggregated model,

Tensor disaggregated model training unit is used for utilizing expert knowledge library and optimum projection to support that tensor machine (OPSTM) carries out the training of tensor disaggregated model to dispersion tensor image; Be specially: utilize Fisher criterion, support that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection, find the optimum projection of image on boundary direction, and calculate optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection; Algorithm is divided into two steps to realize: matrix S between (1), compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved.(2), training sample projected by v, solve quadratic programming problem, in like manner can try to achieve the optimum projection vector on second boundary direction.The present invention to utilize between class scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.To support that tensor machine is generalized to many classification from two classification based on optimum projection, analyze two kinds of projection scheme: 1, the training sample of each sub-classifier is to same direction projection.2, each sub-classifier determines different projection vectors, and then training sample projects respectively on respective projection vector direction.Due to training sample is projected in tensor subspace, inhomogeneous training sample can be separated from each other, and similar training sample can be close to each other.This makes the scheme discrimination choosing same projection vector higher than the scheme choosing non-same projection vector.

Tensor disaggregated model is optimized unit and is used for carrying out the optimization of tensor disaggregated model and test; Be specially: in conjunction with multilinear algebra operation rule, for flow data feature, optimum projection is supported that tensor machine is generalized to online form, and the inequality constrain in optimization problem is converted into equality constraint, concerning diffusion tensor data (second-order tensor), only need to solve two systems of linear equations, carry out simple matrix inversion operation, significantly can reduce calculated amount.In objective function optimization method, take into full account the feature of flow data, utilize stochastic gradient descent method to complete Lagrange multiplier iteration.Assess the cost to keep the prototype structure information of flow data and reduce, utilize the CP of tensor to decompose auxiliary inner product of tensor computing, this while the natural structure information keeping original tensor, can reduce calculated amount and saves storage space.

Referring to Fig. 2, is the process flow diagram of the medical diagnosis on disease householder method based on diffusion tensor technology of the embodiment of the present invention.The medical diagnosis on disease householder method based on diffusion tensor technology of the embodiment of the present invention comprises the following steps:

Step 100: gather dispersion tensor image;

Step 200: registration is carried out to the dispersion tensor image of described collection.Specifically:

For the feature that directional information in dispersion tensor image is more responsive than half-tone information, first carry out the dispersion tensor image registration based on tensor similarity, then carry out the dispersion tensor image registration based on scalar similarity

Step 300: the polyteny core principle component analysis method based on tensor algebra carries out feature extraction and the dimensionality reduction of dispersion tensor image;

In step 300, feature extraction and the dimensionality reduction of dispersion tensor image are specially: first choose suitable kernel function and raw data is mapped to respective feature space, again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional, according to minimum alternately square principle, calculate and solve new feature.

Step 400: the expert knowledge library setting up brain and spinal cord relevant disease;

Step 500: find the optimum projection of above-mentioned dispersion tensor image on boundary direction, and calculate optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection.Specifically:

Be divided into two steps to realize: matrix S between (1), compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved.(2), training sample projected by v, solve quadratic programming problem, in like manner can try to achieve the optimum projection vector on second boundary direction.

Step 600: utilize expert knowledge library and optimum projection to support that tensor machine carries out the training of tensor disaggregated model;

In step 600, the training of tensor disaggregated model is specially: utilize Fisher criterion, supports that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection.The present invention to utilize between class scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.To support that tensor machine is generalized to many classification from two classification based on optimum projection, analyze two kinds of projection scheme: 1, the training sample of each sub-classifier is to same direction projection.2, each sub-classifier determines different projection vectors, and then training sample projects respectively on respective projection vector direction.Due to training sample is projected in tensor subspace, inhomogeneous training sample can be separated from each other, and similar training sample can be close to each other.This makes the scheme discrimination choosing same projection vector higher than the scheme choosing non-same projection vector.

Step 700: carry out the optimization of tensor disaggregated model and test;

In step 700, the optimization of tensor disaggregated model and test are specially: in conjunction with multilinear algebra operation rule, for flow data feature, optimum projection is supported that tensor machine is generalized to online form, and the inequality constrain in optimization problem is converted into equality constraint, concerning diffusion tensor data (second-order tensor), only need to solve two systems of linear equations, carry out simple matrix inversion operation, significantly can reduce calculated amount.In objective function optimization method, take into full account the feature of flow data, utilize stochastic gradient descent method to complete Lagrange multiplier iteration.Assess the cost to keep the prototype structure information of flow data and reduce, utilize the CP of tensor to decompose auxiliary inner product of tensor computing, this while the natural structure information keeping original tensor, can reduce calculated amount and saves storage space.

Step 800: export diagnostic result.

The medical diagnosis on disease backup system based on diffusion tensor technology of the embodiment of the present invention and method, for the feature more responsive than half-tone information of directional information in dispersion tensor image, propose double tolerance method for registering, secondly, feature extraction and dimensionality reduction that a kind of polyteny core principle component analysis method realizes dispersion tensor image are proposed, finally, for the limitation of vector pattern learning algorithm in process tensor structured data, the structural risk minimization principle of combining classification device and existing tensor pattern learning algorithm, and based on optimum projection vector criterion, propose to support tensor machine based on optimum projection, realize the medical diagnosis on disease Real time identification based on tensor pattern algorithm and dispersion tensor image, can under the prerequisite not destroying initial data structure, the original information of abundant excavation image, improve pattern classification precision, greatly can reduce calculated amount and assess the cost, and the real-time of effectively having protected medical diagnosis on disease whole.The present invention is used in the diseases analysis and prediction aspect that modification between astrocytoma, spiritual demyelinating pathology, development of central nervous system exception, schizophrenia, depression, cervical spondylotic myelopathy etc. be associated with brain and spinal cord, and the present invention simultaneously also may be used for the brain of normal person and the prevention of spinal cord relevant disease and health care aspect.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1. based on a medical diagnosis on disease backup system for diffusion tensor technology, it is characterized in that: comprise image pre-processing module, expertise library module, tensor study module and diagnostic result output module; Described image pre-processing module is used for the accurate and dispersion tensor image characteristics extraction of the collection of dispersion tensor image, dispersion tensor image reprovision; Described expertise library module is for setting up the expert knowledge library of brain and spinal cord relevant disease; Described tensor study module is used for utilizing expert knowledge library and tensor learning algorithm carry out the training of tensor disaggregated model and the optimization of tensor disaggregated model to dispersion tensor image and test; Described diagnostic result output module is for exporting diagnostic result.
2. the medical diagnosis on disease backup system based on diffusion tensor technology according to claim 1, is characterized in that, described image pre-processing module comprises image acquisition units, image registration unit and feature extraction unit;
Described image acquisition units is for gathering dispersion tensor image;
Described image registration unit for carrying out the dispersion tensor image registration based on tensor similarity, then carries out the dispersion tensor image registration based on scalar similarity;
Described feature extraction unit is used for the feature extraction and the dimensionality reduction that carry out dispersion tensor image based on the polyteny core principle component analysis method of tensor algebra.
3. the medical diagnosis on disease backup system based on diffusion tensor technology according to claim 2, it is characterized in that, feature extraction and the dimensionality reduction of described dispersion tensor image are specially: first choose suitable kernel function and raw data is mapped to respective feature space; Again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional; According to minimum alternately square principle, calculate and solve new feature.
4. the medical diagnosis on disease backup system based on diffusion tensor technology according to claim 1, is characterized in that, described tensor study module comprises tensor disaggregated model training unit and unit optimized by tensor disaggregated model;
Described tensor disaggregated model training unit is used for utilizing expert knowledge library and optimum projection to support that tensor machine carries out the training of tensor disaggregated model to dispersion tensor image;
Described tensor disaggregated model is optimized unit and is used for carrying out the optimization of tensor disaggregated model and test.
5. the medical diagnosis on disease backup system based on diffusion tensor technology according to claim 4, it is characterized in that, the training of described tensor disaggregated model is specially: support that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection, find the optimum projection of image on boundary direction, and calculating optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection, the projection vector of described calculating optimum specifically comprises: matrix S between compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved; Training sample is projected by v, solves quadratic programming problem, in like manner try to achieve the optimum projection vector on second boundary direction; With scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix between class and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.
6. the medical diagnosis on disease backup system based on diffusion tensor technology according to claim 5, it is characterized in that, the training of described tensor disaggregated model also comprises: support that tensor machine is analyzed two kinds of projection scheme and is based on optimum projection: the training sample of each sub-classifier is to same direction projection, or each sub-classifier determines different projection vectors, then training sample projects respectively on respective projection vector direction.
7., based on a medical diagnosis on disease householder method for diffusion tensor technology, comprising:
Step a: gather dispersion tensor image, and carry out dispersion tensor image reprovision standard and dispersion tensor image characteristics extraction;
Step b: the expert knowledge library setting up brain and spinal cord relevant disease;
Step c: utilize expert knowledge library and tensor learning algorithm carry out the training of tensor disaggregated model and the optimization of tensor disaggregated model to dispersion tensor image and test;
Steps d: export diagnostic result.
8. the medical diagnosis on disease householder method based on diffusion tensor technology according to claim 7, it is characterized in that, in described step a, described dispersion tensor image reprovision standard comprises the dispersion tensor image registration based on tensor similarity and the dispersion tensor image registration based on scalar similarity.
9. the medical diagnosis on disease householder method based on diffusion tensor technology according to claim 7, it is characterized in that, in described step a, described dispersion tensor image characteristics extraction is: the polyteny core principle component analysis method based on tensor algebra carries out feature extraction and the dimensionality reduction of dispersion tensor image; Specifically comprise: first choose suitable kernel function and raw data is mapped to respective feature space, again by tensor product by all Feature Mapping to multilinear subspace, make each subspace can capture the amount of variability of most of orthogonal multidimensional, according to minimum alternately square principle, calculate and solve new feature.
10. the medical diagnosis on disease householder method based on diffusion tensor technology according to claim 7, it is characterized in that, in described step c, the training of described tensor disaggregated model is specially: support that tensor machine carries out pattern-recognition to dispersion tensor image based on optimum projection, find the optimum projection of image on boundary direction, and calculating optimum projection vector by the ratio of the distribution of matrix in matrix and class between the class of sample after maximizing projection, the projection vector of described calculating optimum specifically comprises: matrix S between compute classes bwith matrix S in class wmaximum value the max [(V of the ratio of distribution ts bv)/(V ts wv)], the optimum projection vector v on first boundary direction is solved; Training sample is projected by v, solves quadratic programming problem, in like manner try to achieve the optimum projection vector on second boundary direction; With scatter matrix definition projection coefficient of dispersion R=Tr (S in scatter matrix between class and class b× S b t)/Tr (S w× S w t), R represents the degree of separation of training sample after the projection of optimum projection vector between sample, R value larger expression training sample after projecting sample separation from larger.
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