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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- tensor
- image
- dispersion
- projection
- disease
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/757—Matching configurations of points or features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/24765—Rule-based classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
- G06V2201/032—Recognition 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
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410627555.9A CN104361318B (en) | 2014-11-10 | 2014-11-10 | A kind of medical diagnosis on disease accessory system based on diffusion tensor technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410627555.9A CN104361318B (en) | 2014-11-10 | 2014-11-10 | A kind of medical diagnosis on disease accessory system based on diffusion tensor technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104361318A true CN104361318A (en) | 2015-02-18 |
| CN104361318B CN104361318B (en) | 2018-02-06 |
Family
ID=52528576
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410627555.9A Active CN104361318B (en) | 2014-11-10 | 2014-11-10 | A kind of medical diagnosis on disease accessory system based on diffusion tensor technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104361318B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104715260A (en) * | 2015-03-05 | 2015-06-17 | 中南大学 | Multi-modal fusion image sorting method based on RLS-ELM |
| CN104866713A (en) * | 2015-05-12 | 2015-08-26 | 南京霁云信息科技有限公司 | Kawasaki disease and fever diagnosis system based on embedding of incremental local discrimination subspace |
| CN105184794A (en) * | 2015-09-07 | 2015-12-23 | 中国科学院深圳先进技术研究院 | CSM assistant analysis system and method based on tensor image |
| CN105654110A (en) * | 2015-12-04 | 2016-06-08 | 深圳先进技术研究院 | Supervised learning optimization method under tensor mode and system thereof |
| WO2017092022A1 (en) * | 2015-12-04 | 2017-06-08 | 深圳先进技术研究院 | Optimization method and system for supervised tensor learning |
| CN107330267A (en) * | 2017-06-28 | 2017-11-07 | 首都医科大学宣武医院 | Utilize the white matter fiber brain map construction method of diffusion tensor medical image |
| CN107590806A (en) * | 2017-09-19 | 2018-01-16 | 陈烨 | A kind of detection method and system based on brain medical imaging |
| CN108198622A (en) * | 2018-01-30 | 2018-06-22 | 上海蓬海涞讯数据技术有限公司 | The system that disease complexity quantitatively evaluating is realized based on computer software |
| CN108320804A (en) * | 2018-01-30 | 2018-07-24 | 上海蓬海涞讯数据技术有限公司 | The method for realizing disease complexity quantitatively evaluating based on disease fault network complexity evaluation system |
| TWI639830B (en) * | 2017-03-17 | 2018-11-01 | 長庚大學 | Method for identifying neurological diseases using magnetic resonance imaging images |
| CN109978871A (en) * | 2019-03-30 | 2019-07-05 | 西安电子科技大学 | Merge the fibre bundle screening technique of probabilistic type and the tracking of deterministic type fibre bundle |
| CN110993094A (en) * | 2019-11-19 | 2020-04-10 | 中国科学院深圳先进技术研究院 | Intelligent auxiliary diagnosis method and terminal based on medical images |
| CN111081372A (en) * | 2019-12-14 | 2020-04-28 | 中国科学院深圳先进技术研究院 | Disease diagnosis method, disease diagnosis device, terminal device and computer-readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103383716A (en) * | 2013-06-26 | 2013-11-06 | 北京理工大学 | Senile dementia computer-aided diagnosis method based on 18F-FDG PET image |
| CN103425999A (en) * | 2013-08-27 | 2013-12-04 | 西安电子科技大学 | Brain cognitive state judgment method based on non-negative tensor projection operator decomposition algorithm |
| US20140293069A1 (en) * | 2013-04-02 | 2014-10-02 | Microsoft Corporation | Real-time image classification and automated image content curation |
-
2014
- 2014-11-10 CN CN201410627555.9A patent/CN104361318B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140293069A1 (en) * | 2013-04-02 | 2014-10-02 | Microsoft Corporation | Real-time image classification and automated image content curation |
| CN103383716A (en) * | 2013-06-26 | 2013-11-06 | 北京理工大学 | Senile dementia computer-aided diagnosis method based on 18F-FDG PET image |
| CN103425999A (en) * | 2013-08-27 | 2013-12-04 | 西安电子科技大学 | Brain cognitive state judgment method based on non-negative tensor projection operator decomposition algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 李文: "磁共振扩散张量图像配准的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 牛少波: "基于张量学习的目标识别技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104715260A (en) * | 2015-03-05 | 2015-06-17 | 中南大学 | Multi-modal fusion image sorting method based on RLS-ELM |
| CN104866713B (en) * | 2015-05-12 | 2018-02-13 | 南京霁云信息科技有限公司 | Locally differentiate the Kawasaki disease and fever diagnostic system of subspace insertion based on increment |
| CN104866713A (en) * | 2015-05-12 | 2015-08-26 | 南京霁云信息科技有限公司 | Kawasaki disease and fever diagnosis system based on embedding of incremental local discrimination subspace |
| CN105184794A (en) * | 2015-09-07 | 2015-12-23 | 中国科学院深圳先进技术研究院 | CSM assistant analysis system and method based on tensor image |
| CN105184794B (en) * | 2015-09-07 | 2018-04-17 | 中国科学院深圳先进技术研究院 | A kind of CSM Computer Aided Analysis Systems and method based on tensor image |
| WO2017092022A1 (en) * | 2015-12-04 | 2017-06-08 | 深圳先进技术研究院 | Optimization method and system for supervised tensor learning |
| CN105654110A (en) * | 2015-12-04 | 2016-06-08 | 深圳先进技术研究院 | Supervised learning optimization method under tensor mode and system thereof |
| US10748080B2 (en) | 2015-12-04 | 2020-08-18 | Shenzhen Institutes Of Advanced Technology | Method for processing tensor data for pattern recognition and computer device |
| TWI639830B (en) * | 2017-03-17 | 2018-11-01 | 長庚大學 | Method for identifying neurological diseases using magnetic resonance imaging images |
| CN107330267A (en) * | 2017-06-28 | 2017-11-07 | 首都医科大学宣武医院 | Utilize the white matter fiber brain map construction method of diffusion tensor medical image |
| CN107590806A (en) * | 2017-09-19 | 2018-01-16 | 陈烨 | A kind of detection method and system based on brain medical imaging |
| CN107590806B (en) * | 2017-09-19 | 2021-06-01 | 陈烨 | Detection method and system based on brain medical imaging |
| CN108198622A (en) * | 2018-01-30 | 2018-06-22 | 上海蓬海涞讯数据技术有限公司 | The system that disease complexity quantitatively evaluating is realized based on computer software |
| CN108320804A (en) * | 2018-01-30 | 2018-07-24 | 上海蓬海涞讯数据技术有限公司 | The method for realizing disease complexity quantitatively evaluating based on disease fault network complexity evaluation system |
| CN109978871A (en) * | 2019-03-30 | 2019-07-05 | 西安电子科技大学 | Merge the fibre bundle screening technique of probabilistic type and the tracking of deterministic type fibre bundle |
| CN109978871B (en) * | 2019-03-30 | 2021-06-25 | 西安电子科技大学 | Fiber bundle screening method integrating probability type and determination type fiber bundle tracking |
| CN110993094A (en) * | 2019-11-19 | 2020-04-10 | 中国科学院深圳先进技术研究院 | Intelligent auxiliary diagnosis method and terminal based on medical images |
| CN111081372A (en) * | 2019-12-14 | 2020-04-28 | 中国科学院深圳先进技术研究院 | Disease diagnosis method, disease diagnosis device, terminal device and computer-readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104361318B (en) | 2018-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104361318A (en) | Disease diagnosis auxiliary system and disease diagnosis auxiliary method both based on diffusion tensor imaging technology | |
| US20220122250A1 (en) | Brain feature prediction using geometric deep learning on graph representations of medical image data | |
| Sarraf et al. | Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data | |
| Pachauri et al. | Topology-based kernels with application to inference problems in Alzheimer's disease | |
| Antelmi et al. | Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data | |
| US8861815B2 (en) | Systems and methods for modeling and processing functional magnetic resonance image data using full-brain vector auto-regressive model | |
| Gupta et al. | Natural image bases to represent neuroimaging data | |
| Khagi et al. | Pixel-label-based segmentation of cross-sectional brain MRI using simplified SegNet architecture-based CNN | |
| CN110534195B (en) | Alzheimer disease detection method based on data space transformation | |
| JP2023044669A (en) | Graph model-based brain function registration method | |
| Zhao et al. | Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder | |
| CN113688862B (en) | Brain image classification method based on semi-supervised federal learning and terminal equipment | |
| Zhang et al. | Integrative analysis of patient health records and neuroimages via memory-based graph convolutional network | |
| CN105184794A (en) | CSM assistant analysis system and method based on tensor image | |
| CN115272295A (en) | Dynamic brain function network analysis method and system based on time domain-space domain combined state | |
| Gutierrez-Becker et al. | Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks | |
| Pan et al. | Multi-classification prediction of Alzheimer’s disease based on fusing multi-modal features | |
| Mhiri et al. | Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning | |
| Bône et al. | Learning the spatiotemporal variability in longitudinal shape data sets | |
| Woo et al. | A sparse non-negative matrix factorization framework for identifying functional units of tongue behavior from MRI | |
| Dhinagar et al. | Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer’s Disease Detection | |
| Thirion et al. | Feature characterization in fMRI data: the Information Bottleneck approach | |
| CN112863664A (en) | Alzheimer disease classification method based on multi-modal hypergraph convolutional neural network | |
| CN112330645A (en) | Glioma grading method and device based on attention mechanism | |
| CN109241996B (en) | FMRI brain image analysis method based on gradient characterization similarity and Searchlight |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C53 | Correction of patent of invention or patent application | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wang Shuqiang Inventor after: Tan Weiqi Inventor after: Shen Yanyan Inventor after: Hu Jinxing Inventor after: Yin Ling Inventor after: Zeng Chunxia Inventor before: Wang Shuqiang Inventor before: Hu Yong Inventor before: Tan Weiqi Inventor before: Shen Yanyan Inventor before: Hu Jinxing Inventor before: Yin Ling Inventor before: Zeng Chunxia |
|
| COR | Change of bibliographic data |
Free format text: CORRECT: INVENTOR; FROM: WANG SHUQIANG HU YONG TAN WEIQI SHEN YANYAN HU JINXING YIN LING CENG CHUNXIA TO: WANG SHUQIANG TAN WEIQI SHEN YANYAN HU JINXING YIN LING CENG CHUNXIA |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |