CN104598575A - Brain-computer interactive image retrieval system based on real-time functional magnetic resonance imaging (fMRI) - Google Patents
Brain-computer interactive image retrieval system based on real-time functional magnetic resonance imaging (fMRI) Download PDFInfo
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Abstract
The invention discloses a brain-computer interactive image retrival system based on real-time functional magnetic resonance imaging (fMRI), so as to solve the problem of, needing to improve a brain-computer interactive image retrieval system in the prior art. The brain-computer interactive image retrieval system is used by comprising the following steps that (1) an image semantic real-time classification model, an image feature similarity real-time evaluation model and a visual attention real-time decoding model are trained by collecting fMRI data in advance; (2) a to-be-retrieved image is presented to a subject as visual stimulation, the fMRI data of the subject are collected at the same time, and the semantic class of the to-be-retrieved image is distinguished by using the image semantic classification model; (3) an image which represents the distinguished result of the semantic class and the to-be-retrieved image are presented to the subject together, and whether the semantic class which is distinguished by the image semantic classification model is correct is fed back by the subject through visual attention; (4) a retrieved image is given according to the distinguished result of the semantic class, which is fed back by the subject. The brain-computer interactive image retrieval system is significant in increasing the fMRI image retrieval accuracy.
Description
Technical field
This invention relates to a kind of brain-machine interaction image indexing system, particularly relates to a kind of brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging.
Background technology
The main method of functional mri (functional Magnetic Resonance Imaging) is blood oxygen level dependent (BOLD, Blood Oxygenation Level Dependent) method, the method utilizes MRI equipment to gather the information of brain, main reflection to be activated in brain district capillary the transient change of in units of voxel oxygenated blood red blood cell concentration, be called for short BOLD-fMRI (Blood oxygenation level dependent functional magnetic resonance imaging, BOLD-fMRI).Compared with other cerebral function imaging modes, BOLD-fMRI is imaged on speed and spatial resolution aspect and has very large advantage, especially the brain Cognitive Function Research of the relative complex such as visual performance is made to be carried out in a deep going way, therefore, utilize it to carry out main flow that brain Cognitive Study has become cerebral function imaging.
Real-time functional magnetic resonance imaging (rtfMRI, real-time functional Magnetic Resonance Imaging) adopt the online mode obtaining and process data, make data processing suitable with data acquisition speed, can in scanning process real-time monitoring data quality and one gather repetition time (TR, Time of Repetition) in complete and obtain result in real time, the brain activity state that result reflects is fed back to experimenter in the mode of vision or the sense of hearing, thus the information circuits formed again to experimenter from experimenter to fMRI, the brain-machine interaction based on rtfMRI is made to become possibility.
Existing achievement in research, mainly based on Images Classification and the identification of fMRI, and these researchs adopt the mode of off-line data process usually.Although these models and method can complete the assessment for image, semantic classification and characteristic similarity, also do not possess online image retrieval ability, independently cannot form image indexing system.
For the information decoding of human brain visual performance, be the Disciplinary Frontiers in brain science research and hot issue always, particularly study human brain visual performance related brain areas and can produce what kind of brain response to the image seen, achieve a series of key achievement.Calendar year 2001, the people such as Haxby utilize multi-voxel proton analytical approach to study the difference of 8 class image human brain visual performance fMRI data, give good semantic classification result.2008, the people such as Kay utilized Gabor wavelet pyramid to simulate the receptive field of human brain Visual Neuron, establish the image similarity assessment models based on brain signal, thus realize the accurate identification of 120 width images.The people such as Naselaris in 2009 propose image similarity based on Bayes framework and semantic fusion model.
Within 2007, first LaConte constructs the rtfMRI system can carrying out online classification, this system can adopt full brain image to train, trained listening group is utilized to carry out On-line funchon state classification, this system achieves the classification of two class emotional functions, demonstrates the feasibility using cerebral function state classification method to build brain-computer interface.The people such as Lee in 2009 build rtfMRI system and classify to the signal of the functional areas referred to right-hand man, then classification results are converted to the steering order of mechanical arm, complete the control of brain-computer interface to external unit.The people such as Andersson in 2013 are classified to vision attention force direction by SVM classifier, construct the rtfMRI brain machine interface system that can manipulate tele-robotic and carry out roaming.Also do not study at present and rtfMRI brain-computer interface technology is applied to image retrieval.
Summary of the invention
Instant invention overcomes the problem that the mutual image indexing system of prior art midbrain machine still needs to improve, a kind of brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging of better performances is provided.
Technical solution of the present invention is, provides a kind of brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging with following steps: containing following steps:
Step 1, by gathering fMRI data in advance, the semantic real-time grading model of training image, characteristics of image similarity real-time assessment model and vision attention real-time decoding model;
Step 2, present an image to be retrieved as visual stimulus to tested, gather tested fMRI data simultaneously, use the semantic classes of Image application processing Model checking image to be retrieved;
Step 3, present to tested together with image to be retrieved by the image representing differentiated semantic classes result, whether the tested semantic classes by vision attention feedback image semantic classification Model checking is correct;
Step 4, the result differentiated according to the semantic classification of tested feedback, the semantic real-time grading model of fused images and image similarity real-time assessment model, provide the image retrieved from image library.
The concrete steps of described step 1 are: the tested Ge Nao district registered placement that participates in advance is tested, an Image application processing training experiment and a vision attention classification based training experiment, gather corresponding fMRI data, train respectively based on the realtime graphic semantic classification model of support vector machine, vision attention real-time decoding model and based on the pyramidal image similarity assessment model of Gabor wavelet.
The concrete steps of described step 2 are: present an image to be retrieved as visual stimulus to tested, gather fMRI data simultaneously, stimulus duration is 3 TR(6s), then utilize the image, semantic real-time grading model trained to carry out semantic classification to stimulating image, whole data prediction and assorting process complete in 1 TR.
The concrete steps of described step 3 by: by represent the image of differentiation semantic classes result present to tested together with image to be retrieved, image to be retrieved is placed on the left side of visual stimulus screen, and semantic classes result images is placed on the right side of visual stimulus screen; Testedly differentiate whether result is correctly fed back by vision attention to semanteme, if kind judging is correct, then note seeing category result image, if kind judging mistake, then note treating retrieving images, vision attention reciprocal process continues 2 TR; Then utilize vision attention real-time decoding model to calculate tested visual attention information, thus obtain the whether correct information of semantic classes result of determination, data prediction and vision attention decode procedure complete in 1 TR; The result of vision decoding is presented to tested at next TR by visual stimulus screen.
The concrete steps of described step 4 are: the semantic classification according to tested feedback differentiates result, if differentiate, result is correct, 10 width images the highest with image similarity to be retrieved in image library are exported in visual stimulus screen as result for retrieval by direct use image similarity real-time assessment model; If differentiation erroneous results, use the semantic real-time grading model of Bayes method fused images and image similarity real-time assessment model, the 10 width images providing posterior probability maximum export in visual stimulus screen as result for retrieval, and all calculating of this process and result are presented in 2 TR and complete.
Compared with prior art, the brain-machine interaction image indexing system that the present invention is based on real-time functional magnetic resonance imaging has the following advantages: this system innovation proposes image, semantic real-time grading model and image similarity real-time assessment Model Fusion, and realize online image retrieval in conjunction with rtfMRI technology, real-time brain-machine interaction method is introduced image retrieval, significant to raising fMRI image retrieval accuracy.This image indexing system can as the platform utilizing human brain visual information Intelligent treatment mechanism, also can be regarded as the brain-machine interaction Vision information processing example on real-time functional magnetic resonance imaging basis, the research for human brain Vision information processing mechanism has unique effect and value.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the brain-machine interaction image indexing system that the present invention is based on real-time functional magnetic resonance imaging.
Embodiment
Below in conjunction with the drawings and specific embodiments, the brain-machine interaction image indexing system that the present invention is based on real-time functional magnetic resonance imaging is described further:
As shown in the figure, the present embodiment is mainly divided into model training, and semantic classes judges, the mutual and result for retrieval of classification judged result exports four functional modules.This system, by the preparation function of first online image retrieval of Implement of Function Module, mainly comprises and gathers fMRI data in advance, the semantic real-time grading model of training image, image similarity real-time assessment model and vision attention real-time decoding model; Then online retrieving is realized by rear three modules, comprising: present an image to be retrieved as visual stimulus by second module to tested, gather tested fMRI data simultaneously, first use the semantic classes of Image application processing Model checking image to be retrieved; Then present to tested by the 3rd module together with image to be retrieved by the image representing differentiated semantic classes result, whether the tested semantic classes by vision attention feedback image semantic classification Model checking is correct; Finally by the result that the 4th module differentiates according to the semantic classification of tested feedback, the semantic real-time grading model of fused images and image similarity real-time assessment model, provide the image that image indexing system retrieves.
Concrete steps are as follows, step 1: by gathering fMRI data in advance, the semantic real-time grading model of training image, image similarity real-time assessment model and vision attention real-time decoding model, concrete grammar is: the tested Ge Nao district registered placement that participates in advance is tested, an Image application processing training experiment and a vision attention classification based training experiment, gather corresponding fMRI data, train respectively based on the realtime graphic semantic classification model of support vector machine, vision attention real-time decoding model and based on the pyramidal image similarity assessment model of Gabor wavelet.
Step 2: present an image to be retrieved as visual stimulus to tested, gather tested fMRI data simultaneously, use the semantic classes of image, semantic real-time grading Model checking image to be retrieved, concrete grammar is: present an image to be retrieved as visual stimulus to tested, gather fMRI data, stimulus duration is 3 TR(6s simultaneously).Then utilize the image, semantic real-time grading model trained to carry out semantic classification to stimulating image, whole data prediction and assorting process complete in 1 TR.
Step 3: the image representing differentiated semantic classes result is presented to tested together with image to be retrieved, whether the tested semantic classes by the semantic real-time grading Model checking of vision attention feedback image is correct, concrete grammar by: by represent the image of differentiation semantic classes result present to tested together with image to be retrieved, image to be retrieved is placed on the left side of visual stimulus screen, and semantic classes result images is placed on the right side of visual stimulus screen.Testedly by vision attention, semanteme is differentiated whether result is correctly fed back, if kind judging is correct, then note seeing category result image, if kind judging mistake, then note treating retrieving images.Vision attention reciprocal process continues 2 TR.Then utilize vision attention real-time decoding model to calculate tested visual attention information, thus obtain the whether correct information of semantic classes result of determination, data prediction and vision attention decode procedure complete in 1 TR.The result of vision decoding is presented to tested at next TR by visual stimulus screen.
Step 4: the result differentiated according to the semantic classification of tested feedback, the semantic real-time grading model of fused images and image similarity real-time assessment model, provide the image that image indexing system retrieves, concrete grammar is: the semantic classification according to tested feedback differentiates result, if differentiate, result is correct, 10 width images the highest with image similarity to be retrieved in image library are exported in visual stimulus screen as result for retrieval by direct use image similarity real-time assessment model; If differentiation erroneous results, use the semantic real-time grading model of Bayes method fused images and image similarity real-time assessment model, the 10 width images providing posterior probability maximum export in visual stimulus screen as result for retrieval, and all calculating of this process and result are presented in 2 TR and complete.
Claims (5)
1., based on a brain-machine interaction image indexing system for real-time functional magnetic resonance imaging, it is characterized in that: containing following steps:
Step 1, by gathering fMRI data in advance, the semantic real-time grading model of training image, characteristics of image similarity real-time assessment model and vision attention real-time decoding model;
Step 2, present an image to be retrieved as visual stimulus to tested, gather tested fMRI data simultaneously, use the semantic classes of Image application processing Model checking image to be retrieved;
Step 3, present to tested together with image to be retrieved by the image representing differentiated semantic classes result, whether the tested semantic classes by vision attention feedback image semantic classification Model checking is correct;
Step 4, the result differentiated according to the semantic classification of tested feedback, the semantic real-time grading model of fused images and image similarity real-time assessment model, provide the image retrieved from image library.
2. the brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging according to claim 1, it is characterized in that: the concrete steps of described step 1 are: the tested Ge Nao district registered placement that participates in advance is tested, an Image application processing training experiment and a vision attention classification based training experiment, gather corresponding fMRI data, train respectively based on the realtime graphic semantic classification model of support vector machine, vision attention real-time decoding model and based on the pyramidal image similarity assessment model of Gabor wavelet.
3. the brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging according to claim 1, it is characterized in that: the concrete steps of described step 2 are: present an image to be retrieved as visual stimulus to tested, gather fMRI data simultaneously, stimulus duration is 3 TR(6s), then utilize the image, semantic real-time grading model trained to carry out semantic classification to stimulating image, whole data prediction and assorting process complete in 1 TR.
4. the brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging according to claim 1, it is characterized in that: the concrete steps of described step 3 by: by represent the image of differentiation semantic classes result present to tested together with image to be retrieved, image to be retrieved is placed on the left side of visual stimulus screen, and semantic classes result images is placed on the right side of visual stimulus screen; Testedly differentiate whether result is correctly fed back by vision attention to semanteme, if kind judging is correct, then note seeing category result image, if kind judging mistake, then note treating retrieving images, vision attention reciprocal process continues 2 TR; Then utilize vision attention real-time decoding model to calculate tested visual attention information, thus obtain the whether correct information of semantic classes result of determination, data prediction and vision attention decode procedure complete in 1 TR; The result of vision decoding is presented to tested at next TR by visual stimulus screen.
5. the brain-machine interaction image indexing system based on real-time functional magnetic resonance imaging according to claim 1, it is characterized in that: the concrete steps of described step 4 are: the semantic classification according to tested feedback differentiates result, if differentiate, result is correct, 10 width images the highest with image similarity to be retrieved in image library are exported in visual stimulus screen as result for retrieval by direct use image similarity real-time assessment model; If differentiation erroneous results, use the semantic real-time grading model of Bayes method fused images and image similarity real-time assessment model, the 10 width images providing posterior probability maximum export in visual stimulus screen as result for retrieval, and all calculating of this process and result are presented in 2 TR and complete.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105426446A (en) * | 2015-11-06 | 2016-03-23 | 西安电子科技大学 | Application of Gabor-Zernike characteristics in medical image retrieval |
| CN106022384A (en) * | 2016-05-27 | 2016-10-12 | 中国人民解放军信息工程大学 | Image attention semantic target segmentation method based on fMRI visual function data DeconvNet |
| CN106056602A (en) * | 2016-05-27 | 2016-10-26 | 中国人民解放军信息工程大学 | CNN (convolutional neural network)-based fMRI (functional magnetic resonance imaging) visual function data object extraction method |
| CN107092925A (en) * | 2017-03-30 | 2017-08-25 | 中国人民解放军国防科学技术大学 | Cerebral function magnetic resonance imaging blind source separation method based on SIM algorithms in groups |
| CN108121750A (en) * | 2016-11-30 | 2018-06-05 | 西门子公司 | A kind of model treatment method, apparatus and machine readable media |
| CN109567803A (en) * | 2018-12-18 | 2019-04-05 | 中国人民解放军战略支援部队信息工程大学 | Hippocampus self-control analysis method based on real-time neural feedback technology |
| CN109685804A (en) * | 2019-01-04 | 2019-04-26 | 清华大学深圳研究生院 | A kind of multichannel head NMR zeugmatographic imaging tissue segmentation methods |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778240A (en) * | 2014-02-10 | 2014-05-07 | 中国人民解放军信息工程大学 | Image retrieval method based on functional magnetic resonance imaging and image dictionary sparse decomposition |
-
2015
- 2015-01-14 CN CN201510016541.8A patent/CN104598575B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778240A (en) * | 2014-02-10 | 2014-05-07 | 中国人民解放军信息工程大学 | Image retrieval method based on functional magnetic resonance imaging and image dictionary sparse decomposition |
Non-Patent Citations (2)
| Title |
|---|
| BRUNO A.OLSHAUSEN 等: ""Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?"", 《VISION RESEARCH》 * |
| KENDRICK N.KAY 等: ""Identifying natural images from human brain activity"", 《NATURE》 * |
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| CN105426446A (en) * | 2015-11-06 | 2016-03-23 | 西安电子科技大学 | Application of Gabor-Zernike characteristics in medical image retrieval |
| CN106022384A (en) * | 2016-05-27 | 2016-10-12 | 中国人民解放军信息工程大学 | Image attention semantic target segmentation method based on fMRI visual function data DeconvNet |
| CN106056602A (en) * | 2016-05-27 | 2016-10-26 | 中国人民解放军信息工程大学 | CNN (convolutional neural network)-based fMRI (functional magnetic resonance imaging) visual function data object extraction method |
| CN106056602B (en) * | 2016-05-27 | 2019-06-28 | 中国人民解放军信息工程大学 | FMRI visual performance datum target extracting method based on CNN |
| CN108121750A (en) * | 2016-11-30 | 2018-06-05 | 西门子公司 | A kind of model treatment method, apparatus and machine readable media |
| CN108121750B (en) * | 2016-11-30 | 2022-07-08 | 西门子公司 | Model processing method and device and machine readable medium |
| CN107092925A (en) * | 2017-03-30 | 2017-08-25 | 中国人民解放军国防科学技术大学 | Cerebral function magnetic resonance imaging blind source separation method based on SIM algorithms in groups |
| CN107092925B (en) * | 2017-03-30 | 2019-09-17 | 中国人民解放军国防科学技术大学 | Cerebral function magnetic resonance imaging blind source separation method based on SIM algorithm in groups |
| CN109567803A (en) * | 2018-12-18 | 2019-04-05 | 中国人民解放军战略支援部队信息工程大学 | Hippocampus self-control analysis method based on real-time neural feedback technology |
| CN109567803B (en) * | 2018-12-18 | 2022-07-19 | 中国人民解放军战略支援部队信息工程大学 | Real-time neural feedback technology-based hippocampus self-regulation analysis method |
| CN109685804A (en) * | 2019-01-04 | 2019-04-26 | 清华大学深圳研究生院 | A kind of multichannel head NMR zeugmatographic imaging tissue segmentation methods |
| CN109685804B (en) * | 2019-01-04 | 2021-04-23 | 清华大学深圳研究生院 | Multi-channel head magnetic resonance imaging tissue segmentation method |
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Inventor after: Yan Bin Inventor after: Li Yongli Inventor after: Tong Li Inventor after: Zheng Zaizhou Inventor after: Wang Linyuan Inventor after: Ma Lijia Inventor after: Li Zhonglin Inventor after: Zeng Ying Inventor after: Zhang Chi Inventor before: Yan Bin Inventor before: Li Yongli Inventor before: Tong Li Inventor before: Zheng Zaizhou Inventor before: Wang Linyuan Inventor before: Ma Lijia Inventor before: Li Zhonglin Inventor before: Zeng Ying Inventor before: Zhang Chi |