Method for extracting and classifying fMRI features based on adaptive entropy algorithm for projection clustering (APEC)

A technology of adaptive entropy projection and clustering algorithm, which is applied in computing, computer components, instruments, etc., can solve problems such as poor clustering effect, inability to select the optimal clustering result, and the influence of subjective factors. The effect of objectively selecting questions, improving clustering ability, and clear physical meaning

Active Publication Date: 2015-06-10
NANJING UNIV OF TECH
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[0007] Aiming at the problem of fMRI data feature extraction and recognition, the present invention proposes a fMRI feature extraction and classification method based on an adaptive entropy projection clustering algorithm, which solves the problem of using traditional clustering for high-dimensional dynamic functional connectivity matrix data ...

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  • Method for extracting and classifying fMRI features based on adaptive entropy algorithm for projection clustering (APEC)
  • Method for extracting and classifying fMRI features based on adaptive entropy algorithm for projection clustering (APEC)
  • Method for extracting and classifying fMRI features based on adaptive entropy algorithm for projection clustering (APEC)

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[0027] In order to make the purpose and technical solution of the present invention clearer, the present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments. The functional magnetic resonance data of the brains of two groups of test participants of type A and type B with similar age and educational background were collected by magnetic resonance equipment in resting state scanning mode. Assume that there are currently 30 test participants whose brain status is Class A, and 30 test participants whose brain status is Class B, all of them are right-handed, and the brain function image data is scanned by the same instrument , the number of scan sequences is 224. In order to test the effectiveness of the algorithm, half of the test data of type A and type B were taken out as the training set data, and the remaining half was used as the test set. refer to figure 1 , a fMRI feature extraction and classification meth...

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Abstract

The invention discloses a method for extracting and classifying fMRI features based on adaptive entropy algorithm for projection clustering (APEC). The method comprises the steps of building a dynamic function connecting matrix by the sliding time window method; performing (AEPC) to cluster dynamic function connecting matrixes of parties in A type and B type tests so as to form a plurality of clustering centers; calculating the similarity between the functional connecting matrix of each test party and each clustering center to form a similarity matrix; extracting elements in the similarity matrix as the features; classifying brain data through a training SVM classifier. With the adoption of the method, the generalization of a data classifying model is improved, and moreover, rich brain dynamic structure information can be extracted; the method can be applied to automatic handling and classification of brain data in the study of the biology information technology.

Description

technical field [0001] The present invention relates to an automatic processing and classification method of functional Magnetic Resonance Imaging (fMRI), in particular to a fMRI feature extraction and classification method based on an adaptive entropy projection clustering algorithm, which involves digital images Knowledge areas such as processing, dynamic functional connectivity matrix, Adaptive Entropy Algorithm for Projective Clustering (AEPC) and Support Vector Machine (SVM). technical background [0002] In recent years, with the continuous development of science and technology, the equipment and instruments for observing brain activity have been increasing, and the non-invasive method of measuring brain function activity has greatly promoted the development of brain neurofunction. Currently, the methods used to study brain activity mainly include magnetoencephalography (MEG), electroencephalography (EEG), single photon emission tomography (SPECT), electron emission to...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 梅雪李微微马士林黄嘉爽
Owner NANJING UNIV OF TECH
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