Cluster partition method and system suitable for magnetic resonance image, and magnetic resonance image information processing device
By fusing the functional and structural features of magnetic resonance images using a multi-view algorithm, and combining Eigengap and spectral clustering methods, the problem of strong subjectivity in magnetic resonance image analysis is solved, enabling more accurate quantitative and statistical analysis and expanding its application value.
CN114882261BActive Publication Date: 2026-06-12韩少强
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 韩少强
- Filing Date
- 2022-04-20
- Publication Date
- 2026-06-12
AI Technical Summary
⚠Technical Problem
Current magnetic resonance imaging analysis mainly relies on subjective observation, which fails to fully realize its intrinsic value and lacks quantitative and intelligent statistical analysis methods.
⚗Method used
A multi-view algorithm is used to fuse magnetic resonance image features. By obtaining functional and structural similarity matrices, a similarity network fusion method is used to form a fusion matrix, and the clustering results are determined by combining Eigengap and spectral clustering methods.
🎯Benefits of technology
It enables accurate and comprehensive quantitative analysis and clustering of magnetic resonance imaging information, thereby enhancing its reference and application value.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN114882261B_ABST
Abstract
The application discloses a clustering division method and system suitable for magnetic resonance images and a magnetic resonance image information processing device. The method is realized by fusing magnetic resonance image features by using a multi-view algorithm. The method comprises the following steps: acquiring at least two feature similarity matrices according to magnetic resonance image information; fusing all the acquired feature similarity matrices by using a multi-view algorithm to obtain a fusion matrix, wherein the multi-view algorithm is realized by using a similarity network fusion method; and determining a clustering result of the magnetic resonance image information according to the obtained fusion matrix. The clustering division of the magnetic resonance image information is realized by extracting and fusing the magnetic resonance image features, and the clustering result is more accurate and more comprehensive.
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