Application of mean-shift clustering to Blood oxygen level dependent functional MRI activation detection

BMC Medical Imaging

Jinhu Xiong²  Xin Gao¹  Leo Ai³ 

[1] Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, China;Department of Radiology, University of Iowa, Iowa, USA;Department of Biomedical Engineering, University of Iowa, 200 Hawkins Drive C721 GH, Iowa City, IA 52242, USA

关键词: Clustering;    BOLD;    fMRI;    Mean-shift;   

Others  :  797823
DOI  :  10.1186/1471-2342-14-6

 received in 2013-09-06, accepted in 2014-01-28,  发布年份 2014

【 摘 要 】

Background

Functional magnetic resonance imaging (fMRI) analysis is commonly done with cross-correlation analysis (CCA) and the General Linear Model (GLM). Both CCA and GLM techniques, however, typically perform calculations on a per-voxel basis and do not consider relationships neighboring voxels may have. Clustered voxel analyses have then been developed to improve fMRI signal detections by taking advantages of relationships of neighboring voxels. Mean-shift clustering (MSC) is another technique which takes into account properties of neighboring voxels and can be considered for enhancing fMRI activation detection.

Methods

This study examines the adoption of MSC to fMRI analysis. MSC was applied to a Statistical Parameter Image generated with the CCA technique on both simulated and real fMRI data. The MSC technique was then compared with CCA and CCA plus cluster analysis. A range of kernel sizes were used to examine how the technique behaves.

Results

Receiver Operating Characteristic curves shows an improvement over CCA and Cluster analysis. False positive rates are lower with the proposed technique. MSC allows the use of a low intensity threshold and also does not require the use of a cluster size threshold, which improves detection of weak activations and highly focused activations.

Conclusion

The proposed technique shows improved activation detection for both simulated and real Blood Oxygen Level Dependent fMRI data. More detailed studies are required to further develop the proposed technique.

【 授权许可】

   
2014 Ai et al.; licensee BioMed Central Ltd.

【 图 表 】

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