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

BMC Medical Imaging

Research Article

Leo Ai¹  Xin Gao²  Jinhu Xiong³ 

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

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

DOI  :  10.1186/1471-2342-14-6

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

来源: Springer

【 摘 要 】

BackgroundFunctional 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.MethodsThis 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.ResultsReceiver 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.ConclusionThe 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】

CC BY   
© Ai et al.; licensee BioMed Central Ltd. 2014