Application of mean-shift clustering to Blood oxygen level dependent functional MRI activation detection
Tóm tắt
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. 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. 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. 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.
Tài liệu tham khảo
Bandettini P, Jesmanowicz A, Wong E, Hyde J: Processing strategies for time-course data sets in functional MRI of the human brain. Magn Reson Med. 1993, 30 (2): 161-173. 10.1002/mrm.1910300204.
Boynton G, Engel S, Glover G, Heeger D: Linear systems analysis of functional magnetic resonance imaging in human. J Neurosci. 1996, 16 (13): 4207-4221.
Bullmore E, Brammer M, Williams SCR, Rabe-Hesketh S, Janot N, David A, Mellers J, Howard R, Sham P: Statistical methods of estimation and inference for functional MR image analysis. Magn Reson Med. 1996, 35 (2): 261-277. 10.1002/mrm.1910350219.
Calhoun V, Adali T, McGinty V, Pekar J, Watson T, Pearlson G, fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis. NeuroImage. 2001, 14 (5): 1080-1088. 10.1006/nimg.2001.0921.
Cohen M: Parametric Analysis of fMRI Data Using Linear Systems Methods. NeuroImage. 1997, 6 (2): 93-103. 10.1006/nimg.1997.0278.
Friston K, Holmes A, Worsley K, Poline J, Frith C, Frackowaik R: Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp. 2004, 2 (4): 189-210.
Penny W, Friston K: Mixtures of general linear models for functional neuroimaging. IEEE Trans Med Imag. 2003, 22 (4): 504-514. 10.1109/TMI.2003.809140.
Foreman S, Cohen J, Fitzgerald M, Eddy W, Mintun M, Noll D: Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn Res Med. 1995, 33 (5): 636-647. 10.1002/mrm.1910330508.
Worsley K, Evans A, Marrett S, Neelin P: Three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab. 1992, 12 (6): 900-918. 10.1038/jcbfm.1992.127.
Xiong J, Gao J, Lancaster J, Fox P: Clustered Pixel Analysis for Functional MRI Activation Studies of the Human Brain. Hum Brain Mapp. 1995, 4 (4): 287-301.
MacQueen J: Some Methods for Classification and Analysis of Multivariate Observatoins. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967, Berkeley, USA: University of California Press, 281-297.
Bezdek J, Ehrlich R, Full W: FCM: The fuzzy C-Means Clustering Algorithm. Comput Geosci. 1984, 10 (2): 191-203.
Baumgartner R, Scarth G, Teichtmeister C, Somorjai R, Moser E: Fuzzy Clustering of Gradient-Echo Functional MRI in the Human Visual Cortex Part I: reproducibility. J. Magn Reson Imag. 1997, 7 (6): 1094-1101. 10.1002/jmri.1880070623.
Baumgartner R, Windischberger C, Moser E: Quantification in functional magnetic resonance imaging: fuzzy clustering vs correlation analysis. Magn Reson Imag. 1998, 16 (2): 115-125. 10.1016/S0730-725X(97)00277-4.
Moser E, Diemling M, Baumgartner R: Fuzzy clustering of gradient-echo functional MRI in the human visual cortex. Part II: quantification. J Magn Reson Imag. 1997, 7 (6): 1102-1108. 10.1002/jmri.1880070624.
Singh M, Patel P, Khosla D, Kim T: Segmentation of functional MRI by K-Means Clustering. IEEE Trans Nucl Sci. 1996, 43 (3): 2030-2036. 10.1109/23.507264.
Fukunaga K, Hostetler L: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans Inf Theory. 1975, 21 (1): 32-40. 10.1109/TIT.1975.1055330.
Cheng Y: Mean shift, Mode seeking, and Clustering. IEEE Trans Pattern Anal Mach Intell. 1995, 17 (8): 790-799. 10.1109/34.400568.
Dorin C, Peter M: Mean shift: a robust approach towards feature space analysis. IEEE Trans Pattern Anal Mach Intell. 2002, 24 (5): 603-619. 10.1109/34.1000236.
Mayer A, Greenspan H: An Adaptive Mean-Shift Framework for MRI Brain Segmentation. IEEE Trans Med Imag. 2009, 28 (8): 1238-1250.
Connolly C: The relationship between colour metrics and the appearance of three-dimensional coloured objects. Color Res Appl. 1996, 21: 331-337. 10.1002/(SICI)1520-6378(199610)21:5<331::AID-COL2>3.0.CO;2-Z.
Wyszecki G, Stilles W: Color Science: Concepts and Methods, Quantitative Data, and Formulae. 1982, New York: J Wiley
Parzen E: On Estimation of a Probability Density Function and Mode. Ann Math Statist. 1962, 33 (3): 1065-1076. 10.1214/aoms/1177704472.
Cox R: AFNI: Software for Analysis and Visualition of Functional Magnetic Resonance Neuroimages. Comput Biomed Res. 1996, 29 (3): 169-173.
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