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Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence

Mitra, Pinaki S (2006) Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Functional Magnetic Resonance Imaging (fMRI) has the potential to unlock many of the mysteries of the brain. Although this imaging modality is popular for brain-mapping activities, clinical applications of this technique are relatively rare. For clinical applications, classification models are more useful than the current practice of reporting loci of neural activation associated with particular disorders. Also, since the methods used to account for anatomical variations between subjects are generally imprecise, the conventional voxel-by-voxel analysis limits the types of discoveries that are possible. This work presents a classification-based framework for knowledge discovery from fMRI data. Instead of voxel-centric knowledge discovery, this framework is segment-centric, where functional segments are clumps of voxels that represent a functional unit in the brain. With simulated activation images, it is shown that this segment-based approach can be more successful for knowledge discovery than conventional voxel-based approaches. The spatial coherence principle refers to the homogeneity of behavior of spatially contiguous voxels. Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) - a new algorithm based on the spatial coherence principle is presented here for functional segmentation. With benchmark data, it is shown that the ACEIC method can achieve higher segmentation accuracy than Probabilistic Independent Component Analysis - a popular method used for fMRI data analysis. The spatial coherence principle can also be exploited for voxel-centric image-classification problems. Spatially Coherent Voxels (SCV) is a new feature selection method that uses the spatial coherence principle to eliminate features that are unlikely to be useful for classification. For a Substance Use Disorder dataset, it is demonstrated that feature selection with SCV can achieve higher classification accuracies than conventional feature selection methods.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Mitra, Pinaki
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairGopalakrishnan, Vanathi
Committee MemberChapman, Brian E
Committee MemberStetten, George D
Committee MemberCooper, Gregory F
Committee MemberEddy, William F
Date: 27 September 2006
Date Type: Completion
Defense Date: 26 July 2006
Approval Date: 27 September 2006
Submission Date: 10 August 2006
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: 4D image segmentation; 4D visualization; feature selection; fMRI artifacts; fMRI data analysis; fMRI head-motion; machine learning; medical image classification
Other ID:, etd-08102006-155213
Date Deposited: 10 Nov 2011 19:58
Last Modified: 15 Nov 2016 13:48


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