Mitra, Pinaki S
(2006)
Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
| Title | Member | Email Address | Pitt Username | ORCID |
|---|
| Committee Chair | Gopalakrishnan, Vanathi | | | | | Committee Member | Chapman, Brian E | | | | | Committee Member | Stetten, George D | | | | | Committee Member | Cooper, Gregory F | | | | | Committee Member | Eddy, 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: |
http://etd.library.pitt.edu/ETD/available/etd-08102006-155213/, etd-08102006-155213 |
| Date Deposited: |
10 Nov 2011 19:58 |
| Last Modified: |
15 Nov 2016 13:48 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/9036 |
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