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Proceedings Paper

Tissue classification of large-scale multi-site MR data using fuzzy k-nearest neighbor method
Author(s): Ali Ghayoor; Jane S. Paulsen; Regina E. Y. Kim; Hans J. Johnson
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Paper Abstract

This paper describes enhancements to automate classification of brain tissues for multi-site degenerative magnetic resonance imaging (MRI) data analysis. Processing of large collections of MR images is a key research technique to advance our understanding of the human brain. Previous studies have developed a robust multi-modal tool for automated tissue classification of large-scale data based on expectation maximization (EM) method initialized by group-wise prior probability distributions. This work aims to augment the EM-based classification using a non-parametric fuzzy k-Nearest Neighbor (k-NN) classifier that can model the unique anatomical states of each subject in the study of degenerative diseases. The presented method is applicable to multi-center heterogeneous data analysis and is quantitatively validated on a set of 18 synthetic multi-modal MR datasets having six different levels of noise and three degrees of bias-field provided with known ground truth. Dice index and average Hausdorff distance are used to compare the accuracy and robustness of the proposed method to a state-of-the-art classification method implemented based on EM algorithm. Both evaluation measurements show that presented enhancements produce superior results as compared to the EM only classification.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841V (21 March 2016); doi: 10.1117/12.2216625
Show Author Affiliations
Ali Ghayoor, The Univ. of Iowa (United States)
Jane S. Paulsen, The Univ. of Iowa Hospitals and Clinics (United States)
Regina E. Y. Kim, The Univ. of Iowa Hospitals and Clinics (United States)
Hans J. Johnson, The Univ. of Iowa (United States)
The Univ. of Iowa Hospitals and Clinics (United States)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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