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Proceedings Paper

Automated segmentation of MS lesions in FLAIR, DIR and T2-w MR images via an information theoretic approach
Author(s): Jason E. Hill; Kevin Matlock; Ranadip Pal; Brian Nutter; Sunanda Mitra
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Paper Abstract

Magnetic Resonance Imaging (MRI) is a vital tool in the diagnosis and characterization of multiple sclerosis (MS). MS lesions can be imaged with relatively high contrast using either Fluid Attenuated Inversion Recovery (FLAIR) or Double Inversion Recovery (DIR). Automated segmentation and accurate tracking of MS lesions from MRI remains a challenging problem. Here, an information theoretic approach to cluster the voxels in pseudo-colorized multispectral MR data (FLAIR, DIR, T2-weighted) is utilized to automatically segment MS lesions of various sizes and noise levels. The Improved Jump Method (IJM) clustering, assisted by edge suppression, is applied to the segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions, if present, into a subset of slices determined to be the best MS lesion candidates via Otsu’s method. From this preliminary clustering, the modal data values for the tissues can be determined. A Euclidean distance is then used to estimate the fuzzy memberships of each brain voxel for all tissue types and their 50/50 partial volumes. From these estimates, binary discrete and fuzzy MS lesion masks are constructed. Validation is provided by using three synthetic MS lesions brains (mild, moderate and severe) with labeled ground truths. The MS lesions of mild, moderate and severe designations were detected with a sensitivity of 83.2%, and 88.5%, and 94.5%, and with the corresponding Dice similarity coefficient (DSC) of 0.7098, 0.8739, and 0.8266, respectively. The effect of MRI noise is also examined by simulated noise and the application of a bilateral filter in preprocessing.

Paper Details

Date Published: 21 March 2016
PDF: 12 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842J (21 March 2016); doi: 10.1117/12.2217710
Show Author Affiliations
Jason E. Hill, Texas Tech Univ. (United States)
Kevin Matlock, Texas Tech Univ. (United States)
Ranadip Pal, Texas Tech Univ. (United States)
Brian Nutter, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (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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