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Proceedings Paper

Automated segmentation of MS lesions in brain MR images using localized trimmed-likelihood estimation
Author(s): Alfiia Galimzianova; Žiga Špiclin; Boštjan Likar; Franjo Pernuš
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Paper Abstract

Diagnosis and prognosis of patients with multiple sclerosis (MS) rely on quantitative markers derived from the analysis of magnetic resonance (MR) images. To compute these markers, a segmentation of lesions in the brain tissues, which are characteristic for MS disease, is needed. In this paper, we propose an unsupervised method for segmenting MS lesions that employs localized trimmed-likelihood estimation (TLE) to model the intensity distributions of normal appearing brain tissues (NABT). Compared to the original whole-brain TLE approach, the proposed method employs a set of three-component Gaussian mixture models for each of the spatially localized and non-overlapping subregions of the brain. The subregions were assigned by the customized balanced box decomposition that takes into account the spatial distribution and the cardinality of NABT tissues, as obtained from the initial whole-brain TLE. The proposed method was tested and compared to the original TLE approach on publicly available synthetic BrainWeb datasets. The results indicate a higher average Dice similarity coefficient both for the segmentation of NABT and MS lesions by using the proposed spatially localized TLE as compared to the original whole-brain TLE, which is due to the fact that the proposed method yields a more accurate NABT model and thus detects fewer false NABT outliers.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693E (13 March 2013); doi: 10.1117/12.2006381
Show Author Affiliations
Alfiia Galimzianova, Univ. of Ljubljana (Slovenia)
Žiga Špiclin, Univ. of Ljubljana (Slovenia)
Boštjan Likar, Univ. of Ljubljana (Slovenia)
Franjo Pernuš, Univ. of Ljubljana (Slovenia)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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