NDLI: Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

Content Provider	Springer Nature Link
Author	Sajja, Balasrinivasa Rao Datta, Sushmita He, Renjie Mehta, Meghana Gupta, Rakesh K. Wolinsky, Jerry S. Narayana, Ponnada A.
Copyright Year	2005
Abstract	The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.
Starting Page	142
Ending Page	151
Page Count	10
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	34
Issue Number	1
e-ISSN	15739686
Language	English
Publisher	Springer US
Publisher Date	2006-03-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Segmentation Feature classification Multiple sclerosis Expectation maximization Hidden Markov random field MRI Biochemistry Mechanics Biophysics/Biomedical Physics Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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