TY - JOUR
T1 - Unified approach for multiple sclerosis lesion segmentation on brain MRI
AU - Sajja, Balasrinivasa Rao
AU - Datta, Sushmita
AU - He, Renjie
AU - Mehta, Meghana
AU - Gupta, Rakesh K.
AU - Wolinsky, Jerry S.
AU - Narayana, Ponnada A.
N1 - Funding Information:
This work is supported by National Institutes of Health Grant EB002095 to PAN.
PY - 2006/1
Y1 - 2006/1
N2 - 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.
AB - 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.
KW - Expectation maximization
KW - Feature classification
KW - Hidden Markov random field
KW - MRI
KW - Multiple sclerosis
KW - Segmentation
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U2 - 10.1007/s10439-005-9009-0
DO - 10.1007/s10439-005-9009-0
M3 - Article
C2 - 16525763
AN - SCOPUS:33645153542
SN - 0090-6964
VL - 34
SP - 142
EP - 151
JO - Annals of biomedical engineering
JF - Annals of biomedical engineering
IS - 1
ER -