Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI. Export

@article{citeulike:549365, 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.}, address = {Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX, 77030, USA.}, author = {Sajja, Balasrinivasa R. and Datta, Sushmita and He, Renjie and Mehta, Meghana and Gupta, Rakesh K. and Wolinsky, Jerry S. and Narayana, Ponnada A.}, citeulike-article-id = {549365}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10439-005-9009-0}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16525763}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16525763}, day = {9}, doi = {10.1007/s10439-005-9009-0}, issn = {0090-6964}, journal = {Ann Biomed Eng}, keywords = {markov, medical, mrf}, month = {March}, posted-at = {2006-03-13 09:52:28}, priority = {2}, title = {Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI.}, url = {http://dx.doi.org/10.1007/s10439-005-9009-0}, year = {2006} }

TY - JOUR ID - citeulike:549365 L3 - citeulike-article-id:549365 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. JF - Ann Biomed Eng SN - 0090-6964 AD - Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX, 77030, USA. TI - Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI. KW - markov KW - medical KW - mrf AU - Sajja, Balasrinivasa AU - Datta, Sushmita AU - He, Renjie AU - Mehta, Meghana AU - Gupta, Rakesh AU - Wolinsky, Jerry AU - Narayana, Ponnada PY - 2006/03/09/ UR - http://dx.doi.org/10.1007/s10439-005-9009-0 ER -