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Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

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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.

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Abbreviations

CSF :

Cerebrospinal fluid

EDSS :

Expanded disability status scale

EM :

Expectation maximization

FLAIR :

Fluid attenuation inversion recovery

FNM :

False negative minimization

FPM :

False positive minimization

FSE :

Fast spin echo

GM :

Gray matter

HMRF :

Hidden Markov random field

HMRF-EM :

Hidden Markov random field-expectation maximization

IDL :

Interactive data language

MR :

Magnetic resonance

MRF :

Markov random field

MRI :

Magnetic resonance imaging

MS :

Multiple sclerosis

PCE :

Percentage of correct estimation

PD :

Proton density

POE :

Percentage of overestimation

PUE :

Percentage of underestimation

SI :

Similarity index

SPM :

Statistical parametric mapping

WM :

White matter

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ACKNOWLEDGMENT

This work is supported by National Institutes of Health Grant EB002095 to PAN.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX, 77030, USA

    Balasrinivasa Rao Sajja, Sushmita Datta, Renjie He, Meghana Mehta & Ponnada A. Narayana PhD

  2. Department of Radiodiagnosis, SGPGIMS, Lucknow, 226 014, India

    Rakesh K. Gupta

  3. Department of Neurology, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, TX, 77030, USA

    Jerry S. Wolinsky

  4. Department of Diagnostic and Interventional Imaging, University of Texas at Houston Medical School, 6431 Fannin Street, Houston, TX, 77030, USA

    Ponnada A. Narayana PhD

Authors
  1. Balasrinivasa Rao Sajja
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  2. Sushmita Datta
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  3. Renjie He
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  4. Meghana Mehta
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  5. Rakesh K. Gupta
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  6. Jerry S. Wolinsky
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  7. Ponnada A. Narayana PhD
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Correspondence to Ponnada A. Narayana PhD.

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Sajja, B.R., Datta, S., He, R. et al. Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI. Ann Biomed Eng 34, 142–151 (2006). https://doi.org/10.1007/s10439-005-9009-0

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  • DOI: https://doi.org/10.1007/s10439-005-9009-0

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