Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Resource management with stateful support for analytic applications
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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Neurodegenerative diseases are often associated with loss of brain tissue volume. Our objective was to develop and evaluate a fully automated method to estimate cerebral volume from magnetic resonance images (MRI) of patients with multiple sclerosis (MS). In this study, MRI data from 17 normal subjects and 68 untreated MS patients was used to test the method. Each MRI volume was corrected for image intensity non-uniformity, intensity normalized, brain masked and tissue classified. The classification results were used to compute a normalized metric of cerebral volume based on the Brain to IntraCranial Capacity Ratio (BICCR). This paper shows that the computation of BICCR using automated techniques provides a highly reproducible measurement of relative brain tissue volume that eliminates the need for precise repositioning. Initial results indicate that the measure is both robust and precise enough to monitor MS patients over time to estimate brain atrophy. In addition, brain atrophy may yield a more sensitive endpoint for treatment trials in MS and possibly for other neuro-degenerative diseases such as Huntington's or Alzheimer's disease.