Robust regression and outlier detection
Robust regression and outlier detection
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Automated Estimation of Brain Volume in Multiple Sclerosis with BICCR
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A Spatio-Temporal Model-Based Statistical Approach to Detect Evolving Multiple Sclerosis Lesions
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
A spatio-temporal multi-model data management and analysis environment for tracking MS lesions
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
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We present a method to detect intensity changes in longitudinal volumetric MRI data from patients with multiple sclerosis (MS). Preprocessing includes spatial and intensity normalization. The intra-subject intensity normalization is achieved using a polynomial least trimmed squares method to match the histograms of all images in the series. Viewing the detection of disease activity in MRI as a change-point problem, we present two statistical tests and apply them to a patient's series of grey-level images on a voxel-by-voxel basis. Results are compared with manual lesion segmentation for one MS patient scanned approximately every 5 months for 5 years. Results are also shown for 12 MS patients with 30 monthly scans.