Abstract: In this paper we consider the problem of change
detection and non stationary signals tracking. Using parametric
estimation of signals based on least square lattice adaptive filters we
consider for change detection statistical parametric methods using
likelihood ratio and hypothesis tests. In order to track signals
dynamics, we introduce a compensation procedure in the adaptive
estimation. This will improve the adaptive estimation performances
and fasten it-s convergence after changes detection.
Abstract: In Multiple Sclerosis, pathological changes in the
brain results in deviations in signal intensity on Magnetic Resonance
Images (MRI). Quantitative analysis of these changes and their
correlation with clinical finding provides important information for
diagnosis. This constitutes the objective of our work. A new approach
is developed. After the enhancement of images contrast and the brain
extraction by mathematical morphology algorithm, we proceed to the
brain segmentation. Our approach is based on building statistical
model from data itself, for normal brain MRI and including clustering
tissue type. Then we detect signal abnormalities (MS lesions) as a
rejection class containing voxels that are not explained by the built
model. We validate the method on MR images of Multiple Sclerosis
patients by comparing its results with those of human expert
segmentation.