Multiple sclerosis (MS) is a disease with heterogeneous evolution among the patients. Some classifications have
been carried out according to either the clinical course or the immunopathological profiles. Epidemiological data
and imaging are showing that MS is a two-phase neurodegenerative inflammatory disease. At the early stage it
is dominated by focal inflammation of the white matter (WM), and at a later stage it is dominated by diffuse
lesions of the grey matter and spinal cord. A Clinically Isolated Syndrome (CIS) is a first neurological episode
caused by inflammation/demyelination in the central nervous system which may lead to MS. Few studies have
been carried out so far about this initial stage. Better understanding of the disease at its onset will lead to a
better discovery of pathogenic mechanisms, allowing suitable therapies at an early stage.
We propose a new data processing framework able to provide an early characterization of CIS patients
according to lesion patterns, and more specifically according to the nature of the inflammatory patterns of these
lesions. The method is based on a two layers classification. Initially, the spatio-temporal lesion patterns are
classified using a tensor-like representation. The discovered lesion patterns are then used to identify group of
patients and their correlation to 15 months follow-up total lesion loads (TLL), which is so far the only image-based
figure that can potentially infer future evolution of the pathology.
We expect that the proposed framework can infer new prospective figures from the earliest imaging sign of
MS since it can provide a classification of different types of lesion across patients.
Estimation of the covariance matrix is a pivotal step in landmark based statistical shape analysis. For high dimensional representation of the shapes, often the number of available shape examples is far too small for ML covariance matrix is rank deficient and eigenvectors corresponding to the small eigenvalues. We take a Bayesian approach to the problem and show how the prior information can be used to estimate the covariance matrix from a small number of samples in a high dimensional shape space. The performance of the proposed method is evaluated in the context of reconstructions of high resolution vertebral boundary from an incomplete and lower dimensional representation. The algorithm performs better than the ML method, especially for small numbers of samples in the training set. The superiority of the proposed Bayesian approach was also observed when noisy incomplete lower dimensional representation of the vertebral boundary was used in the reconstruction algorithm. Moreover, unlike other commonly used approaches, e.g., regularization, the presented method does not depend heavily on the choice of the parameter values.