This paper presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. Our goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter can be considered as outliers and can severely bias the estimates of the former. For this purpose, we introduce a first method, which is an extension of the expectation-maximization (EM) algorithm, that estimates parameters of Gaussian mixtures but incorporates an outlier rejection scheme which allows to compute the properties of the desired tissues in presence of atypical data. The second method is based on genetic algorithms and is well suited for estimating the parameters of mixtures of different kind of distributions. We use this property by adding a uniform distribution to the Gaussian mixture for modeling the outliers. The proposed genetic algorithm can efficiently estimate the parameters of this extended mixture for various initial settings. Also, by changing the minimization criterion, estimates of the parameters can be obtained by histogram fitting which considerably reduces the computational cost. Experiments on synthetic and real MR data show that accurate estimates of the gray and white matters parameters are computed.

A layered motion estimation scheme using fuzzy clustering is introduced in this paper. Once motion estimation is performed a modified objective criterion is applied to discard non significant classes.

Keywords: LTS1 Reference LTS-CONF-1995-018 Record created on 2006-06-14, modified on 2016-08-08

This paper presents a hierarchical motion estimation method considering both a large spatial and temporal support. The problem of motion estimation is formulated as a time-varying parameter estimation problem. The main advantage of the proposed algorithm is its ability to constrain the computation of the flow field spatially and temporally at the same time, by defining each motion parameter as a linear combination of some orthogonal time functions. The number of time basis varies according to the complexity of the movement and is determined automatically by means of a multiresolution approach and of statistical tests. We also present two different applications which underline the validity of the chosen approach.