Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization.

In this paper, an MRI image segmentation method based on two-dimensional survival exponential entropy (2DSEE) and particle swarm optimization (PSO) is proposed. The 2DSEE technique does not consider only the cumulative distribution of the gray level information but also takes advantage of the spatial information using the 2D-histogram. The problem with this method is its time-consuming computation that is an obstacle in real time applications for instance. We propose to use PSO algorithm, that was proved very efficient for non convex and combinatorial optimization. The experiments on segmentation of MRI images proved that the proposed method can achieve a satisfactory segmentation with a low computation cost.

Optimized brainstem auditory evoked potentials estimation using simulated annealing.

In this paper, we use a new approach based on Simulated Annealing for estimating the BAEPs (brainstem auditory evoked potentials). Each BAEP is obtained through a hundred of responses to stimulations. In case of endocochlear pathologies, it has been assumed that these signals could be randomly delayed from a response to another one. In such cases, the application of the averaging method systematically leads to "smoothed" BAEPs, thus complicating both identification and interpretation operations. The method presented in this paper consists in minimizing a non linear criterion in order to obtain an alignment of the various responses, before averaging them. Simulated and experimental results are presented, and compared to those produced by the classical method.

BAEP dynamic estimation in case of endocochlear pathologies using a time delay correction method.

Extraction of Brainstem Auditory Evoked Potentials (BAEPs) from the electroencephalogram (EEG) is generally difficult when both BAEP and EEG are non-stationary. In this paper we focus on the problem of BAEP non-stationarities, in particular those observed in some endocochlear pathologies assumed causing random delays of BAEPs due to an abnormal behaviour of the cochlea. The technique developed in this paper, called the Time Delay Correction (TDC) method, allows us to estimate the averaged BAEP by an optimal alignment of responses based on a correlation criterion. We demonstrate that the TDC method avoids wave smoothness, generally produced with the classical ensemble averaging method, especially in the case when the hypothesis of the time delay non-stationarity is verified. The TDC method is performed using simulated annealing (SA) algorithm, since the criterion to be optimized is nonlinear. Real signals recorded from pathological subjects are used to validate the model of non-stationarity.

Application of simulated annealing for estimating BAEPs in endocochlear pathologies.

In this paper, we propose a new approach aimed at handling the temporal Brainstem Auditory Evoked Potentials (BAEPs) non-stationarity. It is pointed out that for some endocochlear pathologies, BAEPs could be randomly delayed from one response to another. This non-stationarity leads to smoothed BAEPs when applying ensemble averaging or any other technique based on BAEPs stationarity. In that case, waves identification is very difficult, sometimes impossible. The problem consists in estimating time delays. Knowing the distribution of delays allows subsequent study of the dynamic of the cochlea and, perhaps, identification of the nature of its pathology. The approach suggested in this paper is based on Simulated Annealing, used to minimize a non-linear criterion involving delays. This technique is advantageously compared to the non-corrected ensemble averaging method, using a set of simulated data based on a realistic model. As an illustration, results based on real signals recorded from two patients are presented and discussed at the end of the paper.