3-D registration on carotid artery imaging data: MRI for different timesteps.

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance (ANOVA) showed that the p-values were <;0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry.

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition.

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35 dB on average.