In this paper, a multiresolution approach is suggested for texture classification of atherosclerotic tissue from B-mode ultrasound. Four decomposition schemes, namely, the discrete wavelet transform, the stationary wavelet transform, wavelet packets (WP), and Gabor transform (GT), as well as several basis functions, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. The mean and standard deviation of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included 1) ranking the features in terms of their divergence values and 2) appropriately thresholding by a nonlinear correlation coefficient. The selected features were subsequently input into two classifiers using support vector machines (SVM) and probabilistic neural networks. WP analysis and the coiflet 1 produced the highest overall classification performance (90% for diastole and 75% for systole) using SVM. This might reflect WP's ability to reveal differences in different frequency bands, and therefore, characterize efficiently the atheromatous tissue. An interesting finding was that the dominant texture features exhibited horizontal directionality, suggesting that texture analysis may be affected by biomechanical factors (plaque strains).

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In this paper, three multiscale transforms with directional character, namely the dual-tree complex wavelet (DTCWT), the finite ridgelet (FRIT) and the fast discrete curvelet (FDCT) transforms, were comparatively assessed with respect to their ability to characterize carotid atherosclerotic plaque from B-mode ultrasound and discriminate between symptomatic and asymptomatic cases. The standard deviation and entropy of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included ranking the features according to their highest separability value and the minimum correlation among them. Due to the rather limited size of the sample population, the selected features were resampled 100 times by the bootstrap technique and divided into training and test sets. For each pair of sets, a support vector machine classifier was trained on the training set and evaluated on the test set. The average overall classification performance for systole (diastole) was 70% (65.2%), 72.6% (70.4%) and 84.9% (73.6%) for the DTCWT, FRIT and FDCT, respectively. These preliminary results showed the superiority of the curvelet transform, in terms of classification accuracy, being of great importance for the diagnosis and management of plaque instability in carotid atheromatous stenosis.

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The wavelet entropy (WE) of rest electroencephalogram (EEG) and of event-related potentials (ERP) carries information about the degree of order or disorder associated with a multi-frequency brain electrophysiological activity. In the present study, WE, relative WE and WE change were estimated for the EEG and ERP signals recorded during a working memory task, from dyslectic children and healthy subjects. The analysis of the two groups (controls vs dyslectics) revealed differentiations mainly in relative WE and WE change that takes into account the variability of rest EEG. These findings indicate that the WE can be employed as a quantitative measure for monitoring EEG and ERP activities and may provide a useful tool in analyzing electrophysiological signals associated with dyslexia.