Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Surgical management of a mobile floating carotid plaque.

A mobile floating carotid plaque (MFCP) is an infrequent pathological lesion with an unknown natural history caused by thinning and rupture of the fibrous cap of the atheromatous plaque; it may result in repeated ischaemic strokes. Duplex carotid ultrasound is a non-invasive technique useful in defining the plaque morphology with high sensitivity and specificity. Due to the lack of evidence, treatment remains controversial. We present a patient with an asymptomatic MFCP diagnosed by ultrasound and treated with carotid endarterectomy without neurological complications due to the procedure and without restenosis nor residual flaps during the follow-up.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images.

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.