Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing.

Background: Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy.

Objective: To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing.

Rapid Assessment of Deep Frying Oil Quality as Well as Water and Fat Contents in French Fries by Low-Field Nuclear Magnetic Resonance.

Most of the health hazards in fried foods are related to unqualified frying oil and excessive oil content. In this study, the feasibility of using low-field nuclear magnetic resonance techniques (LF-NMR) for analysis of the water and oil contents in French fries, as well as simultaneous evaluation of frying oil quality during deep frying, was investigated. Three proton populations were identified and successfully assigned to water and oil relaxation signals.

Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features.

Aimed to rapidly identify the edible oils according to their botanical origin, a novel method was proposed using supervised support vector machine based on low-field nuclear magnetic resonance and relaxation features. The low-field (LF) nuclear magnetic resonance (NMR) signals of 11 types of edible oils were acquired, and 5 features were extracted from the transverse relaxation decay curves and modeled using support vector machines (SVM) for the identification of edible oils. Two SVM classification strategies have been applied and discussed.

The inversion of 2D NMR relaxometry data using L1 regularization.

NMR relaxometry has been used as a powerful tool to study molecular dynamics. Many algorithms have been developed for the inversion of 2D NMR relaxometry data. Unlike traditional algorithms implementing L2 regularization, high order Tikhonov regularization or iterative regularization, L1 penalty term is involved to constrain the sparsity of resultant spectra in this paper.

An inversion method of 2D NMR relaxation spectra in low fields based on LSQR and L-curve.

The low-field nuclear magnetic resonance (NMR) inversion method based on traditional least-squares QR decomposition (LSQR) always produces some oscillating spectra. Moreover, the solution obtained by traditional LSQR algorithm often cannot reflect the true distribution of all the components. Hence, a good solution requires some manual intervention, for especially low signal-to-noise ratio (SNR) data.