Sharif Digital Repository

Purpose: Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. Methods: In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed... 

A simple model is proposed to estimate the critical properties of alkanes-temperature, pressure, and volume. This model is dependent on normal boiling and molecular weight of hydrocarbons. The model has been expanded based on a Taylor series in the form of a polynomial function of normal boiling. Because methane has the simplest structure among hydrocarbons, this model has been calculated around methane. The coefficients of this model were assumed in the form of a linear function of molecular weight. These parameters can be obtained from regression between experimental results and results from this model. By a comparison of this model and previous models, it is shown that this model is... 

Objective: To determine the motor-behavioral and neural correlates of putative functional common variants in the sodium-channel NaV1.8 encoding gene (SCN10A) in vivo in patients with multiple sclerosis (MS). Methods: We recruited 161 patients with relapsing-onset MS and 94 demographically comparable healthy participants. All patients with MS underwent structural MRI and clinical examinations (Expanded Disability Status Scale [EDSS] and Multiple Sclerosis Functional Composite [MSFC]). Whole-brain voxel-wise and cerebellar volumetry were performed to assess differences in regional brain volumes between genotype groups. Resting-state fMRI was acquired from 62 patients with MS to evaluate... 

Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The...