Leakage suppression in human airway tree segmentation using shape optimization based on fuzzy connectivity method

Human airway tree segmentation from computed tomography (CT) images is a very important step for virtual bronchoscopic applications. Imaging artifacts or thin airway walls decrease the contrast between the air and airway wall and make the segmented region to leak from inside of the airway to the parenchyma. This in turn begins the leakage phenomenon to build and then large parts of the lung parenchyma might be erroneously marked as the airway tree instead. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. In this article, we propose a new shape based human airway segmentation scheme to suppress the leakage into surrounding area which is based on fuzzy connectivity (FC) method. Complex medical image features such as weak boundary edges in the CT images of the lung parenchyma have fuzzy properties and can be described by FC in many extents. Our method aims to embed a mathematical shape optimization approach in a FC algorithm. Using the partial derivatives of the image data that is minimized with respect to the polar angle and cylindrical axis direction, a proper cost function based on cylindrical features of the airway branches is proposed. This approach retains the cylindrical properties of the airway branches during the segmentation process. The proposed cost function includes two parts named cylindrical‐shape feature and smoothed final error term. The former term arranges the underlying voxels on a cylindrical shape and the latter term controls and smoothes the final error considering the local minima's problem. To evaluate the efficiency of our proposed optimization technique in term of segmentation accuracy, the cost function is first applied to the simulated data with the spongy shape of leakage and the leakage origin. The impact of each term of the proposed cost function on the final error and the convergence of the algorithm are also evaluated. Then, the cost function with best proper parameters is applied to real image dataset. Comparisons of the results on multidetector CT chest scans show that our segmentation algorithm outperforms the fuzzy region growing algorithm. Quantitative comparisons with manually segmented airway trees also indicate high sensitivity of our segmentation algorithm on peripheral airways. On the basis of the results, it is concluded that the proposed method is able to detect more branches up to the sixth generation with no leakage which provides 2–3 more generations of airways than several other methods do. The extracted airway trees enable image‐guided bronchoscopy to go deeper into the human lung periphery than past studies. The novelty of our proposed method is to apply a shape optimization approach embedded in an efficient FC segmentation algorithm. Hence, our method prevents leakage from its origination in contrast to most previously published works that just set their algorithms to repeat the segmentation steps to reduce leakage. As our results indicate leakage suppression in human airway segmentation instead of readjusting the segmentation parameters, more airway branches can be extracted with correct shape. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 71–84, 2013     

Estimation of the depth of frost penetration in both uniform and layered soils in frost-affected regions

One of the most important factors affecting pavement performance is climate, including frost action and precipitation. The performance of pavements in frost-affected regions depends to a large degree on the depth of frost penetration. In this paper, a simple predictive tool is developed to calculate a new correction coefficient depending upon the thermal ratio and fusion parameter. The new correction coefficient can be used in follow-up calculations to estimate the depth of frost penetration for both uniform and layered soils in frost-affected regions to evaluate the performance of pavement. The results of the proposed method are found to be in excellent agreement with reported data in the literature with average absolute deviation being less than 0.8%. The predictive tool is simple, straightforward and can be readily implemented in any standard spreadsheet programme leading to accurate, smooth and non-oscillatory data points. The prime application of the method is as a quick-and-easy evaluation tool in conceptual development and scoping studies in which the depth of frost penetration for both uniform and layered soils in frost-affected regions is being considered. The method may also serve as a benchmark in numerical and rigorous simulation studies.     

ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

DRAG REDUCTION IN A TURBULENT CHANNEL FLOW WITH HYDRO- PHOBIC WALL

This paper investigates a theoretical prediction of friction drag reduction in turbulent channel flow which is achieved by using superhydrophobic surfaces. The effect of the hydrophobic surface is considered to be a slip boundary condition on the wall, and this new boundary condition is added to Large Eddy Simulation (LES) equations. The predicted drag reduction at Reτ=180 is approximately 30%, which concurs with results obtained from Direct Numerical Simulation (DNS). An important implication of the present finding is that the near-wall turbulence structures are modified with streamwise slip velocity. In addition, a noticeable effect on the turbulence structure occurs when the slip length is greater than a certain value.