University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI

Abstract: Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern

Abstract: This study proposes a methodology to optimize truck arrival patterns to reduce emissions from idling truck engines at marine container terminals. A bi-objective model is developed minimizing both truck waiting times and truck arrival pattern change. The truck waiting time is estimated via a queueing network. Based on the waiting time, truck idling emissions are estimated. The proposed methodology is evaluated with a case study, where truck arrival rates vary over time. We propose a Genetic Algorithm based heuristic to solve the resulting problem. Result shows that, a small shift of truck arrivals can significantly reduce truck emissions, especially at the gate.

A New Variance Ratio Test of Random Walk in Emerging Markets: A Revisit

Abstract: Using a nonparametric variance ratio (VR) test, we revisit the empirical validity of the random walk hypothesis in eight emerging markets in the Middle East and North Africa (MENA). After correcting for measurement biases caused by thin and infrequent trading prevalent in nascent and small stock markets, we cannot reject the random walk hypothesis for the MENA markets. We conclude that a nonparametric VR test is appropriate for emerging stock markets, and argue that our findings can reconcile previously contradictory results regarding the efficiency of MENA markets.