Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

Objective

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features.

Materials and methods

T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.

Results

The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).

Conclusion

A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

     

Double-hierarchy hesitant fuzzy linguistic information-based framework for green supplier selection with partial weight information

Neural Computing and Applications - Green supplier selection (GSS) is a crucial issue in green supply chain management. CAPS indicate that industries spend yearly USD 25 million per procurement,...     

Performance of zinc phosphate coatings obtained by cathodic electrochemical treatment in accelerated corrosion tests

The formation of zinc phosphate coating by cathodic electrochemical treatment and evaluation of its corrosion resistance is addressed. The corrosion behaviour of cathodically phosphated mild steel substrate in 3.5% sodium chloride solution exhibits the stability of these coatings, which lasts for a week's time with no red rust formation. Salt spray test convincingly proves the white rust formation in the scribed region on the painted substrates and in most part of the surface on unpainted surface. The protective ability of the zinc corrosion product formed on the surface of the coated steel is evidenced by the decrease in the loss in weight due to corrosion of the uncoated mild steel, when it is galvanically coupled with cathodically phosphated mild steel. Potentiodynamic polarization curves reveal that E_corr shifts towards higher cathodic values (in the range of −1000 to −1100 mV versus SCE) compared to that of uncoated mild steel and conventionally phosphated mild steel substrates. The i_corr value is also very high for these coatings. EIS studies reveal that zinc dissolution is the predominant reaction during the initial stages of immersion. Subsequently, the non-metallic nature of the coating is progressively increased due to the formation of zinc corrosion products, which in turn enables an increase in corrosion resistance with increase in immersion time. The zinc corrosion products formed may consist of zinc oxide and zinc hydroxychloride.     

A new method to evaluate the feasibility of a dye in DSSC application

Search for better photo sensitizers has always been a challenge in the field of dye sensitized solar cell (DSSC). This paper suggests a new method to identify a good dye for DSSC through the evaluation of energy levels of dye–TiO₂ complex. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of the complex are evaluated using cyclic voltammetry (CV) and spectroscopic technique. These energy levels have been used to predict the performance of the solar cell even without fabricating a cell. The authenticity of this method is also revalidated through the correlation of efficiency of practical cell. Copyright © 2017 John Wiley & Sons, Ltd.     

Time trends in the association of ESRD incidence with area‐level poverty in the US population

The objective of this study was to examine the temporal trends of the association between area‐level poverty status and end‐stage renal disease (ESRD) incidence. We hypothesized that the association between area‐level poverty status and ESRD incidence has increased significantly over time. Patient data from the United States Renal Data System were linked with data from the 2000 and 2010 US census. Area‐level poverty was defined as living in a zip code‐defined area with ≥20% of households living below the federal poverty line. Negative binomial regression models were created to examine the association between area‐level poverty status and ESRD incidence by time period in the US adult population while simultaneously adjusting for the distribution of age, sex, and race/ethnicity within a zip code. Time was categorized as January 1, 1995 through December 31, 2004 (Period 1) and January 1, 2005 through December 31, 2010 (Period 2). The percentage of adults initiating dialysis with area‐level poverty increased from 27.4% during Period 1 to 34.0% in Period 2. After accounting for the distribution of age, sex, and race/ethnicity within a zip code, area‐level poverty status was associated with a 1.24 (95% confidence interval [CI] 1.22, 1.25)‐fold higher ESRD incidence. However, this association differed by time period with 1.04‐fold (95% CI 1.02, 1.05) higher ESRD incidence associated with poverty status for Period 2 compared with the association between ESRD and poverty status in Period 1. Area‐level poverty and its association with ESRD incidence is not static over time.     

Enhanced visible light responsive photocatalysis by ZnO:Mg/RGO nanocomposites

Mg and RGO activated ZnO nanocomposites were prepared using a low-cost soft chemical method. As per the structural studies the samples exhibit wurtzite structure of ZnO with hexagonal crystal system. No secondary phases were observed. The photocatalytic activity of the prepared samples were assessed through the degradation of cationic dyes, methylene blue (MB) and malachite green (MG) under visible light irradiation. The studies revealed that the ZnO:Mg/RGO nanocomposite exhibits enhanced photocatalytic as well as antibacterial behavior compared to bare ZnO. The optical, structural and surface morphological studies support the reports on the photocatalytic and antibacterial activities of the synthesized samples.     

Application of digital image correlation method to biogel

This study adopts the digital image correlation (DIC) method to measure the mechanical properties under tension in agarose gels. A second polynomial stress–strain equation based on a pore model is proposed in this work. It shows excellent agreement with experimental data and was verified by finite element simulation. Evaluation of the planer strain field by DIC allows measurement of strain localization and Poisson's ratio. At high stresses, Poisson's ratio is found to exceed the standard assumption of 0.5 which is shown to be a result of pore water leakage. Local failure strains are found to be approximately twice those determined by crosshead displacements. Viscous properties of agarose gels are investigated by performing the tensile tests at various loading rates. Increases in loading rate do not cause much difference in the shape of stress–strain curves, but result in increases in ultimate stress and strain. POLYM. ENG. SCI., 50:1585–1593, 2010. © 2010 Society of Plastics Engineers     

Reconfigurable Antennas for Advanced Wireless Communications: A Review

Wireless Personal Communications - Modern era of wireless communication relies on the evolution of adaptive antennas. This influences the new age antenna designs to adapt themselves to the changing...     

Tuning violet to green emission in luminomagnetic Dy,Er co-doped ZnO nanoparticles

This paper discusses the synthesis of undoped ZnO, 2?mol% Dy doped ZnO, 2?mol% Er doped ZnO and 1?mol% Dy,Er co-doped ZnO nanoparticles by simple combustion technique and the characterization of their structural, morphological, magnetic and optical properties by X-ray diffraction (XRD), X-Ray Photoelectron Spectroscopy(XPS), Field Emission Scanning Electron Microscope (FESEM), High Resolution Transmission Electron Microscope (HRTEM), Diffuse Reflectance Spectroscope (DRS), Vibrating Sample Magnetometer (VSM) and Photoluminescence(PL). All samples are of hexagonal wurzite type structure which was found from XRD analysis. The effects of annealing on morphology and luminescence emission wavelengths were noticed in FESEM and PL, respectively. As-prepared sample displayed spherical morphology and annealed co-doped sample showed interwoven hexagonal stacking like morphology. VSM revealed the room temperature ferromagnetism in doped samples. The photoluminescence under the UV and IR excitations was observed in experiment. The as-prepared samples had violet region emission at the 325?nm excitation. The annealed samples had green region emission under the same excitation. Due to the annealing effect, the enhancement of upconversion luminescence intensity in co-doped sample in green (535?nm) and red (665?nm) regions was observed at the 980?nm excitation.     

A robust similarity based deep siamese convolutional neural network for gait recognition across views

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies.