Mahdi Alizadeh

Investigation of the electrochemical behavior of indium nitride thin films by plasma-assisted reactive evaporation

Indium nitride (InN) thin films were deposited on Si (111) substrate by plasma-assisted reactive evaporation with a variable radio frequency (RF) power supply. The effects of RF power on the structural, morphological, and optical properties of the films were investigated by X-ray diffraction analysis, scanning electron microscopy, energy-dispersive X-ray analysis, UV-vis transmittance, and micro Raman spectroscopy. The electrochemical behaviors of the InN thin films were investigated in 0.1 M KOH electrolyte towards electrochemical water splitting. Linear sweep voltammograms revealed that the anodic current decreases by increasing RF power for the growth of InN thin films. The charge transfer dynamics between the InN thin film and electrolyte interfaces during the electrochemical process were studied using electrochemical impedance spectroscopy (EIS). Variations in donor density and flat band potentials of the InN thin films were deduced from Mott–Schottky plots. Further, the electrocatalytic behavior of InN thin films was investigated with a K3[Fe(CN)6] redox probe. The good electrochemical behavior of InN thin films showed that this material could be a potential candidate for water splitting application.

Segmentation of Small Bowel Tumors in Wireless Capsule Endoscopy Using Level Set Method

In this paper, we proposed an algorithm to segment small bowel tumors. In order to increase effectiveness of Level Set Method (LSM) we applied adaptive gamma correction method (AGCM) that is based on prior information of illumination of images. We applied this method on 10 small bowel tumor images captured by Wireless Capsule Endoscopy (WCE). The performance measurements (i.e. sensitivity, specificity, and accuracy) by using hand ground method are computed for different parameters of a (0.05, 0.07, 0.09, 0.11, and 0.13) in AGCM, and then compared with traditional LSM and Snake method. The proposed method shows increased sensitivity up to 0.87 in a=0.13 while other performance measurements decrease by increasing value of a. the sensitivity of the other methods are 0.2 and 0.22, respectively. The optimal value of these measurements is 0.73 that takes place in a=0.1.

Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord

Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques, it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion and its directionality, both techniques are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption neglects pathological processes that are not Gaussian therefore reducing the amount of potentially clinically relevant information. Additions to the Gaussian distribution measured by the excess kurtosis, or peakedness, of the probabilistic model provide a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can offer additional information about the micromolecular environment of the pediatric spinal cord. This is accomplished by a more thorough characterization of the nature of random water displacement within the cord. A novel DKI imaging sequence based on a tilted 2D spatially selective radio frequency pulse providing reduced field of view (FOV) imaging was developed, implemented, and optimized on a 3 Tesla MRI scanner, and tested on pediatric subjects (healthy subjects: 15; patients with spinal cord injury (SCI):5). Software was developed and validated for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in mean kurtosis (p < 0.01) and radial kurtosis (p < 0.01) between healthy subjects and subjects with SCI. DKI provides incremental and novel information over conventional diffusion acquisitions when coupled with higher order estimation algorithms.

Numerical investigation of the plasma-aided fabrication of stoichiometric InAs nanodots at early stage of the growth

Using numerical modeling of the plasma sheath and key surface processes, the plasma-aided fabrication of InAs nanodots is investigated at early stage of the growth. Roles of different plasma process parameters, such as electron temperature, electron number density, and ion-to-electron density ratio, in achieving the stoichiometric growth of the nanodots are explored and conditions to achieve a highly stoichiometric InAs composition are discussed. It is shown that the nanodots get larger with increasing the electron temperature and electron number density, whereas they shrink in size with increasing the ion-to-electron density ratio. Moreover, it is shown that with increase in the electron temperature and electron number density stoichiometric saturation state can be reached shortly, which this enables the fabrication of highly stoichiometric array of nanodots within shorter processing time. The results obtained can open a path toward nucleation and growth of an array of nanodots with desired structural co...

Reduced FOV diffusion tensor MR imaging and fiber tractography of pediatric cervical spinal cord injury

Study design:Quantitative study.Objectives:To evaluate the effectiveness of pediatric spinal cord diffusion tensor tractography (DTT) generated from reduced field of view diffusion tensor imaging (DTI) data and investigate whether there are differences in these values between typically developing (TD) subjects and patients with spinal cord injury (SCI).Setting:Temple University Hospital and Shriners Hospitals for Children-Philadelphia, USA.Methods:A total of 20 pediatric subjects including 10 healthy subjects (age 15.13±3.51 years (mean±s.d.) and age range 11–21 years) and 10 subjects with SCI in the cervical area (age 13.8±3.26 years and age range 8–20 years) were recruited, and scanned using a 3.0T MR scanner. Quantitative parameters of DTI and fiber tracking, such as mean fractional anisotropy (FA), apparent diffusion coefficient (ADC), mean length of fiber tracts and tract density, were calculated for each subject.Results:Subjects with SCI showed reduced FA and tract density, and increased ADC values and length of fiber tracts, compared with controls. Statistically significant differences were seen in FA (P=0.0238) and tract density (P=0.0005) between controls and subjects with SCI, whereas there were no significant differences in ADC values and length of fiber tracts. The tractography visually showed that the white matter tracts (blue color) of the SCI patients were overall less abundant and less organized compared with control cases.Conclusion:The results show that DTI and DTT could be used as surrogate markers for quantification and visualization of the injured spinal cord.

A computer aided method to detect bleeding, tumor, and disease regions in Wireless Capsule Endoscopy

Wireless Capsule Endoscopy (WCE) is a relatively new technology to record the entire gastrointestinal (GI) tract, in vivo. A large amount of images (frames) are captured during the WCE examination. Reviewing this number of images by a gastroenterologist would be time consuming and prone to human error. Therefore, a diagnostic computer-aided technique is essential to detect and segment regions of abnormalities. In this study, a novel method based on textural features (such as Gabor filters, local binary pattern, and Haralick) in HSV color space, Fisher score test, and neural networks is presented to detect and differentiate regions such as bleeding, tumor, and other types of gastric diseases including Crohns, Lymphangectasia, Stenosis, Lymphoid Hyperslasia and Xanathoma. The experimental results indicate that this method is able to classify a lesion from a normal region in every single frame and group them into normal and abnormal frames to be considered for surgery/treatment planning by an expert.

Reduced Field of View Diffusion Tensor Imaging and Fiber Tractography of the Pediatric Cervical and Thoracic Spinal Cord Injury

The aim of this study is to assess the utility and effectiveness of diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) of the entire pediatric cervical and thoracic spinal cord toward discrimination of typically developing (TD) controls and subjects with spinal cord injury (SCI). A total of 43 pediatric subjects, including 23 TD subjects ranging in age from 6 to 16 years old and 20 subjects with SCI ranging in age from 7 to 16 years, were recruited and scanned using a 3.0 Tesla magnetic resonance scanner. Reduced field of view diffusion tensor images were acquired axially to cover the entire spinal cord across two slabs. For DTI analysis, motion correction was performed by coregistration of the diffusion-weighted images to the reference image (b0). Streamline deterministic tractography results were generated from the preprocessed data. DTI and DTT parameters of the whole cord, including fractional anisotropy (FA), mean diffusivity (MD), tract length, and tract density, were calculated, averaged across the whole spinal cord, and compared between the TD and SCI groups. Statistically significant decreases have been shown in FA (TD = 0.46 ± 0.11; SCI = 0.37 ± 0.09; p < 0.0001) and tract density (TD = 405.93 ± 243.84; SCI = 268.90 ± 270.34; p < 0.0001). However, the mean length of tracts and MD did not show significant differences. When investigating differences in DTI and DTT parameters above and below the injury site, it was shown that the FA and tract density in patients with cervical SCI decreased significantly in the thoracic region. An identical trend was observed in the cervical region for patients with thoracic SCI as well. When comparing TD and SCI subjects, FA and tract density were the most sensitive parameters in detecting functional changes of the spinal cord in chronic pediatric SCI. The results show that both DTI and DTT have the potential to be imaging biomarkers in the diagnosis of SCI.

Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system

Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.

Controlled Growth of Conductive AlN Thin Films by Plasma-Assisted Reactive Evaporation

In this work, the growth of conductive AlN thin films by plasma-assisted reactive evaporation at different filament-to-substrate distances was presented and discussed. The elemental composition, surface morphology, structural, optical, and electrical properties of the films were examined by energy-dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy, grazing incidence X-ray diffraction (GIXRD), Fourier transform infrared spectroscopy (FTIR), optical measurement, and current–voltage (I–V) characterizations. The electrical study revealed that the films are conductive, as ohmic conductivity was observed from I–V results. The GIXRD results of AlN thin films showed that by decreasing the distance, the intensity of the peak corresponding to metallic Al decreases while that of AlN increases. EDX and XPS results indicated that at shorter distances, the incorporation of N into the AlN films is enhanced. This was further confirmed by FTIR results, which showed that the incorporation of Al-N bonds in the grown AlN films was enhanced by decreasing the distance. It was shown that the optical absorption edge of the grown films shifts from the near-ultraviolet (UV) region to far-UV as the distance is decreased.

Detection of uninformative regions in wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is able to investigate the entire gastrointestinal tract including the small bowel. To reduce the reviewing time of captured images by gastroenterologists and increasing the accuracy rate for automatic detection of abnormalities, it is beneficial to remove regions which have less or no clinical information of small bowel texture (i.e., uninformative regions). In this research study, a multi-stage method including Chan-Vese, color range ratio, adaptive gamma correction method (AGCM), canny color edge detection operator, and morphological processing is proposed to detect these uninformative regions. The results support the effectiveness of the proposed method. In conclusion, the proposed method is a simple method to implement and performed well in removing the uninformative regions of small bowel images.