ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules.     

Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.     

Rotational hydrodynamic diffusion system to study mass transport across boundaries

The design and operation of a new mass transport technique is presented. Rotational hydrodynamic diffusion system (RHDS) is a method that can be adapted for analytical laboratory analysis as well as industrial-scale separation and purification. Although RHDS is not an electrochemical technique, its concept is derived from hydrodynamic rotating disk electrode voltammetry. A diffusion advantage gained using the RHDS is higher flux of probe molecules across the boundary (e.g., membrane or porous media) with increased rotation rate compared to the static two-half-cell (THC) method. The separation concept of RHDS differs from pressurized, agitated, electrodialysis, and reversed osmosis systems in design and theory. The detection mechanism of the RHDS opens the possibility to study mass transport properties of a large variety of molecules using different types of ultrathin membranes. Therefore, the RHDS is a potential alternative to classical mass transport detection methods such as THC, impedance spectroscopy, and cyclic and rotating disk electrode voltammetry. Theoretical analysis on the rotational hydrodynamic flux is derived and compared to experimental flux measured using HCl, KCl, KNO 3, Ni(NO 3) 2, LiCl, camphor sulfonic acid, and K 3Fe(CN) 6 ionic solutions. Values of effective diffusion coefficients of salts across Nucleopore membranes of thickness 6.0 and 10 mum with pore size 0.1 and 0.2 mum, respectively, are presented and discussed.     

Narrow-line waveguide terahertz time-domain spectroscopy of aspirin and aspirin precursors

Low frequency vibrational modes of pharmaceutical molecules are dependent on the molecule as a whole and can be used for identification purposes. However, conventional Fourier transform far-infrared spectroscopy (FT-IR) and terahertz time-domain spectroscopy (THz-TDS) often result in broad, overlapping features that are difficult to distinguish. The technique of waveguide THz-TDS has been recently developed, resulting in sharper spectral features. Waveguide THz-TDS consists of forming an ordered polycrystalline film on a metal plate and incorporating that plate in a parallel-plate waveguide, where the film is probed by THz radiation. The planar order of the film on the metal surface strongly reduces the inhomogeneous broadening, while cooling the waveguide to 77 K reduces the homogeneous broadening. This combination results in sharper absorption lines associated with the vibrational modes of the molecule. Here, this technique has been demonstrated with aspirin and its precursors, benzoic acid and salicylic acid, as well as the salicylic acid isomers 3- and 4-hydroxybenzoic acid. Linewidths as narrow as 20 GHz have been observed, rivaling single crystal measurements.     

Ledipasvir and Sofosbuvir for untreated HCV genotype 1 infection in end stage renal disease patients: A prospective observational study

Introduction: Hepatitis C virus (HCV) infection in end stage renal disease (ESRD) is associated with increased mortality. Recently, numerous directly acting antiviral agents have been approved for the management of HCV. Ledipasvir along with Sofosbuvir has been approved for management of genotype 1 infection in patients with eGFR ≥30 mL/min. However, there is paucity of data regarding its role in the management of patients on dialysis. Material and Methods: This is a single center prospective open label observational study to assess the safety and efficacy of Ledipasvir and Sofosbuvir in hemodialysis (HD) patients who were diagnosed with HCV genotype 1 infection. Eligibility criteria were treatment naive HD patients with normal liver histology. We administered Ledipasvir and Sofosbuvir combination tablet on alternate days for a period of 12 weeks. Primary efficacy end point was the assessment of sustained virological response (SVR12), and the safety end point was the discontinuation of therapy secondary to adverse drug effects. Results: A total of 21 patients were treated with this regimen. Two patients expired during the study period and are not related to the therapy. SVR12 was achieved in all the 19 patients. None of the patients in our study discontinued the therapy or had severe adverse drug effects. One patient had head ache and another patient had giddiness which were managed symptomatically. Conclusion: Ledipasvir and Sofosbuvir combination therapy on alternate days, is effective even in ESRD patients, with excellent SVR12 rates, and it is as safe as in other population groups, without any major adverse reactions.     

Distribution of volatile organic compounds over a semiconductor Industrial Park in Taiwan

This study examined volatile organic compounds (VOC) concentration in ambient air collected during the years 2000--2003 at several different locations of Hsinchu Science-based Industrial Park (HSIP) in Taiwan. A canister automated GC-MS system analyzed the volatile organics in ambient air grasp samples according to T0-15 method. Oxygenated volatiles were the most abundant VOC detected in HSIP followed by aromatics that are commonly used as solvents in the semiconductor industries. The major components measured in the ambient air are 2-propanol (29-135 ppbv), acetone (12-164 ppbv), benzene (0.7-1.7 ppbv), and toluene (13-20 ppbv). At some of the sampling locations, odorous compounds such as carbon disulfide and dimethyl sulfide levels exceed threshold values. The estimated toluene/benzene ratio is very high at most of the sites. However, the total amount of VOC is reduced over the years from 2000 to 2003 due to strict implementation on use and discharge of solvents in industries. There exists no definite seasonal pattern for sporadic occurrence of high levels of some of the volatile organics. Stagnant weather conditions with low wind speeds aid accumulation of toxic species at ground level. The results entail that hi-tech semiconductor industries are still a potential source for harmful organic substances to surrounding microenvironment.     

Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images

Wireless Personal Communications - The research work proposes a novel triangular fuzzy membership (TFM) function-based contrast limited adaptive histogram equalization (CLAHE) for biometric image...     

Acoustic image enhancement using Gaussian and laplacian pyramid – a multiresolution based technique

Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.     

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.