Gripe water as reducing and stabilizing agent for synthesis of size controlled gold nanoparticles.

Green synthesis techniques are emerging as more facile and eco-friendly approach for the synthesis of metal nanoparticles compared to chemical reduction methods. Herein we report a new approach to synthesize gold nanoparticles (AuNPs) using gripe water as a reducing as well as stabilizing agent. Good control over the size of the nanoparticles from 3.2 nm to 25 nm has been achieved with this method by simply varying the experimental conditions. The Surface Plasmon Resonance bands of tunable gold nanospheres with high monodispersity and polydispersity have been obtained by this technique and monitored using UV-Visible spectrum. The morphology and the size of these AuNPs are determined using High Resolution Transmission Electron Microscope (HR-TEM). X-Ray Diffraction (XRD) analysis confirms the crystalline nature and the phase of the AuNPs. The as-synthesized AuNPs exhibit good optical nonlinearity. The nonlinear optical studies have been carried out by Z-scan technique to demonstrate its optical limiting property. The threshold limit of the AuNPs is obtained at a input intensity of 30 mW. The nonlinear refractive index of the nanoparticles is in the order of 10(-9) cm2/W and the third-order nonlinearity is estimated to be 7 x 10(-5) esu.

Single Image based Super Resolution Ultrasound Imaging Using Residual Learning of Wavelet Features.

The generation of super resolution ultrasound images from the low-resolution (LR) brightness mode (B-mode) images acquired by the portable point of care ultrasound systems has been of sufficient interest in the recent past. With the advancements in deep learning, there have been numerous attempts in this direction. However, all the approaches have been concentrated on employing the direct image as the input to the neural network. In this work, a stationary wavelet (SWT) decomposition is employed to extract the features from the input LR image which is passed through a modified residual network and the learned features are combined using the inverse SWT to reconstruct the high resolution (HR) image at a 4× scale factor. The proposed approach when compared to the state-of-the art approaches, results in an improved high resolution reconstruction.Clinical relevance- The proposed approach will enable the generation of high-resolution images from portable ultrasound systems, allowing for easier interpretation and faster diagnostics in primary care settings.

Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.

Eye care professionals generally use fundoscopy to confirm the occurrence of Diabetic Retinopathy (DR) in patients. Early DR detection and accurate DR grading are critical for the care and management of this disease. This work proposes an automated DR grading method in which features can be extracted from the fundus images and categorized based on severity using deep learning and Machine Learning (ML) algorithms. A Multipath Convolutional Neural Network (M-CNN) is used for global and local feature extraction from images. Then, a machine learning classifier is used to categorize the input according to the severity. The proposed model is evaluated across different publicly available databases (IDRiD, Kaggle (for DR detection), and MESSIDOR) and different ML classifiers (Support Vector Machine (SVM), Random Forest, and J48). The metrics selected for model evaluation are the False Positive Rate (FPR), Specificity, Precision, Recall, F1-score, K-score, and Accuracy. The experiments show that the best response is produced by the M-CNN network with the J48 classifier. The classifiers are evaluated across the pre-trained network features and existing DR grading methods. The average accuracy obtained for the proposed work is 99.62% for DR grading. The experiments and evaluation results show that the proposed method works well for accurate DR grading and early disease detection.

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images.

BACKGROUND AND OBJECTIVE: Retinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina. METHODS: In the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm. RESULTS: Training and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases. CONCLUSION: In the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina's actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.

RPE layer detection and baseline estimation using statistical methods and randomization for classification of AMD from retinal OCT.

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is a condition of the eye that affects the aged people. Optical coherence tomography (OCT) is a diagnostic tool capable of analyzing and identifying the disease affected retinal layers with high resolution. The objective of this work is to extract the retinal pigment epithelium (RPE) layer and the baseline (natural eye curvature, particular to every patient) from retinal spectral-domain OCT (SD-OCT) images. It uses them to find the height of drusen (abnormalities) in the RPE layer and classify it as AMD or normal. METHODS: In the proposed work, the contrast enhancement based adaptive denoising technique is used for speckle elimination. Pixel grouping and iterative elimination based on the knowledge of typical layer intensities and positions are used to obtain the RPE layer. Using this estimate, randomization techniques are employed, followed by polynomial fitting and drusen removal to arrive at a baseline estimate. The classification is based on the drusen height obtained by taking the difference between the RPE and baseline levels. We have used a patient, wise classification approach where a patient is classified diseased if more than a threshold number of patient images have drusen of more than a certain height. Since all slices of an affected patient will not show drusen, we are justified in adopting this technique. RESULTS: The proposed method is tested on a public data set of 2130 images/slices, which belonged to 30 patient volumes (15 AMD and 15 Normal) and achieved an overall accuracy of 96.66%, with no false positives. In comparison with existing works, the proposed method achieved higher overall accuracy and a better baseline estimate. CONCLUSIONS: The proposed work focuses on AMD/normal classification using a statistical approach. It does not require any training. The proposed method modifies the motion restoration paradigm to obtain an application-specific denoising algorithm. The existing RPE detection algorithm is modified significantly to make it robust and applicable even to images where the RPE is not very evident/there is a significant amount of perforations (drusen). The baseline estimation algorithm employs a powerful combination of randomization, iterative polynomial fitting, and pixel elimination in contrast to mere fitting techniques. The main highlight of this work is, it achieved an exact estimation of the baseline in the retinal image compared to the existing methods.

A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images.

BACKGROUND AND OBJECTIVE: One of the significant retinal diseases that affected older people is called Age-related Macular Degeneration (AMD). The first stage creates a blur effect on vision and later leads to central vision loss. Most people overlooked the primary stage blurring and converted it into an advanced stage. There is no proper treatment to cure the disease. So the early detection of AMD is essential to prevent its extension into the advanced stage. This paper proposes a novel deep Convolutional Neural Network (CNN) architecture to automate AMD diagnosis early from Optical Coherence Tomographic (OCT) images. METHODS: The proposed architecture is a multiscale and multipath CNN with six convolutional layers. The multiscale convolution layer permits the network to produce many local structures with various filter dimensions. The multipath feature extraction permits CNN to merge more features regarding the sparse local and fine global structures. The performance of the proposed architecture is evaluated through ten-fold cross-validation methods using different classifiers like support vector machine, multi-layer perceptron, and random forest. RESULTS: The proposed CNN with the random forest classifier gives the best classification accuracy results. The proposed method is tested on data set 1, data set 2, data set 3, data set 4, and achieved an accuracy of 0.9666, 0.9897, 0.9974, and 0.9978 respectively, with random forest classifier. Also, we tested the combination of first three data sets and achieved an accuracy of 0.9902. CONCLUSIONS: An efficient algorithm for detecting AMD from OCT images is proposed based on a multiscale and multipath CNN architecture. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in the detection of AMD. The proposed architecture can be applied in rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters.

Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier.

CONTEXT: Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations. AIM: Currently, there is a great interest in the prospects of image processing to provide quantitative information about a skin lesion, that can be relevance for the clinical images and also used as a stand-alone cautioning tool. SETTING AND DESIGN: To accomplish a powerful approach to recognize skin cancer without performing any unnecessary skin biopsies, this article presents a new hybrid technique for the classification of skin images using Firefly with K-Nearest Neighbor algorithm (FKNN). MATERIALS AND METHODS: FKNN classifier is used to predict and classify skin cancer along with threshold-based segmentation and ABCD feature extraction. Image preprocessing and feature extraction techniques are mandatory for any image-based applications. STATISTICAL ANALYSIS USED: Initially, it is essential to eliminate the illumination variation and the other unwanted shadow areas present in the skin image, which is done by homomorphic filtering called preprocessing. RESULTS: The comparison of our proposed method with other existing methods and a comprehensive discussion is explored based on the obtained results. CONCLUSION: The proposed FKNN provides a quantitative information about a skin lesion through hybrid KNN and firefly optimization that helps for recognizing the skin cancer efficiently than other technique with low computational complexity and time.

Automated classification of diabetic retinopathy through reliable feature selection.

Diabetic retinopathy (DR) is a complication of diabetes mellitus that damages the blood vessels in the retina. DR is considered a serious vision-threatening impediment that most diabetic subjects are at risk of developing. Effective automatic detection of DR is challenging. Feature extraction plays an important role in the effective classification of disease. Here we focus on a feature extraction technique that combines two feature extractors, speeded up robust features and binary robust invariant scalable keypoints, to extract the relevant features from retinal fundus images. The selection of top-ranked features using the MR-MR (maximum relevance-minimum redundancy) feature selection and ranking method enhances the efficiency of classification. The system is evaluated across various classifiers, such as support vector machine, Adaboost, Naive Bayes, Random Forest, and multi-layer perception (MLP) when giving input image features extracted from standard datasets (IDRiD, MESSIDOR, and DIARETDB0). The performances of the classifiers were analyzed by comparing their specificity, precision, recall, false positive rate, and accuracy values. We found that when the proposed feature extraction and selection technique is used together with MLP outperforms all the other classifiers for all datasets in binary and multiclass classification.

Nonlinear studies of Pararosanilin dye in liquid and solid media.

Solid-state dye-doped polymers are attractive alternative to the conventional liquid dye solutions. In this paper, nonlinear properties of the dye Pararosanilin has been studied. The third-order nonlinear optical properties of Pararosanilin dye in 1-butanol and dye-doped polymer film were measured by the Z-scan technique using 532 nm diode pumped Nd:Yag laser. This material exhibits negative optical nonlinearity. The dye at 0.4 mM concentration exhibited nonlinear refractive coefficient (n(2) = -6.8 x 10(-8) and -7.11 x 10(-8) (cm(2)/W) in liquid and solid media, respectively), nonlinear absorption coefficient (beta = -7.7 x 10(-4) and -7.93 x 10(-4)cm/W in liquid and solid media, respectively) and susceptibility (chi((3))=3.38 x 10(-6) and 3.53 x 10(-5)esu in liquid and solid media, respectively). These results show that Pararosanilin dye has potential applications in nonlinear optics.

Synthesis of silver nanoparticles through green approach using Dioscorea alata and their characterization on antibacterial activities and optical limiting behavior.

In this work, we have successfully synthesized highly biocompatible and functionalized Dioscorea alata (D. alata) mediated silver nanoparticles with different quantities of its extract for the evaluation of proficient bactericidal activity and optical limiting behavior. The crystalline nature of the synthesized silver nanoparticles was confirmed by powder X-ray powder diffraction (XRD) analysis and furthermore confirmed from SAED pattern of HRTEM Analysis. The Surface Plasmon Resonance band was measured and monitored by UV-Visible spectral studies. The functional groups present in the extract for the reduction and stabilization of the nanoparticles were analyzed by Fourier transform infrared spectroscopy (FTIR) technique. Surface morphology and size of particles were determined by high-resolution transmission electron microscopy analysis (HRTEM). The elemental analysis was made by Energy Dispersive X-ray Spectroscopy (EDX). The synthesized silver nanoparticles (AgNPs) in colloidal form were found to exhibit third order optical nonlinearity as studied by closed aperture Z-scan technique and open aperture technique using 532nm Nd:YAG (SHG) CW laser beam (COHERENT-Compass 215M-50 diode-pumped) output as source. The negative nonlinearity observed was well utilized for the study of optical limiting behavior of the silver nanoparticles. D. alata mediated silver nanoparticles possess very good antimicrobial activity which was confirmed by agar well diffusion assay method.

Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering.

Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter.

Energy transfer distributed feedback dye laser using Rhodamine B-Acid blue 7 dye mixture.

The characteristics of energy transfer distributed feedback dye laser (ETDFDL) are studied both theoretically and experimentally in a mixture of Rhodamine B and Acid blue 7 dyes pumped by 532 nm Nd:YAG laser. The behaviour of donor and acceptor DFDL, the dependence of their pulse width and output power on pump power and donor-acceptor concentrations are studied. Experimentally, the tunability is achieved over the spectral range 565-680 nm using a prism dye cell arrangement. The output energy of DFDL is measured at the emission peaks of donor and acceptor for different pump powers and donor-acceptor concentrations. The output pulse of DFDL is found to be as narrow as 40-ps duration, which is nearly 100-fold shorter than the pump pulse. The pulse linewidth is of the order of 0.1 A.

Photophysics of TICT states of 7-diethylamino-4-methyl coumarin dye by energy transfer techniques.

Coumarin 1 exhibited dual amplified spontaneous emission (ASE) in certain solvents under nitrogen laser excitation. These emissions are known as normal and anomalous emissions. The anomalous emission corresponds to TICT state and it does not have a corresponding fluorescence peak. Energy transfer techniques have been used to study the photophysics of TICT states and the characteristics of dual ASE bands of the dye coumarin 1.

Role of dipole moment of solvents in formation and stabilization of the TICT states in Coumarin 445 under nitrogen laser excitation.

In this paper we report the observation of dual Amplified Spontaneous Emission (ASE) from solutions of 7-ethylamino-4-methyl coumarin dye (Coumarin 445) in certain solvents such as n-butyl acetate, dioxane etc. when pumped by high power nitrogen laser. The two ASE bands appear to be from two different excited species (ICT and TICT conformation) one of which is the precursor of the other. The spectral characteristics of dye Coumarin 445 depend upon the solvent environment. The TICT coumarin photoisomers, which form exciplexes with the solvent molecules, have enough gain to produce amplified spontaneous emission even when there is apparently no detectable fluorescence. The behaviour of this dye in the excited state is studied by measuring the small signal gain and variation of the gain slope with temperature in different solvents. It is observed that polarity of the solvent plays a more dominant role in formation and stabilization of TICT states.

MR image reconstruction based on framelets and nonlocal total variation using split Bregman method.

PURPOSE: An efficient algorithm for magnetic resonance (MR) image reconstruction is needed, especially when sparse sampling is employed to accelerate data acquisition. The aim of this paper is to solve the sparse MRI problem based on nonlocal total variation (NLTV) and framelet sparsity using the split Bregman algorithm. A new method was developed and tested in a variety of MR image acquisitions. METHODS: The proposed method minimizes a linear combination of NLTV, least square data fitting and framelet terms to reconstruct the MR images from undersampled k-space data. The NLTV and framelet sparsity are taken as the L1-regularization functional and solved by using the split Bregman method. Experiments were conducted to compare the proposed algorithm with several different reconstruction methods, including the operator splitting algorithm, variable splitting method, composite splitting algorithm and its accelerated version called the fast composite splitting algorithm. A detailed evaluation study was done on the reconstruction of MR images which represent varying degrees of object structural complexity. Both qualitative visualization-based and quantitative metric-based evaluations were done. RESULTS: Numerical results on various data corresponding to different sampling rates showed the advantages of the new method in preserving geometrical features, textures and fine structures. The proposed algorithm was compared with previous methods in terms of the reconstruction accuracy and computational complexity with favorable results. CONCLUSION: An efficient new algorithm was developed for compressed MR image reconstruction based on NLTV and framelet sparsity. The algorithm effectively solves a hybrid regularizer based on framelet sparsity and NLTV using the split Bregman method. NLTV makes the recovered image quality sharper by preserving the edges or boundaries more accurately, and framelets often improve image quality. The comparison with alternative method yielded results that demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

MR image reconstruction based on iterative Split Bregman algorithm and nonlocal total variation.

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L 1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

Micro-CT image reconstruction based on alternating direction augmented Lagrangian method and total variation.

Micro-computed tomography (micro-CT) plays an important role in pre-clinical imaging. The radiation from micro-CT can result in excess radiation exposure to the specimen under test, hence the reduction of radiation from micro-CT is essential. The proposed research focused on analyzing and testing an alternating direction augmented Lagrangian (ADAL) algorithm to recover images from random projections using total variation (TV) regularization. The use of TV regularization in compressed sensing problems makes the recovered image quality sharper by preserving the edges or boundaries more accurately. In this work TV regularization problem is addressed by ADAL which is a variant of the classic augmented Lagrangian method for structured optimization. The per-iteration computational complexity of the algorithm is two fast Fourier transforms, two matrix vector multiplications and a linear time shrinkage operation. Comparison of experimental results indicate that the proposed algorithm is stable, efficient and competitive with the existing algorithms for solving TV regularization problems.

Coat protein-mediated transgenic resistance of peanut (Arachis hypogaea L.) to peanut stem necrosis disease through Agrobacterium-mediated genetic transformation.

The absence of resistance genes against biotic stresses like Tobacco streak virus (TSV) within compatible peanut germplasm necessitates the deployment of genetic engineering strategy to develop transgenic resistance. Transgenic resistance in peanut (Arachis hypogaea L.) to peanut stem necrosis disease caused by TSV was obtained by transferring coat protein (CP) gene of TSV through Agrobacterium-mediated transformation of de-embryonated cotyledons and immature leaves of peanut cultivars Kadiri 6 (K6) and Kadiri 134 (K134). Integration of the transgene in T1, T2 and T3 generations were confirmed by PCR with gene-specific primers. On the basis of segregation analysis of the PCR amplicons, homozygosity was confirmed in progeny from five transgenic lines. Six transgenic plants from three different single copy transgenic lines homozygous for the transgene were selected for challenge inoculation in T3 generations. The transgenic lines remained symptomless throughout and showed traces or no systemic accumulation of virus indicating the tolerance/resistance to the TSV infection. CP gene expression was observed in transgenic lines by RT-PCR, real-time PCR and ELISA. The findings provide an effective strategy for developing peanut with resistance to peanut stem necrosis disease.

An expert support system for breast cancer diagnosis using color wavelet features.

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Adsorption of Cr(VI) and Pb(II) from aqueous solution using agricultural solid waste.

Areca nut shell, an agricultural solid waste by-product, has been studied for the removal of heavy metals Cr(VI) and Pb(II) from aqueous solution. Parameters, such as equilibrium time, effect of initial metal ion concentration, effect of pH on the removal, were analyzed. An initial pH of 4.0 was found most favourable for Cr(VI) removal and 5.0 for Pb(II) removal. Two theoretical isotherm models, namely Langmuir and Freundlich, were analyzed for the applicability of the experimental data. The Langmuir adsorption capacity (Q0) was calculated. The results of thermodynamic parameters suggest the exothermic nature of the adsorption. The desorption studies were carried out using dilute hydrochloric acid. Maximum desorption of 88% for Cr(VI) and 91% for Pb(II) were achieved. Areca nut shell waste, the low cost adsorbent is found to be effective in the removal of Cr(VI) and Pb(II) ions, and hence it can be applied for the removal of heavy metals from industrial wastewater.