Search

The enhanced Kudryashov’s approach retrieves quiescent bright, dark, and singular solitons to the governing model that is considered with cubic–quartic form of self-phase modulation. The algorithm however fails to retrieve stationary solitons when the nonlinearity is the generalized version of the cubic–quartic form. The current analysis is conducted with a direct approach without an intermediary phase-portrait analysis as in the past.

This paper recovers cubic–quartic optical solitons for Kudryashov’s law having dual form of generalized nonlocal nonlinearity. Hamiltonian type perturbation terms are included and these appear with maximum intensity. Bright, dark and singular solitons have emerged with the implementation of sine-Gordon equation method to the model.

In this paper, the auxiliary equation mapping method is employed to extract optical solitons and other solutions for special cases of Kudryashov’s model in birefringent fibers that is studied without the effect of four wave mixing effects. Bright, dark and singular solitons and other solutions emerge from the auxiliary equation mapping method.

This paper recovers optical solitons for the newly proposed Kudryashov’s equation which governs soliton pulse propagation through optical fibers and photonic crystal fibers. A spectrum of soliton solutions are obtained from a wide range of integration norms. The existence criteria for such solitons are enlisted. Finally, couple of numerical simulations make the paper rounded.

In this work, Kudryashov’s equation is studied with Lie symmetry analysis, which is implemented to describe the propagation pulses in an optical fiber. The equation is converted into system of ordinary differential equations with similarity transformations. These gave way to bright, dark and singular optical soliton solutions to the model.

The current paper considers the enhanced Kudryashov’s technique to retrieve solitons with a governing model having cubic-quintic-septic-nonic and quadrupled structures of self-phase modulation. The results prove that it is redundant to extend the self-phase modulation beyond cubic-quintic nonlinearity or dual-power law of nonlinearity.

The paper addresses optical solitons in magneto-optic waveguides that are studied using Kudryashov’s law of nonlinear refractive index in the presence of chromatic dispersion and Hamiltonian-type perturbation terms. The trial solution approach yielded a variety of soliton solutions, which are listed in this paper.

Highly dispersive solitons in fiber Bragg gratings with quadratic-cubic law of nonlinear refractive index are studied in this paper. The G′/G-expansion approach and the enhanced Kudryashov’s scheme have made this retrieval possible. A deluge of solitons, that emerge from the two integration schemes, are presented.

Artificial Neural Networks (ANNs) are efficient tools successfully used to solve a regression problem. In this paper, the skin lesions are analyzed using a feedforward neural network (FFN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm as a supervised learning method. The proposed model uses four combinations of inputs built on the data from type of skin lesion/database/method of asymmetry computation and searches for four combination of desired outputs such as the type of skin lesion/database/ method of asymmetry computation. Also, the number of hidden neurons has been changed to reach the condition of maximum regression coefficient (R) and minimum mean squared error (MSE). The proposed FFN-LMBP model was validated with 24 images and tested with another 24 images. This study is centered on the most relevant and widely used feature in dermoscopic images, i.e., asymmetry. Two algorithms are implemented to extract handcraft asymmetry values: one algorithm computes the asymmetry of the geometric characteristics (GAF) using the geometric shape of the lesions, and the second one computes the asymmetry based on histogram projections (AHP) on the horizontal and vertical axes. The MED-NODE and PH2 databases are used for skin lesion detection.

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases, (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.

Construction of 3D brain MRI images from 2D brain MRI images is an important step towards an accurate diagnosis and for a correct treatment plan for any disease. The volume segmentation is a challenging task due to the poor quality of DTI brain images. This paper aims to evaluate the efficacy of the spatial statistical Maximum (Max) and Minimum (Min) filters in the construction of 3D brain MRI images. These well-known simple filters were used to analyze the 3D image construction together with noise removal techniques operated by them. Using these semi-processed images, a binarization operation has been applied and a similarity investigation was performed using the Dice score. A dataset of 2D processing images is used for the construction of the 3D images by means of the volume imaging technique. The effectiveness of the 3D construction imaging algorithm was investigated through the mean of the volume computation. 3D volume measurements were performed using ImageJ and a real-time database of brain MRI images that contains image stacks from both healthy and ischemic stroke patients. The Minimum filter provides the best results by keeping the image characteristics and local geometries.

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon’s-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature.

Background: People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Objective: Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. associations while known disease-lncRNA associations are required only. Method: The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. The texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The proposed method has been tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. Results: The noise sensitivity of the fuzzy c–means classifier was overcome by using the image features. The results established that the entropy-based features provided superior clustering results compared to homogeneity. Conclusion: Entropy image feature has a lower spread of the data in the clusters of healthy subjects and myocardial infarction. Also, the Euclidean distance function between the cluster centroids has higher values for both LAX and SAX views for entropy images.

Graphical abstract, Highlights • A Gaussian mixture model to classify the pixel distribution of main brain tissues is introduced. • A hemisphere approach is proposed. • Mixing probabilities at the sub-class and class levels are estimated. • The k-means algorithm optimizes the parameters of the mixture distributions. • A difference in the mixing probabilities between hemispheres is determined., A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm2) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres.

Optical pressure sensors in foot scanner system (FSS) based imaging systems can directly and accurately reflect the elastic changes through the plantar tissue to form different pressure values in different regions. In this work, the computational complexity of the sensor dataset from FSS was reduced using an improved full convolution network (FCN) through the AlexNet platform (FCN-AlexNet-8 s). Initially, plantar pressure imaging pre-process and segmentation techniques were developed to extract the region of interests (ROIs) in the captured images from the optical pressure sensors. The experiment established superior performance in the index evaluation system, including mean square error (MSE), and peak signal to noise ratio (PSNR) compared to the previous related studies. Furthermore, the proposed method was compared with region based convolutional neural network (R-CNN) and fast R-CNN separately in terms of the layers indices, maximum stride and time consuming. Accordingly, the proposed method is beneficial to decrease the computation complexity of plantar pressure sensor datasets and has potential application on shoe-last customization.

This paper obtains conservation laws of Chen–Lee–Liu equation in optical fibers. The conserved densities are retrieved by Lie symmetry analysis and the conserved quantities are finally presented from bright 1-soliton solutions that are reported in the past.

In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

This paper recovers cubic-quartic bright optical solitons with perturbed Fokas–Lenells equation. The Hamiltonian perturbation terms appear with maximal permissible intensity. The semi-inverse variational principle is employed to retrieve such solitons.

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10-5.

Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)).

(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained raw images. The second dataset contained images preprocessed for noise removal and uneven illumination reduction. Further, the images belonging to both datasets were segmented, followed by extracting features considered in terms of form/shape and color such as asymmetry, eccentricity, circularity, asymmetry of color distribution, quadrant asymmetry, fast Fourier transform (FFT) normalization amplitude, and 6th and 7th Hu&rsquo, s moments. The FFT normalization amplitude is an atypical feature that is computed as a Fourier transform descriptor and focuses on geometric signatures of skin lesions using the frequency domain information. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed to ascertain the relevance of the selected features and their capability to differentiate between nevi and melanoma. (3) Results: The ROC curves and AUC were employed for all experiments and selected features. A comparison in terms of the accuracy and AUC was performed, and an evaluation of the performance of the analyzed features was carried out. (4) Conclusions: The asymmetry index and eccentricity, together with F6 Hu&rsquo, s invariant moment, were fairly competent in providing a good separation between malignant melanoma and benign lesions. Also, the FFT normalization amplitude feature should be exploited due to showing potential in classification.

The detection of concealed objects in the baggage by non-invasive methods has become a very important issue in the border control process. This paper is concerned with the multiple inverse scattering of the plane wave on various objects or scatterrers by using µ-diff MATLAB open-source toolbox. The acquired information is corelated with the material nature (i.e. steel, salt, neoprene and PVC) for different measured orientation of the scatterers. The non-invasive detection of concealed objects represents a huge challenge, which has attracted particular attention due to a heightened threat of terrorism. The Fourier series and on four integral equations, i.e. Electric Field Integral Equation (EFIE), Magnetic Field Integral Equation (MFIE), Combined Field Integral Equation (CFIE) and Brakhage-Werner Integral Equation (BWIE) generate the approximation framework able to solve the multiple scattering problems. A multi-frequency scattering data approach is adopted and a low frequency domain is proposed for detecting concealed objects in the luggage.

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8 s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.

The Cucuteni-Tripolye Culture is one of the last brilliant cultural expressions of the Copper Age. The relative rarity of the genuine ceramic samples leads to flooding the marketplace with fake (replica) archaeological artefacts. The current study represented the first attempt to implement the multifractal analysis in the SEM images of fractured surfaces of shreds that belong to genuine domestic ceramics from archaeological sites of the Cucuteni-Tripolye culture. The multifractal analysis was performed at both local (multifractal singularity spectrum) and global (Renyi generalized dimensions) approaches and in a comparative manner between replica and genuine samples. Replica samples were obtained by an experienced team of archaeologists following as much as possible the same raw material, paste preparation technology, shaping, and firing operations. The surface heterogeneity of the samples is measured using fractality through the fractal dimensions D 0 –D 2 . Afterward, a multifractal approach based on the generalized fractal dimension and the singularity spectrum is used to correlate the structural variability on the analyzed surface with the clay quality and firing conditions. A significant separation between the original and replica samples is achieved. The proposed method established high potential for the detection of the fake Cucuteni samples that are often found in the black market of antiquities.

The current work is intended for investigation of complex soliton solutions and invariant analysis of time–dependent complex coupled short pulse equation with Lie symmetry analysis. In this study, invariant conditions of complex short pulse equation are addressed. Next, with the application of this invariant condition symmetries for the main equation are recovered. Finally, these symmetries are utilized to obtained the similarity solutions of the considered system. The method reduces the time–dependent equation to the system of equations in which single independent variable. Consequently, these reduced equations lead to complex soliton solutions. Further, with similarity solutions, complex soliton solutions are yielded for time–dependent complex coupled short pulse equation. These solutions are in terms of hyperbolic and exponential functions.

Recently, gray and white matter volumetric studies of the brain have been adjusted with measured brain diffusion scalar values, such as fractional anisotropy (FA). This is carried out mainly to determine the existence of manifested abnormal water diffusivity. This paper developed a method to quantify the diffusion tensor changes among right and left hemispheres. Morphological differences between the hemorrhagic brain injury and healthy subjects are investigated. Specifically, the diffusion orientation and the integrity of the white matter are expressed by the FA. Diffusion characteristics along the axial and radial directions and the mean diffusivity are assessed. The associated diffusivities are related to the diffusion tensor shape for each hemisphere and for the entire brain, based on linear, planar, and spherical measurements in a three-phase plot. This procedure is used to map and to compare changes in the anisotropy from healthy to hemorrhagic brain injury. These measures are combined and acted as a filtering technique. Only those possible macro structural diffusion measures, which are important in the assessment of fiber-tract organization or fiber-tract degradation, are retained. The results indicate that the proposed approach provides some anisotropy measures to efficiently and to accurately discriminate between the brain injuries. Furthermore, it is an established fact that the FA performs better to separate between healthy and temporal intracerebral hemorrhage (ICH) and ischemic stroke (IS)-induced brain injury subjects, whereas the radial diffusivity was more appropriate to discriminate between the left and right hemispheres versus the whole brain for ICH and IS subjects.

This paper focuses on the acoustic backscattering from objects with non-convex shape in two-dimensional space. The scattering simulations are provided using the boundary integral formulation and Nystrom discretization in a single parameter approach. Several numerical examples covering three non-convex kite-shape cross sections and two wave numbers in acoustic domain are carried out to show the influence of shapes on acoustic scattering. Also, the material absorption properties are considered. The error on the accuracy of simulation results is considered in terms of wave numbers.This paper focuses on the acoustic backscattering from objects with non-convex shape in two-dimensional space. The scattering simulations are provided using the boundary integral formulation and Nystrom discretization in a single parameter approach. Several numerical examples covering three non-convex kite-shape cross sections and two wave numbers in acoustic domain are carried out to show the influence of shapes on acoustic scattering. Also, the material absorption properties are considered. The error on the accuracy of simulation results is considered in terms of wave numbers.

This work retrieves polarized optical soliton solutions for pulses in birefringent fibers that are modeled by the Lakshmanan–Porsezian–Daniel model. The unified approach recovers singular solitons only. This approach fails to retrieve the much needed bright and dark soliton solutions. These singular solitons exist with restricted parametric conditions that are also exhibited.

A new approach for the digital watermarking process is proposed to be part of the pre-processing stage of a computer-aided diagnosis system. We propose to embed a denoised image acting as the watermark image in the original host image with the final goal of improving the quality of demoscopic images for further image processing operation related to CAD. The proposed algorithm uses Discrete Wavelet Transform (DWT) corroborated with some basic properties of Human Visual System such as Contrast Sensitive Function (CSF) and Noise Visibility Function (NVF) with the goal of correlating the texture properties and noise. This approach hides the watermark (i.e. denoised version of the host image) in high-pass subbands that are focused on image features. The main concern is to evaluate the distortion produced to the host image by watermarking and an objective quality measure function, i.e. Weighted Peak Signal-to-Noise Ratio (WPSNR), is used to evaluate the existing differences between the original and watermarked images. The proposed approach is tested using the available skin lesion images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen. The experiment results show the improved performance of the proposed scheme against a 3 × 3 median filtering attack in comparison with the a 5 × 5 median filtering attack.