Luminita Moraru

OPTICAL SOLITONS IN MULTI-DIMENSIONS WITH SPATIO-TEMPORAL DISPERSION AND NON-KERR LAW NONLINEARITY

This paper studies the dynamics of optical solitons in multi-dimensions with spatio-temporal dispersion and non-Kerr law nonlinearity. The integrability aspect is the main focus of this paper. Five different forms of nonlinearity are considered — Kerr law, power law, parabolic law, dual-power law and log law nonlinearity. The traveling wave hypothesis, ansatz approach and the semi-inverse variational principle are the integration tools that are adopted to retrieve the soliton solutions to the governing equation. As a result, several constraint conditions arise out of the integration process and represent necessary conditions for the existence of solitons.

Optimization of breast lesion segmentation in texture feature space approach.

This paper develops a method for semi-automatic detection of breast lesion boundaries by combining the snake evolution techniques with statistical texture information of images. We propose an efficient image energy function in segmentation based on image features, first-order textural features and four n×n masks. The segmentation results were evaluated by using area error rate. The image features were evaluated qualitatively by using the contrast-to-noise ratio and fractal dimension analysis. In our study, standard deviation, skewness and entropy are indicated as being the most relevant image features.

Cardiac Cycle Phase Estimation in 2-D Echocardiographic Images Using an Artificial Neural Network

This paper proposes a new hybrid approach to estimate the cardiac cycle phases in 2-D echocardiographic images as a first step in cardiac volume estimation. We focused on analyzing the atrial systole and diastole events by using the geometrical position of the mitral valve and a set of three image features. The proposed algorithm is based on a tandem of image processing methods and artificial neural networks as a classifier to robustly extract anatomical information. An original set of image features is proposed and derived to recognize the cardiac phases. The aforementioned approach is performed in two denoising scenarios. In the first scenario, the images are corrupted with Gaussian noise, and in the second one with Rayleigh noise distribution. Our hybrid algorithm does not involve any manual tracing of the boundaries for segmentation process. The algorithm is implemented as computer-aided diagnosis (CADi) software. A dataset of 150 images that include both normal and infarct cardiac pathologies was used. We reported an accuracy of 90 % and a 2 ± 0.3 s in terms of execution time of CADi application in a cardiac cycle estimation task. The main contribution of this paper is to propose this hybrid method and a set of image features that can be helpful for automatic detection applications without any user intervention. The results of the employed methods are qualitatively and quantitatively compared in terms of efficiency for both scenarios.

The heat transfer coefficient during the solidification of aluminum

The heat transfer coefficient was measured during the solidification of molten aluminum (minimum purity 99.7 wt.% Al). Heat transfer coefficients were obtained with solidification proceeding vertically downward. Heat transfer during solidification is shown to be a complex process controlled by the macroscale of the thermal expansion and contraction of the mold. The experimental technique applied to determine the heat transfer coefficient is based on the assumption of one-dimensional heat transfer.

Level set method coupled with Energy Image features for brain MR image segmentation.

Abstract Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels’ values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

The melting temperature of 99.97 wt. % Al estimated from activation energy for viscous flow

The viscous flow phenomenon in molten aluminium and the viscosity, in the vicinity of its melting temperature, was analyzed. When the temperature decreases, both the viscosity and the activation energy for viscous flow increase. This is explained on the basis of the atomic rearrangement due to interatomic interactions prior to the separation of the solid phase. The latter was found to be nonlinear as the temperature is decreased wherein the solid phase is separated from the liquid. The second derivative of the activation energy for viscous flow with respect to temperature was found to show discontinuity in the vicinity of the melting temperature. Thus, the second derivative offers a way of estimating the melting temperature of metals. Our estimations indicate that the break of the second derivative vs. temperature could be observed at 938 K.

Study concerning the electrical resistivity of some liquid metals in ultrasonic field

The increasing of the electrical resistivity values for the overheated high-purity liquid aluminium in range 934 K to 1043 K and for aluminium-silicon eutectic alloy in range 850 K to 960 K, during high-energy ultrasonic field presence is reported. We used DC electrical resistivity measurements. The modification of electrical resistivity values in ultrasonic field due to electron-ion interaction processes and the limited current density in liquid metals is discussed. The effect of cavitation at the high temperature is debated.

Quantitative Diffusion Tensor Magnetic Resonance Imaging Signal Characteristics in the Human Brain: A Hemispheres Analysis

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.

Standalone functional CAD system for multi-object case analysis in hepatic disorders

A new algorithm able to automatically diagnose the presence of the hemangioma areas in the hepatic ultrasonographic image is proposed. The algorithm uses a new multi-object approach which decomposes the image into three biological regions: a normal hepatic area, a hemangioma area and other areas. The de-noising process is efficiently accomplished for both Gaussian and Rayleigh noise distributions. Furthermore, a segmentation technique, based on gray level intensity analysis and the Moore-Neighbor contour tracing algorithm for a robust differentiation of the hemangioma area are employed. This new proposed technique is almost fully automatic, fast, and simple and its results are satisfactory.

Speckle noise reduction methods in cardiac cycles

Speckle noise is omnipresent in imagistic and is an important problem in imagistic because it is the main source of noise in echography and echocardiography images and it should be reduced without affecting the image features. In spite of wider study dealing to speckle noise removal, until now there is no comprehensive method that covers all the constraints. In this study, three techniques were used for despeckling in echocardiography images along cardiac cycles for apical two chamber view (A2C), apical four chamber view (A4C), parasternal long axis view (LAX), and parasternal short axis view (SAX) and their results were compared. To assess the performances of filters, the correlation coefficient (CoC) was used. Following this analysis, the proper filtering method is recommended.