A new features extraction approach is presented based on mathematical form the modify soil ratio (MSR) and skewness for numerous environmental studies. This approach is involved the investigate on the separation of features using frequency band combination by ratio to estimate the quantity of these features, and it is exhibited a particular aspect to determine the shape of features according to the position of brightness values in a digital scenes, especially when the utilizing the skewness. In this research, the marginal probability density function G(MSR) derivation for the MSR index is corrected, that mentioned in several sources including the source (Aim et al.). This index can be used on original input features space for three diffe

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors. In this paper, tried to implement an automated segmentation methods of gray level CT images is used to provide information such as anatomical structure and identifying the Region of Interest (ROI) i.e. locate tumor, lesion and other in kidney.
A CT image has inhomogeneity, noise which affects the continuity and accuracy of the images segmentation. In

The conjugate coefficient optimal is the very establishment of a variety of  conjugate gradient methods. This paper proposes a new class coefficient of conjugate gradient (CG) methods for impulse noise removal, which is based on the quadratic model. Our proposed method ensures descent independent of the accuracy of the line search and it is globally convergent under some conditions, Numerical experiments are also presented for the impulse noise removal in images.

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

     Numerical simulations were carried out to evaluate the effects of different aberrations modes on the performance of optical system, when observing and imaging the solar surface. Karhunen-Loeve aberrations modes were simulated as a wave front error in the aperture function of the optical system. To identify and apply the appropriate rectification that removes or reduces various types of aberration, their attribute must be firstly determined and quantitatively described. Wave aberration function is well suitable for this purpose because it fully characterizes the progressive effect of the optical system on the wave front passing through the aperture. The Karhunen-Loeve polynomials for circular aperture were used to

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

The present work aims to achieve pulsed laser deposition ofTiO₂ nanostructures and investigate their nonlinear properties using z-scan technique.The second harmonic Q-switched Nd: YAG laser at repetition rate of 1Hz and wavelength of 532 nm with three different laser fluencies in the range of 0.77-1.1 J/cm² was utilized to irradiate the TiO₂ target. The products of laser-induced plasma were characterized by utilizing UV-Vis absorption spectroscopy, x-ray diffraction (XRD), atomic force Microscope (AFM),and Fourier transform infrared (FTIR). A reasonable agreement was found among the data obtained usingX-Ray diffraction, UV-Vis and Raman spectroscopy. The XRD results showed that the prepared TiO₂

The effect of using three different interpolation methods (nearest neighbour, linear and non-linear) on a 3D sinogram to restore the missing data due to using angular difference greater than 1° (considered as optimum 3D sinogram) is presented. Two reconstruction methods are adopted in this study, the back-projection method and Fourier slice theorem method, from the results the second reconstruction proven to be a promising reconstruction with the linear interpolation method when the angular difference is less than 20°.