FG Mohammed, HM Al-Dabbas, Science International, 2018 - Cited by 2

     Social media and networks rely heavily on images. Those images should be distributed in a private manner. Image encryption is therefore one of the most crucial components of cyber security. In the present study, an effective image encryption technique is developed that combines the Rabbit Algorithm, a simple algorithm, with the Attractor of Aizawa, a chaotic map. The lightweight encryption algorithm (Rabbit Algorithm), which is a 3D dynamic system, is made more secure by the Attractor of Aizawa. The process separates color images into blocks by first dividing them into bands of red, green, and blue (RGB). The presented approach generates multiple keys, or sequences, based on the initial parameters and conditions, which are

The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA).

     A quantitative description of microstructure governs the characteristics of the material. Various heat and excellent treatments reveal micro-structures when the material is prepared. Depending on the microstructure, mechanical properties like hardness, ductility, strength, toughness, corrosion resistance, etc., also vary. Microstructures are characterized by morphological features like volume fraction of different phases, particle size, etc. Relative volume fractions of the phases must be known to correlate with the mechanical properties. In this work, using image processing techniques, an automated scheme was presented to calculate relative volume fractions of the phases, namely Ferrite, Martensite, and Bainite, present in the

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

     Multispectral remote sensing image segmentation can be achieved using a multithresholding technique. This paper studies the effect of changing the window size of the two dimensional (2D) fast Otsu algorithm that presented by Zhang. From the results, it shown that this method behaves as a search machine for the valleys (an automatic threshold), between the gray levels of the histogram with changing the size of slide window.  

Keywords Image Segmentation, (2D) Fast Otsu method, Multithresholding, Automatic thresholding, (2D) histogram image.

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Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)

Optic Disc (OD) localization is a basic step for the screening, identification and appreciation of the risk of diverse ophthalmic pathologies such as glaucoma and diabetic retinopathy.In fact, the fundamental step towards an exact OD segmentation process is the success of OD localization. This paper proposes a fully automatic procedure for OD localization based on two of the OD most relevant features  of high-intensity value and vasculature convergence. Merging ofthese two features renders the proposed method capable of localizing the OD within the variously complicated environments such as the faint disc boundary, unbalanced shading, and the existence of retinal pathologies like cotton wall and exudates,which usually share the same

Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method