This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on real data from the Kadhimiya teaching hospital shows that the proposed CUHF is a better method when compared to the accuracy of the other integrated filters.
Magnetic Resonance Imaging (MRI) is one of the most important diagnostic tool. There are many methods to segment the
tumor of human brain. One of these, the conventional method that uses pure image processing techniques that are not preferred because they need human interaction for accurate segmentation. But unsupervised methods do not require any human interference and can segment the brain with high precision. In this project, the unsupervised classification methods have been used in order to detect the tumor disease from MRI images. These metho
... Show MoreThis investigation aims to study some properties of lightweight aggregate concrete reinforced by mono or hybrid fibers of different sizes and types. In this research, the considered lightweight aggregate was Light Expanded Clay Aggregate while the adopted fibers included hooked, straight, polypropylene, and glass. Eleven lightweight concrete mixes were considered, These mixes comprised of; one plain concrete mix (without fibers), two reinforced concrete mixtures of mono fiber (hooked or straight fibers), six reinforced concrete mixtures of double hybrid fibers, and two reinforced concrete mixtures of triple hybrid fibers. Hardened concrete properties were investigated in this study. G
This investigation aims to study some properties of lightweight aggregate concrete reinforced by mono or hybrid fibers of different sizes and types. In this research, the considered lightweight aggregate was Light Expanded Clay Aggregate while the adopted fibers included hooked, straight, polypropylene, and glass. Eleven lightweight concrete mixes were considered, These mixes comprised of; one plain concrete mix (without fibers), two reinforced concrete mixtures of mono fiber (hooked or straight fibers), six reinforced concrete mixtures of double hybrid fibers, and two reinforced concrete mixtures of triple hybrid fibers. Hardened concrete properties were investigated in this study. G
The aim of this work is study the partical distribution function g(r12,r1) for Carbon ion cases (C+2,C+3,C+4) in the position space using Hartree-Fock's Wave function, and the partitioning technique for each shell which is represented by Carbon Ions [C+2 (1s22s2)], [C+3 (1s22s)] and [C+4 (1s2)]. A comparision has been made among the three Carbon ions for each shell. A computer programs (MATHCAD ver. 2001i) has been used texcute the results.
In this research, the one of the most important model and widely used in many and applications is linear mixed model, which widely used to analysis the longitudinal data that characterized by the repeated measures form .where estimating linear mixed model by using two methods (parametric and nonparametric) and used to estimate the conditional mean and marginal mean in linear mixed model ,A comparison between number of models is made to get the best model that will represent the mean wind speed in Iraq.The application is concerned with 8 meteorological stations in Iraq that we selected randomly and then we take a monthly data about wind speed over ten years Then average it over each month in corresponding year, so we g
... Show MoreDBNRSK Sayed, Journal of Strategic Research in Social Science (JoSReSS), 2020