Background: Multiple sclerosis is a chronic autoimmune inflammatory demyelinating disease of the central nervous system of unknown etiology. Different techniques and magnetic resonance image sequences are widely used and compared to each other to improve the detection of multiple sclerosis lesions in the spinal cord. Objective: To evaluate the ability of MRI short tau inversion recovery sequences in improvementof multiple sclerosis spinal cord lesion detection when compared to T2 weighted image sequences. Type of the study: A retrospective study. Methods: this study conducted from 15thAugust 2013 to 30thJune 2014 at Baghdad teaching hospital. 22 clinically definite MS patients with clinical features suggestive of spinal cord involvement, patients were imaged with sagittal short tau inversion recovery sequences and sagittal T2 weighted. Results: The mean age of the patients was 32.5 ± 6.7years; female to male ratio was 2.7:1. The total number of spinal cord MS lesions was 44 of them 86.4% in the cervical spine, 68.2%of the lesions had less than one vertebra extension,79.6% of the lesions did not show changes in the spinal cord morphology. There was a significant upgrading in the lesions conspicuity at short tau inversion recovery sequence comparing to T2 weighted image, P<0.001. A significant difference had been found in artifact grading between both sequences; P<0.001. Conclusions: short tau inversion recovery magnetic resonance image sequences improve detection of MS spinal cord plaques compared with T2 weighted image and itincreasesthe conspicuity of the visualized T2weighted image lesions, but also it accentuates theartifacts more than T2weighted image.
In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny
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