Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.
PKE Sharquie MD, PDPAA Noaimi MD, DDV, FDSM Al-Ogaily MD, IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 2015
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The Indian costus plasma properties are investigated including electron temperature (Te), "electron density (ne)", "plasma frequency (fp)", " Debye sphere length", and amount of Debye(Nd), using the spectrum of optical emission technique. There are several energies used, with ranging from 300 to 600 mJ. The Boltzmann Plot is used to calculate the temperature; where as Stark's Line Broadening is used to calculate the electron density. The Indian costus was spectroscopically examined in the air with the laser at 10 cm away from the target and the optical fiber at 0.5 cm away. The results were obtained for an electron temperature range of (1.8-2.2) electron volts (ev) and a wavelength range of (300-600) nm. The XRF analysis reveals th
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different
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We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.
The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F
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Objective: to assess the awareness and knowledge of our medical students regarding dose levels of imaging procedures and radiation safety issues, and to conclude how the curriculum of clinical radiology in the college medical program impacts such knowledge.
Subjects and methods: this is a cross-sectional study conducted among 150 medical students in Alkindy College of Medicine between January 2021 to July 2021, regardless of their age or gender. The study included six grades according to the year 2020-2021. A questionnaire consisting of 12 multiple-choice questions was conducted via an online survey using Google Forms. The questions were divided into two parts
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