BACKGROUND: Abdominal aortic calcification (AAC) is a marker of atherosclerosis and a predictor of subsequent vascular disease. To date, there has been little research into the automatic detection and quantification of AAC. METHODS: In this study, lateral dual energy X-ray absorptiometry (DXA) scans are used to detect AAC; this is possible because of the anatomical position of the abdominal aorta anterior to the lumbar spine. The deformable shape modelling techniques active shape (ASM) and active appearance (AAM) models are used to model the calcified aorta and four vertebrae of the lumbar spine L1-L4. RESULTS: ASM and AAM were trained and tested on 14 DXA images. The shape of both calcified aorta and four lumbar vertebrae were extracted automatically from the DXA scans using combined shape and appearance models. CONCLUSION: ASM and AAM were implemented successfully. The calcified aorta obtained from the DXA scans was segmented using this modelling technique. The next step is to develop a new automated method to quantify the calcification within the aorta. arabic 13 English 89
Karima Elmasri(6-2015)

Abstract: In this study, a three dimensional computer code based on the so called SIMPLE algorithm (which stands for Semi-Implicit Method for Pressure-Linked Equations) developed by McGill University-Montréal-Canada is used for the numerical solution of a laminar fluid flow and heat transfer of impinging four jets in the presence of crossflow with constant temperature boundary condition on the impingement surface. The governing equations solved by the computer code are the continuity equation, three components of the momentum equation and the energy equation. The finite volume method is adopted for the discretisation of the governing equation. In the computer program, the finite difference equations are solved via the primitive pressure-velocity approach where the hybrid difference scheme, which is a combination of the central and upwind differences, is used to represent the convective and diffusive fluxes over the control volume surfaces. The results obtained show that in general, for multiple jets, the induced and imposed crossflow have significant effects on both the flow and temperature fields near the impingement surface. A strong crossflow deflects the jet and prevents it from impinging on the surface resulting in lower heat transfer rates and hence lower temperatures difference.The results also show that reducing the jet separation distances causes an increase of the temperature of the flow, and hence better cooling of impingement surface.
إبتسام عمارة (2008)

Cardiovascular disease (CVD) is a major cause of mortality and the main cause of morbidity worldwide. CVD may lead to heart attacks and strokes and most of these are caused by atherosclerosis; this is a medical condition in which the arteries become narrowed and hardened due to an excessive build-up of plaque on the inner artery wall. Arterial calcification and, in particular, abdominal aortic calcification (AAC) is a manifestation of atherosclerosis and a prognostic indicator of CVD. In this paper, a two-stage automatic method to detect and quantify the severity of AAC is described; it is based on the analysis of lateral vertebral fracture assessment (VFA) images. These images were obtained on a dual energy x-ray absorptiometry (DXA) scanner used in single energy mode. First, an active appearance model was used to segment the lumbar vertebrae L1-L4 and the aorta on VFA images; the segmentation of the aorta was based on its position with respect to the vertebrae. In the second stage, feature vectors representing calcified regions in the aorta were extracted to quantify the severity of AAC. The presence and severity of AAC was also determined using an established visual scoring system (AC24). The abdominal aorta was divided into four parts immediately anterior to each vertebra, and the severity of calcification in the anterior and posterior walls was graded separately for each part on a 0-3 scale. The results were summed to give a composite severity score ranging from 0 to 24. This severity score was classified as follows: mild AAC (score 0-4), moderate AAC (score 5-12) and severe AAC (score 12-24). Two classification algorithms (k-nearest neighbour and support vector machine) were trained and tested to assign the automatically extracted feature vectors into the three classes. There was good agreement between the automatic and visual AC24 methods and the accuracy of the automated technique relative to visual classification indicated that it is capable of identifying and quantifying AAC over a range of severity. arabic 30 English 163
Karima Mohamed Ali Elmasri, William Evans, Yulia Hicks(1-2016)