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)

Dual-energy x-ray absorptiometry (DXA) is an established modality for the assessment of bone mineral density. DXA has also been used for the detection of abdominal aortic calcification (AAC) using lateral images taken for vertebral fracture assessment (VFA). In this phantom study, the capability of VFA for the detection of AAC was investigated. A Perspex phantom of variable width in the range 15-30 cm was used to simulate abdominal soft tissue. Aluminium strips of thickness 0.05-2.0 mm were sandwiched between two halves of the phantom to mimic aortic calcification. VFA images of the phantom were acquired in single-energy mode and analysed by placing regions of interest over the aluminium strip and an adjacent area of Perspex. For each phantom width, the minimum detectable aluminium thickness was assessed visually and related to contrast-to-noise ratio (CNR). Linearity of pixel value with aluminium thickness was tested by linear regression and correlation. Repeatability was measured with five repeated scans for selected phantom configurations. The minimum thickness of aluminium that could be visualised increased with phantom width and varied from 0.05 mm at 15 cm Perspex to 0.5 mm at 30 cm Perspex; the CNR threshold was about 0.03. At all phantom widths, the variation of pixel value with aluminium thickness was strongly linear (r²>0.98, p
Karima Elmasri, William David Evans, Yulia Hicks(6-2017)

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)