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Automatic detection and quantification of abdominal aortic calcification in dual energy X-ray absorptiometry

Elmasri, Karima, Hicks, Yulia Alexandrovna ORCID:, Yang, Xin ORCID:, Sun, Xianfang ORCID:, Pettit, Rebecca J. and Evans, William ORCID: 2016. Automatic detection and quantification of abdominal aortic calcification in dual energy X-ray absorptiometry. Procedia Computer Science 96 , pp. 1011-1021. 10.1016/j.procs.2016.08.116

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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

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Abdominal aortic calcification (AAC); segmentation; active appearance model (AAM); dual energy x-ray absortiometry (DXA).
Additional Information: PDF uploaded in accordance with publishers policies at (accessed 28.7.16).
Publisher: Elsevier
ISSN: 1877-0509
Date of First Compliant Deposit: 22 July 2016
Date of Acceptance: 25 July 2016
Last Modified: 16 Jun 2023 19:45

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