Abstract
Abdominal aortic aneurysm (AAA) is a common disease affecting elderly people and increasing in incidence. The most feared complication of AAA is the rupture of which most will result in death. The AAA involves the excessive dilation of the abdominal aorta in diameter. As a result, open surgery or endoluminal repair is indicated in AAA greater than 55mm. Currently screening and assessment of AAA can be achieved by either ultrasound or computed tomography (CT) angiography, where the latter imaging technology is the current gold standard. Each AAA is different having varying percentage of thrombus, total volume, luminal volume and calcification all of which are thought to play a critical role for assessing the rupture risk and determining management. Currently measurement of these parameters is based on manual or semiautomatic CT image segmentation - it is time-consuming, inaccurate and becomes unrealistic in clinical practice. The development of an automated method for the segmentation of AAA CT images is therefore demanding. We introduce in this paper a geostatistically constrained fuzzy c-means based algorithm as an automatic and effective segmentation of such images.