ABSTRACT
Abdominal aortic aneurysm (AAA) is an expansion in the abdominal aorta having a diameter of 3 cm or greater. This cardiovascular disease may cause death if the aneurysm were to rupture. In several cases, varying amounts of calcification have been observed in AAA. Calcification leads to an increased rupture risk. These calcifications are often not included though in rupture risk assessment models due to the fact that calcifications are small, which makes their inclusion a difficult task. Therefore, it is important to develop a tool capable of detecting these calcified deposits. This is expected to lead to a better rupture risk prediction, reducing AAA rupture fatalities. At first, a thorough literature survey was conducted to identify existing solutions to segment different components (lumen, thrombus, calcification, and aortic wall) of the aneurysm. In order to detect the calcification from abdominal aortic aneurysm, a two-staged method was developed in this chapter. In the first stage, the aneurysm is segmented using a newly proposed statistical topology prior based method. In the second stage, the calcifications are detected using the Bayesian classifier.
