Segmentation of thrombus in abdominal aortic aneurysms is complicated by regions of low boundary contrast and by the presence of many neighboring structures in close proximity to the aneurysm wall. This paper presents an automated method that is similar to the well known Active Shape Models (ASM), which combine a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: First, we show how the generalizability of a shape model of curvilinear objects can be improved by modeling the objects axis deformation independent of its cross-sectional deformation. Second, a non-parametric appearance modeling scheme that effectively deals with a highly varying background is presented. In contrast with the conventional ASM approach, the new appearance model trains on both true and false examples of boundary profiles. The probability that a given image profile belongs to the boundary is obtained using k nearest neighbor (kNN) probability density estimation. The performance of this scheme is compared to that of original ASMs, which minimize the Mahalanobis distance to the average true profile in the training set. A set of leave-one-out experiments is performed on 23 datasets. Modeling the axis and cross-section separately reduces the shape reconstruction error in all cases. The average reconstruction error was reduced from 2.2 to 1.6 mm. Segmentation using the kNN appearance model significantly outperforms the original ASM scheme; average volume errors are 5.9% and 46% respectively.