Model-based segmentation of abdominal aortic aneurysms in CTA images
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Model-based segmentation of abdominal aortic aneurysms in CTA images. / de Bruijne, Marleen; van Ginneken, Bram; Niessen, Wiro J.; Loog, Marco; Viergever, Max A.
Proceedings of SPIE. 2003. p. 1560-1571 (Medical Imaging 2003: Image Processing, Vol. 5032).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Model-based segmentation of abdominal aortic aneurysms in CTA images
AU - de Bruijne, Marleen
AU - van Ginneken, Bram
AU - Niessen, Wiro J.
AU - Loog, Marco
AU - Viergever, Max A.
PY - 2003
Y1 - 2003
N2 - 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. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets.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 theboundary 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. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation.A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.
AB - 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. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets.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 theboundary 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. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation.A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.
U2 - 10.1117/12.481367
DO - 10.1117/12.481367
M3 - Article in proceedings
T3 - Medical Imaging 2003: Image Processing
SP - 1560
EP - 1571
BT - Proceedings of SPIE
Y2 - 29 November 2010
ER -
ID: 5555831