A static SMC sampler on shapes for the automated segmentation of aortic calcifications

TY - GEN

T1 - A static SMC sampler on shapes for the automated segmentation of aortic calcifications

AU - Petersen, Peter Kersten

AU - Nielsen, Mads

AU - Brandt, Sami S.

N1 - Conference code: 11

PY - 2010

Y1 - 2010

N2 - In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC) algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering, while being computationally equally costly.

AB - In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC) algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering, while being computationally equally costly.

U2 - 10.1007/978-3-642-15561-1_48

DO - 10.1007/978-3-642-15561-1_48

M3 - Article in proceedings

SN - 978-3-642-15560-4

T3 - Lecture notes in computer science

SP - 666

EP - 679

BT - Computer Vision – ECCV 2010

A2 - Daniilidis, Kostas

A2 - Maragos, Petros

A2 - Paragios, Nikos

PB - Springer

T2 - 11th European Conference on Computer Vision

Y2 - 5 September 2010 through 11 September 2010

ER -