Point Distribution Modelling (PDM) is an efficient generative technique that can be used to incorporate statistical shape priors into image analysis methods like Active Shape Models (ASMs) or Active Appearance Models (AAMs). They are described by a set of landmarks usually manually pinpointed in a training set. Frangi et al. [1] have proposed an automatic auto-landmarking technique capable of dealing with multi-object arrangements. In this paper, we present an experimental extension of this previous work, validating the method provided. Our contributions can be summarized as follows: A two-chamber shape model of the heart is constructed from a large data-set comprising 90 subjects and considering 5 phases of the cardiac cycle. The computational demand of our technique is addressed using Grid computing. The results of our experiments suggest that the method presented in [1] as a proof-of-concept, can truly cope with the large inter-subject and inter-phase deformations present in clinical cardiac data sets including pathologies. The achieved accuracy in our validation is comparable to the former tests.
Ordas, S, Boisrobert, L, Bossa, M, Huguei, M, Laucelli, M, Olmos, S & Frangi, AF 2004, Grid-enabled automatic construction of a two-chamber cardiac PDM from a large database of dynamic 3D shapes. in 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano. 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, vol. 1, pp. 416-419, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, Arlington, VA, United States, 15/04/04.
Ordas, S., Boisrobert, L., Bossa, M., Huguei, M., Laucelli, M., Olmos, S., & Frangi, A. F. (2004). Grid-enabled automatic construction of a two-chamber cardiac PDM from a large database of dynamic 3D shapes. In 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (pp. 416-419). (2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano; Vol. 1).
@inproceedings{8475e2c213614af2a3509df1781a27cc,
title = "Grid-enabled automatic construction of a two-chamber cardiac PDM from a large database of dynamic 3D shapes",
abstract = "Point Distribution Modelling (PDM) is an efficient generative technique that can be used to incorporate statistical shape priors into image analysis methods like Active Shape Models (ASMs) or Active Appearance Models (AAMs). They are described by a set of landmarks usually manually pinpointed in a training set. Frangi et al. [1] have proposed an automatic auto-landmarking technique capable of dealing with multi-object arrangements. In this paper, we present an experimental extension of this previous work, validating the method provided. Our contributions can be summarized as follows: A two-chamber shape model of the heart is constructed from a large data-set comprising 90 subjects and considering 5 phases of the cardiac cycle. The computational demand of our technique is addressed using Grid computing. The results of our experiments suggest that the method presented in [1] as a proof-of-concept, can truly cope with the large inter-subject and inter-phase deformations present in clinical cardiac data sets including pathologies. The achieved accuracy in our validation is comparable to the former tests.",
author = "S. Ordas and L. Boisrobert and M. Bossa and M. Huguei and M. Laucelli and S. Olmos and Frangi, {A. F.}",
year = "2004",
month = dec,
day = "1",
language = "English",
isbn = "0780383885",
series = "2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano",
pages = "416--419",
booktitle = "2004 2nd IEEE International Symposium on Biomedical Imaging",
note = "2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano ; Conference date: 15-04-2004 Through 18-04-2004",
}