Segmentation of cardiac MR and CT image sequences using model-based registration of a 4D statistical model

Dimitrios Perperidis; Raad Mohiaddin; Philip Edwards; Daniel Rueckert

doi:10.1117/12.706778

26 March 2007 Segmentation of cardiac MR and CT image sequences using model-based registration of a 4D statistical model

Dimitrios Perperidis, Raad Mohiaddin, Philip Edwards, Daniel Rueckert

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65121D (2007) https://doi.org/10.1117/12.706778
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

In this paper we present a novel approach to the problem of fitting a 4D statistical shape model of the myocardium to cardiac MR and CT image sequences. The 4D statistical model has been constructed from 25 cardiac MR image sequences from normal volunteers. The model is controlled by two sets of shape parameters. The first set of shape parameters describes shape changes due to inter-subject variability while the second set of shape parameters describes shape changes due to intra-subject variability, i.e. the cardiac contraction and relaxation. A novel fitting approach is used to estimate the optimal parameters of the cardiac shape model. The fitting of the model is performed simultaneously for the entire image sequences. The method has been tested on 5 cardiac MR image sequences. Furthermore, we have also tested the method using a cardiac CT image sequence. The result demonstrate that the method is not only able to fit the 4D model to cardiac MR image sequences, but also to cardiac image sequences from a different modality (CT).

Citation Download Citation

Dimitrios Perperidis, Raad Mohiaddin, Philip Edwards, and Daniel Rueckert "Segmentation of cardiac MR and CT image sequences using model-based registration of a 4D statistical model", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65121D (26 March 2007); https://doi.org/10.1117/12.706778

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