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Hierarchical multi-resolution decomposition of statistical shape models

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Abstract

Active shape models (ASMs) are some of the most actively used model-based segmentation approaches, whose usefulness has been successfully demonstrated in a wide variety of applications. However, they suffer from two important drawbacks: (1) the large number of training samples required to adequately model the object of interest and (2) their dependence on the initialization. Partial solutions for both of these problems have been proposed but come at the expense of a significant increase in computation time, which is of great importance in current applications. Based on the wavelet transform, the present paper proposes a new full multi-resolution framework that allows not only to hierarchically decompose any type of shape into a set of bands of information, but also to create instances of the shape with different degrees of detail. This is a multi-resolution decomposition of the shape space. Combining this new approach with a multi-resolution Gaussian pyramid of the image, the result is a new full multi-resolution hierarchical ASM that reduces both the size of the training set required and the initialization dependence while improving the classical segmentation algorithms accuracy and computational complexity. The advantages of this new algorithm have been successfully verified over two different databases.

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Notes

  1. The absolute running times correspond to implementations performed on a 3.3 GHz Intel\(^{\textregistered }\) Xeon\(^{\textregistered }\) W5590 with 12 GB of RAM. All implementations were in Matlab\(^{\textregistered }\) R2010a 64-bits prioritizing the clarity of the code over the execution speed. Thus, they must be interpreted with caution and not as an absolute reference.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Public University of Navarra, Campus Arrosadia, 31006 , Pamplona, Spain

    Juan J. Cerrolaza, Arantxa Villanueva & Rafael Cabeza

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  1. Juan J. Cerrolaza
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  2. Arantxa Villanueva
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  3. Rafael Cabeza
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Correspondence to Juan J. Cerrolaza.

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Cerrolaza, J.J., Villanueva, A. & Cabeza, R. Hierarchical multi-resolution decomposition of statistical shape models. SIViP 9, 1473–1490 (2015). https://doi.org/10.1007/s11760-014-0616-9

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