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Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies pp 1–31Cite as

Integrating Shape and Texture in 3D Deformable Models: From Metamorphs to Active Volume Models

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Abstract

For the purpose of object boundary extraction, traditional shape-based deformable models rely on external image forces that come primarily from edge or image gradient information. Such reliance on local edge information makes the models prone to get stuck in local minima due to image noise and various other image artifacts. In this chapter, we review a 2D deformable model – Metamorphs, which integrates region texture constraints so as to achieve more robust segmentation. Compared with traditional shape-based models, Metamorphs segmentation result is less dependent on model initialization and not sensitive to noise and spurious edges inside the object of interest. Then, we review Active Volume Model (AVM), a similar and improved approach for 3D segmentation. The shape of this 3D model is considered as an elastic solid, with a simplex-mesh surface made of thousands of vertices. Deformations of the model are derived from a linear system that encodes external forces from the boundary of a Region of Interest (ROI), which is a binary mask representing the object region predicted by the current model. Efficient optimization and fast convergence of the model are achieved using the Finite Element Method (FEM). To further improve segmentation performance, a multiple-surface constraint is also employed to incorporate spatial constraints among multiple objects. It uses two surface distance-based functions to adaptively adjust the weights of contribution from the image-based region information and from spatial constraints among multiple interacting surfaces. Several applications are shown to demonstrate the benefits of these segmentation algorithms based on deformable models that integrate multiple sources of constraints.

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Notes

  1. 1.

    An MSAVM can also be without an outer AVM.

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Acknowledgments

The authors would like to thank Prof. Leon Axel (NYU) for providing the heart CT volume data, and Prof. Panayotis K. Thanos (Brookhaven National Lab) for providing the rat brain data.

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Authors and Affiliations

  1. Department of Computer and Information Science, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA

    Shaoting Zhang

Authors
  1. Tian Shen
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  2. Shaoting Zhang
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  3. Junzhou Huang
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  4. Xiaolei Huang
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  5. Dimitris N. Metaxas
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Corresponding author

Correspondence to Shaoting Zhang .

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Editors and Affiliations

  1. , Department of Bioengineering, University of Louisville, Louisville, 40208, USA

    Ayman S. El-Baz

  2. School of Engineering, Ngee Ann Polytechnic, Clementi Road 535, Singapore, 599489, Singapore

    Rajendra Acharya U

  3. Bristol, United Kingdom

    Majid Mirmehdi

  4. Otter Glen Ct 208, Roseville, 95661-4013, California, USA

    Jasjit S. Suri

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Shen, T., Zhang, S., Huang, J., Huang, X., Metaxas, D.N. (2011). Integrating Shape and Texture in 3D Deformable Models: From Metamorphs to Active Volume Models. In: El-Baz, A., Acharya U, R., Mirmehdi, M., Suri, J. (eds) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8195-0_1

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