Abstract
In this paper, a novel statistical shape modeling method is developed for the vertebral body (VB) segmentation framework. Two-dimensional principal component analysis (2D-PCA) technique is exploited to extract the shape prior. The obtained shape model is then embedded into the image domain to develop a new shape-based segmentation approach. Our framework consists of four main steps: (1) shape model construction using 2D-PCA, (2) the detection of the VB region using the Matched filter, (3) initial segmentation using a new region-based tensor level set model, and (4) registration of the shape priors and initially segmented region to obtain the final segmentation. The proposed method is validated on a Phantom as well as clinical CT images with various Gaussian noise levels. The experimental results show that the noise immunity and the segmentation accuracy of our framework are much higher than scalar level sets approaches. Additionally, the construction of the shape model using 2D-PCA is computationally more efficient than PCA.
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All algorithms are run on a PC with a 2 Ghz Core i7 Quad processor with 6 GB RAM.
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Shalaby, A., Farag, A., Aslan, M. (2014). 2D-PCA Based Tensor Level Set Framework for Vertebral Body Segmentation. In: Yao, J., Klinder, T., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07269-2_4
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DOI: https://doi.org/10.1007/978-3-319-07269-2_4
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