2D-PCA Based Tensor Level Set Framework for Vertebral Body Segmentation

  • Ahmed ShalabyEmail author
  • Aly Farag
  • Melih Aslan
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)


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.


Vertebral Body Training Image Shape Model Segmentation Accuracy Clinical Dataset 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisvilleUSA

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