Interpolation-Based Shape-Constrained Deformable Model Approach for Segmentation of Vertebrae from CT Spine Images

  • Robert KorezEmail author
  • Bulat IbragimovEmail author
  • Boštjan Likar
  • Franjo Pernuš
  • Tomaž Vrtovec
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


This paper presents a method for automatic vertebra segmentation. The method consists of two parts: vertebra detection and vertebra segmentation. To detect vertebrae in an unknown CT spine image, an interpolation-based optimization approach is first applied to detect the whole spine, then to detect the location of individual vertebrae, and finally to rigidly align shape models of individual vertebrae to the detected vertebrae. Each optimization is performed using a spline-based interpolation function on an equidistant sparse optimization grid to obtain the optimal combination of translation, scaling and/or rotation parameters. The computational complexity in examining the parameter space is reduced by a dimension-wise algorithm that iteratively takes into account only a subset of parameter space dimensions at the time. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is built upon the existing shape-constrained deformable model approach. The proposed iterative segmentation consists of two steps that are executed in each iteration. To find adequate boundaries that are distinctive for the observed vertebra, the boundary detection step applies an improved robust and accurate boundary detection using Canny edge operator and random forest regression model that incorporates prior knowledge through image intensities and intensity gradients. The mesh deformation step attracts the mesh of the vertebra shape model to vertebra boundaries and penalizes the deviations of the mesh from the training repository while preserving shape topology.


Vertebra Detection Vertebral Boundaries Individual Vertebrae Canny Edge Operator Random Forest Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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