Volumetric Segmentation of Complex Bone Structures from Medical Imaging Data Using Reeb Graphs

In proceedings of Central European Seminar on Computer Graphics for Students (CESCG'2013), pages 113-120, Apr. 2013
 

Abstract

The exploration of medical imaging datasets often requires a segmentation of the images according to different materials or structures. Model-based algorithms excel in finding closed boundary contours enclosing the structure to be segmented. However, porose structures like Spongiosa have a complex topology and do not exhibit a unique single closed boundary contour. In order to enable segmentation of such complex structures we suggest a new algorithmic framework based on a Reeb graph representing the topological information. Each node in the graph corresponds to a connected region of voxels in a specific image slice while edges indicate connected regions between adjacent slices. Starting with a coarse segmentation, the corresponding graph is refined at critical nodes and the resulting connected components of the graph provide the final segmentation. We present two strategies for identifying critical nodes, one solely based on dynamic thresholding and one based on a single user specified pre-segmentation. The approach is evaluated on a dataset of 193 muCT scans of rodent skulls which are segmented into skull, left and right mandible.

(Best paper award, third place.)

Images

Download Paper

Download Paper

Additional Material

Bibtex

@INPROCEEDINGS{wiens2013,
     author = {Wiens, Vitalis},
      pages = {113--120},
      title = {Volumetric Segmentation of Complex Bone Structures from Medical Imaging Data Using Reeb Graphs},
  booktitle = {Central European Seminar on Computer Graphics for Students (CESCG'2013)},
       year = {2013},
      month = apr,
   abstract = {The exploration of medical imaging datasets often requires a segmentation of the images according to
               different materials or structures. Model-based algorithms excel in finding closed boundary contours
               enclosing the structure to be segmented. However, porose structures like Spongiosa have a complex
               topology and do not exhibit a unique single closed boundary contour. In order to enable segmentation
               of such complex structures we suggest a new algorithmic framework based on a Reeb graph representing
               the topological information. Each node in the graph corresponds to a connected region of voxels in a
               specific image slice while edges indicate connected regions between adjacent slices. Starting with a
               coarse segmentation, the corresponding graph is refined at critical nodes and the resulting
               connected components of the graph provide the final segmentation. We present two strategies for
               identifying critical nodes, one solely based on dynamic thresholding and one based on a single user
               specified pre-segmentation. The approach is evaluated on a dataset of 193 muCT scans of rodent
               skulls which are segmented into skull, left and right mandible.}
}