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Segmentation of Complex Objects with Non-Spherical Topologies from Volumetric Medical Images using 3D
 

Summary: Segmentation of Complex Objects with Non-Spherical
Topologies from Volumetric Medical Images using 3D
Livewire
Miranda Poona, Ghassan Hamarnehb, Rafeef Abugharbieha
aBiomedical Signal and Image Computing Lab, University of British Columbia, Canada
bMedical Image Analysis Lab, Simon Fraser University, Canada
ABSTRACT
Segmentation of 3D data is one of the most challenging tasks in medical image analysis. While reliable au-
tomatic methods are typically preferred, their success is often hindered by poor image quality and significant
variations in anatomy. Recent years have thus seen an increasing interest in the development of semi-automated
segmentation methods that combine computational tools with intuitive, minimal user interaction. In an earlier
work, we introduced a highly-automated technique for medical image segmentation, where a 3D extension of the
traditional 2D Livewire was proposed. In this paper, we present an enhanced and more powerful 3D Livewire-
based segmentation approach with new features designed to primarily enable the handling of complex object
topologies that are common in biological structures. The point ordering algorithm we proposed earlier, which
automatically pairs up seedpoints in 3D, is improved in this work such that multiple sets of points are allowed
to simultaneously exist. Point sets can now be automatically merged and split to accommodate for the presence
of concavities, protrusions, and non-spherical topologies. The robustness of the method is further improved by
extending the `turtle algorithm', presented earlier, by using a turtle-path pruning step. Tests on both synthetic
and real medical images demonstrate the efficiency, reproducibility, accuracy, and robustness of the proposed

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia
Hamarneh, Ghassan - School of Computing Science, Simon Fraser University

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences