Tensor-based graph-cut in riemannian metric space and its application to renal artery segmentation

Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Renal artery segmentation remained a big challenging due to its low contrast. In this paper,we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scalevalued images. Conventional graph-cut methods only use intensity information,which may result in failing in segmentation of small blood vessels. To overcome this drawback,this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about 80% of renal arteries with 1mm precision in diameter.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer-Verlag Italia
Number of pages9
ISBN (Print)9783319467252
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Blood vessel segmentation
  • Graph-cut
  • Hessian matrix
  • Renal artery
  • Riemannian manifold
  • Tensor


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