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 Federico Frangi, Kensaku Mori · Lecture notes in computer science · 2016
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 scale-valued 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.