Virtual Blood Vessels in Complex Background Using Stereo X-Ray Images

Qiuyu Chen, Ryoma Bise, Lin Gu, Yinqiang Zheng, Imari Sato, Jenq-Neng Hwang, Nobuaki Imanishi, Sadakazu Aiso; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 99-106

Abstract


We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carries the risk of over irradiation exposure. In reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented vessels by extracting their centerlines and generating trees. We propose a coarse-to-fine scheme for stereo matching, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian Regression. Finally, we render and visualize the reconstructed model in a HoloLens Based AR system, which can essentially change the way of visualizing medical data. We have evaluated its performance by using synthetic and real stereo X-ray images, and achieved satisfactory quantitative and qualitative results.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Chen_2017_ICCV,
author = {Chen, Qiuyu and Bise, Ryoma and Gu, Lin and Zheng, Yinqiang and Sato, Imari and Hwang, Jenq-Neng and Imanishi, Nobuaki and Aiso, Sadakazu},
title = {Virtual Blood Vessels in Complex Background Using Stereo X-Ray Images},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}