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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 6506049, 11 pages
Research Article

An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation

1Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA, USA
2College of Computer Science and Technology, Zhejiang University, Hangzhou, China
3Radiology Department, Sir Run Run Shaw Hospital, Medical School of Zhejiang University, Hangzhou, China
4Graduate School of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan

Correspondence should be addressed to Yen-Wei Chen;

Received 24 February 2017; Accepted 23 April 2017; Published 23 October 2017

Academic Editor: Pan Lin

Copyright © 2017 Chunhua Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Random walk (RW) method has been widely used to segment the organ in the volumetric medical image. However, it leads to a very large-scale graph due to a number of nodes equal to a voxel number and inaccurate segmentation because of the unavailability of appropriate initial seed point setting. In addition, the classical RW algorithm was designed for a user to mark a few pixels with an arbitrary number of labels, regardless of the intensity and shape information of the organ. Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner. Our strategy is to employ the previous segmented slice to obtain the shape and intensity knowledge of the target organ for the adjacent slice. According to the prior knowledge, the object/background seed points can be dynamically updated for the adjacent slice by combining the narrow band threshold (NBT) method and the organ model with a Gaussian process. Finally, a high-quality image segmentation result can be automatically achieved using Bayes RW algorithm. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for liver segmentation ().