an improved random walker with bayes model for volumetric medical image segmentation

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ID: 198073
2017
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Abstract
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 (p<0.001).
Reference Key
dong2017journalan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Chunhua Dong;Xiangyan Zeng;Lanfen Lin;Hongjie Hu;Xianhua Han;Masoud Naghedolfeizi;Dawit Aberra;Yen-Wei Chen
Journal journal of political philosophy
Year 2017
DOI
10.1155/2017/6506049
URL
http://dx.doi.org/10.1155/2017/6506049
Keywords
medicine (general)medical technology

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