title: Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.
abstract: BACKGROUND
  Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation.
  
  
  METHODS
  In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method.
  
  
  RESULTS
  Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends.
  
  
  CONCLUSIONS
  An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.
authors.affiliation: Computer Science Department, Xiamen University, Xiamen, China. gxxamy@163.com.|Computer Engineering College, Jimei University, Xiamen, China. gxxamy@163.com.
authors.forename: Xiaoxi
authors.initials: X
authors.lastname: Guo
authors.affiliation: Computer Science Department, Xiamen University, Xiamen, China. hsh@xmu.edu.cn.
authors.forename: Shaohui
authors.initials: S
authors.lastname: Huang
authors.affiliation: Computer Science Department, Xiamen University, Xiamen, China. fionafxz@hotmail.com.
authors.forename: Xiaozhu
authors.initials: X
authors.lastname: Fu
authors.affiliation: Computer Science Department, Xiamen University, Xiamen, China. blwang@xmu.edu.cn.
authors.forename: Boliang
authors.initials: B
authors.lastname: Wang
authors.affiliation: Computer Science Department, Xiamen University, Xiamen, China. xyhuang@xmu.edu.cn.
authors.forename: Xiaoyang
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000706:statistics & numerical data*