title: An improved parallel fuzzy connected image segmentation method based on CUDA.
abstract: OBJECTIVE
  Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy.
  
  
  METHODS
  In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again.
  
  
  RESULTS
  Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE.
  
  
  CONCLUSIONS
  The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.
authors.affiliation: Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China.
authors.forename: Liansheng
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China. hsh@xmu.edu.cn.
authors.forename: Shaohui
authors.initials: S
authors.lastname: Huang
mesh_terms: D003196:Computer Graphics; D017143:Fuzzy Logic; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging; D008962:Models, Theoretical; D014057:Tomography, X-Ray Computed
keywords: CUDA; Fuzzy connectedness; Vessel segmentation