An improved parallel fuzzy connected image segmentation method based on CUDA.

Liansheng Wang,Shaohui Huang,Dong Li

doi:10.1186/s12938-016-0165-2

Abstract

PurposeFuzzy 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.MethodsIn 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.ResultsThree 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.ConclusionsThe 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.

Highlights

Vessel segmentation is important for evaluation of vascular-related diseases and has applications in surgical planning
The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDAkFOE
The proposed method has a comparable time cost and has less errors compared to the original compute unified device architecture (CUDA)-kFOE as demonstrated in the experimental results

Summary

Introduction

Vessel segmentation is important for evaluation of vascular-related diseases and has applications in surgical planning. Vascular structure is a reliable mark to localize a tumor, especially in liver surgery. Accurately extracting the liver vessel from CT slices in real time is the most important factor in preliminary examination and hepatic surgical planning. Many methods of vascular segmentation have been proposed. Gooya et al [1] proposed a level-set based geometric regularization method for vascular segmentation. Yi et al [2] used a locally adaptive region growing algorithm

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