An improved parallel fuzzy connected image segmentation method based on CUDA

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.

An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images

“…Shang et al [ 9 ] evaluated the sensitivity by the number of vascular nodes (denoted as SEN∗ in Table 3 ), but this evaluation metric may not be rigorous. In comparison with Guo et al [ 15 ] and Wang et al [ 16 ] which increased the time efficiency of traditional FC, this study focused on improving the segmentation performance and reducing the number of seeds and the sensitivity to initialization. In addition, our method did not require manual interaction to select the seed.…”

“…Currently, computerized liver vessel segmentation techniques can be classified into region growing [ 6 – 8 ], active contour models or level sets [ 9 ], graph cuts [ 10 – 12 ], extreme learning [ 13 ], deep learning [ 14 ], and fuzzy logic [ 15 , 16 ]. However, it is still challenging to extract liver vessel in CT images, especially in those with low contrast [ 4 ].…”

“…Machine learning methods [ 13 , 14 ] can take into account the diversity of liver vessel topologies and features, but they generally require plenty of training samples or long training time. Fuzzy connectedness (FC) methods [ 15 , 16 ] are based on fuzzy logic. FC describes spatial connectedness between each voxel, rather than just focusing on intensity.…”

“…FC describes spatial connectedness between each voxel, rather than just focusing on intensity. Recently, Guo et al [ 15 ] and Wang et al [ 16 ] have demonstrated the potential of FC in liver vessel segmentation. However, for FC-based liver vessel segmentation in CT images, there are still issues to be addressed, including unsatisfying segmentation accuracy (especially for low-contrast CT images), requirement on multiple seeds, and sensitivity to initialization.…”

A Fast and Effective Image Preprocessing Method for Hot Round Steel Surface

“…However, the experimental data used in these literatures are all from public database, without experimental support from actual production line. Some literature studies proved the efficiency of CUDA-based parallel computing in one or two specific processes in image preprocessing [36][37][38][39][40]. For example, Xia et al proposed a CUDA-based image denoising method for steel plate and proved that the speed of the CUDA method was faster than that of the traditional CPU method [40].…”