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Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.

Guo X, Huang S, Fu X, Wang B, Huang X - Biomed Eng Online (2015)

Bottom Line: 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.These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels.This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Xiamen University, Xiamen, China. gxxamy@163.com.

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.

No MeSH data available.


Segmentation results of dataset IV: a histogram of the connectivity scene, b the fuzzy connectivity scene, c segmentation result using our method, d segmentation result using using RRG, e the difference set C.
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Fig4: Segmentation results of dataset IV: a histogram of the connectivity scene, b the fuzzy connectivity scene, c segmentation result using our method, d segmentation result using using RRG, e the difference set C.

Mentions: We applied RRG to these four datasets using the same seeds. Table 3 shows the quantitative comparisons between RRG and our method. Let A be the segmentation result using our method and B be the segmentation result using RRG, a different set Cis the result of A − B as shown in Figures 1e, 2e, 3e and 4e. According to the set C, our method based on fuzzy connectedness has advantage of detecting vascular edge. Due to the influence of partial volume effect, the edge of vessel is blurred and mixed with other tissues such as liver parenchyma, and HU values of vessels decrease from center to the edge. Therefore, it is hard for RRG to collect the edge voxels that should be belonged to the vessel. Besides, different vessel systems may have a weak connectivity in the vessel ends, thus it is able to generate more than one vessel system using only one seed as shown in Figure 1c. According to Table 3 and set C, it is concluded that our method has better segmentation outcomes than RRG.Table 3


Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.

Guo X, Huang S, Fu X, Wang B, Huang X - Biomed Eng Online (2015)

Segmentation results of dataset IV: a histogram of the connectivity scene, b the fuzzy connectivity scene, c segmentation result using our method, d segmentation result using using RRG, e the difference set C.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4472182&req=5

Fig4: Segmentation results of dataset IV: a histogram of the connectivity scene, b the fuzzy connectivity scene, c segmentation result using our method, d segmentation result using using RRG, e the difference set C.
Mentions: We applied RRG to these four datasets using the same seeds. Table 3 shows the quantitative comparisons between RRG and our method. Let A be the segmentation result using our method and B be the segmentation result using RRG, a different set Cis the result of A − B as shown in Figures 1e, 2e, 3e and 4e. According to the set C, our method based on fuzzy connectedness has advantage of detecting vascular edge. Due to the influence of partial volume effect, the edge of vessel is blurred and mixed with other tissues such as liver parenchyma, and HU values of vessels decrease from center to the edge. Therefore, it is hard for RRG to collect the edge voxels that should be belonged to the vessel. Besides, different vessel systems may have a weak connectivity in the vessel ends, thus it is able to generate more than one vessel system using only one seed as shown in Figure 1c. According to Table 3 and set C, it is concluded that our method has better segmentation outcomes than RRG.Table 3

Bottom Line: 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.These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels.This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Xiamen University, Xiamen, China. gxxamy@163.com.

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.

No MeSH data available.