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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.

Purpose: Liver segmentation is a fundamental step in computer-aided liver disease diagnosis and surgery planning. For the sake of high accuracy and efficiency, in this study, the authors present an automatic seed point selection method and an adaptive fast marching method (FMM) for liver segmentation., Methods: The automatic seed point selection method is according to the structure and intensity characteristics of liver. The proposed adaptive FMM is self-adaptive parameter adjustment. The arrival time parameter T in FMM is adjusted according to the intensity statistics of the possible liver region, which can be used to estimate the size of liver region on the corresponding computed tomography (CT) slices. The proposed algorithm consists of the following steps. First, a thresholding operation was applied to remove the ribs, spines, and kidneys, followed by a smooth filter for noise reduction and a nonlinear gray scale converter, which was used to enhance the contrast of the liver parenchyma. Second, the seed points located in the liver were selected automatically. Finally, using the processed image as a speed function, adaptive FMM was employed to generate the liver contour., Results: Clinical validation has been performed on 30 abdominal CT data-sets. The proposed algorithm achieved an overall true positive rate of 98.7%, false negative rate of 1.6%, false positive rate of 5.2%, and the DICE coefficient of 96.7%. It takes about 0.30s for a 512 × 512-pixel slice., Conclusions: The method has been applied successfully to achieve fast and accurate liver segmentation.

In living donor liver transplantation, the volume of the potential graft must be measured to ensure sufficient liver function after surgery. Couinaud divided the liver into 8 functionally independent segments. However, this method is not simple to perform in 3D space directly. Thus, we propose a rapid method to segment the liver based on the hepatic vessel tree. The most important step of this method is vascular projection. By carefully selecting a projection plane, a 3D point can be fixed in the projection plane. This greatly helps in rapid classification. This method was validated by applying it to a 3D liver depicted on CT images, and the result was in good agreement with Couinaud's classification.