Abstract
In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.
Highlights
Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, especially in China with the fifth morbidity and the third mortality [1, 2]
It can be seen that the improved vesselness filter can effectively enhance the vessel while suppressing the background. e vesselness images obtained by using the Jerman’s vesselness filter and the improved vesselness filter are shown in Figure 5. e intensity of the vesselness images ranged from 0 to 1. e contrast of vessel in computed tomography (CT) images increased from Figures 5(a) to 5(c)
Our method was fully automatic. e main contributions of this study were as follows. e Jerman’s vesselness filter was improved by incorporating adaptive sigmoid filtering and a background-suppressing item. e improved vesselness filter effectively enhanced the vessel and suppressed the background. e improved vesselness response was incorporated into the fuzzy affinity function, increasing the segmentation performance of fuzzy connectedness (FC). e fuzzy scene was initialized by two-threshold Otsu with one single seed, reducing the number of seeds and the sensitivity to initialization in traditional FC
Summary
Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, especially in China with the fifth morbidity and the third mortality [1, 2]. Treatment planning and navigation based on medical imaging are essential for these procedures. Among different medical imaging modalities, computed tomography (CT) is commonly used for the guidance of liver tumor treatment. Reedimensional (3D) segmentation of liver vessel is critical in CT-based liver tumor treatment planning and navigation. Us, there is a demand for computerized 3D segmentation of liver vessel in CT images [4, 5]. Computerized liver vessel segmentation techniques can be classified into region growing [6,7,8], active contour models or level sets [9], graph cuts [10,11,12], extreme learning [13], deep learning [14], and fuzzy logic [15, 16]. Region growing methods [6,7,8] are simple with low computational
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