In order to support the diagnosis of hepatic diseases, understanding the anatomical structures of hepatic lobes and
hepatic vessels is necessary. Although viewing and understanding the hepatic vessels in contrast media-enhanced CT
images is easy, the observation of the hepatic vessels in non-contrast X-ray CT images that are widely used for the
screening purpose is difficult. We are developing a computer-aided diagnosis (CAD) system to support the liver
diagnosis based on non-contrast X-ray CT images. This paper proposes a new approach to segment the middle hepatic
vein (MHV), a key structure (landmark) for separating the liver region into left and right lobes. Extraction and
classification of hepatic vessels are difficult in non-contrast X-ray CT images because the contrast between hepatic
vessels and other liver tissues is low. Our approach uses an atlas-driven method by the following three stages. (1)
Construction of liver atlases of left and right hepatic lobes using a learning datasets. (2) Fully-automated enhancement
and extraction of hepatic vessels in liver regions. (3) Extraction of MHV based on the results of (1) and (2). The
proposed approach was applied to 22 normal liver cases of non-contrast X-ray CT images. The preliminary results show
that the proposed approach achieves the success in 14 cases for MHV extraction.
Primary malignant liver tumor, including hepatocellular carcinoma (HCC), caused 1.25 million deaths per year
worldwide. Multiphase CT images offer clinicians important information about hepatic cancer. The presence of HCC is
indicated by high-intensity regions in arterial phase images and low-intensity regions in equilibrium phase images
following enhancement with contrast material. We propose an automatic method for detecting HCC based on edge
detection and subtraction processing. Within a liver area segmented according to our scheme, black regions are selected
by subtracting the equilibrium phase images to the corresponding registrated arterial phase images. From these black
regions, the HCC candidates are extracted as the areas without edges by using Sobel and LoG edge detection filters. The
false-positive (FP) candidates are eliminated by using six features extracted from the cancer and liver regions. Other FPs
are further eliminated by opening processing. Finally, an expansion process is applied to acquire the 3D shape of the
HCC. The cases used in this experiment were from the CT images of 44 patients, which included 44 HCCs. We extracted
97.7% (43/44) HCCs successfully by our proposed method, with an average number of 2.1 FPs per case. The result
demonstrates that our edge-detection-based method is effective in locating the cancer region by using the information
obtained from different phase images.
Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However,
precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape,
internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a
non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False
extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are
available then the overall outcome of segmentation can be improved by subtracting two phase images, and the
connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map,
tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical
over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment,
40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver
region is effective and robust despite the presence of hepatic tumors within the liver.
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