Publications by authors named "Ye-Zhan Zeng"
Article Synopsis
- The paper presents a new automatic technique for segmenting liver vessels using intensity and shape constraints in 3D images.
- It combines two strategies: one for segmenting thin vessels (using a bi-Gaussian filter and 3D region growing) and another for thick vessels (using a hybrid active contour model with K-means clustering).
- The method was tested on abdominal CTA images and demonstrated high accuracy and improved segmentation results compared to existing algorithms.
Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts.
Comput Methods Programs Biomed
October 2017
Article Synopsis
- This paper talks about a new way to better see and separate liver blood vessels in special CT scans, which is really important for medical studies and liver transplants.
- They use techniques to reduce noise in the images but make sure the edges of the vessels are clear.
- The new method works really well, with high accuracy and doesn’t need doctors to pick specific points manually, making it easier to identify important veins in the liver.
Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching.
Comput Methods Programs Biomed
May 2017
Background And Objective: Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching.
Methods: An initial slice is segmented by density peak clustering.
Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes.
View Article and Find Full Text PDFLiver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images.
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