Paper Info
Title
A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT.
Abstract
In abdominal disease diagnosis and various abdominal surgeries planning, segmentation of abdominal blood vessel (ABVs) is a very imperative task. Automatic segmentation enables fast and accurate processing of ABVs. We proposed a fully automatic approach for segmenting ABVs through contrast enhanced CT images by a hybrid of 3D region growing and 4D curvature analysis. The proposed method comprises three stages. First, candidates of bone, kidneys, ABVs and heart are segmented by an auto-adapted threshold. Second, bone is auto-segmented and classified into spine, ribs and pelvis. Third, ABVs are automatically segmented in two sub-steps: (1) kidneys and abdominal part of the heart are segmented, (2) ABVs are segmented by a hybrid approach that integrates a 3D region growing and 4D curvature analysis. Results are compared with two conventional methods. Results show that the proposed method is very promising in segmenting and classifying bone, segmenting whole ABVs and may have potential utility in clinical use.
Year
DOI
Venue
2017
10.1117/12.2254327
Proceedings of SPIE
Keywords
Field
DocType
Abdominal,blood vessel segmentation,computed Tomography (CT),4D curvature,bone segmentation
Computer vision,Vessel segmentation,Abdomen,Segmentation,Rib cage,Image segmentation,Artificial intelligence,Region growing,Curvature analysis,Medical diagnostics,Physics
Conference
Volume
ISSN
Citations 
10134
0277-786X
0
PageRank 
References 
Authors
0.34
1
7
Name
Order
Citations
PageRank
ahmed s maklad121.04
mikio matsuhiro254.05
hajime suzuki322.39
Yoshiki Kawata419254.44
Noboru Niki518866.10
Mitsuo Shimada6387.91
Gen Iinuma732.52