分割
计算机科学
人工智能
曲率
图像分割
计算机视觉
对比度(视觉)
特征(语言学)
骨盆
放射科
模式识别(心理学)
医学
数学
几何学
语言学
哲学
作者
Ahmed S. Maklad,Mikio Matsuhiro,Hidenobu Suzuki,Yoshiki Kawata,Noboru Niki,Mitsuo Shimada,Gen Iinuma
摘要
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.
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