A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT

Ahmed S. Maklad; Mikio Matsuhiro; Hidenobu Suzuki; Yoshiki Kawata; Noboru Niki; Mitsuo Shimada; Gen Iinuma

doi:10.1117/12.2254327

3 March 2017 A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT

Ahmed S. Maklad, Mikio Matsuhiro, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki, Mitsuo Shimada, Gen Iinuma

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101344C (2017) https://doi.org/10.1117/12.2254327
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Citation Download Citation

Ahmed S. Maklad, Mikio Matsuhiro, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki, Mitsuo Shimada, and Gen Iinuma "A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101344C (3 March 2017); https://doi.org/10.1117/12.2254327

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