Efficient ribcage segmentation from CT scans using shape features

Ziyue Xu, Ulas Bagci, Colleen Jonsson, Sanjay Jain, Daniel J. Mollura

Research output: Contribution to journalArticle

Abstract

Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95% with the proposed algorithm.

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Computerized tomography
Bone
Bone and Bones
Scapula
Sternum
Animals
Rib Cage
Thorax

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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title = "Efficient ribcage segmentation from CT scans using shape features",
abstract = "Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95{\%} with the proposed algorithm.",
author = "Ziyue Xu and Ulas Bagci and Colleen Jonsson and Sanjay Jain and Mollura, {Daniel J.}",
year = "2014",
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doi = "10.1109/EMBC.2014.6944229",
language = "English (US)",
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pages = "2899--2902",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
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T1 - Efficient ribcage segmentation from CT scans using shape features

AU - Xu, Ziyue

AU - Bagci, Ulas

AU - Jonsson, Colleen

AU - Jain, Sanjay

AU - Mollura, Daniel J.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95% with the proposed algorithm.

AB - Rib cage structure and morphology is important for anatomical analysis of chest CT scans. A fundamental challenge in rib cage extraction is varying intensity levels and connection with adjacent bone structures including shoulder blade and sternum. In this study, we present a fully automated 3-D algorithm to segment the rib cage by detection and separation of other bone structures. The proposed approach consists of four steps. First, all high-intensity bone structures are segmented. Second, multi-scale Hessian analysis is performed to capture plateness and vesselness information. Third, with the plate/vessel features, bone structures other than rib cage are detected. Last, the detected bones are separated from rib cage with iterative relative fuzzy connectedness method. The algorithm was evaluated using 400 human CT scans and 100 small animal images with various resolution. The results suggested that the percent accuracy of rib cage extraction is over 95% with the proposed algorithm.

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