Extraction of samples from airway and vessel trees in 3D lung CT based on a multi-scale principal curve tracing algorithm

The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchy...

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Vydáno v:2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Ročník 2011; s. 5157 - 5160
Hlavní autoři: You, S., Bas, E., Erdogmus, D.
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2011
Témata:
Algorithms
Biomedical imaging
Computed tomography
Electron tubes
Humans
Image segmentation
Imaging, Three-Dimensional - methods
Interpolation
Kernel
Lung - blood supply
Lung - diagnostic imaging
Lungs
Pattern Recognition, Automated - methods
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Sensitivity and Specificity
Tomography, X-Ray Computed - methods
ISBN:9781424441211, 1424441218
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
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Abstract The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our method detailed herein presents an automatic extraction of samples of both the airways and vessels from the three-dimensional computed tomography (3D-CT) based on the multi-scale principal curve algorithm. The image is first thresholded to find airway or vessel candidates according to their corresponding Hounsfield units (HU). The Frangi filter is then used to extract the tubular structures and remove background noise. Finally, a multi-scale principal curve projection and tracing algorithm is applied on the filtered image to identify the centerlines of the airway and vessel trees.
AbstractList The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our method detailed herein presents an automatic extraction of samples of both the airways and vessels from the three-dimensional computed tomography (3D-CT) based on the multi-scale principal curve algorithm. The image is first thresholded to find airway or vessel candidates according to their corresponding Hounsfield units (HU). The Frangi filter is then used to extract the tubular structures and remove background noise. Finally, a multi-scale principal curve projection and tracing algorithm is applied on the filtered image to identify the centerlines of the airway and vessel trees.
The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our method detailed herein presents an automatic extraction of samples of both the airways and vessels from the three-dimensional computed tomography (3D-CT) based on the multi-scale principal curve algorithm. The image is first thresholded to find airway or vessel candidates according to their corresponding Hounsfield units (HU). The Frangi filter is then used to extract the tubular structures and remove background noise. Finally, a multi-scale principal curve projection and tracing algorithm is applied on the filtered image to identify the centerlines of the airway and vessel trees.The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our method detailed herein presents an automatic extraction of samples of both the airways and vessels from the three-dimensional computed tomography (3D-CT) based on the multi-scale principal curve algorithm. The image is first thresholded to find airway or vessel candidates according to their corresponding Hounsfield units (HU). The Frangi filter is then used to extract the tubular structures and remove background noise. Finally, a multi-scale principal curve projection and tracing algorithm is applied on the filtered image to identify the centerlines of the airway and vessel trees.
Author You, S.
Bas, E.
Erdogmus, D.
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  surname: Erdogmus
  fullname: Erdogmus, D.
  email: erdogmus@ece.neu.edu
  organization: Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
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Snippet The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task...
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SubjectTerms Algorithms
Biomedical imaging
Computed tomography
Electron tubes
Humans
Image segmentation
Imaging, Three-Dimensional - methods
Interpolation
Kernel
Lung - blood supply
Lung - diagnostic imaging
Lungs
Pattern Recognition, Automated - methods
Radiographic Image Enhancement - methods
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Sensitivity and Specificity
Tomography, X-Ray Computed - methods
Title Extraction of samples from airway and vessel trees in 3D lung CT based on a multi-scale principal curve tracing algorithm
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