Paper
27 March 2019 Investigation of extracting interlobular septa with Hessian analysis and radial structure tensor combined with roundness error in micro-CT volume
Author Affiliations +
Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110501E (2019) https://doi.org/10.1117/12.2521646
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore
Abstract
Micro-CT is a nondestructive scanning device that is capable of capturing three dimensional structures at _m level. With the spread of this device uses in medical fields, it is expected that this device may bring further understanding of the human anatomy by analyzing three-dimensional micro structure from volume of in vivo specimens captured by micro-CT. In the topic of micro structure analysis of lung, the methods for extracting surface structures including the interlobular septa and the visceral pleura were not commonly studied. In this paper, we introduce a method to extract sheet structure such as the interlobular septa and the visceral pleura from micro-CT volumes. The proposed method consists of two steps: Hessian analysis based method for sheet structure extraction and Radial Structure Tensor combined with roundness evaluation for hollow-tube structure extraction. We adopted the proposed method on complex phantom data and a medical lung micro-CT volume. We confirmed the extraction of the interlobular septa from medical volume from experiments.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaotian Zhao, Hirohisa Oda, Shota Nakamura, Yuichiro Hayashi, Hayato Itoh, Masahiro Oda, and Kensaku Mori "Investigation of extracting interlobular septa with Hessian analysis and radial structure tensor combined with roundness error in micro-CT volume", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501E (27 March 2019); https://doi.org/10.1117/12.2521646
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KEYWORDS
Lung

Error analysis

Tissues

Lung cancer

Visualization

Blood vessels

Binary data

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