Feature Extraction of Ground-Glass Opacity Nodules using Active Contour Model for Lung Cancer Detection

DOI

10.2991/icmmita-16.2016.240How to use a DOI?

Keywords

Feature of Ground-Glass Opacity Nodules; Active Contour Model; Lung Cancer Detection

Abstract

The proportion of the solid part portion in a GGO nodule is one of features to detect lung cancer. It is difficult to segment the solid part in a GGO nodule completely because tissues surrounding GGO nodules include some impurities like noises in image processing technology. This paper proposes Active Contour Model (ACM) to find the boundary of a GGO nodule because ACM algorithm can remove noises. The size of a GGO nodule can be computed based on the boundary of the GGO nodule. Expectation-Maximization (EM) algorithm can segment the no solid part in GGO nodules because no solid part and solid part have different densities. Experiments show ACM algorithm is more effective than EM algorithm to find the boundary of a GGO nodule. Moreover, our proposal also can reduce the burden of doctors because it can find the boundary of GGO nodules automatically.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanli Miao
AU  - Jianming Wang
AU  - Weiwei Du
AU  - Yanhe Ma
AU  - Hong Zhang
PY  - 2017/01
DA  - 2017/01
TI  - Feature Extraction of Ground-Glass Opacity Nodules using Active Contour Model for Lung Cancer Detection
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.240
DO  - 10.2991/icmmita-16.2016.240
ID  - Miao2017/01
ER  -