Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

Disease Edge Detection of Medical CT Image Using Digital Wavelet Filter

Authors
Woon Cho, Daewon Chung, Yuhan Wu, Joonhyeon Jeon
Corresponding Author
Woon Cho
Available Online March 2018.
DOI
https://doi.org/10.2991/acaai-18.2018.19How to use a DOI?
Keywords
medical images; edge detection; image processing; digital filter
Abstract

The aim of this study is detecting disease edge of the medical CT (Computed Tomography) image. This paper describes the method by using 3-Tap bandpass filter signal and two high frequency sub-band CT images in wavelet domain for detecting part of disease. Simulation result show that the proposed method has high accuracy in detecting the disease edge area as is compared to existing methods. It provide a useful and efficient solution for detecting disease of medical CT image, and this method should be applicable to various medical images.

Copyright
© 2018, 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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Volume Title
Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
Series
Advances in Intelligent Systems Research
Publication Date
March 2018
ISBN
978-94-6252-483-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/acaai-18.2018.19How to use a DOI?
Copyright
© 2018, 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/).

Cite this article

TY  - CONF
AU  - Woon Cho
AU  - Daewon Chung
AU  - Yuhan Wu
AU  - Joonhyeon Jeon
PY  - 2018/03
DA  - 2018/03
TI  - Disease Edge Detection of Medical CT Image Using Digital Wavelet Filter
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 77
EP  - 79
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.19
DO  - https://doi.org/10.2991/acaai-18.2018.19
ID  - Cho2018/03
ER  -