Medical image segmentation by improved 3D adaptive thresholding

Cheol Hwan Kim; Yun Jung Lee

doi:10.1109/ICTC.2015.7354544

Medical image segmentation by improved 3D adaptive thresholding

Cheol Hwan Kim, Yun Jung Lee

School of Electronics Engineering

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

To see CT and MRI medical images of slice type easily, or to print using 3D printer, we should make a 3D model from slice images. In this paper, we suggest the Improved Adaptive Segmentation Algorithm which can perform the segmentation process which is the most important process at making 3D model. The adaptive threshold method can detect object boundary effectively in an image luminance bias, but tends to diverge at flat region. The proposed algorithm checks the bimodality at histogram distribution and makes adaptive threshold algorithm works stably.

Original language	English
Title of host publication	International Conference on ICT Convergence 2015
Subtitle of host publication	Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	263-265
Number of pages	3
ISBN (Electronic)	9781467371155
DOIs	https://doi.org/10.1109/ICTC.2015.7354544
State	Published - 11 Dec 2015
Event	6th International Conference on Information and Communication Technology Convergence, ICTC 2015 - Jeju Island, Korea, Republic of Duration: 28 Oct 2015 → 30 Oct 2015

Publication series

Name	International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015

Conference

Conference	6th International Conference on Information and Communication Technology Convergence, ICTC 2015
Country/Territory	Korea, Republic of
City	Jeju Island
Period	28/10/15 → 30/10/15

Keywords

Adaptive thresholding
Bimodal distribution
Medical image
Segmentation

Access to Document

10.1109/ICTC.2015.7354544

Cite this

Kim, C. H., & Lee, Y. J. (2015). Medical image segmentation by improved 3D adaptive thresholding. In International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015 (pp. 263-265). Article 7354544 (International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2015.7354544

Kim, Cheol Hwan ; Lee, Yun Jung. / Medical image segmentation by improved 3D adaptive thresholding. International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 263-265 (International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015).

@inproceedings{1e885a1d7b2c480e8f62a768c1b14f61,

title = "Medical image segmentation by improved 3D adaptive thresholding",

abstract = "To see CT and MRI medical images of slice type easily, or to print using 3D printer, we should make a 3D model from slice images. In this paper, we suggest the Improved Adaptive Segmentation Algorithm which can perform the segmentation process which is the most important process at making 3D model. The adaptive threshold method can detect object boundary effectively in an image luminance bias, but tends to diverge at flat region. The proposed algorithm checks the bimodality at histogram distribution and makes adaptive threshold algorithm works stably.",

keywords = "Adaptive thresholding, Bimodal distribution, Medical image, Segmentation",

author = "Kim, {Cheol Hwan} and Lee, {Yun Jung}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 6th International Conference on Information and Communication Technology Convergence, ICTC 2015 ; Conference date: 28-10-2015 Through 30-10-2015",

year = "2015",

month = dec,

day = "11",

doi = "10.1109/ICTC.2015.7354544",

language = "English",

series = "International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "263--265",

booktitle = "International Conference on ICT Convergence 2015",

address = "United States",

}

Kim, CH & Lee, YJ 2015, Medical image segmentation by improved 3D adaptive thresholding. in International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015., 7354544, International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 263-265, 6th International Conference on Information and Communication Technology Convergence, ICTC 2015, Jeju Island, Korea, Republic of, 28/10/15. https://doi.org/10.1109/ICTC.2015.7354544

Medical image segmentation by improved 3D adaptive thresholding. / Kim, Cheol Hwan; Lee, Yun Jung.
International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 263-265 7354544 (International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2015/12/11

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N2 - To see CT and MRI medical images of slice type easily, or to print using 3D printer, we should make a 3D model from slice images. In this paper, we suggest the Improved Adaptive Segmentation Algorithm which can perform the segmentation process which is the most important process at making 3D model. The adaptive threshold method can detect object boundary effectively in an image luminance bias, but tends to diverge at flat region. The proposed algorithm checks the bimodality at histogram distribution and makes adaptive threshold algorithm works stably.

AB - To see CT and MRI medical images of slice type easily, or to print using 3D printer, we should make a 3D model from slice images. In this paper, we suggest the Improved Adaptive Segmentation Algorithm which can perform the segmentation process which is the most important process at making 3D model. The adaptive threshold method can detect object boundary effectively in an image luminance bias, but tends to diverge at flat region. The proposed algorithm checks the bimodality at histogram distribution and makes adaptive threshold algorithm works stably.

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KW - Bimodal distribution

KW - Medical image

KW - Segmentation

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DO - 10.1109/ICTC.2015.7354544

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T3 - International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015

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BT - International Conference on ICT Convergence 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Information and Communication Technology Convergence, ICTC 2015

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Kim CH, Lee YJ. Medical image segmentation by improved 3D adaptive thresholding. In International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 263-265. 7354544. (International Conference on ICT Convergence 2015: Innovations Toward the IoT, 5G, and Smart Media Era, ICTC 2015). doi: 10.1109/ICTC.2015.7354544

Medical image segmentation by improved 3D adaptive thresholding

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