Semi-automated enhanced breast tumor segmentation for CT image

Chao Wang; Meng Li; Xia Liu; Zaiyi Liu; Yali Zang; Zhenyu Liu; Di Dong; Changhong Chang; Jie Tian

doi:10.1109/EMBC.2017.8036908

Semi-automated enhanced breast tumor segmentation for CT image

Chao Wang
, Meng Li
, Xia Liu
, Zaiyi Liu
, Yali Zang
, Zhenyu Liu
, Di Dong
, Changhong Chang^*
, Jie Tian

^*Corresponding author for this work

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

3 Scopus citations

Abstract

Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6%, which was higher than that of Random Walk (76.9%) and Graph-Cut (79.8%).

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	648-651
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8036908
State	Published - 13 Sep 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/EMBC.2017.8036908

Cite this

Wang, C., Li, M., Liu, X., Liu, Z., Zang, Y., Liu, Z., Dong, D., Chang, C., & Tian, J. (2017). Semi-automated enhanced breast tumor segmentation for CT image. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 648-651). Article 8036908 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8036908

Wang, Chao ; Li, Meng ; Liu, Xia et al. / Semi-automated enhanced breast tumor segmentation for CT image. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 648-651 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{e8338bd61e93437781fd77d5728d31fb,

title = "Semi-automated enhanced breast tumor segmentation for CT image",

abstract = "Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6\%, which was higher than that of Random Walk (76.9\%) and Graph-Cut (79.8\%).",

author = "Chao Wang and Meng Li and Xia Liu and Zaiyi Liu and Yali Zang and Zhenyu Liu and Di Dong and Changhong Chang and Jie Tian",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

year = "2017",

month = sep,

day = "13",

doi = "10.1109/EMBC.2017.8036908",

language = "英语",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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

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address = "美国",

}

Wang, C, Li, M, Liu, X, Liu, Z, Zang, Y, Liu, Z, Dong, D, Chang, C & Tian, J 2017, Semi-automated enhanced breast tumor segmentation for CT image. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8036908, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 648-651, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 11/07/17. https://doi.org/10.1109/EMBC.2017.8036908

Semi-automated enhanced breast tumor segmentation for CT image. / Wang, Chao; Li, Meng; Liu, Xia et al.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 648-651 8036908 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

TY - GEN

T1 - Semi-automated enhanced breast tumor segmentation for CT image

AU - Wang, Chao

AU - Li, Meng

AU - Liu, Xia

AU - Liu, Zaiyi

AU - Zang, Yali

AU - Liu, Zhenyu

AU - Dong, Di

AU - Chang, Changhong

AU - Tian, Jie

PY - 2017/9/13

Y1 - 2017/9/13

N2 - Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6%, which was higher than that of Random Walk (76.9%) and Graph-Cut (79.8%).

AB - Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6%, which was higher than that of Random Walk (76.9%) and Graph-Cut (79.8%).

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T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 11 July 2017 through 15 July 2017

ER -

Wang C, Li M, Liu X, Liu Z, Zang Y, Liu Z et al. Semi-automated enhanced breast tumor segmentation for CT image. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 648-651. 8036908. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8036908

Semi-automated enhanced breast tumor segmentation for CT image

Abstract

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UN SDGs

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