Neural network analysis applied to tumor segmentation on 3D breast ultrasound images

Sheng Fang Huang; Yen Ching Chen; Kyung Moon Woo

doi:10.1109/ISBI.2008.4541243

Neural network analysis applied to tumor segmentation on 3D breast ultrasound images

Sheng Fang Huang
, Yen Ching Chen
, Kyung Moon Woo

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

19 Citations (Scopus)

Abstract

Our study presents a fully automatic tumor segmentation method using three-dimensional (3D) breast ultrasound (US) images. The proposed method is an approach based on 2D image processing techniques, which considers the variations of contours between two adjacent planes in a 3D dataset. In this approach, a reference image obtained in the previous plane was used to facilitate the segmentation in the next plane. To determine the initial reference image, we extracted five features from regions in each 2D slice and applied neural network analysis to discriminate the tumor from the background. Finally, three area error metrics were calculated to measure the overall performance of the system.

Original language	English
Title of host publication	2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, Proceedings, ISBI
Pages	1303-1306
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2008.4541243
Publication status	Published - 2008 Sept 10
Event	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France Duration: 2008 May 14 → 2008 May 17

Publication series

Name	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Country/Territory	France
City	Paris
Period	08-05-14 → 08-05-17

All Science Journal Classification (ASJC) codes

Biomedical Engineering

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10.1109/ISBI.2008.4541243

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Huang, S. F., Chen, Y. C., & Woo, K. M. (2008). Neural network analysis applied to tumor segmentation on 3D breast ultrasound images. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI (pp. 1303-1306). Article 4541243 (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). https://doi.org/10.1109/ISBI.2008.4541243

@inproceedings{156d8b06b0c24181abff44835351c6cf,

title = "Neural network analysis applied to tumor segmentation on 3D breast ultrasound images",

abstract = "Our study presents a fully automatic tumor segmentation method using three-dimensional (3D) breast ultrasound (US) images. The proposed method is an approach based on 2D image processing techniques, which considers the variations of contours between two adjacent planes in a 3D dataset. In this approach, a reference image obtained in the previous plane was used to facilitate the segmentation in the next plane. To determine the initial reference image, we extracted five features from regions in each 2D slice and applied neural network analysis to discriminate the tumor from the background. Finally, three area error metrics were calculated to measure the overall performance of the system.",

author = "Huang, \{Sheng Fang\} and Chen, \{Yen Ching\} and Woo, \{Kyung Moon\}",

year = "2008",

month = sep,

day = "10",

doi = "10.1109/ISBI.2008.4541243",

language = "English",

isbn = "9781424420032",

series = "2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI",

pages = "1303--1306",

booktitle = "2008 5th IEEE International Symposium on Biomedical Imaging",

note = "2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI ; Conference date: 14-05-2008 Through 17-05-2008",

}

Huang, SF, Chen, YC & Woo, KM 2008, Neural network analysis applied to tumor segmentation on 3D breast ultrasound images. in 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI., 4541243, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, pp. 1303-1306, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI, Paris, France, 08-05-14. https://doi.org/10.1109/ISBI.2008.4541243

Neural network analysis applied to tumor segmentation on 3D breast ultrasound images. / Huang, Sheng Fang; Chen, Yen Ching; Woo, Kyung Moon.
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI. 2008. p. 1303-1306 4541243 (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI).

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

TY - GEN

T1 - Neural network analysis applied to tumor segmentation on 3D breast ultrasound images

AU - Huang, Sheng Fang

AU - Chen, Yen Ching

AU - Woo, Kyung Moon

PY - 2008/9/10

Y1 - 2008/9/10

N2 - Our study presents a fully automatic tumor segmentation method using three-dimensional (3D) breast ultrasound (US) images. The proposed method is an approach based on 2D image processing techniques, which considers the variations of contours between two adjacent planes in a 3D dataset. In this approach, a reference image obtained in the previous plane was used to facilitate the segmentation in the next plane. To determine the initial reference image, we extracted five features from regions in each 2D slice and applied neural network analysis to discriminate the tumor from the background. Finally, three area error metrics were calculated to measure the overall performance of the system.

AB - Our study presents a fully automatic tumor segmentation method using three-dimensional (3D) breast ultrasound (US) images. The proposed method is an approach based on 2D image processing techniques, which considers the variations of contours between two adjacent planes in a 3D dataset. In this approach, a reference image obtained in the previous plane was used to facilitate the segmentation in the next plane. To determine the initial reference image, we extracted five features from regions in each 2D slice and applied neural network analysis to discriminate the tumor from the background. Finally, three area error metrics were calculated to measure the overall performance of the system.

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U2 - 10.1109/ISBI.2008.4541243

DO - 10.1109/ISBI.2008.4541243

M3 - Conference contribution

AN - SCOPUS:51049090141

SN - 9781424420032

T3 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

SP - 1303

EP - 1306

BT - 2008 5th IEEE International Symposium on Biomedical Imaging

T2 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI

Y2 - 14 May 2008 through 17 May 2008

ER -

Huang SF, Chen YC, Woo KM. Neural network analysis applied to tumor segmentation on 3D breast ultrasound images. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI. 2008. p. 1303-1306. 4541243. (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). doi: 10.1109/ISBI.2008.4541243

Neural network analysis applied to tumor segmentation on 3D breast ultrasound images

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