Automatic tumor detection in the constrained region for ultrasound breast CAD

Yeong Kyeong Seong; Moon Ho Park; Eun Young Ko; Kyoung Gu Woo

doi:10.1117/12.911695

Automatic tumor detection in the constrained region for ultrasound breast CAD

Yeong Kyeong Seong
, Moon Ho Park
, Eun Young Ko
, Kyoung Gu Woo

Samsung
Sungkyunkwan University

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

3 Scopus citations

Abstract

In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.

Original language	English
Title of host publication	Medical Imaging 2012
Subtitle of host publication	Computer-Aided Diagnosis
DOIs	https://doi.org/10.1117/12.911695
State	Published - 2012
Externally published	Yes
Event	Medical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States Duration: 7 Feb 2012 → 9 Feb 2012

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	8315
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2012: Computer-Aided Diagnosis
Country/Territory	United States
City	San Diego, CA
Period	7/02/12 → 9/02/12

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast region segmentation
Tumor detection
Ultrasound breast CAD

Access to Document

10.1117/12.911695

Cite this

@inproceedings{dcfc880e376944b495b2840d0fbd83b1,

title = "Automatic tumor detection in the constrained region for ultrasound breast CAD",

abstract = "In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90\%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14\% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.",

keywords = "Breast region segmentation, Tumor detection, Ultrasound breast CAD",

author = "Seong, \{Yeong Kyeong\} and Park, \{Moon Ho\} and Ko, \{Eun Young\} and Woo, \{Kyoung Gu\}",

year = "2012",

doi = "10.1117/12.911695",

language = "English",

isbn = "9780819489647",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

booktitle = "Medical Imaging 2012",

note = "Medical Imaging 2012: Computer-Aided Diagnosis ; Conference date: 07-02-2012 Through 09-02-2012",

}

Seong, YK, Park, MH, Ko, EY & Woo, KG 2012, Automatic tumor detection in the constrained region for ultrasound breast CAD. in Medical Imaging 2012: Computer-Aided Diagnosis., 831529, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 8315, Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, CA, United States, 7/02/12. https://doi.org/10.1117/12.911695

Automatic tumor detection in the constrained region for ultrasound breast CAD. / Seong, Yeong Kyeong; Park, Moon Ho; Ko, Eun Young et al.
Medical Imaging 2012: Computer-Aided Diagnosis. 2012. 831529 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8315).

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

TY - GEN

T1 - Automatic tumor detection in the constrained region for ultrasound breast CAD

AU - Seong, Yeong Kyeong

AU - Park, Moon Ho

AU - Ko, Eun Young

AU - Woo, Kyoung Gu

PY - 2012

Y1 - 2012

N2 - In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.

AB - In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.

KW - Breast region segmentation

KW - Tumor detection

KW - Ultrasound breast CAD

UR - https://www.scopus.com/pages/publications/84874902325

U2 - 10.1117/12.911695

DO - 10.1117/12.911695

M3 - Conference contribution

AN - SCOPUS:84874902325

SN - 9780819489647

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2012

T2 - Medical Imaging 2012: Computer-Aided Diagnosis

Y2 - 7 February 2012 through 9 February 2012

ER -

Automatic tumor detection in the constrained region for ultrasound breast CAD

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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