Detection of breast cancer based on texture analysis from digital mammograms

Eun Byeol Jo; Ju Hwan Lee; Jun Young Park; Sung Min Kim

doi:10.1007/978-3-642-33932-5_85

Detection of breast cancer based on texture analysis from digital mammograms

Eun Byeol Jo
, Ju Hwan Lee
, Jun Young Park
, Sung Min Kim

Department of Biomedical Engineering

Dongguk University

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

5 Scopus citations

Abstract

In this study, we propose a novel breast cancer detection algorithm based on texture properties of mass area. Proposed method extracts the midpoint of mass area by using AHE (Adaptive Histogram Equalization), and selects the ROI (Region of Interest) in the original image. L1-norm based smoothing filter is then employed to stabilize the mass area, and the form of the mass is determined. Additionally, we measured homogeneity and Ranklet using SVM (Support Vector Machine) to analyze texture properties of the selected mass area. As a result, we observed that the proposed method shows the more stable and outstanding performance for Korean women compared with the existing methods.

Original language	English
Title of host publication	Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012
Publisher	Springer Verlag
Pages	893-900
Number of pages	8
Edition	VOL. 2
ISBN (Print)	9783642339318
DOIs	https://doi.org/10.1007/978-3-642-33932-5_85
State	Published - 2013
Event	12th International Conference on Intelligent Autonomous Systems, IAS 2012 - Jeju Island, Korea, Republic of Duration: 26 Jun 2012 → 29 Jun 2012

Publication series

Name	Advances in Intelligent Systems and Computing
Number	VOL. 2
Volume	194 AISC
ISSN (Print)	2194-5357

Conference

Conference	12th International Conference on Intelligent Autonomous Systems, IAS 2012
Country/Territory	Korea, Republic of
City	Jeju Island
Period	26/06/12 → 29/06/12

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

breast cancer
homogeneity
mammogram
Ranklet
SVM (Support Vector Machine)

Access to Document

10.1007/978-3-642-33932-5_85

Cite this

Jo, E. B., Lee, J. H., Park, J. Y., & Kim, S. M. (2013). Detection of breast cancer based on texture analysis from digital mammograms. In Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012 (VOL. 2 ed., pp. 893-900). (Advances in Intelligent Systems and Computing; Vol. 194 AISC, No. VOL. 2). Springer Verlag. https://doi.org/10.1007/978-3-642-33932-5_85

@inproceedings{e0d40e2a178440ddb550daa528217dda,

title = "Detection of breast cancer based on texture analysis from digital mammograms",

abstract = "In this study, we propose a novel breast cancer detection algorithm based on texture properties of mass area. Proposed method extracts the midpoint of mass area by using AHE (Adaptive Histogram Equalization), and selects the ROI (Region of Interest) in the original image. L1-norm based smoothing filter is then employed to stabilize the mass area, and the form of the mass is determined. Additionally, we measured homogeneity and Ranklet using SVM (Support Vector Machine) to analyze texture properties of the selected mass area. As a result, we observed that the proposed method shows the more stable and outstanding performance for Korean women compared with the existing methods.",

keywords = "breast cancer, homogeneity, mammogram, Ranklet, SVM (Support Vector Machine)",

author = "Jo, \{Eun Byeol\} and Lee, \{Ju Hwan\} and Park, \{Jun Young\} and Kim, \{Sung Min\}",

year = "2013",

doi = "10.1007/978-3-642-33932-5\_85",

language = "English",

isbn = "9783642339318",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

number = "VOL. 2",

pages = "893--900",

booktitle = "Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012",

address = "Germany",

edition = "VOL. 2",

note = "12th International Conference on Intelligent Autonomous Systems, IAS 2012 ; Conference date: 26-06-2012 Through 29-06-2012",

}

Jo, EB, Lee, JH, Park, JY & Kim, SM 2013, Detection of breast cancer based on texture analysis from digital mammograms. in Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012. VOL. 2 edn, Advances in Intelligent Systems and Computing, no. VOL. 2, vol. 194 AISC, Springer Verlag, pp. 893-900, 12th International Conference on Intelligent Autonomous Systems, IAS 2012, Jeju Island, Korea, Republic of, 26/06/12. https://doi.org/10.1007/978-3-642-33932-5_85

Detection of breast cancer based on texture analysis from digital mammograms. / Jo, Eun Byeol; Lee, Ju Hwan; Park, Jun Young et al.
Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012. VOL. 2. ed. Springer Verlag, 2013. p. 893-900 (Advances in Intelligent Systems and Computing; Vol. 194 AISC, No. VOL. 2).

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

TY - GEN

T1 - Detection of breast cancer based on texture analysis from digital mammograms

AU - Jo, Eun Byeol

AU - Lee, Ju Hwan

AU - Park, Jun Young

AU - Kim, Sung Min

PY - 2013

Y1 - 2013

N2 - In this study, we propose a novel breast cancer detection algorithm based on texture properties of mass area. Proposed method extracts the midpoint of mass area by using AHE (Adaptive Histogram Equalization), and selects the ROI (Region of Interest) in the original image. L1-norm based smoothing filter is then employed to stabilize the mass area, and the form of the mass is determined. Additionally, we measured homogeneity and Ranklet using SVM (Support Vector Machine) to analyze texture properties of the selected mass area. As a result, we observed that the proposed method shows the more stable and outstanding performance for Korean women compared with the existing methods.

AB - In this study, we propose a novel breast cancer detection algorithm based on texture properties of mass area. Proposed method extracts the midpoint of mass area by using AHE (Adaptive Histogram Equalization), and selects the ROI (Region of Interest) in the original image. L1-norm based smoothing filter is then employed to stabilize the mass area, and the form of the mass is determined. Additionally, we measured homogeneity and Ranklet using SVM (Support Vector Machine) to analyze texture properties of the selected mass area. As a result, we observed that the proposed method shows the more stable and outstanding performance for Korean women compared with the existing methods.

KW - breast cancer

KW - homogeneity

KW - mammogram

KW - Ranklet

KW - SVM (Support Vector Machine)

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M3 - Conference contribution

AN - SCOPUS:84872773757

SN - 9783642339318

T3 - Advances in Intelligent Systems and Computing

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BT - Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012

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T2 - 12th International Conference on Intelligent Autonomous Systems, IAS 2012

Y2 - 26 June 2012 through 29 June 2012

ER -

Detection of breast cancer based on texture analysis from digital mammograms

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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