Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Shelda Sajeev; Mariusz Bajger; Gobert Lee; Chisako Muramatsu; Hiroshi Fujita

doi:10.1117/12.2564272

Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Shelda Sajeev
, Mariusz Bajger
, Gobert Lee
, Chisako Muramatsu
, Hiroshi Fujita

College of Science and Engineering

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

Abstract

The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.

Original language	English
Title of host publication	15th International Workshop on Breast Imaging, IWBI 2020
Editors	Hilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
Place of Publication	Washington, USA
Publisher	SPIE
Number of pages	6
ISBN (Electronic)	9781510638327
ISBN (Print)	9781510638310
DOIs	https://doi.org/10.1117/12.2564272
Publication status	Published - 2020
Event	15th International Workshop on Breast Imaging, IWBI 2020 - Leuven, Belgium Duration: 25 May 2020 → 27 May 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11513
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	15th International Workshop on Breast Imaging, IWBI 2020
Country/Territory	Belgium
City	Leuven
Period	25/05/20 → 27/05/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast cancer
CAD
Dense ROI
Local binary pattern
Machine learning
Mammography
Structured micro-patterns

Access to Document

10.1117/12.2564272Licence: In Copyright

Cite this

Sajeev, S., Bajger, M., Lee, G., Muramatsu, C., & Fujita, H. (2020). Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns. In H. Bosmans, N. Marshall, & C. Van Ongeval (Eds.), 15th International Workshop on Breast Imaging, IWBI 2020 Article 1151323 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11513). SPIE. https://doi.org/10.1117/12.2564272

Sajeev, Shelda ; Bajger, Mariusz ; Lee, Gobert et al. / Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns. 15th International Workshop on Breast Imaging, IWBI 2020. editor / Hilde Bosmans ; Nicholas Marshall ; Chantal Van Ongeval. Washington, USA : SPIE, 2020. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{31d26d3a8b0a4447ba8e6499e127edbd,

title = "Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns",

abstract = "The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.",

keywords = "Breast cancer, CAD, Dense ROI, Local binary pattern, Machine learning, Mammography, Structured micro-patterns",

author = "Shelda Sajeev and Mariusz Bajger and Gobert Lee and Chisako Muramatsu and Hiroshi Fujita",

year = "2020",

doi = "10.1117/12.2564272",

language = "English",

isbn = "9781510638310",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Hilde Bosmans and Nicholas Marshall and \{Van Ongeval\}, Chantal",

booktitle = "15th International Workshop on Breast Imaging, IWBI 2020",

note = "15th International Workshop on Breast Imaging, IWBI 2020 ; Conference date: 25-05-2020 Through 27-05-2020",

}

Sajeev, S , Bajger, M , Lee, G, Muramatsu, C & Fujita, H 2020, Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns. in H Bosmans, N Marshall & C Van Ongeval (eds), 15th International Workshop on Breast Imaging, IWBI 2020., 1151323, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11513, SPIE, Washington, USA, 15th International Workshop on Breast Imaging, IWBI 2020, Leuven, Belgium, 25/05/20. https://doi.org/10.1117/12.2564272

Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns. / Sajeev, Shelda ; Bajger, Mariusz ; Lee, Gobert et al.
15th International Workshop on Breast Imaging, IWBI 2020. ed. / Hilde Bosmans; Nicholas Marshall; Chantal Van Ongeval. Washington, USA: SPIE, 2020. 1151323 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11513).

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

TY - GEN

T1 - Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

AU - Sajeev, Shelda

AU - Bajger, Mariusz

AU - Lee, Gobert

AU - Muramatsu, Chisako

AU - Fujita, Hiroshi

PY - 2020

Y1 - 2020

N2 - The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.

AB - The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.

KW - Breast cancer

KW - CAD

KW - Dense ROI

KW - Local binary pattern

KW - Machine learning

KW - Mammography

KW - Structured micro-patterns

UR - https://www.scopus.com/pages/publications/85086138014

U2 - 10.1117/12.2564272

DO - 10.1117/12.2564272

M3 - Conference contribution

AN - SCOPUS:85086138014

SN - 9781510638310

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - 15th International Workshop on Breast Imaging, IWBI 2020

A2 - Bosmans, Hilde

A2 - Marshall, Nicholas

A2 - Van Ongeval, Chantal

PB - SPIE

CY - Washington, USA

T2 - 15th International Workshop on Breast Imaging, IWBI 2020

Y2 - 25 May 2020 through 27 May 2020

ER -

Sajeev S , Bajger M , Lee G, Muramatsu C, Fujita H. Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns. In Bosmans H, Marshall N, Van Ongeval C, editors, 15th International Workshop on Breast Imaging, IWBI 2020. Washington, USA: SPIE. 2020. 1151323. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2564272

Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this