A computer-aided detection system for automatic mammography mass identification

Hussein Samma; Chee Peng Lim; Ali Samma

doi:10.1007/978-3-642-17534-3_28

A computer-aided detection system for automatic mammography mass identification

Hussein Samma
, Chee Peng Lim
, Ali Samma

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

2 Scopus citations

Abstract

Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	Models and Applications - 17th International Conference, ICONIP 2010, Proceedings
Pages	226-233
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-17534-3_28
State	Published - 2010
Externally published	Yes
Event	17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 22 Nov 2010 → 25 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6444 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Neural Information Processing, ICONIP 2010
Country/Territory	Australia
City	Sydney, NSW
Period	22/11/10 → 25/11/10

UN SDGs

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

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-17534-3_28

Cite this

Samma, H., Lim, C. P., & Samma, A. (2010). A computer-aided detection system for automatic mammography mass identification. In Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings (PART 2 ed., pp. 226-233). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6444 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-17534-3_28

Samma, Hussein ; Lim, Chee Peng ; Samma, Ali. / A computer-aided detection system for automatic mammography mass identification. Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2. ed. 2010. pp. 226-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{6b97ea2830644fb7bbd6452b73d75589,

title = "A computer-aided detection system for automatic mammography mass identification",

abstract = "Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.",

author = "Hussein Samma and Lim, \{Chee Peng\} and Ali Samma",

year = "2010",

doi = "10.1007/978-3-642-17534-3\_28",

language = "English",

isbn = "3642175333",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "226--233",

booktitle = "Neural Information Processing",

edition = "PART 2",

note = "17th International Conference on Neural Information Processing, ICONIP 2010 ; Conference date: 22-11-2010 Through 25-11-2010",

}

Samma, H, Lim, CP & Samma, A 2010, A computer-aided detection system for automatic mammography mass identification. in Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6444 LNCS, pp. 226-233, 17th International Conference on Neural Information Processing, ICONIP 2010, Sydney, NSW, Australia, 22/11/10. https://doi.org/10.1007/978-3-642-17534-3_28

A computer-aided detection system for automatic mammography mass identification. / Samma, Hussein; Lim, Chee Peng; Samma, Ali.
Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2. ed. 2010. p. 226-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6444 LNCS, No. PART 2).

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

TY - GEN

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N2 - Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.

AB - Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.

UR - https://www.scopus.com/pages/publications/78650187790

U2 - 10.1007/978-3-642-17534-3_28

DO - 10.1007/978-3-642-17534-3_28

M3 - Conference contribution

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SN - 9783642175336

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Samma H, Lim CP, Samma A. A computer-aided detection system for automatic mammography mass identification. In Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2 ed. 2010. p. 226-233. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-17534-3_28

A computer-aided detection system for automatic mammography mass identification

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

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Cite this