A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment

Zhili Chen; Liping Wang; Erika R. E. Denton; Reyer Zwiggelaar

doi:10.1007/978-3-642-40246-3_43

A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment

Zhili Chen
, Liping Wang
, Erika R. E. Denton
, Reyer Zwiggelaar

Department of Computer Science

Norfolk and Norwich University Hospitals NHS Foundation Trust

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

1 Citation (Scopus)

Abstract

Mammographic parenchymal patterns have been found to be a strong indicator of breast cancer risk and play an important role in mammographic risk assessment. In this paper, a novel representation of mammographic parenchymal patterns is proposed, which is based on multiscale blobs. Approximately blob-like tissue patterns are detected over a range of scales and parenchymal patterns are represented as a set of blobs. Spatial relations between blobs are considered to reduce the overlap between connected dense tissue regions. Quantitative measures of breast density are calculated from the resulting blobs and used for mammographic risk assessment. The proposed approach is evaluated using the full MIAS database and a large dataset from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The classification accuracies for the MIAS and DDSM databases are up to 79.44% and 76.90%, respectively.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
Subtitle of host publication	15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II
Editors	Richard Wilson, Edwin Hancock, Adrian Bors, William Smith
Publisher	Springer Nature
Pages	346-353
Number of pages	8
Edition	PART 2
ISBN (Electronic)	978-3-642-40246-3
ISBN (Print)	978-3-642-40245-6
DOIs	https://doi.org/10.1007/978-3-642-40246-3_43
Publication status	Published - 07 Aug 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8048 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

breast density
mammographic parenchymal patterns
mammographic risk assessment
multiscale blob detection

Access to Document

10.1007/978-3-642-40246-3_43

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Chen, Z., Wang, L., Denton, E. R. E., & Zwiggelaar, R. (2013). A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. In R. Wilson, E. Hancock, A. Bors, & W. Smith (Eds.), Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II (PART 2 ed., pp. 346-353). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8048 LNCS, No. PART 2). Springer Nature. https://doi.org/10.1007/978-3-642-40246-3_43

Chen, Zhili ; Wang, Liping ; Denton, Erika R. E. et al. / A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II. editor / Richard Wilson ; Edwin Hancock ; Adrian Bors ; William Smith. PART 2. ed. Springer Nature, 2013. pp. 346-353 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Mammographic parenchymal patterns have been found to be a strong indicator of breast cancer risk and play an important role in mammographic risk assessment. In this paper, a novel representation of mammographic parenchymal patterns is proposed, which is based on multiscale blobs. Approximately blob-like tissue patterns are detected over a range of scales and parenchymal patterns are represented as a set of blobs. Spatial relations between blobs are considered to reduce the overlap between connected dense tissue regions. Quantitative measures of breast density are calculated from the resulting blobs and used for mammographic risk assessment. The proposed approach is evaluated using the full MIAS database and a large dataset from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The classification accuracies for the MIAS and DDSM databases are up to 79.44\% and 76.90\%, respectively.",

keywords = "breast density, mammographic parenchymal patterns, mammographic risk assessment, multiscale blob detection",

author = "Zhili Chen and Liping Wang and Denton, \{Erika R. E.\} and Reyer Zwiggelaar",

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Chen, Z, Wang, L, Denton, ERE & Zwiggelaar, R 2013, A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. in R Wilson, E Hancock, A Bors & W Smith (eds), Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8048 LNCS, Springer Nature, pp. 346-353. https://doi.org/10.1007/978-3-642-40246-3_43

A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. / Chen, Zhili; Wang, Liping; Denton, Erika R. E. et al.
Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II. ed. / Richard Wilson; Edwin Hancock; Adrian Bors; William Smith. PART 2. ed. Springer Nature, 2013. p. 346-353 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8048 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

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T1 - A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment

AU - Chen, Zhili

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AU - Denton, Erika R. E.

AU - Zwiggelaar, Reyer

PY - 2013/8/7

Y1 - 2013/8/7

N2 - Mammographic parenchymal patterns have been found to be a strong indicator of breast cancer risk and play an important role in mammographic risk assessment. In this paper, a novel representation of mammographic parenchymal patterns is proposed, which is based on multiscale blobs. Approximately blob-like tissue patterns are detected over a range of scales and parenchymal patterns are represented as a set of blobs. Spatial relations between blobs are considered to reduce the overlap between connected dense tissue regions. Quantitative measures of breast density are calculated from the resulting blobs and used for mammographic risk assessment. The proposed approach is evaluated using the full MIAS database and a large dataset from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The classification accuracies for the MIAS and DDSM databases are up to 79.44% and 76.90%, respectively.

AB - Mammographic parenchymal patterns have been found to be a strong indicator of breast cancer risk and play an important role in mammographic risk assessment. In this paper, a novel representation of mammographic parenchymal patterns is proposed, which is based on multiscale blobs. Approximately blob-like tissue patterns are detected over a range of scales and parenchymal patterns are represented as a set of blobs. Spatial relations between blobs are considered to reduce the overlap between connected dense tissue regions. Quantitative measures of breast density are calculated from the resulting blobs and used for mammographic risk assessment. The proposed approach is evaluated using the full MIAS database and a large dataset from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The classification accuracies for the MIAS and DDSM databases are up to 79.44% and 76.90%, respectively.

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

SP - 346

EP - 353

BT - Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings

A2 - Wilson, Richard

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A2 - Smith, William

PB - Springer Nature

ER -

Chen Z, Wang L, Denton ERE, Zwiggelaar R. A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. In Wilson R, Hancock E, Bors A, Smith W, editors, Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II. PART 2 ed. Springer Nature. 2013. p. 346-353. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-40246-3_43

A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment

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