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

Adran Cyfrifiadureg

Norfolk & Norwich University Hospital

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

1 Dyfyniad (Scopus)

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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.

Iaith wreiddiol	Saesneg
Teitl	Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
Is-deitl	15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II
Golygyddion	Richard Wilson, Edwin Hancock, Adrian Bors, William Smith
Cyhoeddwr	Springer Nature
Tudalennau	346-353
Nifer y tudalennau	8
Argraffiad	PART 2
ISBN (Electronig)	978-3-642-40246-3
ISBN (Argraffiad)	978-3-642-40245-6
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-642-40246-3_43
Statws	Cyhoeddwyd - 07 Awst 2013

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Rhif	PART 2
Cyfrol	8048 LNCS
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

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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. Yn R. Wilson, E. Hancock, A. Bors, & W. Smith (Gol.), 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 gol., tt. 346-353). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 8048 LNCS, Rhif 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. Gol. / Richard Wilson ; Edwin Hancock ; Adrian Bors ; William Smith. PART 2. gol. Springer Nature, 2013. tt. 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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title = "A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment",

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. yn R Wilson, E Hancock, A Bors & W Smith (gol.), 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 gol., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), rhif PART 2, cyfrol. 8048 LNCS, Springer Nature, tt. 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. gol. / Richard Wilson; Edwin Hancock; Adrian Bors; William Smith. PART 2. gol. Springer Nature, 2013. t. 346-353 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 8048 LNCS, Rhif PART 2).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

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

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AU - Denton, Erika R. E.

AU - Zwiggelaar, Reyer

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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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SN - 978-3-642-40245-6

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

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

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Chen Z, Wang L, Denton ERE, Zwiggelaar R. A Multiscale Blob Representation of Mammographic Parenchymal Patterns and Mammographic Risk Assessment. Yn Wilson R, Hancock E, Bors A, Smith W, golygyddion, 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 gol. Springer Nature. 2013. t. 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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