Classification of linear structures in mammograms using random forests

Zezhi Chen; Michael Berks; Susan Astley; Chris Taylor

doi:10.1007/978-3-642-13666-5_21

Classification of linear structures in mammograms using random forests

Zezhi Chen, Michael Berks, Susan Astley, Chris Taylor

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. © 2010 Springer-Verlag.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Publisher	Springer Nature
Pages	153-160
Number of pages	7
Volume	6136
ISBN (Print)	3642136656, 9783642136658
DOIs	https://doi.org/10.1007/978-3-642-13666-5_21
Publication status	Published - 2010
Event	10th International Workshop on Digital Mammography, IWDM 2010 - Girona, Catalonia Duration: 1 Jul 2010 → …

Conference

Conference	10th International Workshop on Digital Mammography, IWDM 2010
City	Girona, Catalonia
Period	1/07/10 → …

Keywords

classification
dual-tree complex wavelet
linear structures
mammography
random forests

Access to Document

10.1007/978-3-642-13666-5_21

Cite this

Chen, Z., Berks, M., Astley, S., & Taylor, C. (2010). Classification of linear structures in mammograms using random forests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci. (Vol. 6136, pp. 153-160). Springer Nature. https://doi.org/10.1007/978-3-642-13666-5_21

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title = "Classification of linear structures in mammograms using random forests",

abstract = "Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. {\textcopyright} 2010 Springer-Verlag.",

keywords = "classification, dual-tree complex wavelet, linear structures, mammography, random forests",

author = "Zezhi Chen and Michael Berks and Susan Astley and Chris Taylor",

year = "2010",

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booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.",

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note = "10th International Workshop on Digital Mammography, IWDM 2010 ; Conference date: 01-07-2010",

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Chen, Z, Berks, M, Astley, S & Taylor, C 2010, Classification of linear structures in mammograms using random forests. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 6136, Springer Nature, pp. 153-160, 10th International Workshop on Digital Mammography, IWDM 2010, Girona, Catalonia, 1/07/10. https://doi.org/10.1007/978-3-642-13666-5_21

Classification of linear structures in mammograms using random forests. / Chen, Zezhi; Berks, Michael; Astley, Susan et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 6136 Springer Nature, 2010. p. 153-160.

Research output: Chapter in Book/Conference proceeding › Conference contribution

TY - GEN

T1 - Classification of linear structures in mammograms using random forests

AU - Chen, Zezhi

AU - Berks, Michael

AU - Astley, Susan

AU - Taylor, Chris

PY - 2010

Y1 - 2010

N2 - Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. © 2010 Springer-Verlag.

AB - Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. © 2010 Springer-Verlag.

KW - classification

KW - dual-tree complex wavelet

KW - linear structures

KW - mammography

KW - random forests

U2 - 10.1007/978-3-642-13666-5_21

DO - 10.1007/978-3-642-13666-5_21

M3 - Conference contribution

SN - 3642136656

SN - 9783642136658

VL - 6136

SP - 153

EP - 160

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

PB - Springer Nature

T2 - 10th International Workshop on Digital Mammography, IWDM 2010

Y2 - 1 July 2010

ER -

Classification of linear structures in mammograms using random forests

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Keywords

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