Automatic Classification of Linear Structures in Mammographic Images

Reyer Zwiggelaar; Christopher J. Taylor; Caroline Boggis

doi:10.1007/10704282_29

Automatic Classification of Linear Structures in Mammographic Images

Reyer Zwiggelaar, Christopher J. Taylor, Caroline Boggis

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

Abstract

Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings
Editors	Chris Taylor, Alain Colchester
Publisher	Springer Nature
Pages	263-272
Number of pages	10
ISBN (Electronic)	978-3-540-48232-1
ISBN (Print)	354066503X, 9783540665038
DOIs	https://doi.org/10.1007/10704282_29
Publication status	Published - 08 Sept 1999
Externally published	Yes
Event	Second International Conference on Medical Image Computing and Computer-Assisted Intervention - Cambridge, United Kingdom of Great Britain and Northern Ireland Duration: 19 Sept 1999 → 22 Sept 1999

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1679
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated title	MICCAI'99
Country/Territory	United Kingdom of Great Britain and Northern Ireland
City	Cambridge
Period	19 Sept 1999 → 22 Sept 1999

Keywords

linear structure
line operator
digital mammography
principal component analysis model
mammographic image

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10.1007/10704282_29

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Zwiggelaar, R., Taylor, C. J., & Boggis, C. (1999). Automatic Classification of Linear Structures in Mammographic Images. In C. Taylor, & A. Colchester (Eds.), Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings (pp. 263-272). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1679). Springer Nature. https://doi.org/10.1007/10704282_29

Zwiggelaar, Reyer ; Taylor, Christopher J. ; Boggis, Caroline. / Automatic Classification of Linear Structures in Mammographic Images. Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings. editor / Chris Taylor ; Alain Colchester. Springer Nature, 1999. pp. 263-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile.",

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Zwiggelaar, R, Taylor, CJ & Boggis, C 1999, Automatic Classification of Linear Structures in Mammographic Images. in C Taylor & A Colchester (eds), Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1679, Springer Nature, pp. 263-272, Second International Conference on Medical Image Computing and Computer-Assisted Intervention, Cambridge, United Kingdom of Great Britain and Northern Ireland, 19 Sept 1999. https://doi.org/10.1007/10704282_29

Automatic Classification of Linear Structures in Mammographic Images. / Zwiggelaar, Reyer; Taylor, Christopher J.; Boggis, Caroline.
Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings. ed. / Chris Taylor; Alain Colchester. Springer Nature, 1999. p. 263-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1679).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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AB - Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile.

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Zwiggelaar R, Taylor CJ, Boggis C. Automatic Classification of Linear Structures in Mammographic Images. In Taylor C, Colchester A, editors, Medical Image Computing and Computer-Assisted Intervention –MICCAI 1999 - 2nd International Conference, Proceedings. Springer Nature. 1999. p. 263-272. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/10704282_29

Automatic Classification of Linear Structures in Mammographic Images

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