A review of automatic mass detection and segmentation in mammographic images

Arnau Oliver; Jordi Freixenet; Joan Marti; Erika R. E. Denton; Reyer Zwiggelaar; Elsa Pérez; Josep Pont

doi:10.1016/j.media.2009.12.005

A review of automatic mass detection and segmentation in mammographic images

Arnau Oliver, Jordi Freixenet, Joan Marti, Erika R. E. Denton, Reyer Zwiggelaar, Elsa Pérez, Josep Pont

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

372 Citations (Scopus)

Abstract

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

Original language	English
Pages (from-to)	87-110
Journal	Medical Image Analysis
Volume	14
Issue number	2
DOIs	https://doi.org/10.1016/j.media.2009.12.005
Publication status	Published - 01 Feb 2010

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http://hdl.handle.net/2160/4050

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title = "A review of automatic mass detection and segmentation in mammographic images",

abstract = "The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.",

author = "Arnau Oliver and Jordi Freixenet and Joan Marti and Denton, {Erika R. E.} and Reyer Zwiggelaar and Elsa P{\'e}rez and Josep Pont",

note = "Oliver, A., Freixenet, J., Marti, J., P{\'e}rez, E., Pont, J., Denton, E. R. E., Zwiggelaar, R. (2010) A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis 14 (2010) 87–110 Sponsorship: Ministerio de Educaci{\'o}n y Ciencia of Spain: Grant TIN2007–60553; CIRIT and CUR of DIUiE of Generalitat de Catalunya: Grant 2008SALUT2009.",

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AU - Oliver, Arnau

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AU - Marti, Joan

AU - Denton, Erika R. E.

AU - Zwiggelaar, Reyer

AU - Pérez, Elsa

AU - Pont, Josep

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N2 - The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

AB - The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

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DO - 10.1016/j.media.2009.12.005

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JO - Medical Image Analysis

JF - Medical Image Analysis

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ER -

A review of automatic mass detection and segmentation in mammographic images

Abstract

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