A review of automatic mass detection and segmentation in mammographic images

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

doi:10.1016/j.media.2009.12.005

A review of automatic mass detection and segmentation in mammographic images

Med Image Anal. 2010 Apr;14(2):87-110. doi: 10.1016/j.media.2009.12.005. Epub 2009 Dec 29.

Authors

Arnau Oliver¹, Jordi Freixenet, Joan Martí, Elsa Pérez, Josep Pont, Erika R E Denton, Reyer Zwiggelaar

Affiliation

¹ Dept. of Computer Architecture and Technology, University of Girona Ed. P-IV, Campus de Montilivi 17071, Girona, Spain. aoliver@eia.udg.edu

PMID: 20071209
DOI: 10.1016/j.media.2009.12.005

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.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms*
Artificial Intelligence*
Breast Neoplasms / diagnostic imaging*
Female
Humans
Mammography / methods*
Pattern Recognition, Automated / methods*
Radiographic Image Enhancement / methods*
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Sensitivity and Specificity