Dept. of Computer Architecture and Technology, University of Girona Ed. P-IV, Campus de Montilivi 17071, Girona, Spain.
Med Image Anal. 2010 Apr;14(2):87-110. doi: 10.1016/j.media.2009.12.005. Epub 2009 Dec 29.
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.
本文旨在回顾现有的乳腺图像中肿块自动检测和分割方法,重点介绍所采用策略之间的关键点和主要差异。主要目的是指出各种方法的优缺点。与仅定性描述和比较不同方法的其他综述不同,本文还提供了定量比较。使用两个不同的乳腺数据库:公共数字化数据库和本地全数字化数据库,对七种肿块检测方法的性能进行了比较。结果以接收者操作特性(ROC)和自由响应接收者操作特性(FROC)分析的形式给出。