Literature DB >> 20071209

A review of automatic mass detection and segmentation in mammographic images.

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

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. Copyright 2009 Elsevier B.V. All rights reserved.

Mesh:

Year:  2009        PMID: 20071209     DOI: 10.1016/j.media.2009.12.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  46 in total

1.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

2.  Discovering mammography-based machine learning classifiers for breast cancer diagnosis.

Authors:  Raúl Ramos-Pollán; Miguel Angel Guevara-López; Cesar Suárez-Ortega; Guillermo Díaz-Herrero; Jose Miguel Franco-Valiente; Manuel Rubio-Del-Solar; Naimy González-de-Posada; Mario Augusto Pires Vaz; Joana Loureiro; Isabel Ramos
Journal:  J Med Syst       Date:  2011-04-09       Impact factor: 4.460

3.  Detection of cancerous masses in mammograms by template matching: optimization of template brightness distribution by means of evolutionary algorithm.

Authors:  Marcin Bator; Mariusz Nieniewski
Journal:  J Digit Imaging       Date:  2012-02       Impact factor: 4.056

4.  Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.

Authors:  Rohith Reddy Gundreddy; Maxine Tan; Yuchen Qiu; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

5.  Breast Density Analysis Using an Automatic Density Segmentation Algorithm.

Authors:  Arnau Oliver; Meritxell Tortajada; Xavier Lladó; Jordi Freixenet; Sergi Ganau; Lidia Tortajada; Mariona Vilagran; Melcior Sentís; Robert Martí
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

6.  Correlation of free-response and receiver-operating-characteristic area-under-the-curve estimates: results from independently conducted FROC∕ROC studies in mammography.

Authors:  Federica Zanca; Stephen L Hillis; Filip Claus; Chantal Van Ongeval; Valerie Celis; Veerle Provoost; Hong-Jun Yoon; Hilde Bosmans
Journal:  Med Phys       Date:  2012-10       Impact factor: 4.071

7.  A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

Authors:  Maxine Tan; Jiantao Pu; Bin Zheng
Journal:  Med Phys       Date:  2014-08       Impact factor: 4.071

8.  Inferring Generative Model Structure with Static Analysis.

Authors:  Paroma Varma; Bryan He; Payal Bajaj; Imon Banerjee; Nishith Khandwala; Daniel L Rubin; Christopher Ré
Journal:  Adv Neural Inf Process Syst       Date:  2017-12

9.  A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

Authors:  Yuchen Qiu; Shiju Yan; Rohith Reddy Gundreddy; Yunzhi Wang; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  J Xray Sci Technol       Date:  2017       Impact factor: 1.535

Review 10.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

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