Literature DB >> 19171527

Computer-aided detection and diagnosis of breast cancer with mammography: recent advances.

Jinshan Tang1, Rangaraj M Rangayyan, Jun Xu, Issam El Naqa, Yongyi Yang.   

Abstract

Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.

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Year:  2009        PMID: 19171527     DOI: 10.1109/TITB.2008.2009441

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  58 in total

1.  Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

Authors:  Shantanu Banik; Rangaraj M Rangayyan; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-03-30       Impact factor: 2.924

2.  Breast masses detection using phase portrait analysis and fuzzy inference systems.

Authors:  Arianna Mencattini; Marcello Salmeri
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-10-11       Impact factor: 2.924

3.  Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

Authors:  P S Vikhe; V R Thool
Journal:  J Med Syst       Date:  2016-01-26       Impact factor: 4.460

4.  Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
Journal:  J Vis Exp       Date:  2013-08-30       Impact factor: 1.355

5.  Characterizing mammography reports for health analytics.

Authors:  Carlos C Rojas; Robert M Patton; Barbara G Beckerman
Journal:  J Med Syst       Date:  2011-06-14       Impact factor: 4.460

6.  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

Review 7.  Histopathological image analysis: a review.

Authors:  Metin N Gurcan; Laura E Boucheron; Ali Can; Anant Madabhushi; Nasir M Rajpoot; B Yener
Journal:  IEEE Rev Biomed Eng       Date:  2009-10-30

8.  Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; Jayasree Chakraborty; Sudipta Mukhopadhyay; J E Leo Desautels
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-09-30       Impact factor: 2.924

9.  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

10.  Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Authors:  Shubhi Sharma; Pritee Khanna
Journal:  J Digit Imaging       Date:  2014-07-09       Impact factor: 4.056

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