Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

被引:238
作者
Jalalian, Afsaneh [1 ]
Mashohor, Syamsiah B. T. [1 ]
Mahmud, Hajjah Rozi [2 ]
Saripan, M. Iqbal B. [1 ]
Ramli, Abdul Rahman B. [1 ]
Karasfi, Babak [3 ]
机构
[1] Univ Putra, Fac Engn, Dept Comp & Commun Syst Engn, Selangor, Malaysia
[2] Univ Putra, Fac Med & Hlth Sci, Dept Imaging, Selangor, Malaysia
[3] Islamic Azad Univ, Dept Comp Engn, Qazvin, Iran
关键词
Computer-aided detection; Computer-aided diagnosis; Breast cancer; Mammography; Ultrasound; SUPPORT VECTOR MACHINES; ARTIFICIAL NEURAL-NETWORKS; MARKOV RANDOM-FIELD; SCREENING MAMMOGRAPHY; CLUSTERED MICROCALCIFICATIONS; WAVELET TRANSFORM; SPECKLE REDUCTION; TEXTURE ANALYSIS; LUNG-CANCER; DETECTION SYSTEM;
D O I
10.1016/j.clinimag.2012.09.024
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:420 / 426
页数:7
相关论文
共 117 条
[1]   An evolutionary artificial neural networks approach for breast cancer diagnosis [J].
Abbass, HA .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2002, 25 (03) :265-281
[2]   The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images [J].
Adam, D ;
Beilin-Nissan, S ;
Friedman, Z ;
Behar, V .
ULTRASONICS, 2006, 44 (02) :166-181
[3]   Support vector machines combined with feature selection for breast cancer diagnosis [J].
Akay, Mehmet Fatih .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) :3240-3247
[4]  
Alam S, 2011, BANGLADESH J MED PHY, V4
[5]  
[Anonymous], ACR STAND 2000 2001
[6]  
[Anonymous], 2011, Int. J. Comput. Sci. Trends Technol.
[7]   A new method of spatial compounding imaging [J].
Behar, V ;
Adam, D ;
Friedman, Z .
ULTRASONICS, 2003, 41 (05) :377-384
[8]   ANALYSIS OF CANCERS MISSED AT SCREENING MAMMOGRAPHY [J].
BIRD, RE ;
WALLACE, TW ;
YANKASKAS, BC .
RADIOLOGY, 1992, 184 (03) :613-617
[9]   Computer-aided detection with screening mammography in a university hospital setting [J].
Birdwell, RL ;
Bandodkar, P ;
Ikeda, DM .
RADIOLOGY, 2005, 236 (02) :451-457
[10]  
Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401