Dual system approach to computer-aided detection of breast masses on mammograms

被引:36
|
作者
Wei, Jun [1 ]
Chan, Heang-Ping [1 ]
Sahiner, Berkman [1 ]
Hadjiiski, Lubomir M. [1 ]
Helvie, Mark A. [1 ]
Roubidoux, Marilyn A. [1 ]
Zhou, Chuan [1 ]
Ge, Jun [1 ]
机构
[1] Univ Michigan, Dept Radiol, Ann Arbor, MI 48109 USA
关键词
computer-aided detection (CAD); mass detection; mammogram; dual system; artificial neural network (ANN);
D O I
10.1118/1.2357838
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
In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a computer-added detection (CAD) system optimized with "average" masses with another CAD system optimized with "subtle" masses. The two single CAD systems have similar image processing steps, which include prescreening, object segmentation, morphological and texture feature extraction, and false positive (FP) reduction by rule-based and linear discriminant analysis (LDA) classifiers. A feed-forward backpropagation artificial neural network was trained to merge the scores from the LDA classifiers in the two single CAD systems and differentiate true masses from normal tissue. For an unknown test mammogram, the two single CAD systems are applied to the image in parallel to detect suspicious objects. A total of three data sets were used for training and testing the systems. The first data set of 230 current mammograms, referred to as the average mass set, was collected from 115 patients. We also collected 264 mammograms, referred to as the subtle mass set, which were one to two years prior to the current exam from these patients. Both the average and the subtle mass sets were partitioned into two independent data sets in a cross validation training and testing scheme. A third data set containing 65 cases with 260 normal mammograms was used to estimate the FP marker rates during testing. When the single CAD system trained on the average mass set was applied to the test set with average masses, the FP marker rates were 2.2, 1.8, and 1.5 per image at the case-based sensitivities of 90%, 85%, and 80%, respectively. With the dual CAD system, the FP marker rates were reduced to 1.2, 0.9, and 0.7 per image, respectively, at the same case-based sensitivities. Statistically significant (P < 0.05) improvements on the free response receiver operating characteristic curves were observed when the dual system and the single system were compared using the test sets with either average masses or subtle masses. (c) 2006 American Association of Physicists in Medicine.
引用
收藏
页码:4157 / 4168
页数:12
相关论文
共 50 条
  • [31] Knowledge-based computer-aided detection of masses on digitized mammograms: A preliminary assessment
    Chang, YH
    Hardesty, LA
    Hakim, CM
    Chang, TS
    Zheng, B
    Good, WF
    Gur, D
    MEDICAL PHYSICS, 2001, 28 (04) : 455 - 461
  • [32] Computer-Aided Detection and Classification of Masses in Digitized Mammograms Using Artificial Neural Network
    Islam, Mohammed J.
    Ahmadi, Majid
    Sid-Ahmed, Maher A.
    ADVANCES IN SWARM INTELLIGENCE, PT 2, PROCEEDINGS, 2010, 6146 : 327 - 334
  • [33] A new approach to develop computer-aided detection schemes of digital mammograms
    Tan, Maxine
    Qian, Wei
    Pu, Jiantao
    Liu, Hong
    Zheng, Bin
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (11): : 4413 - 4427
  • [34] Development of a computer-aided sketch system for mammograms
    Nakagawa, T
    Hara, T
    Fujita, H
    Iwase, T
    Endo, T
    DIGITAL MAMMOGRAPHY, PROCEEDINGS, 2003, : 581 - 583
  • [35] A novel computer-aided diagnosis system of the mammograms
    Xu, Weidong
    Xia, Shunren
    Duan, Huilong
    INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION, 2006, 345 : 639 - 644
  • [36] An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms
    Mazurowski, Maciej A.
    Zurada, Jacek M.
    Tourassi, Georgia D.
    MEDICAL PHYSICS, 2009, 36 (07) : 2976 - 2984
  • [37] Computer-aided detection systems for breast masses: Comparison of performances on full-field digital mammograms and digitized screen-film mammograms
    Wei, Jun
    Hadjiiski, Lubomir M.
    Sahiner, Berkman
    Chan, Heang-Ping
    Ge, Jun
    Roubidoux, Marilyn A.
    Helvie, Mark A.
    Zhou, Chuan
    Wu, Yi-Ta
    Paramagul, Chintana
    Zhang, Yiheng
    ACADEMIC RADIOLOGY, 2007, 14 (06) : 659 - 669
  • [38] Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms
    Gopichandh Danala
    Bhavika Patel
    Faranak Aghaei
    Morteza Heidari
    Jing Li
    Teresa Wu
    Bin Zheng
    Annals of Biomedical Engineering, 2018, 46 : 1419 - 1431
  • [39] Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach
    Dheeba, J.
    Singh, N. Albert
    Selvi, S. Tamil
    JOURNAL OF BIOMEDICAL INFORMATICS, 2014, 49 : 45 - 52
  • [40] Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms
    Danala, Gopichandh
    Patel, Bhavika
    Aghaei, Faranak
    Heidari, Morteza
    Li, Jing
    Wu, Teresa
    Zheng, Bin
    ANNALS OF BIOMEDICAL ENGINEERING, 2018, 46 (09) : 1419 - 1431