Literature DB >> 17153394

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

Jun Wei1, Heang-Ping Chan, Berkman Sahiner, Lubomir M Hadjiiski, Mark A Helvie, Marilyn A Roubidoux, Chuan Zhou, Jun Ge.   

Abstract

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.

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Mesh:

Year:  2006        PMID: 17153394      PMCID: PMC2742210          DOI: 10.1118/1.2357838

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  32 in total

1.  Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size.

Authors:  B Sahiner; H P Chan; N Petrick; R F Wagner; L Hadjiiski
Journal:  Med Phys       Date:  2000-07       Impact factor: 4.071

2.  Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

Authors:  L Hadjiiski; B Sahiner; H P Chan; N Petrick; M A Helvie; M Gurcan
Journal:  Med Phys       Date:  2001-11       Impact factor: 4.071

3.  The life-sparing potential of mammographic screening.

Authors:  B Cady; J S Michaelson
Journal:  Cancer       Date:  2001-05-01       Impact factor: 6.860

4.  Observer studies involving detection and localization: modeling, analysis, and validation.

Authors:  Dev P Chakraborty; Kevin S Berbaum
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

5.  Benefit of mammography screening in women ages 40 to 49 years. Current evidence from randomized controlled trials.

Authors:  C R Smart; R E Hendrick; J H Rutledge; R A Smith
Journal:  Cancer       Date:  1995-04-01       Impact factor: 6.860

6.  Automated computerized classification of malignant and benign masses on digitized mammograms.

Authors:  Z Huo; M L Giger; C J Vyborny; D E Wolverton; R A Schmidt; K Doi
Journal:  Acad Radiol       Date:  1998-03       Impact factor: 3.173

7.  Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification.

Authors:  N Petrick; H P Chan; D Wei; B Sahiner; M A Helvie; D D Adler
Journal:  Med Phys       Date:  1996-10       Impact factor: 4.071

8.  Computer-aided detection with screening mammography in a university hospital setting.

Authors:  Robyn L Birdwell; Parul Bandodkar; Debra M Ikeda
Journal:  Radiology       Date:  2005-08       Impact factor: 11.105

9.  Impact of breast density on computer-aided detection for breast cancer.

Authors:  Rachel F Brem; Jeffrey W Hoffmeister; Jocelyn A Rapelyea; Gilat Zisman; Kevin Mohtashemi; Guarav Jindal; Martin P Disimio; Steven K Rogers
Journal:  AJR Am J Roentgenol       Date:  2005-02       Impact factor: 3.959

10.  Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

Authors:  Mark A Helvie; Lubomir Hadjiiski; Erini Makariou; Heang-Ping Chan; Nicholas Petrick; Berkman Sahiner; Shih-Chung B Lo; Matthew Freedman; Dorit Adler; Janet Bailey; Caroline Blane; Donna Hoff; Karen Hunt; Lynn Joynt; Katherine Klein; Chintana Paramagul; Stephanie K Patterson; Marilyn A Roubidoux
Journal:  Radiology       Date:  2004-02-27       Impact factor: 11.105

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  14 in total

1.  Bilateral analysis based false positive reduction for computer-aided mass detection.

Authors:  Yi-Ta Wu; Jun Wei; Lubomir M Hadjiiski; Berkman Sahiner; Chuan Zhou; Jun Ge; Jiazheng Shi; Yiheng Zhang; Heang-Ping Chan
Journal:  Med Phys       Date:  2007-08       Impact factor: 4.071

2.  Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers.

Authors:  Tingting Mu; Asoke K Nandi; Rangaraj M Rangayyan
Journal:  J Digit Imaging       Date:  2008-02-28       Impact factor: 4.056

Review 3.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

4.  A new automated method for the segmentation and characterization of breast masses on ultrasound images.

Authors:  Jing Cui; Berkman Sahiner; Heang-Ping Chan; Alexis Nees; Chintana Paramagul; Lubomir M Hadjiiski; Chuan Zhou; Jiazheng Shi
Journal:  Med Phys       Date:  2009-05       Impact factor: 4.071

5.  Computer-aided detection of masses in digital tomosynthesis mammography: comparison of three approaches.

Authors:  Heang-Ping Chan; Jun Wei; Yiheng Zhang; Mark A Helvie; Richard H Moore; Berkman Sahiner; Lubomir Hadjiiski; Daniel B Kopans
Journal:  Med Phys       Date:  2008-09       Impact factor: 4.071

6.  Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach.

Authors:  Swatee Singh; Georgia D Tourassi; Jay A Baker; Ehsan Samei; Joseph Y Lo
Journal:  Med Phys       Date:  2008-08       Impact factor: 4.071

7.  Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis.

Authors:  Jun Wei; Heang-Ping Chan; Berkman Sahiner; Chuan Zhou; Lubomir M Hadjiiski; Marilyn A Roubidoux; Mark A Helvie
Journal:  Med Phys       Date:  2009-10       Impact factor: 4.071

8.  Computer-aided detection of breast masses: four-view strategy for screening mammography.

Authors:  Jun Wei; Heang-Ping Chan; Chuan Zhou; Yi-Ta Wu; Berkman Sahiner; Lubomir M Hadjiiski; Marilyn A Roubidoux; Mark A Helvie
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

9.  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

10.  Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method.

Authors:  Xingwei Wang; Lihua Li; Weidong Xu; Wei Liu; Dror Lederman; Bin Zheng
Journal:  Phys Med Biol       Date:  2012-01-21       Impact factor: 3.609

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