&lt;title&gt;Evaluation of an automated computer-aided diagnosis system for the detection of masses on prior mammograms&lt;/title&gt;

Petrick, Nicholas; Chan, Heang-Ping; Sahiner, Berkman; Helvie, Mark A.; Paquerault, Sophie

doi:10.1117/12.387600

Cited by 11 publications

(6 citation statements)

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“…Petrick et al [115] studied the effectiveness of their mass-detection method in the detection of masses in prior mammograms. The dataset used included 92 images region growing after enhancement of contrast in the image.…”

Section: Article In Pressmentioning

confidence: 99%

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Rangayyan

Ayres

Desautels

2007

Journal of the Franklin Institute

302

143

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Section: Article In Pressmentioning

confidence: 99%

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Rangayyan

Ayres

Desautels

2007

Journal of the Franklin Institute

302

143

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“…Petrick et al [36] assessed the effectiveness of their method for the detection of masses in the detection of the related regions in prior mammograms. A set of 92 images, including 54 malignant and 38 benign lesions from 37 cases (22 malignant and 15 benign) was used.…”

Section: Analysis Of Mammograms Acquired Prior To the Detection Of Brmentioning

confidence: 99%

Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis

et al. 2008

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Objective Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. Materials and methods A method based on Gabor filters and phase portrait analysis was used to detect initial candidates for sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 "prior mammograms", including 21 ROIs related to architectural distortion. From the corresponding set of 14 "detection mammograms", 398 ROIs were obtained, including 18 related to breast cancer. For each ROI, the fractal dimension and Haralick's texture features were computed. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. Results The average fractal dimension of the normal (falsepositive) ROIs was significantly higher than that of the ROIs with architectural distortion ( p = 0.006). For the "prior mammograms", the best receiver operating characteristics (ROC) performance achieved, in terms of the area under the

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“…Petrick et al 21 assessed the effectiveness of their method for the detection of masses in the detection of the related regions in prior mammograms. A set of 92 images, including 54 malignant and 38 benign lesions from 37 cases (22 malignant and 15 benign) was used.…”

Section: Analysis Of Prior Mammogramsmentioning

confidence: 99%

Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis

et al. 2008

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Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. A method based on Gabor filters and phase portrait analysis was used to detect initial candidates of sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 "prior mammograms", including 21 ROIs related to architectural distortion. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. The average fractal dimension of the normal (false-positive) ROIs was higher than that of the ROIs with architectural distortion. For the "prior mammograms", the best receiver operating characteristics (ROC) performance achieved was 0.74 with the fractal dimension and 0.70 with fourteen texture features, in terms of the area under the ROC curve.

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<title>Evaluation of an automated computer-aided diagnosis system for the detection of masses on prior mammograms</title>

Cited by 11 publications

References 0 publications

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis

Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis

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