Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Highlights • We propose a new algorithm for breast cancer detection and classification. • Our system can accurately detect the probability of benign or malign breast lesion. • We utilize NSCT transform and super resolution to improve the quality of the images. • The results on MIAS database show sig...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2015-11, Vol.122 (2), p.89-107
Main Authors: Pak, Fatemeh, Kanan, Hamidreza Rashidy, Alikhassi, Afsaneh
Format: Article
Language:English
Subjects:
Algorithms
BI-RADS
Breast cancer
Breast Neoplasms - diagnostic imaging
Computer-Aided Diagnosis (CAD) system
Female
Fuzzy Logic
Humans
Internal Medicine
Machine Learning
Mammography
Mammography - methods
Non-Subsampled Contourlet Transform (NSCT)
Other
Pattern Recognition, Automated - methods
Radiographic Image Enhancement - methods
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Super Resolution (SR)
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Summary:Highlights • We propose a new algorithm for breast cancer detection and classification. • Our system can accurately detect the probability of benign or malign breast lesion. • We utilize NSCT transform and super resolution to improve the quality of the images. • The results on MIAS database show significant performance of the proposed method. • Our system achieves 91.43% and 6.42% as a mean accuracy and FPR, respectively.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2015.06.009