Superpixel texture analysis for classification of breast masses in dense background
Author:
Affiliation:
1. Medical Devices Research Institute, Flinders UniversityBedford ParkAustralia
2. College of Science and Engineering, Flinders UniversityBedford ParkAustralia
Publisher
Institution of Engineering and Technology (IET)
Subject
Computer Vision and Pattern Recognition,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2017.0586
Reference39 articles.
1. ‘World Cancer Research Fund International’. Available athttp://www.wcrf.org/int/cancer‐facts‐figures/data‐specific‐cancers/breast‐cancerstatistics accessed July 2017
2. American Cancer Society Cancer Facts and Figures 2017. Available athttps://www.cancer.org/research/cancer‐facts‐statistics/all‐cancer‐factsfigures/cancer‐facts‐figures‐2017.html accessed July 2017
3. Impact of mammographic breast density on computer‐assisted detection (CAD) in a breast imaging department;Castellano C.R.;Radiologia,2011
4. Breast patterns as an index of risk for developing breast cancer
5. Hormone Replacement Therapy and Breast Cancer: Heterogeneous Risks by Race, Weight, and Breast Density
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5. Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram;Graph Learning in Medical Imaging;2019
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