RMID: A Novel and Efficient Image Descriptor for Mammogram Mass Classification

  • Sk Md ObaidullahEmail author
  • Sajib Ahmed
  • Teresa Gonçalves
  • Luís Rato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)


For mammogram image analysis, feature extraction is the most crucial step when machine learning techniques are applied. In this paper, we propose RMID (Radon-based Multi-resolution Image Descriptor), a novel image descriptor for mammogram mass classification, which perform efficiently without any clinical information. For the present experimental framework, we found that, in terms of area under the ROC curve (AUC), the proposed RMID outperforms, upto some extent, previous reported experiments using histogram based hand-crafted methods, namely Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (CNN). We also found that the highest AUC value (0.986) is obtained when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.


Image descriptor Mammogram image Breast cancer Classification 



The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [30]. The authors are thankful for considering the paper in the proceedings.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sk Md Obaidullah
    • 1
    Email author
  • Sajib Ahmed
    • 1
  • Teresa Gonçalves
    • 1
  • Luís Rato
    • 1
  1. 1.Department of InformaticsUniversity of ÉvoraÉvoraPortugal

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