Breast Lesion Discrimination Using Saliency Features from MRI Sequences and MKL-Based Classification

  • Henry Jhoán Areiza-Laverde
  • Carlos Andrés Duarte-Salazar
  • Liliana Hernández
  • Andrés Eduardo Castro-OspinaEmail author
  • Gloria M. Díaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


Breast MRI interpretation requires that radiologists examine several images, depending on the acquisition protocol that is managed in the health institution, a very subjective and time-consuming process that reports large variability, which affects the final diagnosis and prognosis of the patient. In this paper, we present a computational method for classifying lesions detected in breast MRI studies, which aims to reduce physician subjectivity. The proposed approach take advantage of the ability of the Multiple Kernel Learning (MKL) strategy for optimally fusing the features extracted from the different image sequences that compose a breast MRI Study, which describe the grey level distribution of the original image and its saliency map, computed using the Graph-based Visual Saliency (GBVS) algorithm. Breast lesions were classified as positive and negative findings with an accuracy of \(85.5\%\) and \(84.8\%\) when nine and five sequences were used, respectively.


Breast MRI GBVS Machine learning MKL Visual saliency 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Henry Jhoán Areiza-Laverde
    • 1
  • Carlos Andrés Duarte-Salazar
    • 1
  • Liliana Hernández
    • 2
  • Andrés Eduardo Castro-Ospina
    • 1
    Email author
  • Gloria M. Díaz
    • 1
  1. 1.Instituto Tecnológico MetropolitanoMedellínColombia
  2. 2.Instituto de Alta Tecnología MédicaMedellínColombia

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