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Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features

  • Robert Martí
  • Yago Díez
  • Arnau Oliver
  • Meritxell Tortajada
  • Reyer Zwiggelaar
  • Xavier Lladó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8539)

Abstract

Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.

Keywords

Image Registration Registration Algorithm Feature Combination Cost Matrix Mammographic Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robert Martí
    • 1
  • Yago Díez
    • 1
  • Arnau Oliver
    • 1
  • Meritxell Tortajada
    • 1
  • Reyer Zwiggelaar
    • 2
  • Xavier Lladó
    • 1
  1. 1.Computer Vision and Robotics GroupUniversity of GironaSpain
  2. 2.Department of Computer ScienceAberystwyth UniversityWalesUK

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