Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points

  • Alberto GomezEmail author
  • Kanwal Bhatia
  • Sarjana Tharin
  • James Housden
  • Nicolas Toussaint
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)


We propose a fast feature-based rigid registration framework with a novel feature saliency detection technique. The method works by automatically classifying candidate image points as salient or non-salient using a support vector machine trained on points which have previously driven successful registrations. Resulting candidate salient points are used for symmetric matching based on local descriptor similarity and followed by RANSAC outlier rejection to obtain the final transform. The proposed registration framework was applied to 3D real-time fetal ultrasound images, thus covering the entire fetal anatomy for extended FoV imaging. Our method was applied to data from 5 patients, and compared to a conventional saliency point detection method (SIFT) in terms of computational time, quality of the point detection and registration accuracy. Our method achieved similar accuracy and similar saliency detection quality in \(<5\%\) the detection time, showing promising capabilities towards real-time whole-body fetal ultrasound imaging.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alberto Gomez
    • 1
    Email author
  • Kanwal Bhatia
    • 1
  • Sarjana Tharin
    • 1
  • James Housden
    • 1
  • Nicolas Toussaint
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.Department of Biomedical EngineeringKing’s College LondonLondonUK

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