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Histology to μCT Data Matching Using Landmarks and a Density Biased RANSAC

  • Natalia Chicherova
  • Ketut Fundana
  • Bert Müller
  • Philippe C. Cattin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

The fusion of information from different medical imaging techniques plays an important role in data analysis. Despite the many proposed registration algorithms the problem of registering 2D histological images to 3D CT or MR imaging data is still largely unsolved.

In this paper we propose a computationally efficient automatic approach to match 2D histological images to 3D micro Computed Tomography data. The landmark-based approach in combination with a density-driven RANSAC plane-fitting allows efficient localization of the histology images in the 3D data within less than four minutes (single-threaded MATLAB code) with an average accuracy of 0.25 mm for correct and 2.21 mm for mismatched slices. The approach managed to successfully localize 75% of the histology images in our database. The proposed algorithm is an important step towards solving the problem of registering 2D histology sections to 3D data fully automatically.

Keywords

Point Cloud Histological Image Histological Slice Histological Cross Section Small Euclidean Distance 
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

  • Natalia Chicherova
    • 1
    • 2
  • Ketut Fundana
    • 1
  • Bert Müller
    • 2
  • Philippe C. Cattin
    • 1
  1. 1.Medical Image Analysis CenterUniversity of BaselBaselSwitzerland
  2. 2.Biomaterials Science CenterUniversity of BaselBaselSwitzerland

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