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Patch-Based Segmentation without Registration: Application to Knee MRI

  • Zehan Wang
  • Claire Donoghue
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

Atlas based segmentation techniques have been proven to be effective in many automatic segmentation applications. However, the reliance on image correspondence means that the segmentation results can be affected by any registration errors which occur, particularly if there is a high degree of anatomical variability. This paper presents a novel multi-resolution patch-based segmentation framework which is able to work on images without requiring registration. Additionally, an image similarity metric using 3D histograms of oriented gradients is proposed to enable atlas selection in this context. We applied the proposed approach to segment MR images of the knee from the MICCAI SKI10 Grand Challenge, where 100 training atlases are provided and evaluation is conducted on 50 unseen test images. The proposed method achieved good scores overall and is comparable to the top entries in the challenge for cartilage segmentation, demonstrating good performance when comparing against state-of-the-art approaches customised to Knee MRI.

Keywords

Patch Size Spatial Context Initial Segmentation Coarse Segmentation Label Fusion 
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 2013

Authors and Affiliations

  • Zehan Wang
    • 1
  • Claire Donoghue
    • 1
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonUK

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