Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data

  • Jose Caballero
  • Wenjia Bai
  • Anthony N. Price
  • Daniel Rueckert
  • Joseph V. Hajnal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation.


Discrete Cosine Transform Gaussian Mixture Model Compress Sense Sparse Code Magnetic Resonance Imaging Data 
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

  • Jose Caballero
    • 1
  • Wenjia Bai
    • 1
  • Anthony N. Price
    • 2
  • Daniel Rueckert
    • 1
  • Joseph V. Hajnal
    • 2
  1. 1.Department of ComputingImperial College LondonUK
  2. 2.Division of Imaging Sciences and Biomedical Engineering DepartmentKing’s College London, St. Thomas’ HospitalLondonUK

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