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Discriminative Parameter Estimation for Random Walks Segmentation

Abstract : The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba- bilistic segmentation. We overcome this challenge by treating the opti- mal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.
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Contributor : Puneet Kumar Dokania Connect in order to contact the contributor
Submitted on : Friday, August 30, 2013 - 12:20:38 PM
Last modification on : Tuesday, January 25, 2022 - 3:11:31 AM
Long-term archiving on: : Thursday, April 6, 2017 - 11:01:54 AM


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  • HAL Id : hal-00856020, version 1
  • ARXIV : 1308.6721


Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, et al.. Discriminative Parameter Estimation for Random Walks Segmentation. 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013), Sep 2013, Nagoya, Japan. 8p. ⟨hal-00856020⟩



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