Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report

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 optimal 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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Submitted on : Wednesday, June 5, 2013 - 2:47:17 PM
Last modification on : Thursday, April 25, 2019 - 11:13:09 AM
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  • HAL Id : hal-00830564, version 1
  • ARXIV : 1306.1083

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Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, et al.. Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report. [Research Report] 2013. ⟨hal-00830564⟩

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