Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2013

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.
Fichier principal
Vignette du fichier
learnRW_suppl.pdf (114.33 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00830564 , version 1 (05-06-2013)

Identifiers

Cite

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⟩
798 View
299 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More