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Probabilistic region matching in narrow-band endoscopy for targeted optical biopsy.

Abstract : This paper presents a novel and robust Markov random field (MRF) approach for matching sparse regions between two images related by a non-rigid transformation. The proposed MRF model incorporates appearance based region similarities as well as the spatial correlations of neighboring regions. The method involves initial affine covariant region detection in each image individually followed by a viewpoint invariant representation of the detected regions using region descriptors. The task of matching these descriptors is performed by estimating the maximum a posteriori (MAP) labeling of the proposed MRF model using Belief Propagation. In particular, we introduce a new pair-wise geometric constraint, which is evaluated on the texture of the detected regions while taking the local image geometry into account. The evaluation of the geometric and appearance constraints in the same space allows for their combination in the MRF objective function without a need for learning or tuning any weighting parameters. The performance of the method is compared to that of the current state-of-the-art matching algorithms using datasets with varying levels of difficulties.
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Contributor : Diana Mateus Connect in order to contact the contributor
Submitted on : Monday, January 22, 2018 - 11:46:35 AM
Last modification on : Thursday, March 10, 2022 - 6:30:04 PM


  • HAL Id : hal-01689590, version 1
  • PUBMED : 20426025


Selen Atasoy, Ben Glocker, Stamatia Giannarou, Diana Mateus, Alexander Meining, et al.. Probabilistic region matching in narrow-band endoscopy for targeted optical biopsy.. International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2009, Londres, United Kingdom. pp.499-506. ⟨hal-01689590⟩



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