66,810 research outputs found
A Latent Source Model for Patch-Based Image Segmentation
Despite the popularity and empirical success of patch-based nearest-neighbor
and weighted majority voting approaches to medical image segmentation, there
has been no theoretical development on when, why, and how well these
nonparametric methods work. We bridge this gap by providing a theoretical
performance guarantee for nearest-neighbor and weighted majority voting
segmentation under a new probabilistic model for patch-based image
segmentation. Our analysis relies on a new local property for how similar
nearby patches are, and fuses existing lines of work on modeling natural
imagery patches and theory for nonparametric classification. We use the model
to derive a new patch-based segmentation algorithm that iterates between
inferring local label patches and merging these local segmentations to produce
a globally consistent image segmentation. Many existing patch-based algorithms
arise as special cases of the new algorithm.Comment: International Conference on Medical Image Computing and Computer
Assisted Interventions 201
Sparsity Based Poisson Denoising with Dictionary Learning
The problem of Poisson denoising appears in various imaging applications,
such as low-light photography, medical imaging and microscopy. In cases of high
SNR, several transformations exist so as to convert the Poisson noise into an
additive i.i.d. Gaussian noise, for which many effective algorithms are
available. However, in a low SNR regime, these transformations are
significantly less accurate, and a strategy that relies directly on the true
noise statistics is required. A recent work by Salmon et al. took this route,
proposing a patch-based exponential image representation model based on GMM
(Gaussian mixture model), leading to state-of-the-art results. In this paper,
we propose to harness sparse-representation modeling to the image patches,
adopting the same exponential idea. Our scheme uses a greedy pursuit with
boot-strapping based stopping condition and dictionary learning within the
denoising process. The reconstruction performance of the proposed scheme is
competitive with leading methods in high SNR, and achieving state-of-the-art
results in cases of low SNR.Comment: 13 pages, 9 figure
A Latent Source Model for Patch-Based Image Segmentation
Abstract. Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm
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