Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-guided sequential label fusion for multi-atlas based segmentation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Fast non local means denoising for 3d MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Introduction to the non-rigid image registration evaluation project (NIREP)
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Regression-based label fusion for multi-atlas segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two independent steps are performed, i.e., first constructing graphs based on the fixed image neighborhood and then computing weights based on the heat kernel for all patches in the neighborhood. In this paper, we first show that many existing label fusion methods can be unified into a graph-based framework, and then propose a novel method for simultaneously deriving both graph adjacency structure and graph weights based on the sparse representation, to perform multi-atlas segmentation. Our motivation is that each patch in the input image can be reconstructed by the sparse linear superposition of patches in the atlas images, and the reconstruction coefficients can be used to deduce both graph structure and weights simultaneously. Experimental results on segmenting brain anatomical structures from magnetic resonance images (MRI) show that our proposed method achieves significant improvements over previous patch-based methods, as well as other conventional label fusion methods.