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A transversal approach for patch-based label fusion via matrix completion

Yanrong Guo, Kim-Han Thung, Guorong Wu, Dinggang Shen, Gerard Sanroma, Yaozong Gao
In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal).  ...  Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing  ...  Algorithm for labeling one entire image using matrix-completion based label fusion.  ... 
doi:10.17615/d40a-hd14 fatcat:hondqdxz7rbijhpk3rflycxcmm

Fusion tilings with infinite local complexity [article]

Natalie Priebe Frank, Lorenzo Sadun
2018 arXiv   pre-print
We propose a formalism for tilings with infinite local complexity (ILC), and especially fusion tilings with ILC.  ...  We examine spectral properties of the invariant measures and define a new notion of complexity that applies to ILC tilings.  ...  We thank Ian Putnam for hospitality and many helpful discussions. We thank the Banff International Research Station and the participants of the 2011 Banff  ... 
arXiv:1201.3911v3 fatcat:g7emjrbcknfndac5gz6ssygkqy

Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging

Hossein Arabi, Habib Zaidi
2016 Medical Physics (Lancaster)  
Purpose: The authors evaluate the performance of shape-based averaging (SBA) technique for whole-body bone segmentation from MRI in the context of MRI-guided attenuation correction (MRAC) in hybrid PET  ...  The majority voting (MV) atlas fusion scheme was also evaluated as a conventional and commonly used method. MRI-guided attenuation maps were generated using the different segmentation methods.  ...  Given a matrix D containing all the atlas label maps and hidden true target label map T, the probability mass function of the complete data would be f (D,T |P,Q ), where the goal is to estimate the performance  ... 
doi:10.1118/1.4963809 pmid:27806602 fatcat:of5knkavdrh57hd2dfcukuhfum

3D-SCoBeP: 3D medical image registration using sparse coding and belief propagation

Aminmohammad Roozgard, Nafise Barzigar, Pramode Verma, Samuel Cheng
2014 International Journal of Diagnostic Imaging  
We propose an efficient 3D medical image registration method based on sparse coding and belief propagation for Computed Tomography (CT) and Magnetic Resonance (MR) imaging.  ...  Our objective results based on Root Mean Square Error (RMSE) are smaller than those from MIRT and GP-Registration.  ...  Renee Wagenblatt for editing and correcting grammar mistakes in previous drafts.  ... 
doi:10.5430/ijdi.v2n1p54 fatcat:zcyjqm6zyjbddjex5g6rqgfqzi

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Yanrong Guo, Yaozong Gao, Yeqin Shao, True Price, Aytekin Oto, Dinggang Shen
2014 Medical Physics (Lancaster)  
For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method.  ...  Guo et al.: Deformable segmentation of prostate MRI via DDD learning 072303-2 Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable  ...  frameworks for label fusion.  ... 
doi:10.1118/1.4884224 pmid:24989402 pmcid:PMC4105964 fatcat:ofyrc5vyrjbkdop5eayhrmammu

Quantum Origami: Transversal Gates for Quantum Computation and Measurement of Topological Order [article]

Guanyu Zhu and Mohammad Hafezi and Maissam Barkeshli
2019 arXiv   pre-print
Here, we show that by folding manifolds, modular transformations can be applied in a single shot by independent local unitaries, providing a novel class of transversal logic gates for fault-tolerant quantum  ...  set of gates for quantum computation.  ...  modular transformations via transversal operations.  ... 
arXiv:1711.05752v3 fatcat:qpr6o3nq5nbjxpxvxojdz7jtvq

Alzheimer's disease early detection from sparse data using brain importance maps

Andreas Kodewitz, Sylvie Lelandais, Christophe Montagne, Vincent Vigneron
2013 ELCVIA Electronic Letters on Computer Vision and Image Analysis  
We will present a method to extract information about the location of metabolic changes induced by Alzheimer's disease based on a machine learning approach that directly links features and brain areas  ...  to search for regions of interest (ROIs).  ...  Acknowledgements Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (  ... 
doi:10.5565/rev/elcvia.531 fatcat:lmusqbmpt5adraq7ihbzkfmvcu

MRI classification using semantic random forest with auto-context model

Yang Lei, Tonghe Wang, Xue Dong, Sibo Tian, Yingzi Liu, Hui Mao, Walter J. Curran, Hui-Kuo Shu, Tian Liu, Xiaofeng Yang
2021 Quantitative Imaging in Medicine and Surgery  
During segmentation, the MRI patches were first fed into these random forests to derive patch-based segmentation. By using patch fusion, the final end-to-end segmentation was obtained.  ...  In the training stage, patch-based MRI features were extracted from registered MRI-CT training images, and the most informative elements were selected via feature selection to train an initial random forest  ...  With the integration of auto-context model and patch- Results Comparison with random forest method Discussion In this paper, we have investigated a learning-based approach to classify tissue labels  ... 
doi:10.21037/qims-20-1114 pmid:34888187 pmcid:PMC8611460 fatcat:5vwdtr3is5fozkfpud4pey22j4

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 5677-5686 Fast Multi-Scale Structural Patch Decomposition for Multi-Exposure Image Fusion.  ...  ., +, TIP 2020 5324-5335 High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression

Kevin M. Cherry, Brandon Peplinski, Lauren Kim, Shijun Wang, Le Lu, Weidong Zhang, Jianfei Liu, Zhuoshi Wei, Ronald M. Summers
2015 Medical Image Analysis  
employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly  ...  We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness.  ...  Steven Wank for patient referral. We also thank the anonymous reviewers for their constructive comments which helped improve the manuscript.  ... 
doi:10.1016/ pmid:25461335 pmcid:PMC4314370 fatcat:zqnbchn6bnb3tjzaiwlfbuuyoy

Dualities in one-dimensional quantum lattice models: symmetric Hamiltonians and matrix product operator intertwiners [article]

Laurens Lootens, Clement Delcamp, Gerardo Ortiz, Frank Verstraete
2022 arXiv   pre-print
We illustrate this approach by establishing matrix product operator intertwiners for dualities such as Kramers-Wannier, Jordan-Wigner, Kennedy-Tasaki and the IRF-vertex correspondence, as well as for new  ...  Dual models can be characterized by equivalent but distinct realizations of a given symmetry, encoded into fusion categories.  ...  This work is supported by the Research Foundation Flanders (FWO) via grant nr. G087918N and G0E1820N. LL is supported by a PhD grant from the FWO.  ... 
arXiv:2112.09091v2 fatcat:4csqwc3lfbfxxedvufc4cikrtu

Table of contents

2020 IEEE Transactions on Image Processing  
Yang 7153 MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks .... H. Xu, J. Ma, and X.-P. Zhang 7203 A Local Flatness Based Variational Approach to Retinex ..... M. Tang, F.  ...  Chen 7861 MSdB-NMF: MultiSpectral Document Image Binarization Framework via Non-Negative Matrix Factorization Approach ................................ Y. Esmaeili Salehani, E. Arabnejad, A.  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

Acoustic Seafloor Classification Using the Weyl Transform of Multibeam Echosounder Backscatter Mosaic

Ting Zhao, Giacomo Montereale Gavazzi, Srđan Lazendić, Yuxin Zhao, Aleksandra Pižurica
2021 Remote Sensing  
accuracies, highest for models based on the backscatter Weyl features.  ...  The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace.  ...  We are grateful to the Renard Centre of Marine Geology of the University of Ghent for the use of the sediment laboratory facilities.  ... 
doi:10.3390/rs13091760 fatcat:unadkcyz5fc3hc6eeoqpkoqbji

Fusion: a general framework for hierarchical tilings of R^d [article]

Natalie Priebe Frank, Lorenzo Sadun
2018 arXiv   pre-print
We introduce a formalism for handling general spaces of hierarchical tilings, a category that includes substitution tilings, Bratteli-Vershik systems, S-adic transformations, and multi-dimensional cut-and-stack  ...  For instance, we exhibit a minimal tiling space that is not uniquely ergodic, with one ergodic measure having pure point spectrum and another ergodic measure having mixed spectrum.  ...  The base of the jth tower is the set B n (j) of all tilings in the n-transversal that have a copy of P n (j) with its control point at the origin.  ... 
arXiv:1101.4930v4 fatcat:vusly5uscrf5vmhs475srivcui

LABEL: Pediatric brain extraction using learning-based meta-algorithm

Feng Shi, Li Wang, Yakang Dai, John H. Gilmore, Weili Lin, Dinggang Shen
2012 NeuroImage  
We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction.  ...  In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images.  ...  based brain extraction method (Zhuang et al., 2006) , and 10 pediatric subjects (5-18 years) for a label fusion based brain extraction method (Eskildsen et al., 2011) .  ... 
doi:10.1016/j.neuroimage.2012.05.042 pmid:22634859 pmcid:PMC3408835 fatcat:3knkvitpvjdundgik57hkwqrha
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