Juan Eugenio Iglesias

Summary

Affiliation: University of California
Country: USA

Publications

  1. doi request reprint Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases
    Juan Eugenio Iglesias
    Department of Biomedical Engineering, University of California, Los Angeles, CA 90024, USA
    IEEE Trans Med Imaging 28:1815-24. 2009
  2. ncbi request reprint Synthetic MRI signal standardization: application to multi-atlas analysis
    Juan Eugenio Iglesias
    Medical Imaging Informatics, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 13:81-8. 2010
  3. ncbi request reprint Agreement-based semi-supervised learning for skull stripping
    Juan Eugenio Iglesias
    Medical Imaging Informatics, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 13:147-54. 2010
  4. doi request reprint Fast approximate stochastic tractography
    Juan Eugenio Iglesias
    UCLA, Los Angeles, CA, USA
    Neuroinformatics 10:5-17. 2012
  5. ncbi request reprint Combining generative and discriminative models for semantic segmentation of CT scans via active learning
    Juan Eugenio Iglesias
    University of California, Los Angeles, USA
    Inf Process Med Imaging 22:25-36. 2011
  6. pmc Modeling diffusion-weighted MRI as a spatially variant gaussian mixture: application to image denoising
    Juan Eugenio Iglesias Gonzalez
    Laboratory of Neuro Imaging, University of California, 635 Charles Young Drive South, Suite 225, Los Angeles, California 90095, USA
    Med Phys 38:4350-64. 2011
  7. doi request reprint Robust brain extraction across datasets and comparison with publicly available methods
    Juan Eugenio Iglesias
    Department of Biomedical Engineering, University of California Los Angeles, Los Angeles, CA 90024, USA
    IEEE Trans Med Imaging 30:1617-34. 2011
  8. ncbi request reprint Classification of Alzheimer's disease using a self-smoothing operator
    Juan Eugenio Iglesias
    Laboratory of Neuro Imaging, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 14:58-65. 2011
  9. doi request reprint Deformable templates guided discriminative models for robust 3D brain MRI segmentation
    Cheng Yi Liu
    Laboratory of Neuro Imaging Department of Neurology, UCLA School of Medicine, 635 Charles E Young Drive South, Suite 225, 90095, Los Angeles, CA, USA
    Neuroinformatics 11:447-68. 2013

Collaborators

Detail Information

Publications9

  1. doi request reprint Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases
    Juan Eugenio Iglesias
    Department of Biomedical Engineering, University of California, Los Angeles, CA 90024, USA
    IEEE Trans Med Imaging 28:1815-24. 2009
    ..A novel aspect of the method is that it is also capable of detecting and segmenting the pectoralis in craniocaudal views...
  2. ncbi request reprint Synthetic MRI signal standardization: application to multi-atlas analysis
    Juan Eugenio Iglesias
    Medical Imaging Informatics, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 13:81-8. 2010
    ..The approach was tested on a multi-atlas based hippocampus segmentation framework using a publicly available database, significantly improving the results obtained with other intensity correction methods...
  3. ncbi request reprint Agreement-based semi-supervised learning for skull stripping
    Juan Eugenio Iglesias
    Medical Imaging Informatics, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 13:147-54. 2010
    ..Our system is practical, and it displays significant improvement over supervised approaches, BET and FreeSurfer in two datasets (60 test images)...
  4. doi request reprint Fast approximate stochastic tractography
    Juan Eugenio Iglesias
    UCLA, Los Angeles, CA, USA
    Neuroinformatics 10:5-17. 2012
    ..7 min) over the Monte Carlo sampling scheme, therefore enabling interactive probabilistic tractography: the user can quickly modify the seed region if he is not satisfied with the output without having to wait on average 7 min...
  5. ncbi request reprint Combining generative and discriminative models for semantic segmentation of CT scans via active learning
    Juan Eugenio Iglesias
    University of California, Los Angeles, USA
    Inf Process Med Imaging 22:25-36. 2011
    ..Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field)...
  6. pmc Modeling diffusion-weighted MRI as a spatially variant gaussian mixture: application to image denoising
    Juan Eugenio Iglesias Gonzalez
    Laboratory of Neuro Imaging, University of California, 635 Charles Young Drive South, Suite 225, Los Angeles, California 90095, USA
    Med Phys 38:4350-64. 2011
    ..The goal is to create a general model that can be used in different applications. This study focuses on image denoising and segmentation (primarily the former)...
  7. doi request reprint Robust brain extraction across datasets and comparison with publicly available methods
    Juan Eugenio Iglesias
    Department of Biomedical Engineering, University of California Los Angeles, Los Angeles, CA 90024, USA
    IEEE Trans Med Imaging 30:1617-34. 2011
    ..The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination...
  8. ncbi request reprint Classification of Alzheimer's disease using a self-smoothing operator
    Juan Eugenio Iglesias
    Laboratory of Neuro Imaging, University of California, Los Angeles, USA
    Med Image Comput Comput Assist Interv 14:58-65. 2011
    ..State-of-the-art results are obtained in the classification of 120 brain MRIs from ADNI as normal, mild cognitive impairment, and Alzheimer's...
  9. doi request reprint Deformable templates guided discriminative models for robust 3D brain MRI segmentation
    Cheng Yi Liu
    Laboratory of Neuro Imaging Department of Neurology, UCLA School of Medicine, 635 Charles E Young Drive South, Suite 225, 90095, Los Angeles, CA, USA
    Neuroinformatics 11:447-68. 2013
    ..We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems. ..