M
Michael I. Miller
Researcher at Johns Hopkins University
Publications - 640
Citations - 38471
Michael I. Miller is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Large deformation diffeomorphic metric mapping & Computational anatomy. The author has an hindex of 92, co-authored 599 publications receiving 34915 citations. Previous affiliations of Michael I. Miller include University of Tennessee & Discovery Institute.
Papers
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A New Apolipoprotein B-100 Variant in a Family With Premature Atherosclerosis and Hyperapobetalipoproteinemia
John A. A. Ladias,Peter O. Kwiterovich,Hazel H. Smith,Michael I. Miller,Paul S. Bachorik,Trudy M. Forte,Aldons J. Lusis,Stylianos E. Antonarakis +7 more
TL;DR: A new mutation, ApoB-100 Hopkins, was not linked to the hyperapobetalipoproteinemia phenotype, which also was segregating in this family.
Journal ArticleDOI
Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer's disease.
TL;DR: It is suggested that education in youth may exert direct and indirect influences on brain reserve in regions that are most vulnerable to the neuropathologies of aging, dementia, and specifically, Alzheimer disease.
Journal ArticleDOI
High-performance wire-grid polarizers using jet and Flash™ imprint lithography
Se Hyun Ahn,Shuqiang Yang,Michael I. Miller,Mahadevan GanapathiSubramanian,Marlon Menezes,Jin H. Choi,Frank Y. Xu,Douglas J. Resnick,Sidlgata V. Sreenivasan +8 more
TL;DR: In this article, a roll-based J-FIL process is applied to fabricate large-area flexible bilayer wire-grid polarizers (WGPs) and high-performance WGPs on rigid glass substrates.
Patent
Image search engine
TL;DR: In this paper, a non-invasive imaging system is presented, which includes an imaging scanner suitable to generate an image representing a tissue region of a subject under observation, the tissue region having at least one substructure and the image comprising a plurality of image voxels.
Journal ArticleDOI
Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI.
Thiago Junqueira Ribeiro de Rezende,Brunno Machado de Campos,Johnny Hsu,Yue Li,Can Ceritoglu,Kwame S. Kutten,Marcondes Cavalcante França Junior,Susumu Mori,Michael I. Miller,Andreia V. Faria +9 more
TL;DR: The increasing use of large sample sizes for population and personalized medicine requires high‐throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification.