H
Honghai Zhang
Researcher at University of Iowa
Publications - 48
Citations - 1045
Honghai Zhang is an academic researcher from University of Iowa. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 11, co-authored 43 publications receiving 910 citations.
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Ultrasonics, Ferroelectrics, and Frequency Control
Craig Woody,Mos Kaveh,Michael Insana,M. Insana,E. Angelini,Y. K Im,A. B. Brill,R. Jaszczak,W. C Lem Karl,Milan Sonka,Honghai Zhang,Michael D. Abràmoff,Arash Amini,Stephen R. Aylward,C. Barillot,E. Bullitt,I. Buvat,D. Comaniciu,C. R. Crawford,James S. Duncan,J. A. Fessler,X. P. Hu,R. H. Huesman,M. F. Insana,P. I Rarrázaval,M. Jacob,T. Jiang,Nico Karssemeijer,E. A. Krupinski,P. Liang,Murray H. Loew,A. Manduca,Joseph M. Reinhardt,J. Sijbers,H. Soltanian-Zadeh,D. W. Townsend,Van Leemput,Moshe Kam,Gordon W. D Ay,Roger D. Pollard +39 more
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4-D Cardiac MR Image Analysis: Left and Right Ventricular Morphology and Function
TL;DR: A combination of active shape model (ASM) and active appearance model (AAM) was used to segment the left and right ventricles of normal and Tetralogy of Fallot (TOF) hearts on 4-D (3-D+time) MR images.
4-D cardiac MR image analysis: left and right ventricular morphology and function.
TL;DR: The features obtained from TOF hearts show higher variability compared to normal subjects, suggesting their potential use as disease progression indicators and automated discrimination between normal/TOF subjects achieved 90%-100% sensitivity and specificity.
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Rapid full volume data acquisition by real-time 3-dimensional echocardiography for assessment of left ventricular indexes in children: a validation study compared with magnetic resonance imaging.
Liping Bu,Stephanie Munns,Honghai Zhang,Michelle Disterhoft,Michael Dixon,Alan H. Stolpen,Milan Sonka,Thomas D. Scholz,Larry T. Mahoney,Shuping Ge +9 more
TL;DR: RT 3DE measurements of LV end-systolicVolume, end-diastolic volume, mass, SV, and EF in children using rapid full volume acquisition strategy are feasible, accurate, and reproducible and are comparable with MRI measurements.
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RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI
TL;DR: A novel algorithm, based on grayscale mathematical morphology and LOGISMOS-based graph segmentation, which is rapid, robust and highly accurate for accurate rodent brain skull-stripping even in challenging data is proposed.