M
Milan Sonka
Researcher at University of Iowa
Publications - 519
Citations - 33458
Milan Sonka 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 72, co-authored 505 publications receiving 29555 citations. Previous affiliations of Milan Sonka include University of Iowa Hospitals and Clinics & Roy J. and Lucille A. Carver College of Medicine.
Papers
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Book
Image Processing: Analysis and Machine Vision
TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
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3D Slicer as an image computing platform for the Quantitative Imaging Network.
Andriy Fedorov,Reinhard Beichel,Jayashree Kalpathy-Cramer,Julien Finet,Jean-Christophe Fillion-Robin,Sonia Pujol,Christian Bauer,Dominique Jennings,Fiona M. Fennessy,Milan Sonka,John M. Buatti,Stephen R. Aylward,James V. Miller,Steve Pieper,Ron Kikinis +14 more
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
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Retinal Imaging and Image Analysis
TL;DR: Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed and aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.
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Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
TL;DR: An optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations is developed.
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Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.
TL;DR: Inspired by earlier works, the application of deep learning models to detect COVID-19 patients from their chest radiography images and shows that the generated heatmaps contain most of the infected areas annotated by the authors' board certified radiologist.