Jerry L. Prince
Johns Hopkins University
699 Papers
5.1K Citations
Jerry L. Prince is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 69, co-authored 642 publications. Previous affiliations of Jerry L. Prince include Johns Hopkins University School of Medicine & Georgia Institute of Technology.
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Papers
Snakes, shapes, and gradient vector flow
Chenyang Xu,Jerry L. Prince +1 more
TL;DR: This paper presents a new external force for active contours, which is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image, and has a large capture range and is able to move snakes into boundary concavities.
Current methods in medical image segmentation.
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
2.5K
Adaptive fuzzy segmentation of magnetic resonance images
Dzung L. Pham,Jerry L. Prince +1 more
TL;DR: 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images, and its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
871
Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects.
Hans W. Müller-Gärtner,Jonathan M. Links,Jerry L. Prince,Robert 'Nick' Bryan,Elliot R. McVeigh,Jeffrey P. Leal,Christos Davatzikos,J. James Frost +7 more
TL;DR: In computer simulations and phantom studies, the GM PET algorithm permitted a 100% recovery of the actual tracer concentration in neocortical GM and hippocampus, irrespective of the GM volume, using an algorithm that relates the regional fraction of GM to partial volume effects.
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou,Hayit Greenspan,Christos Davatzikos,James S. Duncan,Bram van Ginneken,Anant Madabhushi,Jerry L. Prince,Daniel Rueckert,Ronald M. Summers +8 more
- 26 Feb 2021
TL;DR: In this paper, the authors present traits of medical imaging, highlight clinical needs and technical challenges in medical imaging and describe how emerging trends in deep learning are addressing these issues, and conclude with a discussion and presentation of promising future directions.
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