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Yaniv Assaf

dblp:02/449 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
0000-0001-6828-7595ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › neuroimaging
diffusion MRI
0.112005
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005
Medical and health informatics › medical imaging
medical image analysis
0.112005
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005
Image and video processing › image restoration
artifact removal
0.012005
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005
Image and video processing
image restoration
0.012005
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI · NIPS 2005

Methods — techniques the papers use, named apart from their topics

variational regularization · 0.1multiple tensor variational framework · 0.1diffusion tensor model · 0.1
YearPublicationVenuePosition
2005 Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI
abstract
Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a non inva- sive method for brain neuronal fibers delineation. Here we show a mod- ification for DT-MRI that allows delineation of neuronal fibers which are infiltrated by edema. We use the Muliple Tensor Variational (MTV) framework which replaces the diffusion model of DT-MRI with a mul- tiple component model and fits it to the signal attenuation with a vari- ational regularization mechanism. In order to reduce free water con- tamination we estimate the free water compartment volume fraction in each voxel, remove it, and then calculate the anisotropy of the remaining compartment. The variational framework was applied on data collected with conventional clinical parameters, containing only six diffusion di- rections. By using the variational framework we were able to overcome the highly ill posed fitting. The results show that we were able to find fibers that were not found by DT-MRI.
Ofer Pasternak, Nir A. Sochen, Nathan Intrator, Yaniv Assaf
NIPS4