Skip to main navigation
Skip to search
Skip to main content
Tel Aviv University Home
Update Request & User Guide (TAU staff only)
Home
Experts
Research units
Research output
Prizes
Activities
Press/Media
Student theses
Search by expertise, name or affiliation
An adaptive mean-shift framework for MRI brain segmentation
Arnaldo Mayer
*
,
Hayit Greenspan
*
Corresponding author for this work
Diagnostic Imaging
Department of Bio-Medical Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
152
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'An adaptive mean-shift framework for MRI brain segmentation'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Adaptive Mean Shift
100%
Brain Atlas
33%
Brain Magnetic Resonance Imaging
100%
Brain Segmentation
100%
Cerebrospinal Fluid
33%
Convergence Mode
33%
Density Point
33%
Feature Space
66%
Gray Matter
33%
High Density
33%
High-dimensional Feature Space
33%
Intensity Feature
66%
Intensity-based
66%
Magnetic Resonance Imaging Data
33%
Magnetic Resonance Imaging Image
33%
Mean Shift Algorithm
33%
Mode Clustering
33%
Multimodal Data
33%
Non-convex Clusters
33%
Nonparametric
33%
Single Dataset
33%
Space Images
33%
Spatial Features
33%
Spatial Intensity
33%
State-of-the-art Techniques
33%
Tissue Segmentation
33%
Tissue Type
33%
White Matter
33%
Engineering
Brain Atlas
50%
Cluster Algorithm
50%
Dimensional Feature Space
50%
Feature Space
100%
Image Space
50%
Joints (Structural Components)
50%
Representative Set
50%
Spatial Feature
50%
Spinal Fluid
50%
State-of-the-Art Method
50%