Paper Info
Title
Brain Tissue Segmentation Based On Dwi/Dti Data
Abstract
We present a method for tissue classification based on diffusion-weighted imaging (DWI)/diffusion tensor imaging (DTI) data. Our motivation is that independent tissue segmentation based on DWI/DTI images provides complementary information to the tissue segmentation result using structural MRI data alone. The basis idea is to classify the brain into two compartments by utilizing the tissue contrast exiting in a single channel, e.g., Apparent Diffusion Coefficient (ADC) image can be used to separate CSF and non-CSF, and the Fractional Anisotropy (FA) image can be used to separate WM from non-WM tissues. Other channels such as eigen values of the tensor, relative anisotropy (RA), and volume ratio (VR) can also be used to separate tissues. We employ the STAPLE algorithm [8] to combine these two-class maps to obtain a complete segmentation of CSF, GM, and WM.
Year
DOI
Venue
2006
10.1109/ISBI.2006.1624851
2006 3RD IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1-3
Keywords
Field
DocType
tensile stress,fractional anisotropy,bioinformatics,anisotropic magnetoresistance,diffusion tensor imaging,image classification,magnetic resonance imaging,virtual reality,eigenvalues,biomedical imaging,image segmentation,volume ratio,diffusion weighted imaging,apparent diffusion coefficient,automation
Computer vision,Effective diffusion coefficient,Diffusion MRI,Pattern recognition,Fractional anisotropy,Computer science,Medical imaging,Segmentation,Image segmentation,Artificial intelligence,Contextual image classification,Magnetic resonance imaging
Conference
ISSN
Citations 
PageRank 
1945-7928
11
0.78
References 
Authors
4
5
Name
Order
Citations
PageRank
Hai Li1110.78
Tianming Liu21033112.95
Geoffrey S. Young3597.58
Lei Guo41661142.63
Stephen T. C. Wong51081134.56