Brain tissue segmentation based on DWI/DTI data

Hai Li, Tianming Liu, Geoffrey Young, Lei Guo, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2006 3rd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages57-60
Number of pages4
Volume2006
StatePublished - Nov 17 2006
Event2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, United States
Duration: Apr 6 2006Apr 9 2006

Other

Other2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
CountryUnited States
CityArlington, VA
Period4/6/064/9/06

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Brain tissue segmentation based on DWI/DTI data'. Together they form a unique fingerprint.

Cite this