DocumentCode
2720052
Title
A Stochastic Approach to 3-D Image Modeling
Author
Joshi, Dhiraj ; Li, Jia ; Wang, James Z.
Author_Institution
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA
fYear
2006
fDate
38899
Firstpage
1
Lastpage
2
Abstract
Statistical modeling methods have been successfully used to segment, classify, and annotate digital images, over the years. In this paper, we present a 3-D hidden Markov model (HMM) for volume image modeling. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Potential applications to 3-D biomedical image analysis are also discussed
Keywords
hidden Markov models; image segmentation; medical image processing; 3-D hidden Markov model; 3-D image modeling; biomedical image analysis; stochastic approach; volume image modeling; volume image segmentation; Biomedical imaging; Computed tomography; Digital images; Gaussian distribution; Hidden Markov models; Image analysis; Image segmentation; Magnetic analysis; Magnetic resonance imaging; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Life Science Systems and Applications Workshop, 2006. IEEE/NLM
Conference_Location
Bethesda, MD
Print_ISBN
1-4244-0277-8
Electronic_ISBN
1-4244-0278-6
Type
conf
DOI
10.1109/LSSA.2006.250410
Filename
4015811
Link To Document