Title :
Parameter estimation and NMR image segmentation
Author :
Liang, Z. ; MacFall, J.
Author_Institution :
Dept. of Radiol., State Univ. of New York, Stony Brook, NY, USA
Abstract :
A statistical method is proposed to classify tissue types and to segment the corresponding tissue regions from relaxation time T1 , T2, and proton density NH weighted nuclear magnetic resonance (NMR) images. The method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three variables (T 1, T2, NH) at each location within that tissue region. The method is tested by a set of T1, T2 , and NH weighted images of the brain acquired with a 1.5-T whole body scanner. The number of tissue types and the parameters of these tissue types are satisfactorily estimated. The regions of different tissue types are successfully segmented
Keywords :
biomedical NMR; image segmentation; medical image processing; parameter estimation; 1.5 T; NMR image segmentation; T1; T2; brain images; image intensities distribution; medical diagnostic imaging; multivariate likelihood function; proton density; statistical method; tissue region; tissue types classification; weighted nuclear magnetic resonance images; whole body scanner; Image segmentation; Markov random fields; Maximum likelihood estimation; Medical treatment; Nuclear magnetic resonance; Parameter estimation; Protons; Radiology; Three dimensional displays; X-ray imaging;
Conference_Titel :
Nuclear Science Symposium and Medical Imaging Conference, 1992., Conference Record of the 1992 IEEE
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-0884-0
DOI :
10.1109/NSSMIC.1992.301525
