Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 8, AUGUST 1998 1165 Quantification and Segmentation of
 

Summary: IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 8, AUGUST 1998 1165
Quantification and Segmentation of
Brain Tissues from MR Images: A
Probabilistic Neural Network Approach
Yue Wang, Member, IEEE, Tšulay Adali, Member, IEEE, Sun-Yuan Kung, Fellow, IEEE, and Zsolt Szabo
Abstract--This paper presents a probabilistic neural network
based technique for unsupervised quantification and segmenta-
tion of brain tissues from magnetic resonance images. It is shown
that this problem can be solved by distribution learning and
relaxation labeling, resulting in an efficient method that may be
particularly useful in quantifying and segmenting abnormal brain
tissues where the number of tissue types is unknown and the dis-
tributions of tissue types heavily overlap. The new technique uses
suitable statistical models for both the pixel and context images
and formulates the problem in terms of model-histogram fitting
and global consistency labeling. The quantification is achieved by
probabilistic self-organizing mixtures and the segmentation by
a probabilistic constraint relaxation network. The experimental
results show the efficient and robust performance of the new
algorithm and that it outperforms the conventional classification

  

Source: Adali, Tulay - Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County

 

Collections: Engineering