Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach.
This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation 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 distributions 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 based approaches.
Wang
Yue
Y
Y. Wang is with the Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064 USA, and is affiliated with the Department of Radiology, Georgetown University School of Medicine, Washington, DC 20007 USA (e-mail:  wang@pluto.ee.cua.edu ).
Adalý
Tülay
T
Kung
Sun-Yuan
SY
Szabo
Zsolt
Z