Format

Send to

Choose Destination
Med Biol Eng Comput. 2006 Mar;44(3):242-9. Epub 2006 Feb 17.

A hybrid tissue segmentation approach for brain MR images.

Author information

1
Radiology Department, Radiology Imaging Lab, University of California at San Diego, 3510 Dunhill Street, San Diego, CA 92121, USA. taosong@ucsd.edu

Abstract

A novel hybrid algorithm for the tissue segmentation of brain magnetic resonance images is proposed. The core of the algorithm is a probabilistic neural network (PNN) in which weighting factors are added to the summation layer, such that partial volume effects can be taken into account in the modeling process. The mean vectors for the probability density function estimation and the corresponding weighting factors are generated by a hierarchical scheme involving a self-organizing map neural network and an expectation maximization algorithm. Unlike conventional PNN, this approach circumvents the need for training sets. Tissue segmentation results from various algorithms are compared and the effectiveness and robustness of the proposed approach are demonstrated.

PMID:
16937165
DOI:
10.1007/s11517-005-0021-1
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center