Title :
A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image
Author :
Song, Tao ; Jamshidi, Mo M. ; Lee, Roland R. ; Huang, Mingxiong
Author_Institution :
California Univ. at San Diego, San Diego
Abstract :
A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN\´s kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated.
Keywords :
Bayes methods; biomedical MRI; brain; image segmentation; medical image processing; probability; self-organising feature maps; Bayesian rule; brain MR image; brain tissue segmentation; covariance matrices; kernel function; magnetic resonance imaging; modified probabilistic neural network; partial volume segmentation; probabilistic density function estimation; self-organizing map neural network; weighted probabilistic neural network classifier; weighting factors; Biological neural networks; Brain; Covariance matrix; Density functional theory; Image segmentation; Kernel; Labeling; Magnetic resonance imaging; Smoothing methods; Yield estimation; Magnetic resonance (MR) image; partial volume effect; self-organizing map (SOM) neural network; tissue classification; weighted probabilistic neural network (WPNN); Algorithms; Brain; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Models, Biological; Models, Statistical; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891635