Title :
Three-dimensional probabilistic neural network using for MR image segmentation
Author :
Yuanfeng, Lian ; Falin, Wu
Author_Institution :
Coll. of Geophys. & Inf. Eng., China Univ. of Pet., Beijing, China
Abstract :
The three-dimensional probabilistic neural network (PNN) is proposed as the core classifier for segmentation of three-dimensional (3-D) magnetic resonance imaging (MRI). The proposed algorithm takes into account the spatial information between image voxels. It adopts the self-organizing map (SOM) neural network to overly segment the 3D MR image, and yield reference voxels necessary for probabilistic density function. The experimental results demonstrate the effectiveness and robustness of the proposed approach.
Keywords :
biomedical MRI; image classification; image segmentation; medical image processing; probability; self-organising feature maps; 3D MR image segmentation; 3D PNN; 3D probabilistic neural network; SOM neural network; image classifier; image voxel spatial information; magnetic resonance imaging; probability density function; self organizing map; Biological neural networks; Image segmentation; Magnetic resonance imaging; Neurons; Probabilistic logic; Tensile stress; Training; MR image; segmentation; structure tensor; three-dimensional adaptive probabilistic neural network;
Conference_Titel :
Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8158-3
DOI :
10.1109/ICEMI.2011.6037870
