Three-dimensional anatomical model-based segmentation of MR brain images through principal axes registration

1995 ◽  
Vol 42 (11) ◽  
pp. 1069-1078 ◽  
Author(s):  
L.K. Arata ◽  
A.P. Dhawan ◽  
J.P. Broderick ◽  
M.F. Gaskil-Shipley ◽  
A.V. Levy ◽  
...  
2017 ◽  
Vol 24 (6) ◽  
pp. 653-659
Author(s):  
Qiang Zheng ◽  
Honglun Li ◽  
Baode Fan ◽  
Shuanhu Wu ◽  
Jindong Xu

2012 ◽  
Vol 490-495 ◽  
pp. 157-161
Author(s):  
Guo Fu Lin

In this paper, a three-dimensional probabilistic approach for MR brain image segmentation is proposed. Based on the noise-free representative reference vectors provided by SOM, the results of the 3D-PNN method are superior to other traditional algorithms. In addition to the 3D-PNN architecture, a fast three-step training method is proposed. The proposed approach also incorporates structure tensor to find appropriate feature sets for the 3D-PNN with respect to resulting classification accuracy. Computational results with simulated MR brain images have shown the promising performance of the proposed method.


2006 ◽  
Author(s):  
Jonggeun Park ◽  
Byungjun Baek ◽  
Choong-Il Ahn ◽  
Kyo Bum Ku ◽  
Dong Kyun Jeong ◽  
...  

1990 ◽  
Vol 3 (1) ◽  
pp. 95-103 ◽  
Author(s):  
P.H. Mowforth ◽  
J. Zhengping

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