A New Image Segmentation Method Based on Three-Dimensional Neural Network

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.

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 62 (6) ◽  
pp. 581-590 ◽  
Author(s):  
Ali Ahmadvand ◽  
Mohammad Reza Daliri ◽  
Mohammadtaghi Hajiali

AbstractIn this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named “DCS-SVM” to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.


2013 ◽  
Vol 67 (16) ◽  
pp. 18-20
Author(s):  
G. EvelinSuji ◽  
Y. V. S. Lakshimi ◽  
G. Wiselin Jiji

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