Brain Image Segmentation Based on the Hybrid of Back Propagation Neural Network and AdaBoost System

2019 ◽  
Vol 92 (3) ◽  
pp. 289-298 ◽  
Author(s):  
Zhen Chao ◽  
Hee-Joung Kim
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Brain Image ◽  
Brain Image Segmentation

scholarly journals Incorporating FCM and Back Propagation Neural Network for Image Segmentation

2014 ◽  
pp. 16-21
Author(s):  
Ebrahim. Aghajari ◽  
Dr.Mrs. Gharpure Damayanti
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Input Image ◽  
Discrete Wavelet ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
The Membership Function

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

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