Automatic Segmentation of Brain Structures Using Geometric Moment Invariants and Artificial Neural Networks

2009 ◽  
pp. 326-337 ◽  
Author(s):  
Mostafa Jabarouti Moghaddam ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Moment Invariants ◽  
Geometric Moment ◽  
Artificial Neural

Measurement of Brain Structures with Artificial Neural Networks: Two- and Three-dimensional Applications

Radiology ◽  
1999 ◽  
Vol 211 (3) ◽  
pp. 781-790 ◽  
Author(s):  
Vincent A. Magnotta ◽  
Dan Heckel ◽  
Nancy C. Andreasen ◽  
Ted Cizadlo ◽  
Patricia Westmoreland Corson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Three Dimensional ◽  
Brain Structures ◽  
Artificial Neural

A TOOL FOR ANALYZING MAGNETOENCEPHALOGRAPHY-DATA BASED ON DIFFERENT ARTIFICIAL NEURAL NETWORKS

2003 ◽  
Vol 13 (06) ◽  
pp. 609-626
Author(s):  
M. BORSCHBACH ◽  
W.-M. LIPPE ◽  
S. NIENDIEK
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Rule ◽  
Brain Structures ◽  
Blind Signal Separation ◽  
Feed Forward Neural Network ◽  
Magnetic Dipoles ◽  
Blind Signal ◽  
Artificial Neural

The localization of intracerebral dipole sources in order to detect pathological events is one purpose of magnetoencephalography (MEG). Another aspect is the analysis of brain processes and brain structures. A system consisting of two different types of Artificial Neural Networks is presented. The structure of a feed forward neural network with two layers and a learning rule designed for the task of Blind Signal Separation (BSS) is used to separate temporarily overlapping neuron activities in the brain. Based on the separated signals, the task of the second type of neural network is to determine the position and strength of the different, underlying magnetic dipoles. Several concepts of neural networks, their limits and potentials concerning both tasks of mining medical data are discussed briefly.

Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

2017 ◽  
Vol 21 ◽  
pp. 263-274 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Dheyaa Ahmed Ibrahim ◽  
Mohamad Khir Abdullah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nasopharyngeal Carcinoma ◽  
Automatic Segmentation ◽  
Artificial Neural
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