Three dimensional image registration using artificial neural networks

1994 ◽  
Author(s):  
D. Piraino ◽  
P. Kotsas ◽  
B. Richmond ◽  
M. Recht ◽  
D. Kormos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Image Registration ◽  
Three Dimensional ◽  
Dimensional Image ◽  
Artificial Neural

scholarly journals Reconstruction of Depolarization Pattern in Myocardial Infarction Cases Using Two Cascaded Stages of Artificial Neural Networks

2021 ◽  
Vol 2128 (1) ◽  
pp. 012016
Author(s):  
Nihal A. Mabrouk ◽  
Abdelreheem M. Khalifa ◽  
Abdelmenem A. Nasser ◽  
Moustafa H. Aly
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Inverse Problem ◽  
Artificial Neural Networks ◽  
Electrical Activity ◽  
Heart Diseases ◽  
Three Dimensional ◽  
The Body ◽  
Forward Model ◽  
Artificial Neural

Abstract Our paper introduces a new technique for diagnosis of various heart diseases without the need of highly experts to investigate the electrocardiogram (ECG). Using the same electrodes of the ECG machine, it will be able to transmit directly the electrical activity inside the heart to a moving picture. Our technique is based on artificial intelligence algorithm using artificial neural networks (ANN). Finding the trans-membrane potential (TMP) inside the heart from the body surface potential (BSP) is known as the inverse problem of ECG. To have a unique solution for the inverse problem the data used should be obtained from a forward model. A three dimensional (3-D) model of cellular activation whole heart embedded in torso is simulated and solved using COMSOL Multiphysics software. In our previous paper, one ANN succeeded in displaying the wave propagation on the surface of a normal heart. In this paper, we used a configuration of ANNs to display different cases of heart with myocardial infarction (MI). To check the system accuracy, eight MI cases with different sizes and locations in the heart are simulated in the forward model. This configuration proved to be highly accurate in displaying each MI case -size and location- presenting the infarction as an area with no electrical activity.

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

Modification of PET fabrics by hyperbranched polymer: a comparative study of artificial neural networks (ANN) and statistical approach

2013 ◽  
Vol 33 (5) ◽  
pp. 445-452 ◽  
Author(s):  
Mahdi Hasanzadeh ◽  
Tahereh Moieni ◽  
Bentolhoda Hadavi Moghadam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Three Dimensional ◽  
Ann Model ◽  
Acid Dyes ◽  
Color Strength ◽  
Ann Models ◽  
Artificial Neural ◽  
Poly Ethylene ◽  
Rsm Model

Abstract Hyperbranched polymers (HBPs) are highly branched, three-dimensional and polydisperse macromolecules and have been employed for modification of poly(ethylene terephthalate) (PET) fabrics. The PET fabrics treatment process parameters, like HBP concentration, temperature and time, play a major role in treatment yield and dyeability of treated PET fabrics by acid dyes. Two different quantitative models, comprising response surface methodology (RSM) and artificial neural networks (ANN), were developed for predicting color strength (K/S value) of treated fabrics. The experiments were conducted based on central composite design (CCD) and a mathematical model was developed. A comparison of the predicted color strength using RSM and ANN was studied. The results obtained indicated that both RSM and ANN models show a very good relationship between the experimental and predicted response values. However, the ANN model shows more accurate results than the RSM model.

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