Neural networks method for identification of the objects behind the screen

IEEE Transactions on Medical Imaging

Volumn 21, Issue 6, 2002, Pages 613-619

  Ratajewicz Mikolajczak, Elzbieta ^a   Sikora, Jan ^b  

^a LUBLIN UNIVERSITY OF TECHNOLOGY   (Poland)

^b WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Electrical impedance tomography; Finite element method; Neural network

Indexed keywords

BRAIN; ELECTRIC IMPEDANCE TOMOGRAPHY; ELECTROMAGNETIC FIELD EFFECTS; FINITE ELEMENT METHOD; INVERSE PROBLEMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

TOMOGRAPHIC IMAGES;

MEDICAL IMAGING;

DIAGNOSTIC AGENT;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BRAIN; COMPARATIVE STUDY; COMPUTER SIMULATION; ELECTRODE; FINITE ELEMENT ANALYSIS; HEAD; HUMAN; IMPEDANCE; METHODOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICS; SUBDURAL HEMATOMA; TOMOGRAPHY;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; ELECTRIC IMPEDANCE; ELECTRODES; FINITE ELEMENT ANALYSIS; HEAD; HEMATOMA, SUBDURAL; HUMANS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; TOMOGRAPHY;

EID: 0036591480     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2002.800581     Document Type: Article

References (13)

1. Defect identification by Eddy current inspection data classification through probabilistic neural networks with elliptical Kernels
2. Rapid Eddy current inversion for 3D problems
3. Identification of circular holes in thin plates using a neural network approach
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5. Neural network approach to electrical impedance tomography
6. The bell function approximation of material coefficients distribution in the electrical impedance tomography
7. An overview of statistical learning theory
8. Hardware implementation of RAM-based neural networks for tomographic data processing