[...]... networks in medical imaging 1 2 1.1 Medical Imaging Techniques and Applications Introduction An artificial neural network (ANN) is an information processing system that is inspired by the way biological nervous systems store and process information like human brains It contains a large number of highly interconnected processing neurons working together in a distributed manner to learn from the input information,... self-organizing map; MTANN, massive training artificial neural network CNN∗ (BP∗ ) BP MLP∗ RBFNN∗ BP [6] [8] [9] [10] [13] Application Comparative summary of feedforward neural network applications in medical imaging Number Neurons Neurons Type of of in Hidden in Network Purpose Type of Input Inputs Layers Output Train/Test/Validation TABLE 1.1: 20 Medical Imaging Techniques and Applications Computational Intelligence. .. Amyloid and Preclinical and Diagnostic Molecular Imaging Laboratory University of Tennessee Graduate School of Medicine Knoxville, Tennessee Department of Electronic and Computer Engineering School of Engineering and Design Brunel University Uxbridge, Middlesex, United Kingdom xiii Chapter 1 Computational Intelligence on Medical Imaging with Artificial Neural Networks Z Q Wu, Jianmin Jiang, and Y H... themselves in accordance with the variety and change of input content; (b) neural networks can optimize the relationship between the inputs and outputs via distributed computing, training, and processing, leading to reliable solutions desired by specifications; (c) medical diagnosis relies on visual inspection, and medical imaging provides the most important tool for facilitating such inspection and visualization... feedback network, and self-organizing map The learning paradigms for the neural networks in medical image processing generally include supervised networks and unsupervised networks In supervised training, the training data set consists of many pairs in the source and target patterns The network processes the source inputs and compares the resulting outputs against the target outputs, and adjusts its... compression, providing a global view on the variety of neural network applications and their potential for further research and developments Neural network applications in CAD represent the mainstream of computational intelligence in medical imaging Their penetration and involvement are comprehensive for almost all medical problems because (a) neural networks can adaptively learn from input information and upgrade... structure and training procedure can be applied to resolve a medical imaging problem; (b) how medical images can be analyzed, processed, and characterized by neural networks; and (c) how neural networks can be expanded further to resolve problems relevant to medical imaging In the concluding section, a comparison of all neural networks is included to provide a global view on computational intelligence. .. extracted by modifying growing neural gas (GNG), which was a neural network–based cluster-seeking algorithm Using the modified GNG (a splitting–merging SOM), corresponding dominant points of contours 16 Medical Imaging Techniques and Applications extracted from two corresponding images are found The contours were the boundaries of the regions generated by segmenting the MR brain image Di Bona and Salvetti... input information, to coordinate internal processing, and to optimize its final output In the past decades, neural networks have been successfully applied to a wide range of areas, including computer science, engineering, theoretical modeling, and information systems Medical imaging is another fruitful area for neural networks to play crucial roles in resolving problems and providing solutions Numerous... typical feedback, and its inspiration is to store certain patterns in a manner similar to the way the human brain stores memories The Hopfield network has no special input or output neurons, but all neurons are both input and output, and all of them connect to all others in both directions After receiving the input simultaneously by all the neurons, Computational Intelligence on Medical Imaging 5 they output . class="bi x0 y0 w1 h0" alt="" Computational Intelligence in Medical Imaging Techniques and Applications Computational Intelligence in Medical Imaging Techniques and Applications Edited by Gerald. Nottingham, United Kingdom in 1994. His research interests include image/video processing in compressed domains, medical imaging, machine learning and AI applications in digital media processing,. and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging -in- Publication Data Computational intelligence in medical imaging techniques and