%0 Journal Article %1 Alirezaie1997 %A Alirezaie, J %A Jernigan, ME %A Nahmias, C %C 345 E 47TH ST, NEW YORK, NY 10017-2394 %D 1997 %I IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC %J IEEE Transactions On Nuclear Science %K image images; learning magnetic network} neural quantization; resonance segmentation; vector {classification; %N 2 %P 194-198 %T Neural network-based segmentation of magnetic resonance images of the brain %V 44 %X This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. We have modified the LVQ for better and more accurate classification. We have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, our method is insensitive to the gray-level variation of MR images between different slices, It shows that tissue segmentation using LVQ ANN also performs be ner and faster than that using back-propagation ANN.

@article{Alirezaie1997, abstract = {{This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. We have modified the LVQ for better and more accurate classification. We have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, our method is insensitive to the gray-level variation of MR images between different slices, It shows that tissue segmentation using LVQ ANN also performs be ner and faster than that using back-propagation ANN.}}, added-at = {2011-03-11T12:21:24.000+0100}, address = {{345 E 47TH ST, NEW YORK, NY 10017-2394}}, affiliation = {{Alirezaie, J (Reprint Author), MCMASTER UNIV,COMMUN RES LAB,HAMILTON,ON L8S 4K1,CANADA. UNIV WATERLOO,WATERLOO,ON N2L 3G1,CANADA. MCMASTER UNIV,DEPT NUCL MED,HAMILTON,ON L8N 3Z5,CANADA.}}, author = {Alirezaie, J and Jernigan, ME and Nahmias, C}, biburl = {https://www.bibsonomy.org/bibtex/2be0c996753c8a6fc6d74c4e27db134c2/jmaiora}, doc-delivery-number = {{WU143}}, interhash = {d07c820b06f67061ef3560ed62233723}, intrahash = {be0c996753c8a6fc6d74c4e27db134c2}, issn = {{0018-9499}}, journal = {{IEEE Transactions On Nuclear Science}}, journal-iso = {{IEEE Trans. Nucl. Sci.}}, keywords = {image images; learning magnetic network} neural quantization; resonance segmentation; vector {classification;}, keywords-plus = {{MULTISPECTRAL ANALYSIS; TISSUE CLASSIFICATION}}, language = {{English}}, month = {{APR}}, number = {{2}}, number-of-cited-references = {{22}}, owner = {Josu}, pages = {{194-198}}, publisher = {{IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC}}, subject-category = {{Engineering, Electrical \& Electronic; Nuclear Science \& Technology}}, times-cited = {{15}}, timestamp = {2011-03-11T12:21:24.000+0100}, title = {{Neural network-based segmentation of magnetic resonance images of the brain}}, type = {{Article}}, unique-id = {{ISI:A1997WU14300012}}, volume = {{44}}, year = 1997 }