Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques: Applications and Techniques

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Gonzalez, Fabio A.
IGI Global, Dec 31, 2009 - Computers - 390 pages
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Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis.

Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.


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From Biomedical ImageAnalysis to BiomedicalImage UnderstandingUsing Machine Learning
Section 2 Feature Extraction
ComputerAided Detectionand Diagnosis of Breast CancerUsing Machine LearningTexture and Shape Features
Machine Learning forAutomated Polyp Detectionin Computed TomographyColonography
Variational ApproachBased Image PreProcessing Techniques forVirtual Colonoscopy
Machine Learning for BrainImage Segmentation
A Genetic AlgorithmBasedLevel Set Curve Evolution forProstate Segmentation onPelvic CT and MRI Images
Genetic Adaptation of LevelSets Parameters for MedicalImaging Segmentation
Assessment of Kidney FunctionUsing Dynamic ContrastEnhanced MRI Techniques
Ensemble of Neural Networksfor Automated Cell PhenotypeImage Classification
ContentBased Access toMedical Image Collections
Predicting Complex Patternsof Human Movements UsingBayesian Online Learning inMedical Imaging Applications
Left Ventricle Segmentation andMotion Analysis in MultiSliceComputerized Tomography
Compilation of References
About the Contributors

Automatic Analysis ofMicroscopic Images inHematological CytologyApplications
Biomedical Microscopic ImageProcessing by Graphs

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About the author (2009)

Fabio A. González is an Associate Professor at the Department of Computer Systems and Industrial Engineering, National University of Colombia. He is the co-leader of the Bioingenium research group. He earned a Computer Systems Engineer degree and a MSc in Math degree from the National University of Colombia in 1993 and 1998 respectively, and a MSc and PhD degrees in Computer Science from the University of Memphis, USA, in 2003. His research work is mainly focused on the foundations of machine learning and its applications to image processing, computer vision, data mining and information retrieval among others. He has published more than 50 research papers and has served as referee in different international journals and conferences.

Eduardo Romero received PhD in Biomedical Sciences from the Université Catholique de Louvain in 2000. Between 2000-2002 he worked as a Senior Researcher at the Communications and Remote sensing laboratory (UCL - Belgium), in the group of Medical Images. During 2003 he was with the group of chemical sensors at the Centro Nacional de Microelectrónica (CNM - Spain). Currently he is associated professor attached to the Telemedicine Centre of the Faculty of Medicine and leads both the Bioingenium group and the Biomedical Engineering postgraduate program. He has published more than 50 research papers and has served as referee in different international journals and conferences. [Editor]

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