L. Karwan, P.S. Szcepaniak: In Memoriam.- L.A. Zadeh: Foreword.- P.J. Lisboa: Preface.- Tutorials: J. Kacprzyk: Fuzzy Sets and Fuzzy Systems: A Brief Introduction.- E. Straszecka: Defining Membership Functions.- T.J. Ross: Membership Functions, Fuzzification and Defuzzification.- R.I. John: Fuzzy Sets and Knowledge Representation.- M. Baczynski, J. Drewniak: Monotonic Fuzzy Implications.- I. Bruha, P. Berka: Discretization and Fuzzification of Numerical Attributes in Attribute-Based Learning.- R. Babuska: Fuzzy Clustering Algorithms with Applications to Rule Extraction.- W. Pedrycz: Neurofuzzy Systems.- E. Sanchez, P. Pierre: Intelligent Decision Making Systems: From Medical Diagnosis to Vocational Guidance.- Case Studies: Signal Processing: E. Czogala, J. Leski: Entropy and Energy Measures of Fuzziness in ECG Signal Processing.- A. Grauel: Fuzzy Logic System for ECG Interpretation.- J. Petersen, G. Stockmanns, W. Nahm: EEG Analysis for Assessment of Depth of Anaesthesia.- Image Processing and Interpretation: G. Berks, D.G.v. Keyserlingk: Fuzzy Sets in Medical Image Processing.- H.R. Tizhoosh, G. Krell, B. Michaelis: Enhancement of Megavoltage Images in Radiation Therapy Using Fuzzy and Neural Image Processing Techniques.- F. Masulli, A. Schenone, A.M. Massone: Fuzzy Clustering Methods for the Segmentation of Multimodal Medical Images.- F. Behloul, A.-O. Boudraa, M. Janier, P. Croisille: Fuzzy Clustering for Parametric Map Construction in Myocardial Perfusion Magnetic Resonance Images.- P.R. Innocent, R.I. John, M. Barnes: Neuro-Fuzzy Models of Radiographic Image Classification.- F. waidelich, H. Eichfeld, R. Graumann: Segmentation of Medical Images Using Fuzzy Technique.- M.-C. Jaulent, V.Bombardier, I. Cherrak, O. Perez-Oramas: Fuzzy Quantification of Artery Lesions in Renal Arteriographies.- I. Bloch: Fusion of Numerical and Structural Image Information in Medical Imaging in the Framework of Fuzzy Sets.- Control, Diagnosis and Therapy: A. Bardossy, A. Blinowska: Fuzzy Reasoning in Pacemaker Control.- D.A. Linkens, M.F. Abbod, J.K. Backory, J.S. Shieh: Closed-Loop Control of Anaesthesia Using Fuzzy Logic.- D.G. Mason, N.D. Edwards: Self-Learning Fuzzy Logic Control of Anaesthetic Intravenous Infusions.- D. Rutkowska, A. Starczewski: Fuzzy Inference Neural Networks and Their Applications to Medical Diagnosis.- J.A. Swope, N.K. Kasabov, M.J.A. Williams: Neuro-Fuzzy Modelling of Heart Rate Signals and Application to Diagnostics.- S. Zahan: Fuzzy Diagnosis by Score-Based Tests - Implementation Issues.- E. Rakus-Andersson, T. Gerstenkorn: A Comparison of Fuzzy Decision Models Supporting the Optimal Therapy.- K. Cerbioni, C. Colosimo, A. Starita, P. Dario: A Neuro-Fuzzy System for Emulation of Human Grasp Movements.- Knowledge Based Systems: W. Duch, R. Adamczak, K. Grabczewski, G. Zal, Y. Hayashi: Fuzzy and Crisp Logical Rule Extraction Methods in Application to Medical Data.- R. Molitor, C.B. Tresp: Extending Description Logics to Vague Knowledge in Medicine.- T.E. Rothenfluh, K. Bögl, K.-P. Adlassnig: Represenation and Acquisition of Knowledge for a Fuzzy Medical Consultation System.- J.M. Garibaldi, E.C. Ifeachor: The Development of a Fuzzy Expert System for the Analysis of Umbilical Cord Blood.- J.Novotny, F. Babinec: Fuzzy Expert System for Exercise Therapy in Diabetics.

Provides an introduction to the fundamental concepts of fuzziness together with a compilation of recent advances in the application to medicine. The tutorials in the first part of the book range from basic concepts through theoretical frameworks to rule simplification through data clustering methodologies and the design of multivariate rule bases through self-learning by mapping fuzzy systems onto neural network structures. The case studies which follow are representative of the wide range of applications currently pursued in relation to medicine. The majority of applications presented in this book are about bridging the gap between low-level sensor measurements and intermediate or high-level data representations. The book offers a comprehensive perspective from leading authorities world-wide and provides a tantalising glimpse into the role of sophisticated knowledge engineering methods in shaping the landscape of medical technology in the future.