Fuzzy logic in medicine
Author(s)
Bibliographic Information
Fuzzy logic in medicine
（Studies in fuzziness and soft computing, v. 83）
Physica-Verlag, c2002
- : hbk
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Note
Includes bibliographical references
Description and Table of Contents
Description
To say that Fuzzy Logic in Medicine, or FLM for short, is an important addi tion to the literature of fuzzy logic and its applications, is an understatement. Edited by two prominent informaticians, Professors S. Barro and R. Marin, it is one of the first books in its field. Between its covers, FLM presents authoritative expositions of a wide spectrum of medical and biological ap plications of fuzzy logic, ranging from image classification and diagnostics to anaesthesia control and risk assessment of heart diseases. As the editors note in the preface, recognition of the relevance of fuzzy set theory and fuzzy logic to biological and medical systems has a long history. In this context, particularly worthy of note is the pioneering work of Profes sor Klaus Peter Adlassnig of the University of Vienna School of Medicine. However, it is only within the past decade that we began to see an accelerat ing growth in the visibility and importance of publications falling under the rubric of fuzzy logic in medicine and biology -a leading example of which is the Journal of the Biomedical Fuzzy Systems Association in Japan. Why did it take so long for this to happen? First, a bit of history.
Table of Contents
A Call for a Stronger Role for Fuzzy Logic in Medicine.- Fuzzy Information Granulation of Medical Images. Blood Vessel Extraction from 3-D MRA Images.- Breast Cancer Classification Using Fuzzy Central Moments.- Awareness Monitoring and Decision-Making for General Anaesthesia.- Depth of Anesthesia Control with Fuzzy Logic.- Intelligent Alarms for Anaesthesia Monitoring Based on a Fuzzy Logic Approach.- Fuzzy Clustering in Medicine: Applications to Electrophysiological Signal Processing.- Fuzzy Logic in a Decision Support System in the Domain of Coronary Heart Disease Risk Assessment.- A Model-based Temporal Abductive Diagnosis Model for an Intensive Coronary Care Unit.- A Fuzzy Model for Pattern Recognition in the Evolution of Patients.- Mass Assignment Methods for Medical Classification Diagnosis.- Acquisition of Fuzzy Association Rules from Medical Data.
by "Nielsen BookData"