Case-Based Reasoning on Images and Signals

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Springer Science & Business Media, Oct 9, 2007 - Computers - 436 pages
This book is the ?rst edited book that deals with the special topic of signals and images within case-based reasoning (CBR). Signal-interpreting systems are becoming increasingly popular in medical, industrial, ecological, biotechnological and many other applications. Existing statisticalandknowledge-basedtechniqueslackrobustness,accuracy,and?- ibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBRstrategiesintosignal-interpretingsystemscansatisfytheserequirements. CBR can be used to control the signal-processing process in all phases of a signal-interpreting system to derive information of the highest possible qu- ity. Beyond this CBR o?ers di?erent learning capabilities, for all phases of a signal-interpretingsystem,thatsatisfydi?erentneedsduringthedevelopment process of a signal-interpreting system. In the outline of this book we summarize under the term “signal” signals of 1-dimensional, 2-dimensional or 3-dimensional nature. The unique data and the necessary computation techniques require ext- ordinary case representations, similarity measures and CBR strategies to be utilised. Signalinterpretation(1D,2D,or3Dsignalinterpretation)istheprocessof mapping the numerical representation of a signal into logical representations suitable for signal descriptions. A signal-interpreting system must be able to extract symbolic features from the raw data e.g., the image (e.g., irregular structure inside the nodule, area of calci?cation, and sharp margin). This is a complex process; the signal passes through several general processing steps before the ?nal symbolic description is obtained. The structure of the book is divided into a theoretical part and into an application-oriented part.
 

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Contents

Introduction to CaseBased Reasoning for Signals and Images
1
Similarity
25
Distance Function Learning for Supervised Similarity Assessment
91
Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations
127
Graph Matching
149
Memory Structures and Organization in CaseBased Reasoning
174
Learning a Statistical Model for Performance Prediction in CaseBased Reasoning
195
A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images
213
Extracting Knowledge from Sensor Signals for CaseBased Reasoning with Longitudinal Time Series Data
247
Prototypes and CaseBased Reasoning for Medical Applications
285
CaseBased Reasoning for Image Segmentation by Watershed Transformation
319
SimilarityBased Retrieval for Biomedical Applications
354
Medical Imagery in CaseBased Reasoning
389
InstanceBased Relevance Feedback in Image Retrieval Using Dissimilarity Spaces
419
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