Case-Based Reasoning on Images and Signals: (73 Studies in Computational Intelligence)

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 intoan application-oriented part.

Table of Contents:
to Case-Based Reasoning for Signals and Images.- Similarity.- Distance Function Learning for Supervised Similarity Assessment.- Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations.- Graph Matching.- Memory Structures and Organization in Case-Based Reasoning.- Learning a Statistical Model for Performance Prediction in Case-Based Reasoning.- A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images.- Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data.- Prototypes and Case-Based Reasoning for Medical Applications.- Case-Based Reasoning for Image Segmentation by Watershed Transformation.- Similarity-Based Retrieval for Biomedical Applications.- Medical Imagery in Case-Based Reasoning.- Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces.