About the Editors.
List of Contributors.
1 Introduction.
2 Evolutionary Computation: A Brief Overview (Stefano
Cagnoni and Leonardo Vanneschi).
2.1 Introduction.
2.2 Evolutionary Computation Paradigms.
2.2.1 Genetic Algorithms.
2.2.2 Evolution Strategies.
2.2.3 Evolutionary Programming.
2.2.4 Genetic Programming.
2.2.5 Other Evolutionary Techniques.
2.2.6 Theory of Evolutionary Algorithms.
2.3 Conclusions.
3 A Review of Medical Applications of Genetic and
Evolutionary Computation (Stephen L. Smith).
3.1 Medical Imaging and Signal Processing.
3.1.1 Overview.
3.1.2 Image Segmentation.
3.1.3 Image Registration, Reconstruction and Correction.
3.1.4 Other Applications.
3.2 Data Mining Medical Data and Patient Records.
3.3 Clinical Expert Systems and Knowledge-based Systems.
3.4 Modelling and Simulation of Medical Processes.
3.5 Clinical Diagnosis and Therapy.
4 Applications of GEC in Medical Imaging.
4.1 Evolutionary Deformable Models for Medical Image
Segmentation: A Genetic Algorithm Approach to Optimizing Learned,
Intuitive, and Localized Medial-based Shape Deformation
(Chris McIntosh and Ghassan Hamarneh).
4.1.1 Introduction.
4.1.1.1 Statistically Constrained Localized and Intuitive
Deformations.
4.1.1.2 Genetic Algorithms.
4.1.2 Methods.
4.1.2.1 Population Representation.
4.1.2.2 Encoding the Weights for GAs.
4.1.2.3 Mutations and Crossovers.
4.1.2.4 Calculating the Fitness of Members of the GA
Population.
4.1.3 Results.
4.1.4 Conclusions.
4.2 Feature Selection for the Classification of
Microcalcifications in Digital Mammograms using Genetic Algorithms,
Sequential Search and Class Separability (Santiago E.
Conant-Pablos, Rolando R. Hernandez-Cisneros, and Hugo
Terashima-Marin).
4.2.1 Introduction.
4.2.2 Methodology.
4.2.2.1 Pre-processing.
4.2.2.2 Detection of Potential Microcalcifications
(Signals).
4.2.2.3 Classification of Signals into Microcalcifications.
4.2.2.4 Detection of Microcalcification Clusters.
4.2.2.5 Classification of Microcalcification Clusters into
Benign and Malignant.
4.2.3 Experiments and Results.
4.2.3.1 From Pre-processing to Signal Extraction.
4.2.3.2 Classification of Signals into Microcalcifications.
4.2.3.3 Microcalcification Clusters Detection and
Classification.
4.2.4 Conclusions and Future Work.
4.3 Hybrid Detection of Features within the Retinal Fundus
using a Genetic Algorithm (Vitoantonio Bevilacqua, Lucia
Cariello, Simona Cambo, Domenico Daleno, and Giuseppe
Mastronardi).
4.3.1 Introduction.
4.3.2 Acquisition and Processing of Retinal Fundus Images.
4.3.2.1 Retinal Image Acquisition.
4.3.2.2 Image Processing.
4.3.3 Previous Work.
4.3.4 Implementation.
4.3.4.1 Vasculature Extraction.
4.3.4.2 A Genetic Algorithm for Edge Extraction.
4.3.4.3 Skeletonization Process.
4.3.4.4 Experimental Results.
5 New Analysis of Medical Data Sets using GEC.
5.1 Analysis and Classification ofMammography Reports using
Maximum Variation Sampling (Robert M. Patton, Barbara G.
Beckerman, and Thomas E. Potok).
5.1.1 Introduction.
5.1.2 Background.
5.1.3 Related Works.
5.1.4 Maximum Variation Sampling.
5.1.5 Data.
5.1.6 Tests.
5.1.7 Results & Discussion.
5.1.8 Summary.
5.2 An Interactive Search for Rules in Medical Data using
Multiobjective Evolutionary Algorithms (Daniela Zaharie, D.
Lungeanu, and Flavia Zamfirache).
5.2.1 Medical Data Mining.
5.2.2 Measures for Evaluating the Rules Quality.
5.2.2.1 Accuracy Measures.
5.2.2.2 Comprehensibility Measures.
5.2.2.3 Interestingness Measures.
5.2.3 Evolutionary Approaches in Rules Mining.
5.2.4 An Interactive Multiobjective Evolutionary Algorithm for
Rules Mining.
5.2.4.1 Rules Encoding.
5.2.4.2 Reproduction Operators.
5.2.4.3 Selection and Archiving.
5.2.4.4 User Guided Evolutionary Search.
5.2.5 Experiments in Medical Rules Mining.
5.2.5.1 Impact of User Interaction.
5.2.6 Conclusions.
5.3 Genetic Programming for Exploring Medical Data using
Visual Spaces (Julio J. Valdes, Alan J. Barton, and
Robert Orchard).
5.3.1 Introduction.
5.3.2 Visual Spaces.
5.3.2.1 Visual Space Realization.
5.3.2.2 Visual Space Taxonomy.
5.3.2.3 Visual Space Geometries.
5.3.2.4 Visual Space Interpretation Taxonomy.
5.3.2.5 Visual Space Characteristics Examination.
5.3.2.6 Visual Space Mapping Taxonomy.
5.3.2.7 Visual Space Mapping Computation.
5.3.3 Experimental Settings.
5.3.3.1 Implicit Classical Algorithm Settings.
5.3.3.2 Explicit GEP Algorithm Settings.
5.3.4 Medical Examples.
5.3.4.1 Data Space Examples.
5.3.4.2 Semantic Space Examples.
5.3.5 Future Directions.
6 Advanced Modelling, Diagnosis and Treatment using
GEC.
6.1 Objective Assessment of Visuo-spatial Ability using
Implicit Context Representation Cartesian Genetic Programming
(Michael A. Lones and Stephen L. Smith).
6.1.1 Introduction.
6.1.2 Evaluation of Visuo-spatial Ability.
6.1.3 Implicit Context Representation CGP.
6.1.4 Methodology.
6.1.4.1 Data Collection.
6.1.4.2 Evaluation.
6.1.4.3 Parameter Settings.
6.1.5 Results.
6.1.6 Conclusions.
6.2 Towards an Alternative to Magnetic Resonance Imaging for
Vocal Tract Shape Measurement using the Principles of Evolution
(David M. Howard, Andy M. Tyrrell, and Crispin
Cooper).
6.2.1 Introduction.
6.2.2 Oral Tract Shape Evolution.
6.2.3 Recording the Target Vowels.
6.2.4 Evolving Oral Tract Shapes.
6.2.5 Results.
6.2.5.1 Oral Tract Areas.
6.2.5.2 Spectral Comparisons.
6.2.6 Conclusions.
6.3 How Genetic Algorithms can Improve Pacemaker Efficiency
(Laurent Dumas and Linda El Alaoui).
6.3.1 Introduction.
6.3.2 Modeling of the Electrical Activity of the Heart.
6.3.3 The Optimization Principles.
6.3.3.1 The Cost Function.
6.3.3.2 The Optimization Algorithm.
6.3.3.3 A New Genetic Algorithm with a Surrogate Model.
6.3.3.4 Results of AGA on Test Functions.
6.3.4 A Simplified Test Case for a Pacemaker Optimization.
6.3.4.1 Description of the Test Case.
6.3.4.2 Numerical Results.
6.3.5 Conclusion.
7 The Future for Genetic and Evolutionary Computation in
Medicine: Opportunities, Challenges and Rewards.
7.1 Opportunities.
7.2 Challenges.
7.3 Rewards.
7.4 The Future for Genetic and Evolutionary Computation in
Medicine.
Appendix: Introductory Books and Useful Links.
Index.
