Genetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists.
Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and treatments, finally providing hints about possible future advancements of genetic and evolutionary computation in medicine.
- Explores the rapidly growing area of genetic and evolutionary computation in context of its viable and exciting payoffs in the field of medical applications.
- Explains the underlying theory, typical applications and detailed implementation.
- Includes general sections about the applications of GEC to medicine and their expected future developments, as well as specific sections on applications of GEC to medical imaging, analysis of medical data sets, advanced modelling, diagnosis and treatment.
- Features a wide range of tables, illustrations diagrams and photographs.
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.
