Genetic and Evolutionary Computation: Medical Applications
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.
List of Contributors.
2 Evolutionary Computation: A Brief Overview (Stefano Cagnoni and Leonardo Vanneschi).
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.
3 A Review of Medical Applications of Genetic and Evolutionary Computation (Stephen L. Smith).
3.1 Medical Imaging and Signal Processing.
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).
22.214.171.124 Statistically Constrained Localized and Intuitive Deformations.
126.96.36.199 Genetic Algorithms.
188.8.131.52 Population Representation.
184.108.40.206 Encoding the Weights for GAs.
220.127.116.11 Mutations and Crossovers.
18.104.22.168 Calculating the Fitness of Members of the GA Population.
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. Hernández-Cisneros, and Hugo Terashima-Marín).
22.214.171.124 Detection of Potential Microcalcifications (Signals).
126.96.36.199 Classification of Signals into Microcalcifications.
188.8.131.52 Detection of Microcalcification Clusters.
184.108.40.206 Classification of Microcalcification Clusters into Benign and Malignant.
4.2.3 Experiments and Results.
220.127.116.11 From Pre-processing to Signal Extraction.
18.104.22.168 Classification of Signals into Microcalcifications.
22.214.171.124 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).
4.3.2 Acquisition and Processing of Retinal Fundus Images.
126.96.36.199 Retinal Image Acquisition.
188.8.131.52 Image Processing.
4.3.3 Previous Work.
184.108.40.206 Vasculature Extraction.
220.127.116.11 A Genetic Algorithm for Edge Extraction.
18.104.22.168 Skeletonization Process.
22.214.171.124 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).
5.1.3 Related Works.
5.1.4 Maximum Variation Sampling.
5.1.7 Results & Discussion.
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.
126.96.36.199 Accuracy Measures.
188.8.131.52 Comprehensibility Measures.
184.108.40.206 Interestingness Measures.
5.2.3 Evolutionary Approaches in Rules Mining.
5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining.
220.127.116.11 Rules Encoding.
18.104.22.168 Reproduction Operators.
22.214.171.124 Selection and Archiving.
126.96.36.199 User Guided Evolutionary Search.
5.2.5 Experiments in Medical Rules Mining.
188.8.131.52 Impact of User Interaction.
5.3 Genetic Programming for Exploring Medical Data using Visual Spaces (Julio J. Valdés, Alan J. Barton, and Robert Orchard).
5.3.2 Visual Spaces.
184.108.40.206 Visual Space Realization.
220.127.116.11 Visual Space Taxonomy.
18.104.22.168 Visual Space Geometries.
22.214.171.124 Visual Space Interpretation Taxonomy.
126.96.36.199 Visual Space Characteristics Examination.
188.8.131.52 Visual Space Mapping Taxonomy.
184.108.40.206 Visual Space Mapping Computation.
5.3.3 Experimental Settings.
220.127.116.11 Implicit Classical Algorithm Settings.
18.104.22.168 Explicit GEP Algorithm Settings.
5.3.4 Medical Examples.
22.214.171.124 Data Space Examples.
126.96.36.199 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).
6.1.2 Evaluation of Visuo-spatial Ability.
6.1.3 Implicit Context Representation CGP.
188.8.131.52 Data Collection.
184.108.40.206 Parameter Settings.
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).
6.2.2 Oral Tract Shape Evolution.
6.2.3 Recording the Target Vowels.
6.2.4 Evolving Oral Tract Shapes.
220.127.116.11 Oral Tract Areas.
18.104.22.168 Spectral Comparisons.
6.3 How Genetic Algorithms can Improve Pacemaker Efficiency (Laurent Dumas and Linda El Alaoui).
6.3.2 Modeling of the Electrical Activity of the Heart.
6.3.3 The Optimization Principles.
22.214.171.124 The Cost Function.
126.96.36.199 The Optimization Algorithm.
188.8.131.52 A New Genetic Algorithm with a Surrogate Model.
184.108.40.206 Results of AGA on Test Functions.
6.3.4 A Simplified Test Case for a Pacemaker Optimization.
220.127.116.11 Description of the Test Case.
18.104.22.168 Numerical Results.
7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards.
7.4 The Future for Genetic and Evolutionary Computation in Medicine.
Appendix: Introductory Books and Useful Links.