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).
18.104.22.168 Statistically Constrained Localized and Intuitive Deformations.
22.214.171.124 Genetic Algorithms.
126.96.36.199 Population Representation.
188.8.131.52 Encoding the Weights for GAs.
184.108.40.206 Mutations and Crossovers.
220.127.116.11 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).
18.104.22.168 Detection of Potential Microcalcifications (Signals).
22.214.171.124 Classification of Signals into Microcalcifications.
126.96.36.199 Detection of Microcalcification Clusters.
188.8.131.52 Classification of Microcalcification Clusters into Benign and Malignant.
4.2.3 Experiments and Results.
184.108.40.206 From Pre-processing to Signal Extraction.
220.127.116.11 Classification of Signals into Microcalcifications.
18.104.22.168 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.
22.214.171.124 Retinal Image Acquisition.
126.96.36.199 Image Processing.
4.3.3 Previous Work.
188.8.131.52 Vasculature Extraction.
184.108.40.206 A Genetic Algorithm for Edge Extraction.
220.127.116.11 Skeletonization Process.
18.104.22.168 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.
22.214.171.124 Accuracy Measures.
126.96.36.199 Comprehensibility Measures.
188.8.131.52 Interestingness Measures.
5.2.3 Evolutionary Approaches in Rules Mining.
5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining.
184.108.40.206 Rules Encoding.
220.127.116.11 Reproduction Operators.
18.104.22.168 Selection and Archiving.
22.214.171.124 User Guided Evolutionary Search.
5.2.5 Experiments in Medical Rules Mining.
126.96.36.199 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.
188.8.131.52 Visual Space Realization.
184.108.40.206 Visual Space Taxonomy.
220.127.116.11 Visual Space Geometries.
18.104.22.168 Visual Space Interpretation Taxonomy.
22.214.171.124 Visual Space Characteristics Examination.
126.96.36.199 Visual Space Mapping Taxonomy.
188.8.131.52 Visual Space Mapping Computation.
5.3.3 Experimental Settings.
184.108.40.206 Implicit Classical Algorithm Settings.
220.127.116.11 Explicit GEP Algorithm Settings.
5.3.4 Medical Examples.
18.104.22.168 Data Space Examples.
22.214.171.124 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.
126.96.36.199 Data Collection.
188.8.131.52 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.
184.108.40.206 Oral Tract Areas.
220.127.116.11 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.
18.104.22.168 The Cost Function.
22.214.171.124 The Optimization Algorithm.
126.96.36.199 A New Genetic Algorithm with a Surrogate Model.
188.8.131.52 Results of AGA on Test Functions.
6.3.4 A Simplified Test Case for a Pacemaker Optimization.
184.108.40.206 Description of the Test Case.
220.127.116.11 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.