Atjaunināt sīkdatņu piekrišanu

E-grāmata: Genetic and Evolutionary Computation: Medical Applications

5.00/5 (2 ratings by Goodreads)
Edited by (Universita degli Studi di Parma), Edited by (University of York)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 26-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119956785
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 126,05 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Genetic and Evolutionary Computation: Medical ApplicationsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 26-Jul-2011
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119956785
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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 xi
List of Contributors
xiii
1 Introduction
1(2)
2 Evolutionary Computation: A Brief Overview
3(14)
Stefano Cagnoni
Leonardo Vanneschi
2.1 Introduction
3(1)
2.2 Evolutionary Computation Paradigms
4(8)
2.2.1 Genetic Algorithms
5(2)
2.2.2 Evolution Strategies
7(1)
2.2.3 Evolutionary Programming
8(1)
2.2.4 Genetic Programming
8(2)
2.2.5 Other Evolutionary Techniques
10(1)
2.2.6 Theory of Evolutionary Algorithms
11(1)
2.3 Conclusions
12(5)
3 A Review of Medical Applications of Genetic and Evolutionary Computation
17(28)
Stephen L. Smith
3.1 Medical Imaging and Signal Processing
18(7)
3.1.1 Overview
18(1)
3.1.2 Image Segmentation
18(3)
3.1.3 Image Registration, Reconstruction and Correction
21(3)
3.1.4 Other Applications
24(1)
3.2 Data Mining Medical Data and Patient Records
25(2)
3.3 Clinical Expert Systems and Knowledge-based Systems
27(2)
3.4 Modelling and Simulation of Medical Processes
29(5)
3.5 Clinical Diagnosis and Therapy
34(11)
4 Applications of GEC in Medical Imaging
45(66)
4.1 Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial-based Shape Deformation
47(22)
Chris McIntosh
Ghassan Hamarneh
4.1.1 Introduction
47(7)
4.1.1.1 Statistically Constrained Localized and Intuitive Deformations
54(3)
4.1.1.2 Genetic Algorithms
57(1)
4.1.2 Methods
58(1)
4.1.2.1 Population Representation
58(1)
4.1.2.2 Encoding the Weights for GAs
58(1)
4.1.2.3 Mutations and Crossovers
59(1)
4.1.2.4 Calculating the Fitness of Members of the GA Population
60(2)
4.1.3 Results
62(1)
4.1.4 Conclusions
63(6)
4.2 Feature Selection for the Classification of Microcalcifications in Digital Mammograms using Genetic Algorithms, Sequential Search and Class Separability
69(16)
Santiago E. Conant-Pablos
Rolando R. Hernandez-Cisneros
Hugo Terashima-Marin
4.2.1 Introduction
69(2)
4.2.2 Methodology
71(1)
4.2.2.1 Pre-processing
71(1)
4.2.2.2 Detection of Potential Microcalcifications (Signals)
72(2)
4.2.2.3 Classification of Signals into Microcalcifications
74(2)
4.2.2.4 Detection of Microcalcification Clusters
76(1)
4.2.2.5 Classification of Microcalcification Clusters into Benign and Malignant
77(1)
4.2.3 Experiments and Results
77(1)
4.2.3.1 From Pre-processing to Signal Extraction
77(2)
4.2.3.2 Classification of Signals into Microcalcifications
79(2)
4.2.3.3 Microcalcification Clusters Detection and Classification
81(1)
4.2.4 Conclusions and Future Work
82(3)
4.3 Hybrid Detection of Features within the Retinal Fundus using a Genetic Algorithm
85(26)
Vitoantonio Bevilacqua
Lucia Cariello
Simona Cambo
Domenico Daleno
Giuseppe Mastronardi
4.3.1 Introduction
85(3)
4.3.2 Acquisition and Processing of Retinal Fundus Images
88(1)
4.3.2.1 Retinal Image Acquisition
89(1)
4.3.2.2 Image Processing
90(1)
4.3.3 Previous Work
91(2)
4.3.4 Implementation
93(1)
4.3.4.1 Vasculature Extraction
94(5)
4.3.4.2 A Genetic Algorithm for Edge Extraction
99(4)
4.3.4.3 Skeletonization Process
103(1)
4.3.4.4 Experimental Results
104(7)
5 New Analysis of Medical Data Sets using GEC
111(62)
5.1 Analysis and Classification of Mammography Reports using Maximum Variation Sampling
113(20)
Robert M. Patton
Barbara G. Beckerman
Thomas E. Potok
5.1.1 Introduction
113(1)
5.1.2 Background
114(2)
5.1.3 Related Works
116(2)
5.1.4 Maximum Variation Sampling
118(4)
5.1.5 Data
122(2)
5.1.6 Tests
124(1)
5.1.7 Results & Discussion
124(5)
5.1.8 Summary
129(4)
5.2 An Interactive Search for Rules in Medical Data using Multiobjective Evolutionary Algorithms
133(16)
Daniela Zaharie
D. Lungeanu
Flavia Zamfirache
5.2.1 Medical Data Mining
133(1)
5.2.2 Measures for Evaluating the Rules Quality
134(1)
5.2.2.1 Accuracy Measures
135(1)
5.2.2.2 Comprehensibility Measures
135(1)
5.2.2.3 Interestingness Measures
136(1)
5.2.3 Evolutionary Approaches in Rules Mining
137(1)
5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining
138(1)
5.2.4.1 Rules Encoding
139(1)
5.2.4.2 Reproduction Operators
139(1)
5.2.4.3 Selection and Archiving
140(1)
5.2.4.4 User Guided Evolutionary Search
141(2)
5.2.5 Experiments in Medical Rules Mining
143(1)
5.2.5.1 Impact of User Interaction
144(2)
5.2.6 Conclusions
146(3)
5.3 Genetic Programming for Exploring Medical Data using Visual Spaces
149(24)
Julio J. Valdes
Alan J. Barton
Robert Orchard
5.3.1 Introduction
149(1)
5.3.2 Visual Spaces
150(1)
5.3.2.1 Visual Space Realization
150(1)
5.3.2.2 Visual Space Taxonomy
150(1)
5.3.2.3 Visual Space Geometries
151(1)
5.3.2.4 Visual Space Interpretation Taxonomy
151(2)
5.3.2.5 Visual Space Characteristics Examination
153(1)
5.3.2.6 Visual Space Mapping Taxonomy
154(1)
5.3.2.7 Visual Space Mapping Computation
155(2)
5.3.3 Experimental Settings
157(1)
5.3.3.1 Implicit Classical Algorithm Settings
158(1)
5.3.3.2 Explicit GEP Algorithm Settings
159(2)
5.3.4 Medical Examples
161(1)
5.3.4.1 Data Space Examples
161(3)
5.3.4.2 Semantic Space Examples
164(6)
5.3.5 Future Directions
170(3)
6 Advanced Modelling, Diagnosis and Treatment using GEC
173(50)
6.1 Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming
175(16)
Michael A. Lones
Stephen L. Smith
6.1.1 Introduction
175(1)
6.1.2 Evaluation of Visuo-spatial Ability
176(2)
6.1.3 Implicit Context Representation CGP
178(2)
6.1.4 Methodology
180(1)
6.1.4.1 Data Collection
181(1)
6.1.4.2 Evaluation
181(1)
6.1.4.3 Parameter Settings
182(2)
6.1.5 Results
184(2)
6.1.6 Conclusions
186(5)
6.2 Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement using the Principles of Evolution
191(18)
David M. Howard
Andy M. Tyrrell
Crispin Cooper
6.2.1 Introduction
191(3)
6.2.2 Oral Tract Shape Evolution
194(1)
6.2.3 Recording the Target Vowels
195(1)
6.2.4 Evolving Oral Tract Shapes
196(3)
6.2.5 Results
199(1)
6.2.5.1 Oral Tract Areas
200(1)
6.2.5.2 Spectral Comparisons
201(3)
6.2.6 Conclusions
204(5)
6.3 How Genetic Algorithms can Improve Pacemaker Efficiency
209(14)
Laurent Dumas
Linda El Alaoui
6.3.1 Introduction
209(2)
6.3.2 Modeling of the Electrical Activity of the Heart
211(2)
6.3.3 The Optimization Principles
213(1)
6.3.3.1 The Cost Function
213(1)
6.3.3.2 The Optimization Algorithm
213(1)
6.3.3.3 A New Genetic Algorithm with a Surrogate Model
214(1)
6.3.3.4 Results of AGA on Test Functions
215(1)
6.3.4 A Simplified Test Case for a Pacemaker Optimization
216(1)
6.3.4.1 Description of the Test Case
216(2)
6.3.4.2 Numerical Results
218(2)
6.3.5 Conclusion
220(3)
7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards
223(6)
7.1 Opportunities
224(1)
7.2 Challenges
224(2)
7.3 Rewards
226(1)
7.4 The Future for Genetic and Evolutionary Computation in Medicine
227(2)
Appendix: Introductory Books and Useful Links 229(2)
Index 231
Stephen Smith, Department of Electronics, University of York, UK Stephen Smith is a senior lecturer within the Department of Electronics at the University of York. His research interests include evolutionary algorithms and assisted clinical diagnosis. He is co-organiser of the annual GECCO (Genetic and Evolutionary Computation Conference), and co-workshop organiser for the Medical Applications of Genetic and Evolutionary Computation Workshop. His editorial experience includes current service as subject area editor for the Journal of Systems Architecture and guest editor for a special issue of the BioSystems journal.

Stefano Cagnoni, Universitą degli Studi di Parma, Italy Stefano Cagnoni is an associate professor in the department of computer engineering at the University of Parma. His research interests are in the fields of computer vision, robotics, evolutionary computation and neural networks. He is secretary of the Italian Association for Artificial Intelligence and Co-chairman of EvoIASP, the EvoNet working group on applications of Evolutionary Computation to Image and Signal Processing. He is co-editor of Genetic and Evolutionary Computation for Image Processing and Analysis, soon to publish with Hindawi Press.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811199567856e.html
Keywords: