Kacprzyk / Pietka / Kawa Information Technologies in Biomedicine

1. Auflage 2008
ISBN: 978-3-540-68168-7
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 574 Seiten

As the medical information systems have been integrated in order to address the core of medicine, including patient care in ambulatory and in-patient setting, computer assisted diagnosis and treatment, telemedicine, and home care we are witnessing radical changes in the Information Technologies. This will continue in the years to come. This book presents a comprehensive study in this field and contains carefully selected articles contributed by experts of information technologies. It is an interdisciplinary collection of papers that have both a theoretical and applied dimension. In particular, it includes the following sections: - Image Processing and CAD, - Signal Processing, - Biotechnology, - Data Analysis, - Multimedia, - Biomechanics. This book is a great reference tool for scientists who deal with problems of designing and implementing information processing tools employed in systems that assist the clinicians in patient diagnosis and treatment.

Autoren/Hrsg.

Kacprzyk, J.

Pietka, Ewa

Kawa, Jacek

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;Part I Invited Papers;14
3.1;Exploring the Knowledge of Human Expert beyond His Willing Expression;15
3.1.1;Introduction;15
3.1.1.1;Rising a Need for New Knowledge;15
3.1.1.2;Current Validation Rules and Their Limitations;16
3.1.1.3;Objective Measurement of Behavior;16
3.1.2;Methodology;16
3.1.2.1;Human Expert as Experiment Subject;16
3.1.2.2;Knowledge Exploration Techniques;17
3.1.3;Pursuit of the Human Eye in Course of the Visual ECG Interpretation;18
3.1.3.1;Perceptual Model Concept;18
3.1.3.2;Eye Tracking Method;18
3.1.3.3;Experiment Setup and Participants;19
3.1.3.4;Scanpath Signal Processing;20
3.1.3.5;Results of the Human Eye Pursuit;21
3.1.4;Pursuit of the Human Choice in Course of Diagnostic Result Selection;22
3.1.4.1;Expert’s Choice as Indicator of Medical Data Relevance;22
3.1.4.2;Usual Interface with Hidden Poll Functionality;23
3.1.4.3;Statistical Processing;24
3.1.4.4;Results of the Pursuit of Human-Made Relevance Indication;24
3.1.5;Conclusions;25
3.1.6;References;25
3.2;Application Problems of Implants Used in Interventional Cardiology;27
3.2.1;Introduction;27
3.2.2;Biophysical Conditions of the Heart-Coronary Vessels System;28
3.2.3;Conditions of Using Metal Biomaterials for Coronary Stents;30
3.2.4;Forming the Physical and Chemical Properties of the Coronary Stents Surface Layer;33
3.2.5;Summary;37
3.2.6;References;38
3.3;Computer Enhanced Orthopedics;40
3.3.1;Introduction;40
3.3.2;Computer Enhanced Orthopaedic Education;41
3.3.3;Orthopedic PACS;43
3.3.4;Computer Assisted Preoperative Planning;45
3.3.5;Computer Assisted Surgical Navigation in Orthopedic Surgery;46
3.3.6;Robotics in Orthopedic Surgery;47
3.3.7;Virtual Reality Orthopedic Surgery;48
3.3.8;Computerized Design of Orthopedic Implants;48
3.3.9;Telemedicine in Orthopedics;49
3.3.10;Conclusions and Final Remarks;50
3.3.11;References;50
3.4;Computer-Aided Diagnosis: From Image Understanding to Integrated Assistance;56
3.4.1;Introduction;56
3.4.2;Rationales;57
3.4.2.1;Human Limitations;57
3.4.2.2;Computer-Aided Diagnosis;58
3.4.3;CAD Statement and Development;59
3.4.3.1;Computational Image Descriptors;59
3.4.3.2;Semantic Visualization;59
3.4.3.3;Medical Knowledge Platform;60
3.4.3.4;Cognitive Resonance for Image Understanding;61
3.4.3.5;Content-Based Indexing of Medical Images;62
3.4.4;Integrated Interpretation Assistance;64
3.4.5;Conclusions;64
3.4.6;References;65
4;Part II Image Processing and CAD;67
4.1;Biomedical Structures Representation by Morphological Spectra;68
4.1.1;Introduction;68
4.1.2;Morphological Spectra;69
4.1.3;Spectral Representation of Single Irregular Objects;71
4.1.4;Spectral Representation of Classes of Objects;72
4.1.5;Conclusions;76
4.1.6;References;76
4.2;Medical Image Analysis Using Potential Active Contours;77
4.2.1;Introduction;77
4.2.2;Preprocessing;78
4.2.3;Detection of an Eyeball;80
4.2.4;Summary;83
4.2.5;References;83
4.3;Potential Active Contours – Basic Concepts, Mechanisms and Features;85
4.3.1;Introduction – Physical Background;85
4.3.2;Energy Function;87
4.3.3;Simulated Annealing;89
4.3.4;Move Generator;91
4.3.4.1;Gaussian Move Generator;91
4.3.4.2;Equalization of Chances;92
4.3.4.3;Modification of the Number of Sources;93
4.3.4.4;Move Generator – Suggestions for Further Research;94
4.3.5;Summary;94
4.3.6;References;95
4.4;Fractal Magnification of Medical Images;96
4.4.1;Introduction;96
4.4.2;Fundamentals of Fractal Magnification;97
4.4.3;Proposed Magnification Method;97
4.4.3.1;Image Fidelity;97
4.4.3.2;Time Cost Reduction;99
4.4.4;Experimental Results;101
4.4.5;Conclusions and Future Work;103
4.4.6;References;103
4.5;Fuzzy Clustering in Segmentation of Abdominal Structures Based on CT Studies;104
4.5.1;Introduction;104
4.5.2;Traditional Live-Wire Algorithm;104
4.5.2.1;Local Cost Map Definition;105
4.5.2.2;Graph Searching;105
4.5.2.3;Live-Wire-on-the-Fly;107
4.5.3;Modified Live-Wire Algorithm;108
4.5.3.1;Wavelet Cost Map Definition;109
4.5.3.2;Fuzzy c-Means Clustering;109
4.5.3.3;Live-Wire Modification;110
4.5.4;Results;112
4.5.5;Conclusions;114
4.5.6;References;115
4.6;The Clusterization Process in an Adaptative Method of Image Segmentation;116
4.6.1;Introduction;116
4.6.2;The Idea of Homogeneous Areas;116
4.6.3;The Algorithm;117
4.6.3.1;Generating the Initial Set of Areas;117
4.6.3.2;Merging the Areas;118
4.6.3.3;The Adaptation of the Threshold Variance;119
4.6.3.4;The Edge Strength Factor;121
4.6.4;Conclusions;122
4.6.5;References;122
4.7;Content-Based Indexing of Medical Images for Digital Radiology Applications;124
4.7.1;Introduction;124
4.7.2;Materials and Methods;125
4.7.2.1;IShark System;125
4.7.2.2;Features for Retrieval of Various Modalities Images;126
4.7.2.3;Features for Retrieval of Mammographic Image;127
4.7.3;Testing Scenarios and Results;129
4.7.3.1;Various Modalities Images Indexing;129
4.7.3.2;Mammographic Images Indexing;129
4.7.4;Conclusions;130
4.7.5;References;131
4.8;Shape and Texture Feature Extraction for Retrieval Mammogram in Databases;132
4.8.1;Introduction;132
4.8.2;Mammograms Feature Extraction;134
4.8.3;Results and Conclusion;137
4.8.4;References;138
4.9;Mathematical Morphology (MM) Features for Classification of Cancerous Masses in Mammograms;140
4.9.1;Introduction;140
4.9.2;Proposed MM Features for Mass Malignancy and Spicularity;141
4.9.3;Existing Features for Mass Malignancy and Spicularity;144
4.9.4;Experiments;146
4.9.5;Conclusions;148
4.9.6;References;148
4.10;Stroke Display Extensions: Three Forms of Visualization;150
4.10.1;Ischemic Stroke Conditioning;150
4.10.1.1;Pathophysiology;151
4.10.1.2;CT Findings in Acute Stroke;152
4.10.1.3;Hypodensity Models;153
4.10.2;Materials and Methods;154
4.10.2.1;Algorithm of Stroke Display;155
4.10.3;Experimental Study;156
4.10.4;Conclusions;158
4.10.5;References;158
4.11;Automated Fuzzy-Connectedness-Based Segmentation in Extraction of Multiple Sclerosis Lesions;160
4.11.1;Introduction;160
4.11.2;Fuzzy Connectedness;160
4.11.2.1;Object Segmentation;161
4.11.3;Initial Conditions - Fast Segmentation Overview;162
4.11.4;Automated Fuzzy Connectedness Segmentation;163
4.11.4.1;Fuzzy Affinity Parameters Estimation;163
4.11.4.2;Selecting Regions of Analysis and Segmentation Seed Points;164
4.11.4.3;Segmentation;165
4.11.5;Results and Conclusions;165
4.11.6;References;166
4.12;Computer-Interactive Methods of Brain Cortical Evaluation;168
4.12.1;Introduction;168
4.12.2;Methods of Cortical and Subcortical Atrophy Evaluation;169
4.12.2.1;The Volumetric Method;169
4.12.2.2;The Fractal Method;170
4.12.3;Research Description;171
4.12.4;Research Results;172
4.12.5;Concluding Remarks;174
4.12.6;References;175
4.13;Automatic Registration of MRI Brain;176
4.13.1;Introduction;176
4.13.2;A Fuzzy Image Idea;176
4.13.3;Similarity Measures;179
4.13.4;A- and B-Group Images Registration;181
4.13.5;Results;182
4.13.6;References;183
4.14;Magnetic Resonance Image Classification Using Fractal Analysis;184
4.14.1;Introduction;184
4.14.2;Materials and Methods;185
4.14.3;Results;186
4.14.4;Discussion;188
4.14.5;References;189
4.15;Application of MLBP Neural Network for Exercise ECG Test Records Analysis in Coronary Artery Diagnosis;190
4.15.1;Introduction;190
4.15.2;Materials and Method;191
4.15.2.1;Neural Network;191
4.15.2.2;Medical Database;192
4.15.3;Results and Discussion;193
4.15.4;Conclusion;194
4.15.5;References;194
4.16;Volumetric Analysis of Tumours and Their Blood Vessels;195
4.16.1;Introduction;195
4.16.2;Methodology;196
4.16.2.1;Tumour Segmentation;196
4.16.2.2;Tumour Volume Estimation;198
4.16.2.3;Capillary Veins Segmentation;198
4.16.2.4;Capillary Veins Counting;199
4.16.2.5;The Quotient of the Tumour Volume to the Number of Its Capillary Veins;200
4.16.3;Results of Pilot Investigation;200
4.16.4;Conclusions and Future Work;201
4.16.5;References;201
4.17;Pre- and Postprocessing Stages in Fuzzy Connectedness-Based Lung Nodule CAD;203
4.17.1;Introduction;203
4.17.2;Lung Nodule Segmentation Scheme;204
4.17.3;Preprocessing Stage;204
4.17.3.1;Thresholding and Labeling of Connected Objects;204
4.17.3.2;Nodule Detection;206
4.17.4;Postprocessing Stage;208
4.17.5;Results and Conclusions;209
4.17.6;References;210
4.18;Modeling and Simulation of Airway Tissues Stresses during Pulmonary Recruitment;211
4.18.1;Introduction;211
4.18.2;ModelDescription;212
4.18.3;Method of Solution;213
4.18.4;Results;214
4.18.5;Conclusions;217
4.18.6;References;217
4.19;Compression of Bronchoscopy Video: Coding Usefulness and Efficiency Assessment;219
4.19.1;Introduction;219
4.19.2;Methods;221
4.19.2.1;Video Compression;221
4.19.2.2;Numerical Video Quality Assessment;222
4.19.3;Experiments;224
4.19.4;Conclusions;226
4.19.5;References;227
4.20;Fuzzy Rule-Based System for the Diagnosis of Laryngeal Pathology Based on Contact Endoscopy Images;228
4.20.1;Introduction;228
4.20.2;Feature Selection;229
4.20.3;Feature Space Clustering;231
4.20.4;Generating Fuzzy Rules by Learning from Examples;231
4.20.5;Mapping between Input and the Output by Using a Defuzzification Method;233
4.20.6;Experimental Results and Future Work;233
4.20.7;References;234
4.21;Synthesis of Medical Images in the Domain of Melanocytic Skin Lesions;236
4.21.1;Introduction;236
4.21.2;Structure of the Source Dataset;237
4.21.3;Methodology of the Research;238
4.21.4;Synthesis of Lesion’s Asymmetry;238
4.21.5;Synthesis of Lesion’s Border;240
4.21.6;Synthesis Colors and Structures of the Lesion’s;240
4.21.7;Program Implementation;241
4.21.8;Summary and Conclusion;241
4.21.9;References;242
4.22;Identification of Layers in a Tomographic Image of an Eye Based on the Canny Edge Detection;243
4.22.1;Introduction;243
4.22.2;Canny Filtering;243
4.22.3;Edge Line Properties;245
4.22.4;ModifiedActiveContour;245
4.22.5;The Final Analysis of the Contour Line;249
4.22.6;Summary;249
4.22.7;References;250
5;Part III Signal Processing;251
5.1;Diagnostic Quality-Derived Patient-Oriented Optimization of ECG Interpretation;252
5.1.1;Introduction;252
5.1.2;Materials and Methods;253
5.1.2.1;Adaptive ECG Interpretation System Overview;253
5.1.2.2;Concept of Multidimensional Quality Estimate;255
5.1.2.3;Concept of Weighted Accuracy Estimate;255
5.1.2.4;Providing the Uniform Data;256
5.1.3;Results;257
5.1.4;Conclusions;258
5.1.5;References;259
5.2;Projective Versus Linear Filtering for Repolarization Duration Measurement;260
5.2.1;Introduction;260
5.2.2;Methods;261
5.2.2.1;Projective Filtering of Time-Aligned ECG Beats;261
5.2.2.2;Resampling the ECG Signal;261
5.2.2.3;Low Pass Differentiator for Repolarization Duration Measurement;261
5.2.3;Numerical Experiments and Discussion;263
5.2.4;Conclusions;266
5.2.5;References;266
5.3;An Application of Robust Kernel-Based Filtering of Biomedical Signals;268
5.3.1;Introduction;268
5.3.2;Kernel Methods;269
5.3.3;Kernel-Robust Filter;269
5.3.4;NumericalExperiments;271
5.3.4.1;A Width of the GK Filter and a Width $\sigma$^{2} of RBF Function;271
5.3.4.2;Signals with Gaussian and Real Muscle Noise;273
5.3.5;Conclusions;274
5.3.6;References;275
5.4;Weighted Averaging of ECG Signal Using Criterion Function Minimization;276
5.4.1;Introduction;276
5.4.2;Signal Averaging Methods;277
5.4.2.1;Traditional Arithmetic Averaging;278
5.4.2.2;Weighted Averaging Method WACFM;278
5.4.2.3;Proposed Weighted Averaging Method;279
5.4.3;NumericalExperiments;279
5.4.4;Conclusion;283
5.4.5;References;283
5.5;Empirical Bayesian Approach to Weighted Averaging of ECG Signal Using Cauchy Distribution;284
5.5.1;Introduction;284
5.5.2;Signal Averaging Methods;285
5.5.2.1;Traditional Arithmetic Averaging;286
5.5.2.2;Weighted Averaging Method EBWA;286
5.5.2.3;Proposed Extension of EBWA Method;287
5.5.3;NumericalExperiments;288
5.5.4;Conclusion;291
5.5.5;References;291
5.6;An Approach to Estimation of the Angular Eye-Ball Speed Based on the EOG Signal;292
5.6.1;Introduction;292
5.6.2;Estimation of the Eye-Ball Angular Speed;293
5.6.3;NumericalExperiment;296
5.6.4;Conclusions;298
5.6.5;References;299
5.7;Ensuring the Real Time Signal Transmission Using GSM/Internet Technology for Remote Fetal Monitoring;300
5.7.1;Introduction;300
5.7.2;Methodology;301
5.7.2.1;Instrumentation;301
5.7.2.2;Communication between MI and SC;303
5.7.2.3;Buffer Control;304
5.7.3;Conclusion;306
5.7.4;References;307
5.8;Prediction of Newborn Sex with Neural Networks Approach to Fetal Cardiotocograms Classification;308
5.8.1;Introduction;308
5.8.2;Research Material and Methodology;309
5.8.3;Results;311
5.8.4;Conclusions;314
5.8.5;References;314
5.9;Coping with Limitation of Bedside Measurement Instrumentation for Reliable Assessment of Fetal Heart Rate Variability;316
5.9.1;Introduction;316
5.9.2;Methodology;317
5.9.3;Results;319
5.9.4;Conclusions;322
5.9.5;References;322
5.10;Relationships between Isopotential Areas in EEG Maps before, during and after the Seizure Activity;324
5.10.1;Introduction;324
5.10.2;Material andMethod;324
5.10.2.1;Relationship between the Areas $A_{-20}$ and $A_{20}$ before and after the Seizure Activity Episode;327
5.10.2.2;Relationship between the Areas $A_{-20}$ and $A_{20}$ during the Seizure Activity;327
5.10.3;Results;328
5.10.3.1;Relationship between the Areas $A_{-20}$ and $A_{20}$ before and after the Seizure Activity Episode;328
5.10.3.2;Relationship between the Areas $A_{-20}$ and $A_{20}$ during the Seizure Activity;329
5.10.4;Discussion;330
5.10.5;References;333
5.11;Assessment of Uterine Contractile Activity during a Pregnancy Based on a Nonlinear Analysis of the Uterine Electromyographic Signal;334
5.11.1;Introduction;334
5.11.2;Biological Basis for Pregnant Uterine Activity;335
5.11.2.1;Measurements and a Nonlinear Signal Analysis;336
5.11.2.2;Measurement of EHG;336
5.11.3;Estimation of the Sample Entropy Statistic;337
5.11.4;Results;339
5.11.5;Conclusion;339
5.11.6;References;340
6;Part IV Biotechnology;341
6.1;Use of Computer System for Cell Hybridisation in Biotechnology and Medicine;342
6.1.1;Introduction;342
6.1.2;Characteristics of the System;344
6.1.3;Control Software;345
6.1.4;References;349
6.2;Clustering as a Method of Image Simplification;352
6.2.1;Introduction;352
6.2.2;Related Research Review;352
6.2.3;Microscopic Image Characterization;354
6.2.4;Methods;355
6.2.4.1;Clustering Method;355
6.2.4.2;Details of the Proposed Method;357
6.2.5;Material;358
6.2.6;Results;358
6.2.6.1;How Initialization Influences the Results;358
6.2.6.2;Evaluation of Influence of Selected Weighting Coefficients;358
6.2.6.3;How Cardinal Coefficients Influences the Results;361
6.2.7;Discussion and Conclusion;361
6.2.8;References;362
6.3;Application to Estimate Haplotypes for Multiallelic Present-Absent Loci;364
6.3.1;Introduction;364
6.3.2;Algorithm;365
6.3.2.1;Maximum Likelihood Approach to Estimate Haplotypes;365
6.3.2.2;Extended EM Algorithm;366
6.3.3;NullHap: Validation and Comparison with Other Applications to Haplotype Estimation;367
6.3.4;Conclusion;371
6.3.5;References;371
6.4;Detection of Mitotic Cell Fraction in Neural Stem Cells in Cultures;372
6.4.1;Introduction;372
6.4.2;Related Works;373
6.4.3;Microscopic Investigation of the Cell Growth;374
6.4.3.1;Cell Growing Process;374
6.4.3.2;Characteristics of the Images Sequence and Cells in the Images;374
6.4.4;ProposedMethod;376
6.4.4.1;Mathematical Morphology Methods in Image Processing;376
6.4.4.2;Proposed Method Workflow;378
6.4.5;Experiments;380
6.4.5.1;Experimental Material;380
6.4.5.2;Parameters Choice;380
6.4.6;Conclusions;381
6.4.7;References;382
6.5;Protein Molecular Viewer for Visualizing Structures Stored in the PDBML Format;384
6.5.1;Introduction;384
6.5.2;Related Works;385
6.5.3;The Protein Molecular Viewer;386
6.5.3.1;PMV Work Architecture;387
6.5.3.2;Display Modes;387
6.5.3.3;Marking Selected Structural Regions;388
6.5.3.4;Additional Features of the PMV;390
6.5.3.5;Software and Hardware Requirements;391
6.5.3.6;The PMV Software Availability;391
6.5.4;Concluding Remarks;392
6.5.5;References;392
6.6;Fuzzy Support Vector Machine for Genes Expression Data Analysis;394
6.6.1;Introduction;394
6.6.2;Support Vector Machine;394
6.6.3;Fuzzy Support Vector Machine for Two Classes;395
6.6.4;FSVM for Multi-class Problem;397
6.6.4.1;One-against-One;397
6.6.4.2;One-against-All;398
6.6.5;Experimental Results;398
6.6.5.1;Two-Classes;398
6.6.5.2;Multi-classes;400
6.6.6;Conclusions;400
6.6.7;References;401
6.7;Predictive Performance of Top Differentially Expressed Genes in Microarray Gene Expression Studies;402
6.7.1;Introduction;402
6.7.2;Performance of Class Prediction Using Gene Ranking Feature Selection;403
6.7.3;Empirical Study;405
6.7.4;Conclusions;408
6.7.5;References;408
6.8;A Study on Diagnostic Potential of a Computer-Assisted System for Identification of Neoplastic Urothelial Nuclei from the Bladder;410
6.8.1;Introduction;410
6.8.2;Materials and Methods;412
6.8.2.1;Image Analysis;414
6.8.2.2;Features Selection;418
6.8.3;Results;420
6.8.4;Conclusions;423
6.8.5;References;423
7;Part V Data Analysis;425
7.1;Control of Hand Bioprosthesis Via Sequential Recognition of Patient’s Intent Using Combination of Fuzzy Sets and Dempster-Shafer Theory;426
7.1.1;Introduction;426
7.1.2;Control System of Bio-prosthesis;427
7.1.3;Algorithm of Sequential Recognition;429
7.1.4;Experimental Investigations;430
7.1.5;FinalRemarks;432
7.1.6;References;432
7.2;Matching Knowledge and Evidence in a Model of Medical Diagnosis;434
7.2.1;Introduction;434
7.2.2;The Dempster-Shafer theory;434
7.2.3;Matching Symptoms and Observations;435
7.2.3.1;Example;436
7.2.3.2;Choice of Inference Threshold;438
7.2.4;Tests;438
7.2.5;Conclusions;440
7.2.6;References;441
7.3;Nonparametric Regression for Analyzing Correlation between Medical Parameters;442
7.3.1;Introduction;442
7.3.2;Materials and Methods;443
7.3.3;Nonparametric Regression;444
7.3.3.1;Kernel Regression;444
7.3.3.2;Results;446
7.3.4;Summary;448
7.3.5;References;449
7.4;Experiments on Linear Combiners;450
7.4.1;Introduction;450
7.4.2;Problem Statement;451
7.4.3;Analytical Estimation of Upper Errors;452
7.4.4;Experimental Investigation;454
7.4.4.1;Conditions of Experiment;454
7.4.4.2;Experimental Results Evaluation;456
7.4.5;FinalRemarks;456
7.4.6;References;456
7.5;Processing of Missing Data in a Fuzzy System;458
7.5.1;Introduction;458
7.5.2;Processing of Incomplete Data in Theory;459
7.5.2.1;Fuzzy Inference System;459
7.5.2.2;Representation of Missing Data;459
7.5.3;Dealing with Incomplete Data on Exaple of Iris Data;461
7.5.3.1;Applied Fuzzy Inference Systems;461
7.5.3.2;Example for a Single Case;462
7.5.3.3;Results;462
7.5.4;Conclusions;464
7.5.5;References;465
7.6;Knowledge-Based Decision Hybrid System forthe Doctor’s Work Support;466
7.6.1;Introduction;466
7.6.2;Knowledge Representation in HYBRIDEX System;467
7.6.3;HYBRIDEX System Conclusion Mechanism;468
7.6.4;HYBRIDEX System Realization;470
7.6.5;Conclusion;471
7.6.6;References;472
7.7;Features for Text Comparison;473
7.7.1;Introduction;473
7.7.2;The General Description of the Problem;473
7.7.3;The Description of the Defined Features;474
7.7.4;Application;477
7.7.5;Conclusion;480
7.7.6;References;480
7.8;Possibility of Use a Fuzzy Loss Function in Medical Diagnostics;481
7.8.1;Introduction;481
7.8.2;Hierarchical Classifier;482
7.8.3;Medical Description of the Problem;482
7.8.4;DataDescription;483
7.8.5;Description of the Method;484
7.8.6;Results of Recognition Algorithm;485
7.8.7;Conclusion;485
7.8.8;References;485
7.9;An Application of a Generalized Additive Model for an Identification of a Nonlinear Relation between a Course of Menstrual Cycles and a Risk of Endometrioid Cysts;487
7.9.1;Introduction;487
7.9.2;Modeling of Clinical Data;488
7.9.2.1;Clinical Data;488
7.9.2.2;Identification of the Relationship between a Menstrual Cycles Characteristics and a Risk of Endometrioid Cysts;488
7.9.3;Results;490
7.9.4;Discussion;491
7.9.5;References;491
7.10;Recognition of the Ventilatory Response to the Intermittent Chemical Stimuli in Awake Animals;493
7.10.1;Introduction;493
7.10.2;Materials and Methods;494
7.10.3;Results and Discussion;496
7.10.4;References;498
8;Part VI Multimedia;500
8.1;Telesfor – Telemedical Real-Time Communication Support System;501
8.1.1;Introduction;501
8.1.2;Motivation and Expected Functionality;502
8.1.3;Technologies;502
8.1.3.1;ConferenceXP;503
8.1.3.2;Other Technologies;504
8.1.4;Telesfor’s Architecture;504
8.1.5;Session Example;506
8.1.6;Summary;507
8.1.7;References;508
8.2;Multimedia Program for Teaching Autistic Children;509
8.2.1;Introduction;509
8.2.2;The Application;511
8.2.2.1;Teaching How to Get in Touch;511
8.2.2.2;Acquainting with Facial Gestures;512
8.2.2.3;Renting a Film;512
8.2.2.4;Everyday Behaviour and Activities;513
8.2.3;Usability of the Application;514
8.2.4;Conclusions;515
8.2.5;References;516
8.3;Multimedia System for Accessible Distant Education;517
8.3.1;Introduction;517
8.3.2;Daisy Standard;517
8.3.3;Managemend System for Internet Multimedia Library;518
8.3.4;Interactive Multimedia Daisy Book Browser;519
8.3.5;System Implementation;521
8.3.6;References;521
9;Part VII Biomechanics;522
9.1;Biomechanical Behaviour of Double Threaded Screw in Tibia Fixation;523
9.1.1;Introduction;523
9.1.2;Methods;523
9.1.2.1;Numerical Model;524
9.1.2.2;Boundary Conditions;524
9.1.3;Results;526
9.1.3.1;Tibia;526
9.1.3.2;Tibia – Double Threaded Screw System;526
9.1.4;Conclusions;528
9.1.5;References;529
9.2;Biomechanical Analysis of Lumbar Spine Stabilization by Means of Transpedicular Stabilizer;531
9.2.1;Introduction;531
9.2.2;Materials and Methods;532
9.2.3;Results;534
9.2.4;Conclusions;537
9.2.5;References;538
9.3;FEM Analisys of the Expandable Intramedullar Nail;539
9.3.1;Introduction;539
9.3.2;Materials and Methods;539
9.3.3;Results;542
9.3.3.1;Results of the Femur – Expandable Intramedullary Nail System Analysis;542
9.3.3.2;Results of the Expansion of the Intramedullary Nail Analysis;544
9.3.4;Conclusion;545
9.3.5;References;546
9.4;Biomechanical Analysis of Plate for Corrective Osteotomy of Tibia;547
9.4.1;Introduction;547
9.4.2;Materials and Methods;548
9.4.3;Results;549
9.4.3.1;Results of Tibia – Stainless Steel Plate System;549
9.4.4;Results of Tibia – Ti-6Al-4V Plate System;550
9.4.5;Conclusion;551
9.4.6;References;551
9.5;Kinematic Analysis of Complex Therapeutic Movements of the Upper Limb;553
9.5.1;Introduction;553
9.5.2;Method;554
9.5.3;Results;556
9.5.4;Conclusions;560
9.5.5;References;560
9.6;Influence of Model Discretization Density in FEM Numerical Analysis on the Determined Stress Level in Bone Surrounding Dental Implants;561
9.6.1;Introduction;561
9.6.2;Materials and Methods;562
9.6.3;Results;564
9.6.4;Conclusion;568
9.6.5;References;569
9.7;Computer Simulations of Electric Properties of Organic and Non-organic Compounds;570
9.7.1;Introduction;570
9.7.1.1;Description of RC Model;571
9.7.1.2;Distribution of Relaxation Times;571
9.7.2;Results and Discussion;572
9.7.3;Concluding Remarks;574
9.7.4;References;574
10;Author Index;575