• covers wide range of imaging technologies including MRT, CT, nanoESCA, raman mapping, and multimodal imaging.
• includes both current and developing applications
• includes a comparison of the relative merits and weaknesses among the technogies for application areas

Table of Contents:

1. Evaluation of Spectroscopic Images
2. Evaluation of Tomographic Data
3. X-Ray Imaging & CT
4. MRT
5. Tracer Imaging
6. Electron Tomography
7. Fluourescence and Optical Imaging
8. IR Imaging
9. Raman Mapping
10. CARS Microscopy
11. Terahertz Imaging
12. Acoustic Microscopy
13. Scanning Electrochemical Microscopy
14. Multimodal Imaging

Vibrational Spectroscopy for Medical Diagnosis by Max Diem, Peter R. Griffiths, and John M. Chalmers Hardcover - 258 pages Shipped in CLICK HERE Cat.# JW-BME8 $190.90 BUY Published:  2008   ISBN:  9780470012147

This comprehensive, state-of-the-art volume tackles one of the most recent and challenging roles for vibrational spectroscopy- that of encompassing the various biomedical applications that have emerged in recent years. The individual contributions in this section range from applications in pathology and in clinical chemistry, to the detection of cancer and the identification of pathogenic micro-organisms. Near-infrared spectroscopy, once considered to be the stepchild of vibrational spectroscopy, figures prominently in the biomedical applications; for example to monitor such analytes as oxy- and deoxyhemoglobin circulating in the blood.

Table of Contents:

List of Contributors xv.

Introduction to Spectral Imaging, and Applications to Diagnosis of Lymph Nodes
Infrared and Raman Microspectroscopic Studies of Individual Human Cells
Infrared Spectroscopy in the Identification of Microorganisms
Antemortem Identification of Transmissible Spongiform Encephalopathy (TSE) from Serum by Mid-IR Spectroscopy

Head and Neck Cancer: A Clinical Overview, and Observations from.

Synchrotron-sourced Mid-infrared Spectroscopy Investigations
Infrared Spectroscopic Imaging Protocols for High-throughput Histopathology
Spectral Histopathology of the Human Cervix
Raman Spectroscopy as a Potential Tool for Early Diagnosis of Malignancies in Esophageal and Bladder Tissues
Neuro-oncological Applications of Infrared and Raman Spectroscopy
Resonance Raman Spectroscopy of Erythrocytes

Glossary
Appendix: Infrared and Raman Spectra of Selected Cellular
Components
Index

Molecular Mechanisms and Models of Aging by Efstathios S. Gonos, Ioannis P. Trougakos, and Niki Chondrogianni Softcover - 300 pages Shipped in CLICK HERE Cat.# JW-BME9 $157.30 BUY Published:  2008   ISBN:  9781573316866

Aging is an inevitable consequence of life for nearly all organisms. It mostly reflects the outcome of complicated interactions between genetic factors along with the accumulation of a variety of deleterious stochastic changes over time. Thus, aging is an adaptive process, not caused by a single factor or process, but it is rather a multifactorial procedure modulated by the interplay among genetic and environmental factors. In the coming years, human aging will be one of the biggest challenges faced by industrialized countries as the average life expectancy is continuously increasing.

This volume gathers together contributions from the top scientists and covers the whole range of modern research on aging. More specifically, the following topics are included: replicative senescence, telomeres and telomerase, cancer and aging, DNA repair, free radicals and oxidative stress, proteolysis and proteins maintenance, Immunity, neurodegeneration, model organisms, intervention as well as evolutional and epidemiological aspects.

Overall, increasing our understanding of the aging process at the molecular level and applying available interventions will help to protect and preserve healthy aging.

Table of Contents:

Preface: E.S. Gonos, I.P. Trougakos, and N. Chondrogianni

Part I: Overview of Mammalian Aging and Senescence:

1. Clonal attenuation of somatic cells in aging mammals: a review of supportive evidence and its biomedical significance

Part II: Cell Signaling and Replicative Senescence:

2. Exposure of human diploid fibroblasts to hypoxia extends proliferative lifespan
3. Cyclin D1 over-expression permits the reproducible detection of senescent human vascular smooth muscle cells

Part III: Biomarkers of Aging:

4. Morphological changes associated with aging: age spots and the micro-inflammatory model of skin aging
5. Molecular mechanisms of skin aging: State of the art

Part IV: DNA Repair and Aging:

6. Acquisition of oxidative DNA damage during senescence: the first step towards carcinogenesis?
7. The effect of the histone deacetylase inhibitor, trichostatin A, in human peripheral blood lymphocytes as a function of donor age

Part V: Free Radicals, Oxidative Stress, Proteolysis and Proteins Maintenance:

8. Transient increased extracellular release of H2O2 during establishment of UVB induced premature senescence
9. Importance of the Lon Protease in Mitochondrial Maintenance and the Significance of Declining Lon in Aging
10. Protein oxidative modifications and replicative senescence of WI-38 human embryonic fibroblasts
11. Lipofuscin: formation, distribution and metabolic consequences

Part VI: Intervention:

12. Stress-Mediated Hormetic Modulation of Aging, Wound Healing and Angiogenesis in Human Cells
13. Retarding aging in humans in reality by diminishing risks to health
14. Zinc, metallothioneins and longevity. Effect of zinc supplementation
15. Ghrelin receptor (GHS-R1a) agonists show potential as interventive agents during aging

Part VII: Molecular Links between Aging and Cancer:

16. Functional significance of minor structural and expression changes of stress chaperone mortalin
17. GRO-alpha: A Potential Marker for Cancer and Aging Silenced by RNA Interference
18. Caspase-3 and Bcl-2 expression along ageing in adrenal zona reticularis after dexamethasone administration

Part VIII: Age-Related Diseases:

19. Nutraceutical strategy in ageing: Targeting heat shock protein and inflammatory profile via IL-6 polymorphism understanding
20. Role of TLR4 polymorphisms in inflammatory responses: implications for unsuccessful ageing Caruso.
21. Pro-NGF, sortilin and age-related neurodegeneration
22. Identification of fasting-induced genes in the rat hypothalamus: relationship with neuroprotection
23. Acute myocardial infarction and pro-inflammatory gene variants
24. Bone mineral density in Ukrainan women of different age

Part IX: Model Organisms:

25. Sex-specific effects of the DAF-12 steroid receptor on ageing in Caenorhabditis elegans
26. Modulating longevity in Drosophila by over- and under-expression of glutamate-cysteine ligase
27. Modelling Werner’s syndrome in Drosophila melanogaster: hyper-recombination in flies lacking WRN-like exonuclease
28. Protein synthesis is a novel determinant of ageing in C. Elegans

Part X: Evolutionary and Epidemiological Aspects of Aging:

29. Calorie restriction, post-reproductive life span and programmed aging: a plea for rigour
30. Non-pathological senescence arises from unsuitable external influences

Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics by Atanu Biswas, Sujay Datta, Jason Fine, & Mark Segal Softcover - 582 pages Shipped in CLICK HERE Cat.# JW-BME10 $141.80 BUY Published:  2008   ISBN:  9780471947530

With the increasing use of biotechnology in medical research and the sophisticated advances in computing, it has become essential for practitioners in the biomedical sciences to be fully educated on the role statistics plays in ensuring the accurate analysis of research findings. Statistical Advances in the Biomedical Sciences explores the growing value of statistical knowledge in the management and comprehension of medical research and, more specifically, provides an accessible introduction to the contemporary methodologies used to understand complex problems in the four major areas of modern-day biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics.

Composed of contributions from eminent researchers in the field, this volume discusses the application of statistical techniques to various aspects of modern medical research and illustrates how these methods ultimately prove to be an indispensable part of proper data collection and analysis. A structural uniformity is maintained across all chapters, each beginning with an introduction that discusses general concepts and the biomedical problem under focus and is followed by specific details on the associated methods, algorithms, and applications. In addition, each chapter provides a summary of the main ideas and offers a concluding remarks section that presents novel ideas, approaches, and challenges for future research.

Table of Contents:

SECTION I. CLINICAL TRIALS

1. Phase I Clinical Trials in Oncology
1.1 Introduction
1.2 Phase I Trials in Healthy Volunteers
1.3 Phase I Trials With Toxic Outcomes Enrolling Patients
1.4 Other Design Problems in Dose Finding
1.5 Concluding Remarks
References

2. Phase II Clinical Trials
2.1 Introduction
2.2 Frequentist methods in phase II clinical trials
2.3 Bayesian methods in phase II clinical trials
2.4 Decision theoretic methods in phase II clinical trials
2.5 Clinical trials combining phases II and III
2.6 Outstanding issues in phase II clinical trials
References

3. Response Adaptive Designs in Phase III Clinical Trials
3.1 Introduction
3.3 Adaptive Designs for Binary Treatment Responses Incorporating Covariates
3.4 Adaptive Designs for Categorical Responses
3.5 Adaptive Designs for Continuous Responses
3.6 Optimal Adaptive Designs
3.7 Delayed Responses in Adaptive Designs
3.8 Biased Coin Designs
3.9 Real Adaptive Clinical Trials
3.10 Data Study for Different Adaptive Scheme
3.11 Concluding Remarks
References

4. Inverse Sampling for Clinical Trials: A Brief Review of Theory and Practice
4.1 Introduction
4.2 Two-Sample Randomized Inverse Sampling for Clinical Trials
4.3 An Example of Inverse Sampling: Boston ECMO
4.4 Inverse Sampling in Adaptive Designs
4.5 Concluding

5. The Design and Analysis Aspects of Cluster Randomized Trials (Hrishikesh Chakraborty)
5.1 Introduction: Cluster Randomized Trials
5.2 Intra-Cluster Correlation Coefficient and Confidence Interval
5.3 Sample Size Calculation for Cluster Randomized Trials
5.4 Analysis of Cluster Randomized Trial Data
5.5 Concluding Remarks
References

SECTION II. EPIDEMIOLOGY

6. HIV Dynamics Modeling and Prediction of Clinical Outcomes in AIDS Clinical Research
6.1 Introduction
6.2 HIV Dynamic Model and Treatment Effects Models
6.3 Statistical Methods for Predictions of Clinical Outcomes
6.4 Simulation Study
6.5 Clinical Data Analysis
6.6 Concluding Remarks
References

7. Spatial Epidemiology
7.1 Space and Disease
7.2 Basic Spatial Questions and Related Data
7.3 Quantifying Pattern in Point Data
7.4 Predicting Spatial Observations
7.5 Concluding Remarks
References

8. Modeling Disease Dynamics: Cholera as a Case Study
8.1 Introduction
8.2 Data Analysis via Population Models
8.3 Sequential Monte Carlo
8.4 Modeling Cholera
8.5 Concluding Remarks
References

9. Misclassification and Measurement Error Models in Epidemiological Studies
9.1 Introduction
9.2 A Few Examples
9.3 Binary Regression Models with Two Types of Errors
9.4 Bivariate Binary Regression Models with Two Types of Errors
9.5 Models for Analyzing Mixed Misclassified Binary and Continuous Responses
9.6 Atom Bomb Data Analysis
9.7 Concluding Remarks
References

SECTION III. SURVIVAL ANALYSIS

10. Semiparametric Maximum Likelihood Inference in Survival Analysis
10.1 Introduction
10.2 Examples of Survival Models
10.3 Basic Estimation and Limit Theory
10.4 The Bootstrap
10.5 The Profile Sampler
10.6 The Piggyback Bootstrap
10.7 Other Approaches
10.8 Concluding Remarks
References

11. An Overview of the Semi-Competing Risks Problem
11.1 Introduction
11.2 Nonparametric Inferences
11.3 Semiparmetric One-Sample Inference
11.4 Semiparametric Regression Method
11.5 Concluding Remarks
References

12. Tests for Time-Varying Covariate Effects within Aalen's Additive Hazards Model
12.1 Introduction
12.2 Model Specification and Inferential Procedures
12.3 Numerical Results
12.4 Concluding Remarks
12.5 Summary
References

13. Analysis of Outcomes Subject to Induced
Dependent Censoring: A Marked Point Process Perspective
13.1 Introduction
13.2 Induced Dependent Censoring and Associated Identifiability Issues
13.3 Marked Point Process
13.4 Modeling Strategy for Testing and Regression
13.5 Concluding Remarks
References

14. Analysis of Dependence in Multivariate Failure-Time Data
14.1 Introduction
14.2 Nonparametric Bivariate Survivor Function Estimation
14.3 Non- and Semi-Parametric Estimation of Dependence Measures
14.4 Concluding Remarks
References

15. Robust Estimation for Analyzing
Recurrent Events Data in the Presence of Terminal Events
15.1 Introduction
15.2 Inference Procedures
15.3 Large Sample Properties
15.4 Numerical Results
15.5 Concluding Remarks
References

16. Tree-Based Methods for Survival Data
16.1 Introduction
16.2 Review of CART
16.3 Trees for Survival Data
16.4 Simulations to Compare Different Splitting Methods
16.5 Example: Breast Cancer Prognostic Study
16.6 Random forest for Survival Data
16.7 Concluding Remarks
References

17. Bayesian Estimation of the Hazard Function with Randomly Right-Censored Data
17.1 Introduction
17.2 Bayesian Functional Model Using Monotone Wavelet Approximation
17.3 Estimation of the Sub-Density F*
17.4 Simulations
17.5 Example
17.6 Concluding Remarks
References

SECTION IV. GENOMICS AND PROTEOMICS

18. The Effects of Inter-Gene Associations on Statistical Inferences From Microarray Data
18.1 Introduction
18.2 Inter-Gene Correlation
18.3 Differential Expression
18.4 Time Course Experiments
18.5 Meta-Analysis
18.6 Concluding Remarks
References

19. A Comparison of Methods for Meta-Analysis of Gene Expression Data
19.1 Introduction
19.2 Background
19.3 Example
19.4 Cross Comparison of Gene Signatures
19.5 Best Common Mean Difference Method
19.6 Effect Size Method
19.7 Probability of Expression (POE) Assimilation Method
19.8 Comparison of Three Methods
19.9 Conclusions
References

20. Statistical Methods for Identifying Differentially Expressed Genes in Replicated Microarray Experiments: A Review
20.1 Introduction
20.2 Normalization
20.3 Methods for Selecting Differentially Expressed Genes
20.4 Simulation Study
20.5 Concluding Remarks
References

21. Clustering of Microarray Data via Mixture Models
21.1 Introduction
21.2 Clustering of Microarray Data
21.3 Notation
21.4 Clustering of Tissue Samples
21.5 The EMMIX-GENE Clustering Procedure
21.6 Clustering of gene profile
21.7 EMMIX-WIRE
21.8 ML Estimation via the EM Algorithm
21.9 Model Selection
21.10 Example: Clustering of Time-Course Data
21.11 Concluding Remarks
References

22. Censored Data Regression in
High-Dimension and Low-Sample-Size Settings for Genomic Applications
22.1 Introduction
22.2 Censored Data Regression Models
22.3 Regularized Estimation for Censored Data Regression Models
22.4 Survival Ensemble Methods
22.5 Nonparametric Pathway-Based Regression Models
22.6 Dimension-Reduction-Based Methods and Bayesian Variable Selection Methods
22.7 Criteria for Evaluating Different Procedures
22.8 Application to a Real Data Set and Comparisons
22.9 Discussion and Future Research Topics
22.10 Concluding Remarks
References

23. Analysis of Case-Control Studies in Genetic Epidemiology
23.1 Introduction
23.2 Maximum Likelihood Analysis of Case-Control Data with Complete Information
23.3 Haplotype-Based Genetic Analysis with Missing Phase Information
23.4 Concluding Remarks
References

24. Assessing Network Structure in the Presence of Measurement Error
24.1 Introduction
24.2 Graphs of Biological Data
24.3 Statistics on Graphs
24.4 Graph Theoretic Models
24.5 Types of Measurement Error
24.6 Exploratory Data Analysis
24.7 Influence of Measurement Error on Graph Statistics
24.8 Biological Implications
24.9 Conclusions
References

25. Prediction of RNA Splicing Signals
25.1 Introduction
25.2 Existing Approaches to Splice Site Identification
25.3 Splice Site Recognition Contemporary Classifiers
25.4 Results
25.5 Concluding Remarks
References

26. Statistical Methods for Biomarker Discovery Using Mass Spectrometry (Bradley M. Broom and Kim-Anh Do).

26.1 Introduction
26.2 Biomarker Discovery
26.3 Statistical Methods for Pre-Processing
26.4 Statistical Methods for Multiple Testing, Classification and Applications spectra
26.5 Potential Statistical Developments
26.6 Concluding Remarks
References

27. Genetic Mapping of Quantitative Traits: Model-Free Sib-Pair Linkage Approaches
27.1 Introduction
27.2 The Basic QTL Framework for Sib-Pairs
27.3 The Haseman-Elston Regression Framework
27.4 Nonparametric Alternatives
27.5 The Modified Nonparametric Regression
27.6 Comparison with Linear Regression Methods
27.7 Significance Levels and Empirical Power
27.8 An Application to Real Data
27.9 Concluding Remarks
References

SECTION V. MISCELLANEOUS TOPICS

28. Robustness Issues in Biomedical Studies
28.1 Introduction: The Need for Robust Procedures
28.2 Standard Tools for Robustness
28.3 The Robustness Question in Biomedical Studies
28.4 Robust Estimation in the Logistic Regression Model
28.5 Robust Estimation for Censored Survival Data
28.6 Adaptive Robust Methods in Clinical Trials
28.7 Concluding Remarks
References

29. Recent Advances in the Analysis of Episodic Hormone Data
29.1 Introduction
29.2 A General Biophysical Model
29.3 Bayesian Deconvolution Model (BDM)
29.4 Nonlinear Mixed Effects Partial Splines Models
29.5 Concluding Remarks
References

30. Models for Carcinogenesis
30.1 Introduction
30.2 Statistical Models
30.3 Multistage Models
30.4 Two-Stage Clonal Expansion Model
30.5 Physiologically Based Pharmacokinetic Models
30.6 Statistical Methods
30.7 Concluding Remarks
References

Author Index
Subject Index

Electrochemical Sensors, Biosensors and their Biomedical Applications Edited by Xueji Zhang, Huangxian Ju, and Joseph Wang Hardcover - 616 pages Shipped in CLICK HERE Cat.# EL-BME1 $135.45 BUY Published:  2008   ISBN:  9780123737380

Nitric Oxide (NO) Electrochemical Sensors; Biosensors for Pesticides; Electrochemical Glucose Biosensors; New Trends in Ion Selective Electrodes; Recent Developments in Electrochemical Immunoassays and Immunosensors; Superoxide Electrochemical Sensors and Biosensors: Principle, Development and Applications; Detection of Charged Macromolecules by means of Field-Effect Devices (FEDs): Possibilities and Limitations; Electrochemical Sensors for the Determination of Hydrogen Sulfide Production in Biological Samples; Aspects of Recent Development of Immunosensors; Microelectrodes for In Vivo Determination of pH; Biochips-Fundamentals and Applications; Powering Fuel Cells through Biocatalysis; Chemical and Biological Sensors Based on Electroactive Inorganic Polycrystals; Nanoparticle-Based Biosensors and Bioassays; Electrochemical Sensors Based on Carbon Nanotubes; Biosensors Based on Immobilization of Biomolecules in Sol-gel Matrices; Biosensors Based on Direct Electron-Transfer of Protein.

Biomedical Information Technology Edited by David Dagan Feng Softcover - 552 pages Shipped in CLICK HERE Cat.# EL-BME2 $110.00 BUY Published:  2008   ISBN:  9780123735836

Key Features

• The world's most recognized authorities give their ";best practices"; ready for implementation
• Provides professionals with the most up to date and mission critical tools to evaluate the latest advances in the field and current integrated clinical applications
• Gives new staff the technological fundamentals and updates experienced professionals with the latest practical integrated clinical applications

Description
The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data. The field continues to evolve with the development of new applications to fit the needs of the biomedicine. From molecular imaging to healthcare knowledge management, the storage, access and analysis of data contributes significantly to biomedical research and practice. All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modelling, processing, registration, visualization, communication, and large-scale biological computing. In addition the book contains practical integrated clinical applications for disease detection, diagnosis, surgery, therapy, and biomedical knowledge discovery, including the latest advances in the field, such as ubiquitous M-Health systems and molecular imaging applications.

Table of Contents:

Section I: Technological Fundamentals

Chapter 1. Medical Imaging Dr. Xiaofeng Zhang, Dr. Nadine Smith and Prof. Andrew Webb

1.1 Introduction 1.2 Digital radiography 1.3 Computed tomography 1.4 Nuclear medicine 1.5 Ultrasonic imaging 1.6 Magnetic resonance imaging 1.7 Diffuse optical imaging 1.8 Biosignals 1.9 Appendix 1.10 Exercises 1.11 Bibliography 1.12 Index

Chapter 2. Electronic Medical Record (EMR)

2.1 Introduction 2.2 Medical data and patient record 2.3 Terminology standards – vocabulary and a clinical coding system1 2.4 Information exchange standards 2.5 Usability issues in EMR 2.6 User interface 2.7 Evaluation 2.8 EMR system – a case study: a web-based imaging electronic patient history 2.9 Summary 2.10 Exercise 2.11 Bibliography and references 2.12 Index

Chapter 3. Image Data Compression and Storage

3.1 Introduction 3.2 Picture compression 3.3 Compression in the dicom standard 3.4 Data compression for dynamic functional images 3.5 Summary 3.6 Exercises 3.7 References 3.8 Index

Chapter 4. Content-Based Medical Image Retrieval

4.1 Introduction 4.2 CBMIR by physical visual features 4.3 CBMIR by geometric spatial features 4.4 CBMIR by combination of semantic and visual features 4.5 CBMIR by physiological functional features 4.6 Summary 4.7 Exercises 4.8 Bibliography and references 4.9 Index

Chapter 5. Data Modeling and Simulation

5.1 Introduction 5.2 Compartmental models 5.3 Model identification 5.4 Model validation 5.5 Simulation 5.6 Case study 5.7 Quantification of medical images 5.8 Exercises 5.9 Bibliography and references

Chapter 6. Techniques for Parametric Imaging

6.1 Introduction 6.2 Parametric image estimation methods 6.3 Noninvasive methods 6.4 Clinical applications of parametric images 6.5 Summary 6.6 Exercise 6.7 Bibliography and references 6.8 Index

Chapter 7. Data Processing and Analysis

7.1 Introduction 7.2 Medical image enhancement 7.3 Medical image segmentation 7.4 Medical image feature extraction 7.5 Medical image interpretation 7.6 Summary 7.7 Exercises 7.8 Bibliography 7.9 Index

Chapter 8. Data Registration and Fusion

8.1 Introduction 8.2 Fundamentals of biomedical image registration and fusion 8.3 Feature-based medical image registration 8.4 Intensity-based registration 8.5 Hybrid registration and hierarchical registration 8.6 Hardware registration 8.7 Assessment of registration accuracy 8.8 Applications of biomedical image registration and fusion 8.9 Summary 8.10 Exercises 8.11 Bibliography and references 8.12 Index

Chapter 9. Data Visualization and Display

9.1 Introduction 9.2 Two-Dimensional (2D) visualization techniques 9.3 Three-Dimensional (3D) visualization techniques 9.4 Volume navigation interface 9.5 Volume enhancement and manipulation 9.6 Large data visualization and optimization 9.7 Dual-modality PET/CT visualization 9.8 Data display devices 9.9 Applications of biomedical visualization 9.10 Summary 9.11 Exercises 9.12 Bibliography and references 9.13 Index

Chapter 10. Data Communication and Network Infrastructue

10.1 Introduction 10.2 Transmission and communication technologies 10.3 The internet and World Wide Web 10.4 Wireless and mobile technologies in m-health 10.5 Sensor networks for health monitoring 10.6 Applications of wireless technologies in telemedicine 10.7 Summary 10.8 Exercises 10.9 Bibliography and references 10.10 Index

Chapter 11. Data Security and Protection for Medical Images

11.1 Introduction 11.2 Overview of cryptographic system 11.3 Digital watermarking 11.4 Medical image watermarking 11.5 Region-based reversible watermarking for secure pet image management 11.6 Summary 11.7 Exercise 11.8 Bibliography 11.8 Index

Chapter 12. Biological Computing

12.1 Introduction 12.2 Overview of genomic methods 12.3 Overview of proteomic methods 12.4 Bioinformatics and information infrastructure 12.5 Data mining and large-scale biological databases 12.6 Biological event-driven, time-driven and hybrid simulation techniques 12.7 Summary 12.8 Bibliography

Section II: Integrated Applications

Chapter 13. PACS and Medical Imaging Informatics (MII) for Filmless Hospitals

13.1 Introduction 13.2 PACS infrastructure 13.3 PACS components and workflow 13.4 PACS controller and image archive 13.5 Large-scale PACS implementation 13.6 PACS clinical experiences 13.7 Summary 13.8 Exercises 13.9 Bibliography and references 13.9 Index

Chapter 14. KMeX: A Knowledge-Based Digital Library for Retrieving Scenario-Specific Medical Text Documents

14.1 Introduction 14.2 Extracting key concepts from documents 14.3 Transforming similar queries into query templates 14.4 Topic-oriented directory 14.5 Phrase-based vector space model for automatic document retrieval 14.6 Knowledge-based scenario-specific query expansion 14.7 A system architecture for retrieving scenario-specific free text documents 14.8 Summary 14.9 Exercises 14.10 Bibliography

Chapter 15. Integrated Multimedia Patient Record Systems

15.1 Introduction 15.2 Multimedia patient record 15.3 Multimedia patient record system architecture components 15.4 Electronic medical chart components 15.5 Objects comprising the multimedia patient record 15.6 Capturing multimedia data at the source 15.7 DICOM image acquisition 15.8 Remote data and image viewing across the healthcare network 15.9 Impact on patient care 15.10 Summary 15.11 References

Chapter 16. Computer-Aided Diagnosis (CAD)

16.1 Introduction 16.2 CAD 16.3 CAD for cancer screening 16.4 CAD for differential diagnosis 16.5 Intelligent CAD workstations – indices of similarity and human/computer interfaces 16.6 Summary 16.7 Exercises 16.8 Bibliography 16.9 Index

Chapter 17. Clinical Decision Support Systems (CDSS)

17.1 Introduction 17.2 Overview of CDSS 17.3 Human diagnostic reasoning 17.4 A structure for characterising CDSS 17.5 Decision support tools 17.6 Decision support systems in the hospital and other healthcare settings