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Chi tiết tài liệu

Signal and Image Analysis for Biomedical and Life Sciences

Tác giả: Changming, Sun; Bednarz, Tomasz; Pham, Tuan D.; Vallotton, Pascal; Wang, Dadong

Chủ đề:

Chủ đề: Medicine; Biology

Nhà xuất bản: Springer

Năm xuất bản: 2015

Loại tài liệu: Book

Mô tả vật lý: 286 p.

Định danh: 978-3-319-10984-8

Ngôn ngữ: en

Liên kết: Advances in Experimental Medicine and Biology;Volume 823

Đọc trực tuyến

Tải tài liệu

With an emphasis on applications of computational models for solving modern challenging problems in biomedical and life sciences, this book aims to bring collections of articles from biologists, medical/biomedical and health science researchers together with computational scientists to focus on problems at the frontier of biomedical and life sciences. The goals of this book are to build interactions of scientists across several disciplines and to help industrial users apply advanced computational techniques for solving practical biomedical and life science problems.

Cover
Advances in Experimental Medicine and Biology
Title pages
Preface
Contents
Contributors
Acronyms
Part I Signal Analysis
- 1 Visual Analytics of Signalling Pathways Using Time Profiles
  - 1.1 Introduction
    - 1.1.1 Challenges in Visualising High-Throughput Time-Series Post-translationally Modified Proteomic Datasets
    - 1.1.2 Aims
  - 1.2 Methods
    - 1.2.1 Phosphorylation Dataset for Insulin Response
      - 1.2.1.1 Data Representation
    - 1.2.2 Heat Map of the Time-Series Data
      - 1.2.2.1 Selecting a Single Time Point for Each Phosphorylation
    - 1.2.3 The Minardo Layout
  - 1.3 Results
    - 1.3.1 Evaluation of the Minardo Visualisation Strategy
      - 1.3.1.1 Requested Features
    - 1.3.2 Minardo in the International DREAM8 Competition
      - 1.3.2.1 Proposed Workflow
  - 1.4 Discussion and Further Work
    - 1.4.1 Minardo as a Web-Based Tool
    - 1.4.2 Lessons from the Usability Study
    - 1.4.3 Using 3D Structure Information
    - 1.4.4 Going Beyond Static Roadmaps
    - 1.4.5 Visualisation for Multiple Experiments
    - 1.4.6 Limitations
  - References
- 2 Modeling of Testosterone Regulation by Pulse-ModulatedFeedback
  - 2.1 Introduction
  - 2.2 A Pulse-Modulated Mathematical Model of Testosterone Regulation
  - 2.3 Parameter Estimation
    - 2.3.1 Estimating the GnRH Impulses
      - 2.3.1.1 Estimating Firing Times and Weights
      - 2.3.1.2 Estimating the Parameters
    - 2.3.2 Estimating the Testosterone Dynamics
  - 2.4 Experimental Results
  - 2.5 Simulations of the Closed-Loop System
  - References
- 3 Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences
  - 3.1 Introduction
  - 3.2 Multiple Change-Point Problem
  - 3.3 Framework of the Algorithms
    - 3.3.1 Quickest Change-Point Detection
    - 3.3.2 The Cross-Entropy Method
      - 3.3.2.1 Bonferroni Correction for Multiple Hypothesis Testing
  - 3.4 Numerical Results
    - 3.4.1 Results on Artificially Generated Data
    - 3.4.2 Results on Real Data
      - 3.4.2.1 Fibroblast Cell Lines Data
      - 3.4.2.2 Breast Tumor Data
  - References
- 4 Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease
  - 4.1 Introduction
  - 4.2 The Extraocular Muscles
  - 4.3 Smooth Pursuit
  - 4.4 Eye Tracking
  - 4.5 Parkinson's Disease
  - 4.6 Probability Density Estimation
    - 4.6.1 Stochastic Variables
    - 4.6.2 Kernel Density Estimation
    - 4.6.3 Orthogonal Series Approximation
    - 4.6.4 Finding the Outlier Region
  - 4.7 Non-parametric Method
  - 4.8 Parametric Method
  - 4.9 Visual Stimuli
  - 4.10 Experiment
  - 4.11 Results
    - 4.11.1 Non-parametric Method
    - 4.11.2 Parametric Method
  - References
- 5 Identification of the Reichardt Elementary Motion Detector Model
  - 5.1 Background
  - 5.2 Mathematical Model of EMD
    - 5.2.1 Single Frequency Sinusoidal Signal
      - 5.2.1.1 Symmetrical and Non-symmetrical EMD Model
    - 5.2.2 EMD Response to a L2 Pulse
  - 5.3 Identification Approach
    - 5.3.1 Identification of a Single EMD
      - 5.3.1.1 Pure Time-Delay Model
    - 5.3.2 Identification of a Layer of EMDs
      - 5.3.2.1 Identifiability Properties for EMD-Layer Estimation
      - 5.3.2.2 Spatial Excitation of a Sum of Sinusoidal Gratings: An Example
      - 5.3.2.3 Visualization
  - 5.4 Experiments
    - 5.4.1 Periodicity in the Experimental Data
    - 5.4.2 Identification of a Single EMD
    - 5.4.3 Identification of a Layer of EMDs
  - References
- 6 Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics
  - 6.1 Introduction
  - 6.2 Variability Analysis of Physiological Time Series: Advantages, Challenges and Multi-complexity Generalization
  - 6.3 Diagnostics and Monitoring of Neurological Abnormalities
  - 6.4 Detection of Emerging Physiological States and Regime Changes
  - 6.5 Biometrics Based on Gait Time Series Analysis
  - References
- 7 Development of a Motion Capturing and Load Analyzing System for Caregivers Aiding a Patient to Sit Up in Bed
  - 7.1 Introduction
  - 7.2 The Fundamental Principals of Using Motion Capture to Analyze the Parts of an Action not Visible to the Camera
  - 7.3 Detection and Analysis of the Motion of Aiding a Person to Sit Up
    - 7.3.1 Subjects
    - 7.3.2 Experimental Parameters
    - 7.3.3 Measurements
    - 7.3.4 Results
      - 7.3.4.1 The Manner of Using the Entire Body
      - 7.3.4.2 Angle of the Lumbar Vertebrae
      - 7.3.4.3 The Patient's Impressions of the Caregivers
    - 7.3.5 Discussion
  - 7.4 Evaluation (Kinetic Analysis) of the Burden of Nursing Care in Relation to the Use of a Force Plate and a Human Model
    - 7.4.1 Purpose
    - 7.4.2 Methods
      - 7.4.2.1 Subjects
      - 7.4.2.2 Ethical Considerations
      - 7.4.2.3 Experimental Methods
      - 7.4.2.4 Measuring the Moment of the Lumbar Joints and the Shearing Stress Brought to Bear on the Lumbar Vertebrae
    - 7.4.3 Results
    - 7.4.4 Discussion
  - References
- 8 Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means
  - 8.1 Introduction
  - 8.2 Experimental Data
  - 8.3 Methodology
    - 8.3.1 K-Means Algorithm and K-Means++ Algorithm
    - 8.3.2 Multi-scale K-Means (MSK-Means) Algorithm
    - 8.3.3 Delay Permutation Entropy
    - 8.3.4 Detecting Nonlinear Structure with DPE
      - 8.3.4.1 Support Vector Machine
  - 8.4 Experiments for Detecting Seizures
    - 8.4.1 Evaluating Delay Lag for Exact Order m
    - 8.4.2 Accuracy of Detecting Seizures with Different λ DPE
    - 8.4.3 Identifying Seizures from Other Sets
  - 8.5 Experiments for Detecting Epileptic Zone
    - 8.5.1 Statistical Analysis of Relation Between DPE Index and λ
    - 8.5.2 Relation Between λ and Classification Accuracy
  - References
- 9 Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring
  - 9.1 Introduction
  - 9.2 EEG Signals
  - 9.3 EEG Topomaps
  - 9.4 Motion Estimation
    - 9.4.1 Full-Search (FS) Block Matching Algorithm
    - 9.4.2 Three Step Search (TSS) Algorithm
    - 9.4.3 New Three Step Search (NTSS) Algorithm
    - 9.4.4 Four Step Search (FSS) Algorithm
    - 9.4.5 Diamond Search (DS) Algorithm
    - 9.4.6 Two Dimensional Logarithmic Search (TDLS) Algorithm
    - 9.4.7 Orthogonal Search Algorithm (OSAlg)
    - 9.4.8 Tracking Brain Activity Using FS Motion Estimation Algorithm
  - 9.5 Experimental Setup and Data Acquisition
  - 9.6 Discussion of Results
  - References
Part II Image Analysis
- 10 Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery
  - 10.1 Introduction
  - 10.2 Ultra High-Resolution Imagery
    - 10.2.1 Data Acquisition Protocol
    - 10.2.2 Significance of High Image Resolution
  - 10.3 Methods and Algorithms
    - 10.3.1 Noise Levels and Linear-Structure Enhancement
    - 10.3.2 Dimensionality Reduction by Skeletonization
    - 10.3.3 Quantitative Understanding of the Vasculature Hierarchy
  - 10.4 Results
  - References
- 11 Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks
  - 11.1 Introduction
  - 11.2 Software Architecture
    - 11.2.1 Platform as a Service
    - 11.2.2 Software as a Service
  - 11.3 Launch of the Service
  - 11.4 Supported Functions
  - 11.5 Workflows
  - 11.6 Use Case: Detecting Neurites
  - References
- 12 Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey
  - 12.1 Introduction
  - 12.2 Data
  - 12.3 Classification Models
  - 12.4 Model Performance
  - 12.5 Semi Automated Performance Enhancement
  - 12.6 Case Study: New Zealand Honey
    - 12.6.1 Data and Methods
    - 12.6.2 Performance Assessment
    - 12.6.3 Discussion
  - References
- 13 Digital Image Processing and Analysis for Activated Sludge Wastewater Treatment
  - 13.1 Introduction
  - 13.2 Introduction to as Systems in WWTPs
    - 13.2.1 Characterization of Wastewater
      - 13.2.1.1 Odor
      - 13.2.1.2 Turbidity
      - 13.2.1.3 PH
      - 13.2.1.4 Dissolved Oxygen
      - 13.2.1.5 Biochemical Oxygen Demand
      - 13.2.1.6 Chemical Oxygen Demand
      - 13.2.1.7 Total Organic Carbon
      - 13.2.1.10 Volatile Suspended Solids
      - 13.2.1.11 Food to Micro-organisms Ratio
      - 13.2.1.8 Total Solids
      - 13.2.1.9 Total Suspended Solids
    - 13.2.2 Wastewater Treatment Processes
    - 13.2.3 Aerobic Wastewater Treatment
      - 13.2.3.1 Abnormal Conditions
  - 13.3 Image Processing and Analysis
    - 13.3.1 Image Acquisition
    - 13.3.2 Image Preprocessing
      - 13.3.2.1 Background Subtraction
      - 13.3.2.2 Z-Stacking
      - 13.3.2.3 Image Stitching/Virtual Slide
    - 13.3.3 Image Segmentation
    - 13.3.4 Image Analysis
  - References
- 14 A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis
  - 14.1 Introduction
  - 14.2 System Design
  - 14.3 Root Segmentation
  - 14.4 Root Tip Detection
  - 14.5 Root Tip Tracking
  - 14.6 Ellipse Fitting
  - 14.7 Camera Self Calibration
    - 14.7.1 Internal Calibration
    - 14.7.2 Camera Pose Estimation
  - 14.8 Results
  - References
Index