Table of Contents

PART I: DIAGNOSIS METHODS IN TRADITIONAL CHINESE MEDICINE
Chapter 1 Introduction
1．1 Diagnosis Methods in Traditional Chinese Medicine
1．1．1 Tongue Diagnosis
1．1．2 Pulse Diagnosis
1．1．3 Breath Odor Diagnosis
1．2 Computerized TCM Diagnosis
1．2．1 Computerized Tongue Diagnosis
1．2．2 Computerized Pulse Diagnosis
1．2．3 Computerized Breath Odor Diagnosis
1．3 Summary
References
PART Ⅱ: COMPUTERIZED TONGUE IMAGE ANALYSIS
Chapter 2 Tongue Image Acquisition and Preprocessing
2．1 Tongue Image Acquisition
2．1．1 Requirement Analysis
2．1．2 System Design and Implementation
2．1．3 Performance Analysis
2．2 Color Correction
2．2．1 Color Correction Algorithms
2．2．2 Evaluation of Correction Algorithms
2．2．3 Discussion
2．3 Summary
References
Chapter 3 Automated Tongue Segmentation
3．1 Bi-Elliptical Deformable Contour
3．1．1 Bi-Elliptical Deformable Template for the Tongue
3．1．2 Combined Model for Tongue Segmentation
3．1．3 Results and Analysis
3．2 Snake with Polar Edge Detector
3．2．1 The Segmentation Algorithm
3．2．2 Experimental Results
3．3 Gabor Magnitude-based Edge Detection and Fast Marching
3．3．1 2D Gabor Magnitude-based Edge Detection
3．3．2 Contour Detection Using Fast Marching and Active Contour Model
3．3．3 Experimental Results
3．4 Summary
References
Chapter 4 Tongue Image Feature Analysis
4．1 Color Feature Analysis
4．1．1 Exploratory Tongue Color Analysis
4．1．2 Statistical Analysis of Tongue Color Distribution
4．2 Tongue Texture Analysis
4．3 Tongue Shape Analysis
4．3．1 Shape Correction
4．3．2 Extraction of Shape Features
4．3．3 Tongue Shape Classification
4．4 Extraction of Other Local Pathological Features
4．4．1 Petechia
4．4．2 Tongue Crack
4．4．3 Tongueprint
4．4．4 Sublingual Veins
4．5 Summary
References
Chapter 5 Computerized Tongue Diagnosis
5．1 Bayesian Network for Computerized Tongue Diagnosis
5．1．1 Quantitative Pathological Features Extraction
5．1．2 Bayesian Networks
5．1．3 Experimental Results
5．2 Diagnosis Based on Hyperspectral Tongue Images
5．2．1 Hyperspectral Tongue Images
5．2．2 The SVM Classifier Applied to Hyperspectral Tongue Images
5．2．3 Experimental Results
5．3 Summary
References
PART Ⅲ: COMPUTERIZED PULSE SIGNAL ANALYSIS
Chapter 6 Pulse Signal Acquisition and Preprocessing
6．1 Pressure Pulse Signal Acquisition
6．1．1 Application Scenario and Requirement Analysis
6．1．2 System Architecture
6．1．3 Multi-Channel Pulse Signals
6．2 Baseline Wander Correction of Pulse Signals
6．2．1 Detecting the Onsets of Pulse Wave
6．2．2 Wavelet Based Cascaded Adaptive Filter
6．2．3 Results on Actual Pulse Signals
6．3 Summary
References
Chapter 7 Feature Extraction of Pulse Signals
7．1 Spatial Feature Extraction
7．1．1 Fiducit-Point-based Methods
7．1．2 Approximate Entropy
7．2 Frequency Feature Extraction
7．2．1 Hilbert-Hnang Transform
7．2．2 Wavelet and Wavelet Packet Transform
7．3 AR Model
7．4 Gaussian Mixture Model
7．4．1 Two-term Gaussian Model
7．4．2 Feature Selection
7．4．3 FCM Clustering
7．5 Summary
References
Chapter 8 Classification of Pulse Signals
8．1 Pulse Waveform Classification
8．1．1 Modules of Pulse Waveform Classification
8．1．2 The EDFC and GEKC Classifiers
8．1．3 Experimental Results
8．2 Arrhythmic Pulses Detection
8．2．1 Clinical Value of Pulse Rhythm Analysis
8．2．2 Automatic Recognition of Pulse Rhythms
8．2．3 Experimental Results
8．3 Combination of Heterogeneous Features for Pulse Diagnosis
8．3．1 Multiple Kernel Learning
8．3．2 Experimental Results and Discussion
8．4 Summary
References
PART IV: COMPUTERIZED ODOR SIGNAL ANALYSIS
Chapter 9 Breath Analysis System: Design and Optimization
9．1 Breath Analysis
9．2 Design of Breath Analysis System
9．2．1 Description of the System
9．2．2 Signal Sampling and Preprocessing
9．3 Sensor Selection
9．3．1 Linear Discriminant Analysis
9．3．2 Sensor Selection in Breath Analysis System
9．3．3 Comparison Experiment and Performance Analysis
9．4 Summary
References
Chapter 10 Feature Extraction and Classification of Breath Odor Signals
10．1 Feature Extraction of Odor Signals
10．1．1 Geometry Features
10．1．2 Principal Component Analysis
10．1．3 Wavelet Packet Decomposition
10．1．4 Gaussian Function Representation
10．1．5 Gaussian Basis Representation
10．1．6 Experimental Results
10．2 Common Classifiers for Odor Signal Classification
10．2．1 K Nearest Neighbor
10．2．2 Artificial Neural Network
10．2．3 Support Vector Machine
10．3 Sparse Representation Classification
10．3．1 Data Expression
10．3．2 Test Sample Representation by Training Samples
10．3．3 Samples Sampling Errors
10．3．4 Voting Rules
10．3．5 Identification Steps
10．4 Support Vector Ordinal Regression
10．4．1 Problem Analysis
10．4．2 Basic Idea of Support Vector Regression
10．4．3 Support Vector Ordinal Regression
10．4．4 The Dual Problem
10．4．5 Identification Steps
10．5 Evaluation on Classification methods
10．5．1 Evaluation on SRC
10．5．2 Evaluation on SRC
10．6 Summary
References
Index