医学生物识别 数字化中医数据分析 英文版
作者：张大鹏，左旺孟，李乃民 著
出版时间：2015年版
内容简介
现代信息技术引入中医（TCM）不仅可以获得中医数千年的客观进程，同时对现代医学也提供了新的发现。这本书是基于作者十余年的研究成果，全面、系统介绍中医数据计算机处理和分析。
《医学生物识别：数字化中医数据分析（英文版）》分4个部分共10章，主要介绍中医舌象、脉冲信号和呼吸气味信号三种类型的数据，通过计算机数据分析（CTDA）、图像分析、脉冲分析和气味分析，实现中医数据化的基本理论、技术和方法。第一部分（第1章）是对本书内容的简要介绍；第2部分（第2章~第5章）介绍中医舌诊诊断特征，按颜色、纹理、形状和其他病理特点进行数据提取和分析；第3部分（第6章~第8章）讲述中医脉诊的脉冲数据分析；第4部分（第9章~第10章）对呼吸气味数据的采集、分析进行的讲解。
《医学生物识别：数字化中医数据分析（英文版）》研究基础扎实，内容翔实、严谨。可作为计算机中医药数据分析领域研究人员的专业用书，也可供计算机图像识别、中医学等专业研究生参考使用。
目录
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
