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E-raamat: Tongue Image Analysis

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 28-Feb-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811021671
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Tongue Image AnalysisSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 28-Feb-2017
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811021671
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This is the first book offering a systematic description of tongue image analysis and processing technologies and their typical applications in computerized tongue diagnostic (CTD) systems. It features the most current research findings in all aspects of tongue image acquisition, preprocessing, classification, and diagnostic support methodologies, from theoretical and algorithmic problems to prototype design and development of CTD systems. 
The book begins with a very in-depth description of CTD on a need-to-know basis which includes an overview of CTD systems and traditional Chinese medicine (TCM) in order to provide the information on the context and background of tongue image analysis. The core part then introduces algorithms as well as their implementation methods, at a know-how level, including image segmentation methods, chromatic correction, and classification of tongue images. Some clinical applications based on these methods are presented for the show-how purpose in the CTD research field. Case studies highlight different techniques that have been adopted to assist the visual inspection of appendicitis, diabetes, and other common diseases. Experimental results under different challenging clinical circumstances have demonstrated the superior performance of these techniques. 
In this book, the principles of tongue image analysis are illustrated with plentiful graphs, tables, and practical experiments to provide insights into some of the problems. In this way, readers can easily find a quick and systematic way through the complicated theories and they can later even extend their studies to special topics of interest. This book will be of benefit to researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, clinical practice, and TCM, as well as those involved in interdisciplinary research.
Part I Background
1 Introduction to Tongue Image Analysis
3(16)
1.1 Tongue Inspection for Medical Applications
3(3)
1.2 Computerized Tongue Diagnosis System
6(1)
1.3 Research Review on Tongue Image Analysis
7(4)
1.3.1 Tongue Image Acquisition
7(1)
1.3.2 Tongue Image Preprocessing
8(2)
1.3.3 Qualitative Feature Extraction
10(1)
1.3.4 Diagnostic Classification
11(1)
1.4 Issues and Challenges
11(8)
1.4.1 Inconsistent Image Acquisition
12(1)
1.4.2 Inaccurate Color Correction
13(1)
1.4.3 Subjective Tongue Color Extraction and Classification
14(1)
References
14(5)
2 Tongue Images Acquisition System Design
19(28)
2.1 Introduction
19(3)
2.2 System Framework and Requirement Analysis
22(5)
2.2.1 System Framework
23(1)
2.2.2 Requirement Analysis
24(3)
2.3 Optimal System Design
27(8)
2.3.1 Illuminant
27(1)
2.3.2 Lighting Condition
28(2)
2.3.3 Imaging Camera
30(2)
2.3.4 Color Correction
32(1)
2.3.5 System Implementation and Calibration
33(2)
2.4 Performance Analysis
35(7)
2.4.1 Illumination Uniformity
36(1)
2.4.2 System Consistency
37(4)
2.4.3 Accuracy
41(1)
2.4.4 Typical Tongue Images
41(1)
2.5 Summary
42(5)
References
43(4)
Part II Tongue Image Segmentation and Shape Classification
3 Tongue Image Segmentation by Bi-elliptical Deformable Contour
47(24)
3.1 Introduction
47(3)
3.2 Bi-elliptical Deformable Template for the Tongue
50(5)
3.2.1 Definitions and Notations
50(1)
3.2.2 The Tongue Template
51(1)
3.2.3 Energy Function for the Tongue Template
52(3)
3.3 Combined Model for Tongue Segmentation
55(7)
3.3.1 Two Kinds of Template Forces
56(3)
3.3.2 Bi-elliptical Deformable Contours
59(3)
3.4 Experiment Results and Analysis
62(7)
3.5 Summary
69(2)
References
69(2)
4 A Snake-Based Approach to Automated Tongue Image Segmentation
71(18)
4.1 Introduction
71(2)
4.2 Automated Segmentation Algorithm for Tongue Images
73(7)
4.2.1 Polar Edge Detection of Tongue Image
73(2)
4.2.2 Filtering and Binarization of the Edge Image
75(1)
4.2.3 Initialization and ACM
76(2)
4.2.4 Summary of the Automated Tongue Segmentation Method
78(2)
4.3 Experiments and Discussion
80(7)
4.3.1 Evaluation on the Edge Filtering Algorithm
80(1)
4.3.2 Qualitative Evaluation
80(2)
4.3.3 Quantitative Evaluation
82(5)
4.4 Summary
87(2)
References
87(2)
5 Tongue Segmentation in Hyperspectral Images
89(14)
5.1 Introduction
89(2)
5.2 Setup of the Hyperspectral Device
91(1)
5.3 Segmentation Framework
92(4)
5.3.1 Hyperspectral Image Calibration
93(1)
5.3.2 Segmentation
94(2)
5.4 Experiments and Comparisons
96(5)
5.4.1 Criteria of Evaluation
98(1)
5.4.2 Comparison with the BEDC
99(2)
5.5 Summary
101(2)
References
101(2)
6 Tongue Segmentation by Gradient Vector Flow and Region Merging
103(12)
6.1 Introduction
103(1)
6.2 Initial Segmentation
104(2)
6.3 Extraction of Tongue Area
106(2)
6.3.1 Similarity Metric
106(1)
6.3.2 The Extraction of the Tongue Body by Using the MRSM Algorithm
107(1)
6.4 Experimental Results and Discussions
108(4)
6.4.1 Experimental Results
108(1)
6.4.2 Qualitative Evaluation
109(1)
6.4.3 Quantitative Evaluation
110(1)
6.4.4 Running Time of the Proposed Method
111(1)
6.4.5 Limitations of the Proposed Method
112(1)
6.5 Summary
112(3)
References
113(2)
7 Tongue Segmentation by Fusing Region-Based and Edge-Based Approaches
115(18)
7.1 Introduction
115(2)
7.2 Extraction of the ROI to Enhance Robustness
117(3)
7.3 Combining Region-Based and Edge-Based Approaches
120(7)
7.3.1 Region-Based Approach: Improved MSRM
121(2)
7.3.2 Optimal Edge-Based Approach: Fast Marching
123(2)
7.3.3 The Fusion Approach as a Solution
125(2)
7.4 Experiments and Comparisons
127(3)
7.4.1 Qualitative Evaluation
127(2)
7.4.2 Quantitative Evaluation
129(1)
7.5 Summary
130(3)
References
130(3)
8 Tongue Shape Classification by Geometric Features
133(24)
8.1 Introduction
133(1)
8.2 Shape Correction
134(5)
8.2.1 Automatic Contour Extraction
135(1)
8.2.2 The Length Criterion
135(1)
8.2.3 The Area Criterion
136(1)
8.2.4 The Angle Criterion
137(1)
8.2.5 Correction by Combination
138(1)
8.3 Feature Extraction
139(5)
8.3.1 The Length-Based Feature
140(1)
8.3.2 The Area-Based Feature
141(2)
8.3.3 The Angle-Based Feature
143(1)
8.4 Shape Classification
144(4)
8.4.1 Modeling the Classification as a Hierarchy
144(2)
8.4.2 Calculating Relative Weights
146(1)
8.4.3 Calculating the Global Weights
147(1)
8.4.4 Fuzzy Shape Classification
147(1)
8.5 Experimental Results and Performance Analysis
148(4)
8.5.1 Accuracy of Shape Correction
148(1)
8.5.2 Accuracy of Shape Classification
149(3)
8.6 Summary
152(5)
References
152(5)
Part III Tongue Color Correction and Classification
9 Color Correction Scheme for Tongue Images
157(22)
9.1 Introduction
157(2)
9.2 Color Space for Tongue Analysis
159(2)
9.3 Color Correction Algorithms
161(8)
9.3.1 Definitions of Algorithms
162(1)
9.3.2 Evaluation of the Correction Algorithms
163(1)
9.3.3 Experiments and Results
164(5)
9.4 Experimental Results and Performance Analysis
169(7)
9.4.1 Color Correction by Different Cameras
170(1)
9.4.2 Color Correction Under Different Lighting Conditions
171(2)
9.4.3 Performance Analysis
173(1)
9.4.4 Correction on Real Tongue Images
174(2)
9.5 Summary
176(3)
References
177(2)
10 Tongue Colorchecker for Precise Correction
179(28)
10.1 Introduction
179(2)
10.2 Tongue Color Space
181(2)
10.3 Determination of the Number of Colors
183(7)
10.3.1 Setting for Number Deciding Experiment
184(3)
10.3.2 Results of Number Determination
187(3)
10.4 Optimal Colors Selection
190(5)
10.4.1 Objective Function
190(2)
10.4.2 Selection Algorithms
192(3)
10.5 Experimental Results and Performance Analysis
195(9)
10.5.1 Experimental Configuration
195(1)
10.5.2 Parameter Optimization
196(8)
10.6 Summary
204(3)
References
204(3)
11 Tongue Color Analysis for Medical Application
207(18)
11.1 Introduction
207(2)
11.2 Tongue Image Acquisition Device and Dataset
209(1)
11.3 Tongue Color Gamut and Color Features Extraction
210(5)
11.3.1 Tongue Color Gamut
210(2)
11.3.2 Tongue Color Features
212(3)
11.4 Results and Discussion
215(7)
11.4.1 Healthy Versus Disease Classification
215(1)
11.4.2 Typical Disease Analysis
216(6)
11.5 Summary
222(3)
References
223(2)
12 Statistical Analysis of Tongue Color and Its Applications in Diagnosis
225(26)
12.1 Introduction
225(2)
12.2 Tongue Image Acquisition and Database
227(4)
12.2.1 Tongue Image Acquisition Device
227(1)
12.2.2 Color Correction of Tongue Images
228(2)
12.2.3 Tongue Image Database
230(1)
12.3 Tongue Color Distribution Analysis
231(13)
12.3.1 Tongue Color Gamut: Generation and Modeling
231(8)
12.3.2 Tongue Color Centers
239(3)
12.3.3 Distribution of Typical Image Features
242(2)
12.4 Color Feature Extraction
244(4)
12.4.1 Tongue Color Feature Vector
245(1)
12.4.2 Typical Samples of Tongue Color Representation
245(3)
12.5 Summary
248(3)
References
248(3)
13 Hyperspectral Tongue Image Classification
251(14)
13.1 Introduction
251(2)
13.2 Hyperpectral Images for Tongue Diagnosis
253(1)
13.3 The Classifier Applied to Hyperspectral Tongue Images
254(3)
13.3.1 Linear SVM: Linearly Separable
254(1)
13.3.2 Linear SVM: Linearly Non-separable
255(1)
13.3.3 Non-linear SVM
256(1)
13.4 Experimental Results and Performance Analysis
257(3)
13.4.1 Comparing Linear and Non-linear SVM, RBFNN, and K-NN Classifiers
257(1)
13.4.2 Evaluating the Diagnostic Performance of SVM
258(2)
13.5 Summary
260(5)
References
261(4)
Part IV Tongue Image Analysis and Diagnosis
14 Computerized Tongue Diagnosis Based on Bayesian Networks
265(16)
14.1 Introduction
265(1)
14.2 Tongue Diagnosis Using Bayesian Networks
266(3)
14.3 Quantitative Pathological Features Extraction
269(3)
14.3.1 Quantitative Color Features
269(1)
14.3.2 Quantitative Texture Features
270(2)
14.4 Experimental Results
272(7)
14.4.1 Several Issues
273(1)
14.4.2 Bayesian Network Classifier Based on Textural Features
274(1)
14.4.3 Bayesian Network Classifier Based on Chromatic Features
275(1)
14.4.4 Bayesian Network Classifier Based on Combined Features
276(3)
14.5 Summary
279(2)
References
279(2)
15 Tongue Image Analysis for Appendicitis Diagnosis
281(14)
15.1 Introduction
281(1)
15.2 Chromatic and Textural Features for Tongue Diagnosis
282(2)
15.2.1 The Image of the Tongue of a Patient with Appendicitis
282(1)
15.2.2 Quantitative Features of the Color of the Tongue
283(1)
15.2.3 Quantitative Features of the Texture of the Tongue
283(1)
15.3 Identification of Filiform Papillae
284(3)
15.3.1 Typical Figures and Statistics of Filiform Papillae
284(2)
15.3.2 Filter for Filiform Papillae
286(1)
15.4 Experimental Results and Analysis
287(6)
15.4.1 Evaluation Basis for Diagnosis
288(1)
15.4.2 Performance of Metrics for Color
288(2)
15.4.3 Performance of Textural Metrics
290(1)
15.4.4 Performance of the FPF
291(2)
15.5 Summary
293(2)
References
293(2)
16 Diagnosis Using Quantitative Tongue Feature Classification
295(8)
16.1 Introduction
295(1)
16.2 Tongue Image Samples
296(1)
16.3 Quantitative Chromatic and Textural Measurements
296(2)
16.4 Feature Selection
298(1)
16.5 Results and Analysis
298(1)
16.6 Summary
299(4)
References
301(2)
17 Detecting Diabetes Mellitus and Nonproliferative Diabetic Retinopathy Using CTD
303(26)
17.1 Introduction
303(2)
17.2 Capture Device and Tongue Image Preprocessing
305(1)
17.3 Tongue Color Features
306(5)
17.3.1 Tongue Color Gamut
306(1)
17.3.2 Color Feature Extraction
307(4)
17.4 Tongue Texture Features
311(2)
17.5 Tongue Geometric Features
313(4)
17.6 Numerical Results and Discussion
317(6)
17.6.1 Healthy Versus DM Classification
317(4)
17.6.2 NPDR Versus DM-Sans NPDR Classification
321(2)
17.7 Summary
323(6)
References
324(5)
Part V Book Recapitulation
18 Book Review and Future Work
329(4)
18.1 Book Recapitulation
329(1)
18.2 Future Work
330(3)
Index 333
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in Computer Science from the Harbin Institute of Technology (HIT). From 1986 to 1988 he was a postdoctoral fellow at Tsinghua University and then an associate professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. Currently, he is a Chair Professor at the Hong Kong Polytechnic University, where he is the Founding Director of Biometrics Research Centre (UGC/CRC), which has been supported by the Hong Kong SAR Government since 1998. He also serves as Visiting Chair Professor at Tsinghua University and HIT, and Adjunct Professor at Shanghai Jiao Tong University, Peking University, the National University of Defense Technology and the University of Waterloo. So far, he has been published more than 10 books and 400 international journal papers. He was listedas a highly cited researcher in Engineering by Thomson Reuters in 2014 and in 2015, respectively. Professor Zhang is a Croucher senior research fellow, distinguished speaker of the IEEE computer society, and a Fellow of both the IEEE and the IAPR. Hongzhi Zhang received his Ph.D. degree in computer science and technology from Harbin institute of Technology (HIT), China, in 2007. He is an associate Professor at the School of Computer Science and Technology, HIT, where he has taught for over 15 years. He teaches biomedical image processing and has investigated computerized tongue diagnosis at the Research Center of Perception and Computing. He is a member of  the IEEE, the Chinese Association for Artificial Intelligence (CAAI), and the  China Society of Integrated Traditional Chinese and Western Medicine. His research interests include theoretic approaches to problems in biomedical imaging, biometric image analysis, computer vision, and signal processing. His research has been supported by grants from the National Natural Science Foundation of China. He is the author of more than 70 international journal and conference papers.   Bob Zhang received his Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, Canada, in 2011. After graduating from Waterloo, he remained with the Center for Pattern Recognition and Machine Intelligence, and was later a postdoctoral researcher at the Department of Electrical and Computer Engineering at Carnegie Mellon University, Pittsburgh, USA. He is currently an assistant professor in the Department of Computer and Information Science, University of Macau, Taipa, Macau. His research interests focus on medical biometrics, biometrics security, pattern recognition, and image processing. Dr. Zhang is a Technical Committee Member of the IEEE Systems, Man, and Cybernetics Society, an Associate Editor for the International Journal of Image and Graphics, as well as an editorial board member for the International Journal of INFORMATION.
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