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E-grāmata: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical ImagingPalielināt
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This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world.

Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment.

Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

Part I Advanced Machine Learning in Computer-Aided Systems
Multi-modality Feature Learning in Diagnoses of Alzheimer's Disease
3(28)
Daoqiang Zhang
Chen Zu
Biao Jie
Tingting Ye
1 Introduction
4(1)
2 Subjects
5(3)
2.1 Data Acquisition
7(1)
2.2 Image Analysis
7(1)
3 Multi-task Feature Selection (MTFS)
8(6)
3.1 Method
8(1)
3.2 Multimodal Data Fusion and Classification
9(1)
3.3 Validation
10(1)
3.4 Results
11(3)
4 Manifold Regularized Multi-task Feature Selection (M2TFS)
14(5)
4.1 Manifold Regularized MTFS (M2TFS)
15(1)
4.2 Classification
16(1)
4.3 Results
17(2)
5 Label-Aligned Multi-task Feature Selection (LAMTFS)
19(4)
5.1 Method
19(1)
5.2 Experiments and Results
20(3)
6 Discriminative Multi-task Feature Selection (DMTFS)
23(4)
6.1 Method
23(2)
6.2 Experimental Results
25(2)
7 Conclusion
27(4)
References
28(3)
A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN
31(28)
Nima Tajbakhsh
Kenji Suzuki
1 Introduction
32(1)
2 Methods
33(5)
2.1 Massive-Training Artificial Neural Networks (MTANNs)
33(2)
2.2 Convolutional Neural Networks (CNNs)
35(1)
2.3 Bag of Visual Words with Fisher Encoding
36(2)
3 Datasets
38(1)
3.1 Database for Lung Nodule Detection
38(1)
3.2 Database for Colorectal Polyp Detection
39(1)
3.3 Database for Lung Nodule Classification
39(1)
4 Candidate Generation and Data Augmentation
39(1)
5 Experiments
40(11)
5.1 CNNs Versus Fisher Vectors
40(5)
5.2 CNNs Versus MTANNs
45(6)
6 Discussion
51(3)
7 Conclusion
54(5)
References
55(4)
Introduction to Binary Coordinate Ascent: New Insights into Efficient Feature Subset Selection for Machine Learning
59(28)
Amin Zarshenas
Kenji Suzuki
1 Introduction
60(1)
2 Methods
61(5)
2.1 Coordinate Descent Algorithm
61(1)
2.2 Binary Coordinate Ascent Algorithm
62(2)
2.3 BCA-Based Wrapper FS
64(2)
3 Experimental Results
66(7)
4 Discussion
73(7)
5 Conclusion
80(7)
References
81(6)
Part II Computer-Aided Detection
Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography
87(24)
Atsushi Teramoto
Hiroshi Fujita
1 Introduction
88(2)
1.1 Related Works
88(1)
1.2 Objectives
89(1)
2 Methods
90(9)
2.1 Method Overview
90(1)
2.2 Nodule Detection Using CT Images
90(4)
2.3 Nodule Detection in PET Images
94(2)
2.4 Integration and False Positive Reduction
96(3)
3 Experiments
99(6)
3.1 Materials
99(1)
3.2 Evaluation Methods
100(2)
3.3 Results
102(3)
4 Discussions
105(3)
5 Conclusion
108(3)
References
109(2)
Detecting Mammographic Masses via Image Retrieval and Discriminative Learning
111(24)
Menglin Jiang
Shaoting Zhang
Dimitris N. Metaxas
1 Introduction
112(2)
2 Related Work
114(3)
2.1 Learning-Based CAD Methods
114(2)
2.2 CBIR-Based CAD Methods
116(1)
3 Mass Detection via Retrieval and Learning
117(4)
3.1 Local Feature Voting-Based Mass Retrieval
118(2)
3.2 Learning Similarity Thresholds
120(1)
3.3 Detection of Masses
120(1)
4 Experiments
121(5)
4.1 Dataset
121(1)
4.2 Mass Detection Performance
122(1)
4.3 Mass Retrieval Performance
123(3)
5 Conclusions and Discussions
126(9)
References
127(8)
Part III Computer-Aided Diagnosis
High-Order Statistics of Micro-Texton for HEp-2 Staining Pattern Classification
135(30)
Xian-Hua Han
Yen-Wei Chen
1 Introduction
136(3)
2 Medical Context
139(2)
3 Micro-Structure Representation in Differential Excitation Domain
141(5)
3.1 Local Binary Pattern
141(1)
3.2 Weber Local Descriptors
142(4)
4 Extraction of Image Representation for HEp-2 Cell
146(3)
4.1 The Adaptive WLD Space Modeled by Mixture Gaussian
146(3)
5 High-Order Statistics of Adaptive WLD Model
149(4)
5.1 The Fisher Kernel
150(1)
5.2 Coded Vector with Higher Order Statistics
151(2)
6 Experiments
153(8)
7 Conclusion
161(4)
References
162(3)
Intelligent Diagnosis of Breast Cancer Based on Quantitative B-Mode and Elastography Features
165(28)
Chung-Ming Lo
Ruey-Feng Chang
1 Introduction
166(1)
2 Intensity-Invariant B-Mode Texture Analysis for BI-RADS 3
167(6)
2.1 Patients and Data Acquisition
167(1)
2.2 Tumor Segmentation
168(1)
2.3 Speckle Detection
168(1)
2.4 Ranklet Transform
169(3)
2.5 Statistical Analysis
172(1)
2.6 Result and Discussion
172(1)
3 Quantization of Multichannel Distributions in Color Shear-Wave Imaging
173(7)
3.1 Patients and Data Acquisition
174(1)
3.2 Shear-Wave Elastography (SWE) Features
175(2)
3.3 Performance Evaluation
177(1)
3.4 Results and Discussion
178(2)
4 The Integration of Qualitative BI-RADS and Quantitative Strain Features in Elastography
180(8)
4.1 Patients and Data Acquisition
181(1)
4.2 Tumor Segmentation
181(1)
4.3 Quantitative Features
182(4)
4.4 Statistical Analysis
186(1)
4.5 Results and Discussion
187(1)
5 Conclusion
188(5)
References
188(5)
Categorization of Lung Tumors into Benign/Malignant, Solid/GGO, and Typical Benign/Others
193(16)
Yasushi Hirano
1 Extraction
193(6)
1.1 Lung Tumors
193(4)
1.2 Lung Blood Vessels
197(2)
2 Classification
199(7)
2.1 Benign/Malignant Classification
199(4)
2.2 Solid/GGO Classification
203(1)
2.3 Typical Benign/Others Classification
204(2)
3 Discussion
206(1)
4 Conclusion
207(2)
References
207(2)
Fuzzy Object Growth Model for Neonatal Brain MR Understanding
209(16)
Saadia Binte Alam
Syoji Kobashi
Jayaram K. Udupa
1 Introduction
209(1)
2 Proposed Method
210(8)
2.1 Growth Index
210(3)
2.2 Construction of Fuzzy Object Growth Model
213(4)
2.3 Fuzzy Connected Image Segmentation with Fuzzy Object Growth Model
217(1)
3 Image Data
218(1)
4 Experimental Results
218(3)
5 Conclusion
221(4)
References
221(4)
Part IV Computer-Aided Prognosis
Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis
225(42)
Huiguang He
Hongwei Wen
Dai Dai
Jieqiong Wang
1 Introduction
226(5)
1.1 Multi-modal MRI
226(1)
1.2 Computer Aided Diagnosis
227(1)
1.3 Neurologic and Psychiatric Diseases
228(3)
2 Method
231(12)
2.1 Image Preprocessing and Original Feature Extraction
231(6)
2.2 Feature Selection
237(4)
2.3 Classification Methods
241(1)
2.4 Feature Fusion
242(1)
2.5 Evaluation
243(1)
3 Result
243(12)
3.1 Accurate Prediction of AD Patients
243(6)
3.2 Accurate Identification of ADHD Children
249(1)
3.3 Accurate Identification of TS Children
250(2)
3.4 A Diagnosis Model for TS Children Based on Brain Structural Network
252(3)
4 Discussion
255(5)
4.1 Alzheimer's Disease
255(2)
4.2 ADHD
257(1)
4.3 TS
258(2)
5 Conclusion
260(7)
References
260(7)
Radiomics in Medical Imaging---Detection, Extraction and Segmentation
267(70)
Jie Tian
Di Dong
Zhenyu Liu
Yali Zang
Jingwei Wei
Jiangdian Song
Wei Mu
Shuo Wang
Mu Zhou
1 Introduction
268(6)
1.1 Computer-Aided Diagnosis
268(1)
1.2 Radiomics
268(1)
1.3 Radiomics Pipeline
269(1)
1.4 Data Acquisition
269(1)
1.5 Lesion Segmentation
270(1)
1.6 Feature Extraction and Selection
271(1)
1.7 Knowledge Discovery
271(1)
1.8 Deep Learning Pipeline
272(1)
1.9 Clinical Results
273(1)
2 Radiomics in CT Imaging
274(18)
2.1 Background Knowledge
274(7)
2.2 Radiomics Development of Lung Cancer
281(1)
2.3 Generic Radiomics Approach to Lung Cancer
282(8)
2.4 Future of Radiomics in Lung Cancer
290(2)
2.5 Summary
292(1)
3 Radiomics in MRI Imaging
292(21)
3.1 Introduction
292(2)
3.2 Automated Brain Tumor Segmentation
294(3)
3.3 GBM: Feature Extraction
297(10)
3.4 GBM Prediction
307(1)
3.5 Radiogenomics in GBM
308(5)
4 Radiomics in PET Imaging
313(24)
4.1 Cervical Cancer
313(1)
4.2 Tumor Segmentation of Cervical Cancer
314(4)
4.3 Tumor Characterization of Cervical Cancer
318(2)
4.4 Application of Informatics Analysis and Data Mining in Cervical Cancer
320(1)
4.5 Influencing Factors
321(1)
References
322(15)
Part V Computer-Aided Therapy and Surgery
Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques
337(24)
Tong Lin
Yucheng Lin
1 Introduction
337(3)
1.1 Prior Work on Tumor Gating
338(1)
1.2 Prior Work on Tumor Tracking
339(1)
2 Tumor Gating
340(3)
2.1 Fluoroscopic Image Data
340(1)
2.2 Dimensionality Reduction Techniques
341(1)
2.3 Artificial Neural Network (ANN)
342(1)
2.4 Simulated Treatment Delivery
343(1)
3 Tumor Tracking
343(5)
3.1 Image Data
343(1)
3.2 Outline of the Tracking Method
343(3)
3.3 Principal Component Analysis (PCA)
346(1)
3.4 Regression Analysis
346(2)
4 Experimental Results
348(6)
4.1 Results on Tumor Gating
348(5)
4.2 Results on Tumor Tracking
353(1)
5 Discussions
354(7)
5.1 Summary of Our Work
354(1)
5.2 Limits of Our Work
355(1)
5.3 Follow-Up Work by Other Authors
356(1)
References
357(4)
Image Guided and Robot Assisted Precision Surgery
361(23)
Fang Chen
Jia Liu
Hongen Liao
1 Introduction
361(2)
2 Image Processing Based Guidance for CAS
363(8)
2.1 Overviews of Image Processing Based Guidance
363(2)
2.2 Related Techniques and Examples for Image Processing Based Guidance
365(6)
3 3D Augmented Reality Based Image Guidance in CAS
371(7)
3.1 Overview of 3D Augmented Reality
371(2)
3.2 Related Techniques and Examples of 3D AR Based Image Guidance
373(4)
3.3 Applications of 3D AR Based Image Guidance for Precise Surgery
377(1)
4 Image-Guided Surgical Robots
378(5)
4.1 Overview of Surgical Robots
378(1)
4.2 Classification of Surgical Robots
379(3)
4.3 Application of Surgical Robots for Precise Surgery
382(1)
5 Summary and Future Directions
383(1)
References 384
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