Medical Imaging and Computer-Aided Diagnosis: Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020)

Preface
Organization
	Honorable Chair
	General Chair
	Program Chairs
	Publication Chair
	Keynote Speakers
	Technical Program Committee
Contents
Computer Modeling and Laser Stereolithography in Cranio-Orbital Reconstructive Surgery
	1 Introduction
	2 Materials and Methods
	3 Results
	4 Discussion
	5 Conclusion
	References
Sparse Representation Label Fusion Method Combining Pixel Grayscale Weight for Brain MR Segmentation
	1 Introduction
	2 Methods
		2.1 Atlas Registration
		2.2 Parse Representation Method
		2.3 Pixel Grayscale Weight Setting
		2.4 Label Fusion
	3 Experiments and Results
		3.1 Segmentation Evaluation Index
		3.2 Influence of the Iterations
		3.3 Detailed Segmentation Results
	4 Discussion and Conclusion
	References
Deep Learning for Mental Illness Detection Using Brain SPECT Imaging
	1 Introduction
	2 Main Results: CNN Models for Single Conditions
		2.1 CNN Model
		2.2 Cross-validation with Few Samples
		2.3 The Amber Zone
	3 Conclusion and Future Work
	References
Vessel Segmentation and Stenosis Quantification from Coronary X-Ray Angiograms
	1 Introduction
	2 Methodology
		2.1 Data Acquisition
		2.2 Vessel Segmentation and Edge Detection
		2.3 Quantitative Coronary Arteriography
	3 Results
		3.1 Contour Detection
		3.2 Stenosis Quantification
	4 Conclusions
	References
Improved Brain Tumor Segmentation and Diagnosis Using an SVM-Based Classifier
	1 Introduction
	2 Background
	3 Methodology
	4 Results and Discussions
	5 Conclusion and Future Scope
	References
3D-Reconstruction and Semantic Segmentation of Cystoscopic Images
	1 Introduction
	2 3D Reconstruction
		2.1 Endoscope Calibration
		2.2 Structure-from-Motion
		2.3 Current Work and Results
	3 Deep Learning
		3.1 Feed-Forward Neural Networks
		3.2 RaVeNNA 4pi: Semantic Segmentation
	4 Conclusion and Outlook
	References
A Biomedical Survey on Osteoporosis Classification Techniques
	1 Introduction
		1.1 Motivation
	2 Related Works
	3 Medical Assessment of Osteoporosis
		3.1 Background
	4 Classification of Osteoporosis Diagnosis Techniques
		4.1 Radiographic Techniques
		4.2 Biochemistry Bio-Markers Classification
		4.3 Invasive Techniques
		4.4 Osteoporosis Biosensors Classification
	5 Bone Turnover Markers (BTMs)
		5.1 Advantages of Using BTMs
		5.2 Disadvantage of Using BTMs
	6 Proposed Simulations and Experimental Results
		6.1 Bone Stress Properties in Osteoporosis
	7 Conclusion and Future Works
	References
Segment Medical Image Using U-Net Combining Recurrent Residuals and Attention
	1 Introduction
	2 Related Work
		2.1 Deep Learning
		2.2 Medical Segmentation Based on Deep Learning
		2.3 Segmentation Research Based on U-Net
	3 Method
		3.1 U-Net Module
		3.2 Recurrent Residuals Module
		3.3 Attention Units
	4 Experiment
		4.1 Implementation Details
		4.2 Evaluation Metric
		4.3 Result
	5 Discussion and Conclusions
	References
A New Importance-Performance Analysis by Size-Insensitive Integrity-Based Fuzzy C-Means
	1 Introduction
	2 Methodology
		2.1 Conventional IPA Method
		2.2 Importance-Performance Analysis by Size-Insensitive Integrity-Based Fuzzy C-Means (IPASIIBFCM)
	3 Results and Analysis
		3.1 Samples Collection
		3.2 Traditional IPA Analysis
		3.3 IPASIIBFCM Analysis
	4 Conclusion
	References
Gingivitis Identification via GLCM and Artificial Neural Network
	1 Introduction
	2 Dataset
	3 Methodology
		3.1 Gray-Level Co-occurrence Matrix
		3.2 Artificial Neural Network
		3.3 Genetic Algorithm
		3.4 10-Fold Cross-Validation
	4 Experiment Results and Discussions
		4.1 Statistical Results
		4.2 Training Algorithm Comparison
		4.3 Comparison to State-of-the-Art Approaches
	5 Conclusions
	References
A Novel Classification Method of Medical Image Segmentation Algorithm
	1 Introduction
	2 The Novel Classification Method
		2.1 Segmentation Method Based on Organ Section Shape
		2.2 Based on the Outline of the Organ Section
		2.3 Based on Pixel Features of the Organs
	3 Conclusion
	References
Pathological Changes Discover Network: Discover the Pathological Changes of Perivascular Dermatitis by Semi-supervised Learning
	1 Introduction
	2 Proposed Methods
		2.1 Pathological Changes Discover Module
		2.2 Restricted Boundary Loss
		2.3 Pathological Changes Guided Module
	3 Experiments
		3.1 Dataset and Preprocessing
		3.2 Evaluation Metrics
		3.3 Pathological Changes Analysis
		3.4 Perivascular Dermatitis Classification
	4 Conclusion
	References
Optical Micro-scanning Reconstruction Technique for a Thermal Microscope Imaging System
	1 Introduction
	2 Different Interpolation Reconstruction Models
		2.1 Local Gradient Interpolation Reconstruction Model
		2.2 Interpolation Model Based on Gradient Threshold
		2.3 Micro-scanning Error Correction Technique Based on Local Gradient Interpolation Reconstruction
	3 Simulation Research
	4 Experimental Research
	5 Conclusion
	References
A Survey for Traditional, Cascaded Regression, and Deep Learning-Based Face Alignment
	1 Introduction
	2 Algorithm
		2.1 Traditional Algorithms
		2.2 Cascade Regression-Based Algorithm
		2.3 Deep Learning-Based Algorithm
	3 Experiments
		3.1 Experiment on Tradition Algorithms
		3.2 Experiment on Cascade Regression-Based Algorithm
		3.3 Experiment on Deep Learning-Based Algorithm
	4 Conclusion and Future Work
	References
Automatic Detection and Counting of Malaria Parasite-Infected Blood Cells
	1 Introduction
		1.1 Background
		1.2 The Current Study
	2 Related Work
	3 Method
		3.1 Data
		3.2 Network Architecture
		3.3 Counting
	4 Results
	5 Discussion
	6 Conclusion
	References
Classification of Chest Diseases Using Wavelet Transforms and Transfer Learning
	1 Introduction
	2 Background and Previous Work
		2.1 Wavelet Transform
		2.2 Augmentation
		2.3 Deep Neural Network (DNN)
	3 Dataset
	4 Methodology
		4.1 Wavelet Transforms
		4.2 Deep Neural Network (DNN)
	5 Results and Discussion
	References
Performance Analysis of Different 2D and 3D CNN Model for Liver Semantic Segmentation: A Review
	1 Introduction
	2 Related Work
		2.1 ResNet
		2.2 FCN
		2.3 U-Net
		2.4 3D U-Net
	3 Results
	4 Discussion
	5 Conclusion
	References
Application of Image Segmentation and Convolutional Neural Network in Classification Algorithms for Mammary X-ray Molybdenum Target Image
	1 Introduction
	2 Materials and Methods
	3 Image Processing and Model Designing
		3.1 ROI Location
		3.2 Mini-image Segmentation
		3.3 Model Design
	4 Results
	5 Discussion
	6 Conclusion
	References
Fusion Segmentation of Head Medical Image with Partially Annotated Data
	1 Introduction
	2 Method
		2.1 DeepLabv3+
		2.2 U-Net
		2.3 Mean Teachers
		2.4 Full Pipeline
		2.5 Loss Function
	3 Experiment
		3.1 Data
		3.2 Preprocess
		3.3 Detail
		3.4 Result
	4 Conclusion
	References
Application of U-Shaped Convolutional Neural Network Based on Attention Mechanism in Liver CT Image Segmentation
	1 Introduction
	2 Related Works
		2.1 U-Shaped Convolutional Neural Networks
		2.2 Attention Mechanism
	3 Methodology
	4 Experiments and Results
		4.1 Experimental Setup
		4.2 Experimental Results and Analysis
	5 Conclusions
	References
Design of Photovoltaic Power Intelligent Patrol Robot
	1 Information
	2 System Hardware Design
		2.1 Photovoltaic Power Supply Circuit Design
		2.2 Video Transmission Control Circuit Design
		2.3 Inclination Adjustment Circuit and Structural
	3 Software Design
		3.1 Wireless LAN Module Driver Transplantation
		3.2 Design of Video Transmission Server
		3.3 Design of Control Lower Computer Program
		3.4 Design of Solar Panel Inclination Angle Adjustment Program
	4 System Test
		4.1 Booster Circuit Conversion Efficiency
		4.2 Video Transmission Image Resolution and Frame Rate
	5 Summary
	References
Application of Intelligent Calculation Method in the Cage Simulation
	1 Instruction
	2 Modelling
	3 Intelligent Calculation
	4 Conclusion
	References
An Analysis of Multi-organ Segmentation Performance of CNNs on Abdominal Organs with an Emphasis on Kidney
	1 Introduction
	2 Related Work
	3 Results
	4 Discussion
	5 Conclusion
	References
Author Index