Medical imaging and computer-aided diagnosis : proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020)

Medical imaging and computer-aided diagnosis : proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020) / Ruidan Su, Han Liu, editors.

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the developm...

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Bibliographic Details
Corporate Author: International Conference on Medical Imaging and Computer-Aided Diagnosis Oxford, England
Other Authors: Su, Ruidan, Liu, Han
Format: eBook
Language:English
Published: Singapore : Springer, 2020.
Series:Lecture notes in electrical engineering ; v. 633.
Subjects:
Diagnostic imaging > Data processing > Congresses.
Diagnosis > Data processing > Congresses.
Imaging systems & technology.
Image processing.
Pattern recognition.
Radiology.
Biomedical engineering.
Technology & Engineering > Electronics > General.
Computers > Computer Graphics.
Computers > Computer Vision & Pattern Recognition.
Medical > Diagnostic Imaging.
Technology & Engineering > Biomedical.
Diagnosis > Data processing
Diagnostic imaging > Data processing
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • 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

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