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Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures

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Cover of 'Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures'

Table of Contents

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    Book Overview
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    Chapter 1 Shape-Based Pose Estimation of Robotic Surgical Instruments
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    Chapter 2 3D Endoscope System Using Asynchronously Blinking Grid Pattern Projection for HDR Image Synthesis
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    Chapter 3 Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis
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    Chapter 4 Progressive Hand-Eye Calibration for Laparoscopic Surgery Navigation
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    Chapter 5 Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN)
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    Chapter 6 Motion Vector for Outlier Elimination in Feature Matching and Its Application in SLAM Based Laparoscopic Tracking
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    Chapter 7 Image-Based Smoke Detection in Laparoscopic Videos
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    Chapter 8 Fully Automatic Detection of Distal Radius Fractures from Posteroanterior and Lateral Radiographs
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    Chapter 9 Automated Characterization of Pyelocalyceal Anatomy Using CT Urograms to Aid in Management of Kidney Stones
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    Chapter 10 Local Phase-Based Learning for Needle Detection and Localization in 3D Ultrasound
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    Chapter 11 Intracranial Volume Quantification from 3D Photography
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    Chapter 12 Automatic Near Real-Time Evaluation of 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip
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    Chapter 13 Automatic Sentinel Lymph Node Localization in Head and Neck Cancer Using a Coupled Shape Model Algorithm
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    Chapter 14 Towards an Automated Segmentation of the Ventro-Intermediate Thalamic Nucleus
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    Chapter 15 Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer
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    Chapter 16 Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important?
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    Chapter 17 Hybrid Tracking for Improved Registration of Laparoscopic Ultrasound and Laparoscopic Video for Augmented Reality
Attention for Chapter 3: Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis
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