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Abdominal Imaging. Computational and Clinical Applications

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Cover of 'Abdominal Imaging. Computational and Clinical Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Abdominal Imaging. Computational and Clinical Applications
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    Chapter 2 Dual-Energy Electronic Cleansing for Artifact-Free Visualization of the Colon in Fecal-Tagging CT Colonography
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    Chapter 3 Computer-Aided Polyp Detection for Laxative-Free CT Colonography
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    Chapter 4 Comparative Performance of Random Forest and Support Vector Machine Classifiers for Detection of Colorectal Lesions in CT Colonography
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    Chapter 5 Image-Enhanced Capsule Endoscopy Preserving the Original Color Tones
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    Chapter 6 3D Non-rigid Motion Correction of Free-Breathing Abdominal DCE-MRI Data
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    Chapter 7 Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts
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    Chapter 8 Ensemble Detection of Colorectal Lesions for CT Colonography
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    Chapter 9 Automated Detection of Colorectal Lesions in Non-cathartic CT Colonography
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    Chapter 10 Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy
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    Chapter 11 Automated Detection and Diagnosis of Crohn’s Disease in CT Enterography
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    Chapter 12 Quantitative Evaluation of Lumbar Disc Herniation Based on MRI Image
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    Chapter 13 Liver Tumor Segmentation Using Kernel-Based FGCM and PGCM
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    Chapter 14 Improving Diagnosis and Intervention: A Complete Approach for Registration of Liver CT Data
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    Chapter 15 Abdominal Images Non-rigid Registration Using Local-Affine Diffeomorphic Demons
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    Chapter 16 FIST: Fast Interactive Segmentation of Tumors
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    Chapter 17 Intraoperative Registration for Liver Tumor Ablation
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    Chapter 18 Sliding Geometries in Deformable Image Registration
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    Chapter 19 Simulation of Portal Vein Clamping and the Impact of Safety Margins for Liver Resection Planning
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    Chapter 20 Liver Segmentation in CT Images for Intervention Using a Graph-Cut Based Model
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    Chapter 21 Anatomical Feature-Guided Mutual Information Registration of Multimodal Prostate MRI
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    Chapter 22 Abdominal Multi-Organ Segmentation of CT Images Based on Hierarchical Spatial Modeling of Organ Interrelations
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    Chapter 23 Organ Segmentation from 3D Abdominal CT Images Based on Atlas Selection and Graph Cut
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    Chapter 24 Liver Segmental Anatomy and Analysis from Vessel and Tumor Segmentation via Optimized Graph Cuts
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    Chapter 25 Liver and Tumor Segmentation and Analysis from CT of Diseased Patients via a Generic Affine Invariant Shape Parameterization and Graph Cuts
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    Chapter 26 A Bayesian Framework for Estimating Respiratory Liver Motion from Sparse Measurements
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    Chapter 27 Semi–automated Subcutaneous and Visceral Adipose Tissue Quantification in Computed Tomography
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    Chapter 28 Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs
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    Chapter 29 Discontinuity Preserving Registration of Abdominal MR Images with Apparent Sliding Organ Motion
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    Chapter 30 Segmentation of Cervical Images by Inter-subject Registration with a Statistical Organ Model
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    Chapter 31 A New Method for Contour Determination of the Prostate in Ultrasound Images
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    Chapter 32 Analyses of Missing Organs in Abdominal Multi-Organ Segmentation
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    Chapter 33 Geometric Modelling of Patient-Specific Hepatic Structures Using Cubic Hermite Elements
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Abdominal Imaging. Computational and Clinical Applications
Published by
Springer, Berlin, Heidelberg, January 2011
DOI 10.1007/978-3-642-28557-8
978-3-64-228556-1, 978-3-64-228557-8

Yoshida, Hiroyuki, Sakas, Georgios, Linguraru, Marius George


Hiroyuki Yoshida, Georgios Sakas, Marius George Linguraru

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Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 30%
Student > Master 11 24%
Student > Bachelor 4 9%
Researcher 3 7%
Student > Doctoral Student 3 7%
Other 4 9%
Unknown 7 15%
Readers by discipline Count As %
Engineering 15 33%
Computer Science 9 20%
Medicine and Dentistry 7 15%
Physics and Astronomy 4 9%
Business, Management and Accounting 1 2%
Other 2 4%
Unknown 8 17%