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Computational Intelligence in Biomedical Imaging

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Cover of 'Computational Intelligence in Biomedical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Brain Disease Classification and Progression Using Machine Learning Techniques
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    Chapter 2 The Role of Content-Based Image Retrieval in Mammography CAD
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    Chapter 3 A Novel Image-Based Approach for Early Detection of Prostate Cancer Using DCE-MRI
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    Chapter 4 Computational Intelligent Image Analysis for Assisting Radiation Oncologists’ Decision Making in Radiation Treatment Planning
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    Chapter 5 Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography
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    Chapter 6 Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation
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    Chapter 7 Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Images
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    Chapter 8 Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization
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    Chapter 9 Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing
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    Chapter 10 Image Segmentation for Connectomics Using Machine Learning
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    Chapter 11 Image Analysis Techniques for the Quantification of Brain Tumors on MR Images
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    Chapter 12 Respiratory and Cardiac Function Analysis on the Basis of Dynamic Chest Radiography
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    Chapter 13 Adaptive Noise Reduction and Edge Enhancement in Medical Images by Using ICA
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    Chapter 14 Subtraction Techniques for CT and DSA and Automated Detection of Lung Nodules in 3D CT
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Title
Computational Intelligence in Biomedical Imaging
Published by
Springer-Verlag New York, January 2014
DOI 10.1007/978-1-4614-7245-2
ISBNs
978-1-4614-7245-2, 978-1-4614-7244-5, 978-1-4939-4233-6
Editors

Kenji Suzuki

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Denmark 1 2%
Unknown 53 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 27%
Student > Master 11 20%
Researcher 8 15%
Professor 4 7%
Lecturer > Senior Lecturer 2 4%
Other 11 20%
Unknown 4 7%
Readers by discipline Count As %
Engineering 17 31%
Computer Science 13 24%
Physics and Astronomy 4 7%
Agricultural and Biological Sciences 3 5%
Medicine and Dentistry 3 5%
Other 5 9%
Unknown 10 18%