Machine Learning in Medical Imaging - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783642243189, 9783642243196

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Subject-Specific Cardiac Segmentation Based on Reinforcement Learning with Shape Instantiation

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_37 ◽

2011 ◽

pp. 300-307 ◽

Cited By ~ 1

Author(s):

Lichao Wang ◽

Su-Lin Lee ◽

Robert Merrifield ◽

Guang-Zhong Yang

Keyword(s):

Reinforcement Learning ◽

Cardiac Segmentation ◽

Subject Specific

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Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_2 ◽

2011 ◽

pp. 10-17 ◽

Cited By ~ 2

Author(s):

Peter Mysling ◽

Kersten Petersen ◽

Mads Nielsen ◽

Martin Lillholm

Keyword(s):

Automatic Segmentation ◽

Active Shape Model ◽

Shape Model ◽

Active Shape ◽

Model Approach

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Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_8 ◽

2011 ◽

pp. 59-66 ◽

Cited By ~ 2

Author(s):

Wei Liu ◽

Suyash P. Awate ◽

Jeffrey S. Anderson ◽

Deborah Yurgelun-Todd ◽

P. Thomas Fletcher

Keyword(s):

Monte Carlo ◽

Hidden Markov Models ◽

Expectation Maximization ◽

Resting State ◽

Markov Models ◽

Hidden Markov ◽

Resting State Fmri ◽

Functional Networks ◽

Monte Carlo Expectation Maximization

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Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_20 ◽

2011 ◽

pp. 159-166 ◽

Cited By ~ 12

Author(s):

Katherine R. Gray ◽

Paul Aljabar ◽

Rolf A. Heckemann ◽

Alexander Hammers ◽

Daniel Rueckert

Keyword(s):

Random Forest ◽

Manifold Learning ◽

Imaging Data

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Hot Spots Conjecture and Its Application to Modeling Tubular Structures

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_28 ◽

2011 ◽

pp. 225-232 ◽

Cited By ~ 5

Author(s):

Moo K. Chung ◽

Seongho Seo ◽

Nagesh Adluru ◽

Houri K. Vorperian

Keyword(s):

Hot Spots ◽

Tubular Structures ◽

Hot Spots Conjecture

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Multi-Kernel Classification for Integration of Clinical and Imaging Data: Application to Prediction of Cognitive Decline in Older Adults

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_4 ◽

2011 ◽

pp. 26-34 ◽

Cited By ~ 3

Author(s):

Roman Filipovych ◽

Susan M. Resnick ◽

Christos Davatzikos

Keyword(s):

Older Adults ◽

Cognitive Decline ◽

Imaging Data ◽

Data Application ◽

Kernel Classification

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Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_44 ◽

2011 ◽

pp. 360-367

Author(s):

Jian-Wu Xu ◽

Kenji Suzuki

Keyword(s):

Machine Learning ◽

Ct Colonography ◽

Machine Learning Techniques ◽

Computer Aided Detection ◽

Learning Techniques ◽

Computer Aided

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3D Segmentation in CT Imagery with Conditional Random Fields and Histograms of Oriented Gradients

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_40 ◽

2011 ◽

pp. 326-334 ◽

Cited By ~ 3

Author(s):

Chetan Bhole ◽

Nicholas Morsillo ◽

Christopher Pal

Keyword(s):

Random Fields ◽

Conditional Random Fields ◽

3D Segmentation ◽

Histograms Of Oriented Gradients

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Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_17 ◽

2011 ◽

pp. 134-141 ◽

Cited By ~ 1

Author(s):

Jiayin Zhou ◽

Qi Tian ◽

Vincent Chong ◽

Wei Xiong ◽

Weimin Huang ◽

...

Keyword(s):

Support Vector Machine ◽

Skull Base ◽

Support Vector ◽

Skull Base Tumors

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An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_15 ◽

2011 ◽

pp. 117-125 ◽

Cited By ~ 1

Author(s):

Uyen T. V. Nguyen ◽

Alauddin Bhuiyan ◽

Kotagiri Ramamohanarao ◽

Laurence A. F. Park

Keyword(s):

Blood Vessel ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation

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Machine Learning in Medical Imaging - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Subject-Specific Cardiac Segmentation Based on Reinforcement Learning with Shape Instantiation

Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach

Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia

Hot Spots Conjecture and Its Application to Modeling Tubular Structures

Multi-Kernel Classification for Integration of Clinical and Imaging Data: Application to Prediction of Cognitive Decline in Older Adults

Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques

3D Segmentation in CT Imagery with Conditional Random Fields and Histograms of Oriented Gradients

Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method

An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging

Machine Learning in Medical Imaging - Lecture Notes in Computer Science
Latest Publications