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Title:
Segmenting Brain Tumors Using Pseudo–Conditional Random Fields | SpringerLink
Description:
Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard...
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Keywords {🔍}
google, scholar, segmentation, brain, tumor, random, fields, springer, chapter, vol, heidelberg, eds, miccai, medical, image, lee, learning, lncs, computing, greiner, classification, privacy, cookies, information, publish, conference, paper, science, images, support, spatial, discriminative, content, data, search, computerassisted, intervention, wang, murtha, magnetic, resonance, based, vector, imaging, university, journal, research, segmenting, tumors, pseudoconditional,
Topics {✒️}
pseudo–conditional random fields chi-hoon lee brain tumor segmentation neighboring voxels conditional random fields ca/~btap/research/pcrf/ segmenting brain tumors tumor segmentation random fields variants brain tumor medical image computing privacy choices/manage cookies support vector machines achieve accuracy similar multilevel segmentation brain tumors paper cite paper lee main content log advanced image technology brain cancer approximate parameter learning kernel based method random fields model spatial dependency computational medical imaging treat voxels magnetic resonance images european economic area notable performance improvement noise robust textural learning algorithm improves magnetic resonance imaging wright state university automated segmentation incorporate spatial constraints labeling sequence data conditions privacy policy efficient spatial classification regularized discriminative classifier computer science brain classification modeling spatial dependencies gibbs prior models accepting optional cookies axel fichtinger székely conference series international conference
Schema {🗺️}
ScholarlyArticle:
headline:Segmenting Brain Tumors Using Pseudo–Conditional Random Fields
pageEnd:366
pageStart:359
image:https://media.springernature.com/w153/springer-static/cover/book/978-3-540-85988-8.jpg
genre:
Computer Science
Computer Science (R0)
isPartOf:
name:Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
isbn:
978-3-540-85988-8
978-3-540-85987-1
type:Book
publisher:
name:Springer Berlin Heidelberg
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
type:Organization
author:
name:Chi-Hoon Lee
affiliation:
name:University of Alberta
address:
name:Department of Computing Science, University of Alberta, Canada
type:PostalAddress
type:Organization
name:Yahoo! Inc
address:
name:Yahoo! Inc, USA
type:PostalAddress
type:Organization
type:Person
name:Shaojun Wang
affiliation:
name:Wright State University
address:
name:Department of Computing Science, Wright State University, USA
type:PostalAddress
type:Organization
type:Person
name:Albert Murtha
affiliation:
name:University of Alberta
address:
name:Cross Cancer Institute, University of Alberta, Canada
type:PostalAddress
type:Organization
type:Person
name:Matthew R. G. Brown
affiliation:
name:University of Alberta
address:
name:Department of Computing Science, University of Alberta, Canada
type:PostalAddress
type:Organization
type:Person
name:Russell Greiner
affiliation:
name:University of Alberta
address:
name:Department of Computing Science, University of Alberta, Canada
type:PostalAddress
type:Organization
type:Person
keywords:Conditional Random Field, Segmentation Task, Tumor Segmentation, Neighboring Voxels, Brain Tumor Segmentation
description:Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard classifiers, such as Logistic Regression (LR) and Support Vector Machines (SVM), typically have limited accuracy as they treat voxels as independent and identically distributed (iid). Approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. However, previous random field systems involved computationally intractable formulations, which are typically solved using some approximation. Here, we present pseudo-conditional random fields (PCRFs), which achieve accuracy similar to other random fields variants, but are significantly more efficient. We formulate a PCRF as a regularized discriminative classifier that relaxes the classification decision for each voxel by considering the labels and features of neighboring voxels.
datePublished:2008
isAccessibleForFree:1
context:https://schema.org
Book:
name:Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
isbn:
978-3-540-85988-8
978-3-540-85987-1
Organization:
name:Springer Berlin Heidelberg
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
name:University of Alberta
address:
name:Department of Computing Science, University of Alberta, Canada
type:PostalAddress
name:Yahoo! Inc
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address:
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name:University of Alberta
address:
name:Cross Cancer Institute, University of Alberta, Canada
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name:University of Alberta
address:
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type:PostalAddress
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name:Yahoo! Inc
address:
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type:PostalAddress
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name:Shaojun Wang
affiliation:
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address:
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type:PostalAddress
type:Organization
name:Albert Murtha
affiliation:
name:University of Alberta
address:
name:Cross Cancer Institute, University of Alberta, Canada
type:PostalAddress
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name:Matthew R. G. Brown
affiliation:
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address:
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type:PostalAddress
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name:Department of Computing Science, University of Alberta, Canada
name:Yahoo! Inc, USA
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name:Cross Cancer Institute, University of Alberta, Canada
name:Department of Computing Science, University of Alberta, Canada
name:Department of Computing Science, University of Alberta, Canada
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