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LINK . SPRINGER . COM {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Link.springer.com Make Money
  6. Keywords
  7. Topics
  8. Schema
  9. External Links
  10. Analytics And Tracking
  11. Libraries

We are analyzing https://link.springer.com/chapter/10.1007/978-3-540-85988-8_43.

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...
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

  • Education
  • Virtual Reality
  • Science

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 7,626,432 visitors per month in the current month.

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How Does Link.springer.com Make Money? {💸}

We don't see any clear sign of profit-making.

Not all websites are made for profit; some exist to inform or educate users. Or any other reason why people make websites. And this might be the case. Link.springer.com might be making money, but it's not detectable how they're doing it.

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
      address:
         name:Yahoo! Inc, USA
         type:PostalAddress
      name:Wright State University
      address:
         name:Department of Computing Science, Wright State University, USA
         type:PostalAddress
      name:University of Alberta
      address:
         name:Cross Cancer Institute, University of Alberta, Canada
         type:PostalAddress
      name:University of Alberta
      address:
         name:Department of Computing Science, University of Alberta, Canada
         type:PostalAddress
      name:University of Alberta
      address:
         name:Department of Computing Science, University of Alberta, Canada
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      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
      name:Shaojun Wang
      affiliation:
            name:Wright State University
            address:
               name:Department of Computing Science, Wright State University, USA
               type:PostalAddress
            type:Organization
      name:Albert Murtha
      affiliation:
            name:University of Alberta
            address:
               name:Cross Cancer Institute, University of Alberta, Canada
               type:PostalAddress
            type:Organization
      name:Matthew R. G. Brown
      affiliation:
            name:University of Alberta
            address:
               name:Department of Computing Science, University of Alberta, Canada
               type:PostalAddress
            type:Organization
      name:Russell Greiner
      affiliation:
            name:University of Alberta
            address:
               name:Department of Computing Science, University of Alberta, Canada
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Department of Computing Science, University of Alberta, Canada
      name:Yahoo! Inc, USA
      name:Department of Computing Science, Wright State University, USA
      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

External Links {🔗}(61)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Clipboard.js

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