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Towards automatic polyp detection with a polyp appearance model

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Title: Towards automatic polyp detection with a polyp appearance model
Authors: Bernal, J., Sánchez, J.1, Vilariño, F.1
Source: Pattern Recognition. Sep2012, Vol. 45 Issue 9, p3166-3182. 17p.
Subjects: POLYP (Computer system), Mathematical models, Colonoscopy, Image segmentation, Classification, Algorithms, Image analysis
Abstract: Abstract: This work aims at automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. [Copyright &y& Elsevier]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 76306190
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Towards automatic polyp detection with a polyp appearance model
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  Data: <searchLink fieldCode="AR" term="%22Bernal%2C+J%2E%22">Bernal, J.</searchLink><br /><searchLink fieldCode="AR" term="%22Sánchez%2C+J%2E%22">Sánchez, J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vilariño%2C+F%2E%22">Vilariño, F.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Sep2012, Vol. 45 Issue 9, p3166-3182. 17p.
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  Data: <searchLink fieldCode="DE" term="%22POLYP+%28Computer+system%29%22">POLYP (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Colonoscopy%22">Colonoscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink>
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  Data: Abstract: This work aims at automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.patcog.2012.03.002
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 3166
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        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Colonoscopy
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Image analysis
        Type: general
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      – TitleFull: Towards automatic polyp detection with a polyp appearance model
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              M: 09
              Text: Sep2012
              Type: published
              Y: 2012
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