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Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching.

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Title: Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching.
Authors: Kilic, Niyazi1 niyazik@istanbul.edu.tr, Ucan, Osman N.1, Osman, Onur2
Source: Expert Systems. Nov2009, Vol. 26 Issue 5, p378-390. 13p. 4 Black and White Photographs, 2 Diagrams, 2 Charts.
Subjects: POLYP (Computer system), Combinatorial optimization, Genetic algorithms, Biological neural networks, Neural circuitry, Tomography, Colon (Anatomy), Genetic programming, Combinatorics
Abstract: In this study, an automatic three-dimensional computer-aided detection system for colonic polyps was developed. Computer-aided detection for computed tomography colonography aims at facilitating the detection of colonic polyps. First, the colon regions of whole computed tomography images were carefully segmented to reduce computational burden and prevent false positive detection. In this process, the colon regions were extracted by using a cellular neural network and then the regions of interest were determined. In order to improve the segmentation performance of the study, weights in the cellular neural network were calculated by three heuristic optimization techniques, namely genetic algorithm, differential evaluation and artificial immune system. Afterwards, a three-dimensional polyp template model was constructed to detect polyps on the segmented regions of interest. At the end of the template matching process, the volumes geometrically similar to the template were emhanced. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems is the property of Wiley-Blackwell 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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  Data: Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching.
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. Nov2009, Vol. 26 Issue 5, p378-390. 13p. 4 Black and White Photographs, 2 Diagrams, 2 Charts.
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  Data: <searchLink fieldCode="DE" term="%22POLYP+%28Computer+system%29%22">POLYP (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Colon+%28Anatomy%29%22">Colon (Anatomy)</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorics%22">Combinatorics</searchLink>
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  Data: In this study, an automatic three-dimensional computer-aided detection system for colonic polyps was developed. Computer-aided detection for computed tomography colonography aims at facilitating the detection of colonic polyps. First, the colon regions of whole computed tomography images were carefully segmented to reduce computational burden and prevent false positive detection. In this process, the colon regions were extracted by using a cellular neural network and then the regions of interest were determined. In order to improve the segmentation performance of the study, weights in the cellular neural network were calculated by three heuristic optimization techniques, namely genetic algorithm, differential evaluation and artificial immune system. Afterwards, a three-dimensional polyp template model was constructed to detect polyps on the segmented regions of interest. At the end of the template matching process, the volumes geometrically similar to the template were emhanced. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell 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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        Value: 10.1111/j.1468-0394.2009.00499.x
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 378
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      – SubjectFull: POLYP (Computer system)
        Type: general
      – SubjectFull: Combinatorial optimization
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Biological neural networks
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      – SubjectFull: Neural circuitry
        Type: general
      – SubjectFull: Tomography
        Type: general
      – SubjectFull: Colon (Anatomy)
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      – SubjectFull: Genetic programming
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      – SubjectFull: Combinatorics
        Type: general
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      – TitleFull: Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching.
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            NameFull: Kilic, Niyazi
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            NameFull: Ucan, Osman N.
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            NameFull: Osman, Onur
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              Text: Nov2009
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              Y: 2009
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