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Automated Polyp Detection in Colon Capsule Endoscopy.

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Title: Automated Polyp Detection in Colon Capsule Endoscopy.
Authors: Mamonov, Alexander V.1, Figueiredo, Isabel N.2, Figueiredo, Pedro N.3, Richard Tsai, Yen-Hsi4
Source: IEEE Transactions on Medical Imaging. Jul2014, Vol. 33 Issue 7, p1488-1502. 15p.
Subjects: Colon polyps, POLYP (Computer system), Capsule endoscopy, Digital cameras, Medical imaging systems, Medical decision making
Abstract: Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Medical Imaging is the property of IEEE 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: Automated Polyp Detection in Colon Capsule Endoscopy.
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  Data: <searchLink fieldCode="DE" term="%22Colon+polyps%22">Colon polyps</searchLink><br /><searchLink fieldCode="DE" term="%22POLYP+%28Computer+system%29%22">POLYP (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Capsule+endoscopy%22">Capsule endoscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+cameras%22">Digital cameras</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+decision+making%22">Medical decision making</searchLink>
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  Data: Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Medical Imaging is the property of IEEE 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: 10.1109/TMI.2014.2314959
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      – Code: eng
        Text: English
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      – SubjectFull: Colon polyps
        Type: general
      – SubjectFull: POLYP (Computer system)
        Type: general
      – SubjectFull: Capsule endoscopy
        Type: general
      – SubjectFull: Digital cameras
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      – SubjectFull: Medical imaging systems
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      – SubjectFull: Medical decision making
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      – TitleFull: Automated Polyp Detection in Colon Capsule Endoscopy.
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            NameFull: Mamonov, Alexander V.
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            NameFull: Figueiredo, Isabel N.
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            NameFull: Figueiredo, Pedro N.
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            NameFull: Richard Tsai, Yen-Hsi
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              Text: Jul2014
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              Y: 2014
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