Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study

Konukoğlu, Ender; Açar, Burak; Paik, David S.; Beaulieu, Christopher F.; Rosenberg, Jacob; Napel, Sandy

doi:10.1109/tmi.2007.901429

Cited by 23 publications

(11 citation statements)

References 22 publications

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“…Konukoglu 36 proposed polyp enhancing level sets method to improve the performance of CTC CAD algorithms, especially for smaller polyps. CAD performance increased 5%-10% when higher quality CTC data was tested.…”

Section: Discussionmentioning

confidence: 99%

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

et al. 2011

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Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic Bspline interpolations significantly improved the sensitivity for small polyp detection. Conclusions:The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

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Section: Discussionmentioning

confidence: 99%

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

et al. 2011

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“…However, the sensitivity is not perfect, an evaluation with small lesions and polyps submerged in liquid should be performed, and the FP number can be further improved. Konukoglu et al [23] introduced a preprocessing stage that enhances the polyps via a PDE evolution based on the heat equation, and showed how it improves the CAD performance. Proprietary algorithms, [24], [25], have been reported as well, but with no better results than the methods mentioned above.…”

Section: A Virtual Colonoscopy Cad Reviewmentioning

confidence: 99%

A Complete System for Polyps Flagging in Virtual Colonoscopy

Fiori¹,

Musé²,

Sapiro³

2011

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp flagging based on a simple segmentation technique that enhances polyps, a multi-scale candidate polyp delineation and new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The proposed algorithm is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy, obtaining very promising results. For polyps greater than 6mm in size we achieve 100% sensitivity with just 0:8 false positives per study, and for polyps greater than 3mm in size we achieve 100% sensitivity with 2:2 false positives per study. Abstract-Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp flagging based on a simple segmentation technique that enhances polyps, a multi-scale candidate polyp delineation, and new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The proposed algorithm is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy, obtaining very promising results. For polyps greater than 6mm in size we achieve 100% sensitivity with just 0.8 false positives per study, and for polyps greater than 3mm in size we achieve 100% sensitivity with 2.2 false positives per study.

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“…Konukoglu et al [1], [18] proposed a method that is in some sense the inverse of the approach that is proposed in the current paper. Effectively, a wall evolution algorithm is described based on a level-set formulation that regularizes and enhances polyps as a preprocessing step to CTC CAD algorithms.…”

Section: A Previous Workmentioning

confidence: 99%

Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

Wijk

Ravesteijn

Vos

et al. 2010

IEEE Trans. Med. Imaging

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Abstract-Today's computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps. We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm. We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.

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Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study

Cited by 23 publications

References 22 publications

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

A Complete System for Polyps Flagging in Virtual Colonoscopy

Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

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