2010
DOI: 10.1109/tmi.2009.2031323
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Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

Abstract: 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 … Show more

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Cited by 61 publications
(34 citation statements)
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“…We validate its effectiveness by evaluating the impacts on large-scale colon and lung CAD system performances (879 and 770 volumes respectively). The results are very encouraging and significantly outperform the recent state-of-the-arts [1,2,5,[11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 72%
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“…We validate its effectiveness by evaluating the impacts on large-scale colon and lung CAD system performances (879 and 770 volumes respectively). The results are very encouraging and significantly outperform the recent state-of-the-arts [1,2,5,[11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 72%
“…Curvature features have demonstrated to be very helpful for parsing surface voxels [11,12,17], but appear not to provide much additional information gain on classifying volume voxels, especially in the case of nodule voxel detection. The other reason is that the previous work only focuses on solitary solid nodules [5,15] to show that curvature may be useful, whereas we train a single classifier P BT n to handle all three types of nodules, under various anatomical contexts [6,23].…”
Section: Supervised Probabilistic Voxel Map Labeling ℘ In Roimentioning
confidence: 99%
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“…The second principal curvature (K2) partial differential equation (PDE, or flow) for polyp detection was recently introduced by van Wijk et al [4]. The aim of this section is to model the K2 flow feature's distribution and combine it into the joint statistical likelihood term of the Bayesian framework.…”
Section: Shape Modelmentioning
confidence: 99%
“…Shape-based methods typically rely on various shape features derived from either first order differential geometric quantities [1]; or from second order quantities computed using Hessian matrices [2][3][4]. The shape features take advantage of the fact that polyps tend to have rounded shapes or contain at least local spherical elements; while colonic folds are elongated shapes.…”
Section: Introductionmentioning
confidence: 99%