2010
DOI: 10.1007/s00450-010-0137-x
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Discriminative Generalized Hough transform for localization of joints in the lower extremities

Abstract: A fully automatic iterative training approach for the generation of discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and integrating this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each m… Show more

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Cited by 14 publications
(4 citation statements)
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“…The problem of such approaches is that they do not generalize well to other structures or image modalities. Learning-based approaches such as Discriminative Generalized Hough transform [3] and Marginal Space Learning (MSL) [4], [5] are more general and can be adapted to a wide variety of detection tasks by simply exchanging the training data.…”
Section: Related Workmentioning
confidence: 99%
“…The problem of such approaches is that they do not generalize well to other structures or image modalities. Learning-based approaches such as Discriminative Generalized Hough transform [3] and Marginal Space Learning (MSL) [4], [5] are more general and can be adapted to a wide variety of detection tasks by simply exchanging the training data.…”
Section: Related Workmentioning
confidence: 99%
“…Some techniques depend on the grey-level histogram analysis data generated from the image to detect the location of the liver in the CT scans [8]. Generalized Hough Transform has been used in many approaches to detect the liver shape in CT images [9]. It is considered as a robust and an efficient tool to detect any arbitrary shape in 2D images.…”
Section: Introductionmentioning
confidence: 99%
“…Besides PDM, the Hough transform may be extended into a probabilistic shape model as well [ 20 ]. However, compared to a PDM, its versatility is restricted since appearance information is not part of the model and it is unclear how the trained information may be used for subsequent boundary delineation.…”
Section: Introductionmentioning
confidence: 99%