A Quantitative Evaluation of the Hip Prosthesis Segmentation Quality in X-Ray Images

Oprea, Alina; Vertan, Constantin

doi:10.1109/isscs.2007.4292685

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…On the other hand, several studies attempted to automate the segmentation of hip implant. The early work on implant segmentation considered the analysis of images based on hand-crafted features such as histogram thresholding [4] [5], Active Contour method initialized by using the Fast randomized circle detection method [3], and the region growing method initialized by applying the Hough transforms [2]. These methods are not generalized well towards THR radiographs and could provide good results only when the implant components are clearly presented in the X-ray images.…”

Section: Takedownmentioning

confidence: 99%

Simultaneous Hip Implant Segmentation and Gruen Landmarks Detection

Alzaid,

Lineham,

Dogramadzi

et al. 2024

IEEE J. Biomed. Health Inform.

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Section: Takedownmentioning

confidence: 99%

Simultaneous Hip Implant Segmentation and Gruen Landmarks Detection

Alzaid,

Lineham,

Dogramadzi

et al. 2024

IEEE J. Biomed. Health Inform.

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“…Fortunately, the segmentation of the prosthetic implant is quite straightforward [38] so binary registration is a valid alternative here. Herein, we used active contours [36] to segment the implant.…”

Section: Registration Of Hip Prosthesis X-ray Imagesmentioning

confidence: 99%

Parametric estimation of affine deformations of planar shapes

Domokos

Kató

2010

Pattern Recognition

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We consider the estimation of affine transformations aligning a known 2D shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the shapes and then compute the transformation parameters from these landmarks. Here we propose a novel approach where the exact transformation is obtained as the solution of a polynomial system of equations. The method has been tested on synthetic as well as on real images and its robustness in the presence of segmentation errors and additive geometric noise has also been demonstrated. We have successfully applied the method for the registration of hip prosthesis X-ray images. The advantage of the proposed solution is that it is fast, easy to implement, has linear time complexity, works without established correspondences and provides an exact solution regardless of the magnitude of transformation.

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“…There are two main challenges in registering hip X-ray images: One is the highly non-linear radiometric distortion [8] which makes any greylevel-based method unstable. Fortunately, the segmentation of the prosthetic implant is quite straightforward [9] so shape registration is a valid alternative here. Herein, we used the proposed fuzzy segmentation method to segment the implant.…”

Section: Experiments On Real X-ray Imagesmentioning

confidence: 99%

Recovering Affine Deformations of Fuzzy Shapes

et al. 2009

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Abstract. Fuzzy sets and fuzzy techniques are attracting increasing attention nowadays in the field of image processing and analysis. It has been shown that the information preserved by using fuzzy representation based on area coverage may be successfully utilized to improve precision and accuracy of several shape descriptors; geometric moments of a shape are among them. We propose to extend an existing binary shape matching method to take advantage of fuzzy object representation. The result of a synthetic test show that fuzzy representation yields smaller registration errors in average. A segmentation method is also presented to generate fuzzy segmentations of real images. The applicability of the proposed methods is demonstrated on real X-ray images of hip replacement implants.

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A Quantitative Evaluation of the Hip Prosthesis Segmentation Quality in X-Ray Images

Cited by 14 publications

References 4 publications

Simultaneous Hip Implant Segmentation and Gruen Landmarks Detection

Simultaneous Hip Implant Segmentation and Gruen Landmarks Detection

Parametric estimation of affine deformations of planar shapes

Recovering Affine Deformations of Fuzzy Shapes

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