Logarithmic Model-Based Dynamic Range Enhancement of Hip X-Ray Images

Florea, Corneliu; Vertan, Constantin; Florea, Laura

doi:10.1007/978-3-540-74607-2_53

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…. , 8) representation. Some typical examples of these images and their registration accuracies are shown in Fig.…”

Section: Quantitative Evaluation On Synthetic Imagesmentioning

confidence: 99%

“…Even visual inspection can benefit from registration as clinically significant prosthesis movement can be very small. 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.…”

Section: Experiments On Real X-ray Imagesmentioning

confidence: 99%

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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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“…. , 8) representation. Some typical examples of these images and their registration accuracies are shown in Fig.…”

Section: Quantitative Evaluation On Synthetic Imagesmentioning

confidence: 99%

Section: Experiments On Real X-ray Imagesmentioning

confidence: 99%

Recovering Affine Deformations of Fuzzy Shapes

et al. 2009

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“…X-ray images are good examples as they usually exhibit highly nonlinear radiometric distor-tions [17,18] making registration hard to solve. Therefore binary registration (i.e.…”

Section: Introductionmentioning

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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“…on the whole real axis and we are faced with results that do not belong anymore to the interval [0, D] -the only ones with physical meaning. Such an approach was discussed for instance in [2] for Xray enhancement or in [3] for the creation of high dynamic range images by bracketing.…”

Section: Introductionmentioning

confidence: 99%