Registration of plantar pressure images

Oliveira, Francisco P. M.; Tavares, João Manuel R. S.

doi:10.1002/cnm.1461

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Regularization generally refers to any method that includes additional information to solve a problem. Other numerical methods and regularized processing techniques applied to biomedical field can be found, for example, in . This study focused on the analysis of a proper regularized interpolator for LS_NUFFT on MRI data.…”

Section: Discussionmentioning

confidence: 99%

Regularization techniques on least squares non‐uniform fast Fourier transform

Gibiino

Positano

Landini

et al. 2013

Numer Methods Biomed Eng

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Non-Cartesian acquisition strategies are widely used in MRI to dramatically reduce the acquisition time while at the same time preserving the image quality. Among non-Cartesian reconstruction methods, the least squares non-uniform fast Fourier transform (LS_NUFFT) is a gridding method based on a local data interpolation kernel that minimizes the worst-case approximation error. The interpolator is chosen using a pseudoinverse matrix. As the size of the interpolation kernel increases, the inversion problem may become ill-conditioned. Regularization methods can be adopted to solve this issue. In this study, we compared three regularization methods applied to LS_NUFFT. We used truncated singular value decomposition (TSVD), Tikhonov regularization and L₁-regularization. Reconstruction performance was evaluated using the direct summation method as reference on both simulated and experimental data. We also evaluated the processing time required to calculate the interpolator. First, we defined the value of the interpolator size after which regularization is needed. Above this value, TSVD obtained the best reconstruction. However, for large interpolator size, the processing time becomes an important constraint, so an appropriate compromise between processing time and reconstruction quality should be adopted.

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Section: Discussionmentioning

confidence: 99%

Regularization techniques on least squares non‐uniform fast Fourier transform

Gibiino

Positano

Landini

et al. 2013

Numer Methods Biomed Eng

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“…6 two 3D medical images using the intensity-based method [1]. One of the fields of application that deserves our particular attention is the analysis of plantar pressure images that are especially important when studying the diabetic foot, when designing better shoes or for plantar orthosis [7,8,[11][12][13]. Fig.…”

Section: Applicationsmentioning

confidence: 99%

Analysis of Biomedical Images Based on Automated Methods of Image Registration

Tavares

2014

Advances in Visual Computing

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Abstract. This paper gives a review of the methods that we have developed over the last few years to register 2D/3D images and image sequences. Initially, we will introduce a feature-based method, which will be followed up by a summary of the intensity-based methods. Then we will present some applications of our methods using biomedical images with more automated image analysis tasks. The main applications are related to plantar pressure images and SPECT brain images, including both static images and image sequences. IntroductionImage registration, i.e., the process of transforming an image so that features contained in it are properly adjusted to the homologous features contained in a second image, is a paramount research topic. Computational methods of image registration are frequently applied to static bi-dimensional (2D) and tridimensional (3D) images, as well as to 2D and 3D image sequences. For example, in Biomedical Imaging, these methods have been assuming an essential role in supporting enhanced image-based analysis, by addressing the fusion of information acquired by different imaging techniques, from different directions or even acquired at different times. These computational methods are also used for more effective segmentation of bio structures, the building of more realistic models, and the extraction of more reliable information, among other complex tasks. Within image registration, there is image matching and image interpolation. Image matching establishes correspondences between the related features in two related images, and image interpolation is carried out as the transformations are applied to images in the discrete domain [1].Here, we will introduce the computational methodologies for registering static images and image sequences that we have developed, including matching features presented in the input images. Furthermore, we present and discuss practical cases involving static images, image sequences and images acquired by different imaging techniques. Finally, the conclusions and future perspectives are pointed out.

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“…Such comparison of slices should play an essential role in the registration of CT images. References that deal with the registration on images find a wide application in medicine [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Speeding-up the Fitting of the Model Defining the Ribs-bounded Contour

Bilinskas

Dzemyda

Sabaliauskas

2017

Applied Computer Systems

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-The method for analysing transversal plane images from computer tomography scans is considered in the paper. This method allows not only approximating ribs-bounded contour but also evaluating patient rotation around the vertical axis during a scan. In this method, a mathematical model describing the ribs-bounded contour was created and the problem of approximation has been solved by finding the optimal parameters of the mathematical model using least-squares-type objective function. The local search has been per-formed using local descent by quasi-Newton methods. The benefits of analytical derivatives of the function are disclosed in the paper.

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Registration of plantar pressure images

Cited by 8 publications

References 19 publications

Regularization techniques on least squares non‐uniform fast Fourier transform

Regularization techniques on least squares non‐uniform fast Fourier transform

Analysis of Biomedical Images Based on Automated Methods of Image Registration

Speeding-up the Fitting of the Model Defining the Ribs-bounded Contour

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