Dynamic Pedobarography Transitional Objects by Lagrange’s Equation with FEM, Modal Matching and Optimization Techniques

Tavares

2010

Med Biol Eng Comput

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This article presents a framework to register (or align) plantar pressure images based on a hybrid registration approach, which first establishes an initial registration that is subsequently improved by the optimization of a selected image (dis)similarity measure. The initial registration has two different solutions: one based on image contour matching and the other on image cross-correlation. In the final registration, a multidimensional optimization algorithm is applied to one of the following (dis)similarity measures: the mean squared error (MSE), the mutual information, and the exclusive or (XOR). The framework has been applied to intra- and inter-subject registration. In the former, the framework has proven to be extremely accurate and fast (<70 ms on a normal PC notebook), and obtained superior XOR and identical MSE values compared to the best values reported in previous studies. Regarding the inter-subject registration, by using rigid, similarity, affine, projective, and polynomial (up to the fourth degree) transformations, the framework significantly optimized the image (dis)similarity measures. Thus, it is considered to be very accurate, fast, and robust in terms of noise, as well as being extremely versatile, all of which are regarded as essential features for near-real-time applications.

Section: Introductionmentioning

confidence: 99%

“…Some current pedobarographic equipment, such as those based on light reflection techniques [17], can corrupt data acquired with noise that has a Gaussian distribution. The effect of this kind of noise on the proposed framework was studied and according to the results was shown to be robust.…”

Section: Introductionmentioning

confidence: 99%

Novel framework for registration of pedobarographic image data

Tavares

2010

Med Biol Eng Comput

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“…There are some studies on the alignment of pedobarographic image pairs; for example, those based on: principal axes transformation [6]; modal matching [3,17,23,24]; principal axes combined with a search based on the steepest descent gradient optimization algorithm [15]; optimization based on genetic algorithms [16]; foot size and foot progression angle [8]; matching the contours represented in the input images [13]; optimization of the cross-correlation or phase correlation computed in the frequency domain [11]; and using a hybrid approach that combines a feature based solution with an intensity based solution [12].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal alignment of pedobarographic image sequences

Sousa

Santos

et al. 2011

Med Biol Eng Comput

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The total number of words of the manuscript, including entire text from title page to figure legends: 4300The number of words of the abstract: 237The number of figures: 4The number of tables: 1 2 Abstract This paper presents a methodology to align plantar pressure image sequences simultaneously in time and space. The spatial position and orientation of a foot in a sequence are changed to match the foot represented in a second sequence. Simultaneously with the spatial alignment, the temporal scale of the first sequence is transformed with the aim of synchronizing the two input footsteps. Consequently, the spatial correspondence of the foot regions along the sequences as well as the temporal synchronizing is automatically attained, making the study easier and more straightforward. In terms of spatial alignment, the methodology can use one of four possible geometric transformation models: rigid, similarity, affine or projective. In the temporal alignment, a polynomial transformation up to the 4 th degree can be adopted in order to model linear and curved time behaviors. Suitable geometric and temporal transformations are found by minimizing the mean squared error (MSE) between the input sequences. The methodology was tested on a set of real image sequences acquired from a common pedobarographic device. When used in experimental cases generated by applying geometric and temporal control transformations, the methodology revealed high accuracy. Additionally, the intra-subject alignment tests from real plantar pressure image sequences showed that the curved temporal models produced better MSE results (p<0.001) than the linear temporal model. This paper represents an important step forward in the alignment of pedobarographic image data, since previous methods can only be applied on static images.

“…In addition, pedobarographic image registration supports pixel-level statistics, which makes possible the extraction of biomechanically-relevant information more effectively than traditional regional techniques [3]. Several studies on the registration of pedobarographic images have been developed; for example, using principal axes transformation [4], modal matching [5,6], principal axes combined with steepest descent gradient search [7], optimization with evolutionary algorithms [8], based on foot size and progression angle [9], contours matching [10], optimization of the cross-correlation (CC) computed in the frequency domain [11], phase correlation [11], and optimization of an image (dis)similarity measure using an iterative scheme [12]. In this work, the later four methodologies are studied; thus, their fundamentals are introduced and a discussion about their results is presented.…”

Section: Introductionmentioning

confidence: 99%

Registration of plantar pressure images

Numer Methods Biomed Eng

Tavares

2011

SUMMARYWe present an analysis of four different algorithms used to register plantar pressure images: a first one based on the matching of the external contours of the feet, a second algorithm based on the technique of phase correlation, a third one based on the direct optimization of the cross-correlation (CC) and using the Fourier transform, and a fourth and last algorithm that is based on the iterative optimization of an intensity (dis)similarity measure. In terms of accuracy, the later algorithm achieved the best registration results; on the other hand, the algorithm based on the matching of contours was the fastest, but its accuracy was inferior to the accuracy of the remaining algorithms.