Novel framework for registration of pedobarographic image data

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

doi:10.1007/s11517-010-0700-4

Cited by 14 publications

(25 citation statements)

References 19 publications

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“…By assessing the accuracy of the alignment results from real image sequences based on the MSE, we concluded that higher degree polynomials produced lower MSE values pressure images [12,16]. Besides, the squared root of the MSE represents the mean pressure differences between the plantar pressure images that are relevant biomechanical information and important for statistical analysis.…”

Section: Discussionmentioning

confidence: 97%

“…The algorithm described in Oliveira and Tavares [12] is used to align the two peak pressure images. This 2D alignment algorithm can be divided into two main steps: First, an initial alignment is obtained by maximizing the cross-correlation between the peak plantar pressure images [11].…”

Section: Initial Spatial Transformationmentioning

confidence: 99%

“…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%

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Spatio-temporal alignment of pedobarographic image sequences

Oliveira

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.

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Section: Discussionmentioning

confidence: 97%

Section: Initial Spatial Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal alignment of pedobarographic image sequences

Oliveira

Sousa

Santos

et al. 2011

Med Biol Eng Comput

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“…A two-step approach is considered in [12] to optimize the (dis)similarity measure adopted: in the begin, is determined a pre-registration; afterwards, in the second step, the optimization algorithm starts from the pre-registration solution searching iteratively for the geometric transformation that optimizes the adopted (dis)similarity measure. In that framework, for the pre-registration the user can choose the matching of the represented contours [10], the correlation of the images' phases or the cross-correlation of the input images [11].…”

Section: Registration Based On the Optimization Of An Image Intensitymentioning

confidence: 99%

“…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

Oliveira

Tavares

2011

Numer Methods Biomed Eng

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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.

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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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Novel framework for registration of pedobarographic image data

Cited by 14 publications

References 19 publications

Spatio-temporal alignment of pedobarographic image sequences

Spatio-temporal alignment of pedobarographic image sequences

Registration of plantar pressure images

Analysis of Biomedical Images Based on Automated Methods of Image Registration

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