Isnardo Reducindo

Evaluation of multimodal medical image registration based on Particle Filter

This paper presents a performance evaluation of a new multimodal image registration algorithm which is based on Bayesian estimation theory, specifically on Particle Filters. The results point to an efficient, easy to implement and robust to noise algorithm. The registration method showed good performance when using partial data, and it was compared with an algorithm based on maximization of mutual information and a Hyperplanes optimization method. Finally, we showed that the algorithm may be parallelizable, so that it is possible to reduce the computation time for image registration.

Non-rigid multimodal image registration based on local variability measures and optical flow

In this paper, we present a novel methodology for multimodal non-rigid medical image registration. The proposed approach is based on combining an optical flow technique with a pixel intensity transformation by using a local variability measure, such as statistical variance or Shannon entropy. The methodology is basically composed by three steps: first, we approximate the global deformation using a rigid registration based on a global optimization technique, called particle filtering; second, we transform both target and source images into a new intensity space where they can be compared; and third, we obtain the optical flow between them by using the Horn and Shuck algorithm in an iterative scales-space framework. After these steps, the non-rigid registration is made up by adding the resulting vector fields, computed by the rigid registration, and the optical flow. The proposed algorithm was tested using a synthetic intensity mapping and non-rigid deformation of MRI images. Preliminary results show that the methodology seems to be a good alternative for non-rigid multimodal registration, obtaining an average error of less than two pixels in the estimation of the deformation vector field.

An exploration of multimodal similarity metrics for parametric image registration based on particle filtering

This paper presents an analysis of different multimodal similarity metrics for parametric image registration based on particle filtering. Our analysis includes four similarity metrics found in the literature and we propose a new metric based on the discretization of the kernel predictability, function recently introduced by Gómez-García et al. (2008), that we call histogram kernel predictability (HKP). Hence the metrics studied in this work are mutual information, normalized mutual information, kernel predictibility with gaussian and truncated parabola functions, and HKP. The evaluations include tests varying the number of particles in the filter, the type of pixel sampling, the number of bins used to calculate the histograms, the noise in the images, and the computation time. Furthermore, we also conducted a geometric analysis to inspect convexity properties of the metrics under discussion. The overall evaluation suggests that the normalized mutual information is the best similarity metric for parametric image registration.

Non-rigid Multimodal Image Registration Based on the Expectation-Maximization Algorithm

In this paper, we present a novel methodology for multimodal non-rigid image registration. The proposed approach is formulated by using the Expectation-Maximization (EM) technique in order to estimate a displacement vector field that aligns the images to register. In this approach, the image alignment relies on hidden stochastic random variables which allow to compare the intensity values between images of different modality. The methodology is basically composed of two steps: first, we provide an initial estimation of the the global deformation vector field by using a rigid registration technique based on particle filtering, obtaining, at the same time, an initial estimation of the joint conditional intensity distribution of the registered images; second, we approximate the remaining deformations by applying an iterative EM-technique approach, where at each step, a new estimation of the joint conditional intensity distribution and the displacement vector field are computed. The proposed algorithm was tested with different kinds of medical images; preliminary results show that the methodology is a good alternative for non-rigid multimodal registration.

Rigid Multimodal/Multispectral Image Registration Based on the Expectation-Maximization Algorithm

In this paper, we present a novel methodology for multimodal/multispectral rigid image registration. The proposed approach is formulated by using the Expectation-Maximization (EM) technique in order to estimate a geometric transformation that aligns the multiparametric images to register. In this approach, the images alignment relies on hidden stochastic random variables which allow to compare the multiparametric intensity values between images of different modalities. The methodology is basically composed by an EM-technique approach, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the rigid transformation are computed. The proposed algorithm was tested with different kinds of medical images; our results show that the proposed methodology is a good alternative for rigid multimodal/multispectral registration.

Integración de plataforma LMS y algoritmo de código abierto para detección y prevención de plagio en Educación Superior

The work shows the results of the first phase of a research project, aimed at addressing from a new perspective the problem of plagiarism in the digital era and as part of the processes of academic training, which is pursued through the integration of technological tools . It is an application for plagiarism detection built from two open source applications, a platform for distance education and an adaptive plagiarism detection algorithm. The methodology for the development, integration and implementation of the aforementioned tool is also presented in general terms, as well as the analysis of statistical data from a sample of students at the higher level of the Faculty of Information Sciences (FCI) of the Autonomous University of San Luis Potosi (UASLP), Mexico. The initial analysis yields data of interest and probabilistic models as a first approach to the problem, which will be analyzed from an academic focus, in such a way as to identify the degree of participation of students in this type of practice as part of their university education.

Multimodal image registration based on the expectation–maximisation methodology

In this study, a new framework for multimodal image registration is proposed based on the expectation–maximisation (EM) methodology. This framework allows to address simultaneously parametric and elastic registrations independently on the modality of the target and source images without making any assumptions about their intensity relationship. The EM formulation for the image registration problem leads to a regularised quadratic optimisation scheme to compute the displacement vector field (DVF) that aligns the images and depends on their joint intensity distribution. At the first stage, a parametric transformation is assumed for the DVF, where the resulting quadratic optimisation is computed recursively to calculate its optimal parameters. Next, a general unknown deformation models the elastic part of the DVF, which is represented by an additive structure. The resulting optimisation process by the EM formulation results in a cost function that involves data and regularisation terms, which is also solved recursively. A comprehensive evaluation of the parametric and elastic proposals is carried out by comparing to state-of-the-art algorithms and images from different application fields, where an advantage is visualised by the authors’ proposal in terms of a compromise between accuracy and robustness.

An innovative multimodal/multispectral image registration method for medical images based on the Expectation-Maximization algorithm.

In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantification Used in 3D Brain Images

In this work, we propose a novel fully automated method to solve the 3D multimodal non-rigid image registration problem. The proposed strategy overcomes the monomodal intensity restriction of fluid-like registration (FLR) models, such as Demons-based registration algorithms, by applying a mapping that relies on an intensity uncertainty quantification in a local neighbourhood, bringing the target and source images into a common domain where they are comparable, no matter their image modalities or mismatched intensities between them. The proposed methodology was tested with T1, T2 and PD weighted brain magnetic resonance (MR) images with synthetic deformations, and CT-MR brain images from a radiotherapy clinical case. The performance of the proposed approach was evaluated quantitatively by standard indices that assess the correct alignment of anatomical structures of interest. The results obtained in this work show that the addition of the local uncertainty mapping properly resolve the monomodal restriction of FLR algorithms when same anatomic counterparts exists in the images to register, and suggest that the proposed strategy can be an option to achieve multimodal 3D registrations.