Isnardo Reducindo

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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(More)
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(More)
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.(More)
This paper presents a novel non-rigid multimodal registration method that relies on three basic steps: first, an initial approximation of the deformation field is obtained by a parametric registration technique based on particle filtering; second, an intensity mapping based on local variability measures (LVM) is applied over the two images in order to(More)
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(More)
This paper presents a study of parametric multimodal image registration based on particle filtering. We show that this methodology offers high performance over traditional approaches based on mutual information and gradient descendent optimization. The evaluation was performed with a set of medical images, and the tests include evaluations under different(More)
In this work, we present a novel fully automated elastic registration method for magnetic resonance (MR) images with mismatched intensities, which combines a novel mapping based on an intensity uncertainty quantification in a local region, with a fluid-like registration technique. The proposed methodology can be summarized in two global steps: first, a(More)