@inbook {757, title = {Techniques for tracking: image registration}, booktitle = {Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video}, year = {2018}, publisher = {IET}, organization = {IET}, chapter = {15}, issn = {978-1-78561-290-9}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {616, title = {Influence of ultrasound speckle tracking strategies for motion and strain estimation}, journal = {Medical Image Analysis}, volume = {32}, year = {2016}, month = {2016}, pages = {184 - 200}, abstract = {

Abstract Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.

}, issn = {1361-8415}, doi = {http://dx.doi.org/10.1016/j.media.2016.04.002}, url = {http://www.sciencedirect.com/science/article/pii/S1361841516300202}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {541, title = {A Maximum Likelihood Approach to Diffeomorphic Speckle Tracking for 3D Strain Estimation in Echocardiography}, journal = {Medical Image Analysis}, year = {2015}, pages = {-}, abstract = {

Abstract The strain and strain-rate measures are commonly used for the analysis and assessment of regional myocardial function. In echocardiography (EC), the strain analysis became possible using Tissue Doppler Imaging (TDI). Unfortunately, this modality shows an important limitation: the angle between the myocardial movement and the ultrasound beam should be small to provide reliable measures. This constraint makes it difficult to provide strain measures of the entire myocardium. Alternative non-Doppler techniques such as Speckle Tracking (ST) can provide strain measures without angle constraints. However, the spatial resolution and noisy appearance of speckle still make the strain estimation a challenging task in EC. Several maximum likelihood approaches have been proposed to statistically characterize the behavior of speckle, which results in a better performance of speckle tracking. However, those models do not consider common transformations to achieve the final B-mode image (e.g. interpolation). This paper proposes a new maximum likelihood approach for speckle tracking which effectively characterizes speckle of the final B-mode image. Its formulation provides a diffeomorphic scheme than can be efficiently optimized with a second-order method. The novelty of the method is threefold: First, the statistical characterization of speckle generalizes conventional speckle models (Rayleigh, Nakagami and Gamma) to a more versatile model for real data. Second, the formulation includes local correlation to increase the efficiency of frame-to-frame speckle tracking. Third, a probabilistic myocardial tissue characterization is used to automatically identify more reliable myocardial motions. The accuracy and agreement assessment was evaluated in a set of 16 synthetic image sequences for three different scenarios: normal, acute ischemia and acute dyssynchrony. The proposed method was compared to six speckle tracking methods. Results revealed that the proposed method is the most accurate method to measure the motion and strain with an average median motion error of 0.42\ mm and a median strain error of 2.0 {\textpm} 0.9\%, 2.1 {\textpm} 1.3\% and 7.1 {\textpm} 4.9\% for circumferential, longitudinal and radial strain respectively. It also showed its capability to identify abnormal segments with reduced cardiac function and timing differences for the dyssynchrony cases. These results indicate that the proposed diffeomorphic speckle tracking method provides robust and accurate motion and strain estimation.

}, doi = {http://dx.doi.org/10.1016/j.media.2015.05.001}, url = {http://www.sciencedirect.com/science/article/pii/S1361841515000687}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Johan G. Bosch and Santiago Aja-Fern{\'a}ndez} } @article {542, title = {Probabilistic Tissue Characterization for Ultrasound Images}, journal = {Insight Journal}, year = {2015}, abstract = {

This document describes the derivation of the mixture models commonly used in the literature to describe the probabilistic nature of speckle: The Gaussian Mixture Model, the Rayleigh Mixture Model, the Gamma Mixture Model and the Generalized Gamma Mixture Model. New algorithms were implemented using the Insight Toolkit
ITK for tissue characterization by means of a mixture model.


The source code is composed of a set of reusable ITK filters and classes. In addition to an overview of our implementation, we provide the source code, input data, parameters and output data that the authors used for validating the different probabilistic tissue characterization variants described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

}, url = {http://www.insight-journal.org/browse/publication/955}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @article {534, title = {A local fuzzy thresholding methodology for multiregion image segmentation}, journal = {Knowledge-Based Systems}, volume = {83}, year = {2015}, month = {07/2015}, pages = {1-12}, abstract = {

Abstract Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.

}, issn = {0950-7051}, doi = {http://dx.doi.org/10.1016/j.knosys.2015.02.029}, url = {http://www.sciencedirect.com/science/article/pii/S095070511500129X}, author = {Santiago Aja-Fern{\'a}ndez and Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero} } @article {curiale2014fully, title = {Fully Automatic Detection of Salient Features in 3-D Transesophageal Images}, journal = {Ultrasound in medicine \& biology}, volume = {40}, year = {2014}, month = {07/2014}, pages = {2868-2884}, publisher = {Elsevier}, chapter = {2868}, author = {Ariel H. Curiale and Haak, Alexander and Gonzalo Vegas-S{\'a}nchez-Ferrero and Ren, Ben and Santiago Aja-Fern{\'a}ndez and Johan G. Bosch} } @inbook {curiale2013speckle, title = {Speckle tracking in interpolated echocardiography to estimate heart motion}, booktitle = {Functional Imaging and Modeling of the Heart}, year = {2013}, pages = {325{\textendash}333}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez} } @conference {curiale2013strain, title = {Strain rate tensor estimation from echocardiography for quantitative assessment of functional mitral regurgitation}, booktitle = {Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on}, year = {2013}, pages = {788{\textendash}791}, publisher = {IEEE}, organization = {IEEE}, author = {Ariel H. Curiale and Gonzalo Vegas-S{\'a}nchez-Ferrero and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} } @conference {405, title = {Cuantificaci{\'o}n de la insuficiencia mitral funcional mediante el esfuerzo y la velocidad del miocardio}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2011}, address = {Centro de Cirug{\'\i}a de M{\'\i}nima Invasi{\'o}n Jes{\'u}s Us{\'o}n}, author = {Ariel H. Curiale and S{\'a}nchez-Ferrero, G Vegas and Teresa P{\'e}rez-Sanz and Santiago Aja-Fern{\'a}ndez} }