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Debora Gil, Oriol Rodriguez-Leon, Petia Radeva, & Josepa Mauri. (2008). "Myocardial Perfusion Characterization From Contrast Angiography Spectral Distribution " . IEEE Transactions on Medical Imaging, 27(5), 641–649.
Abstract: Despite recovering a normal coronary flow after acute myocardial infarction, percutaneous coronary intervention does not guarantee a proper perfusion (irrigation) of the infarcted area. This damage in microcirculation integrity may detrimentally affect the patient survival. Visual assessment of the myocardium opacification in contrast angiography serves to define a subjective score of the microcirculation integrity myocardial blush analysis (MBA). Although MBA correlates with patient prognosis its visual assessment is a very difficult task that requires of a highly expertise training in order to achieve a good intraobserver and interobserver agreement. In this paper, we provide objective descriptors of the myocardium staining pattern by analyzing the spectrum of the image local statistics. The descriptors proposed discriminate among the different phenomena observed in the angiographic sequence and allow defining an objective score of the myocardial perfusion.
Keywords: Contrast angiography; myocardial perfusion; spectral analysis.
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Debora Gil, Aura Hernandez-Sabate, Oriol Rodriguez, Josepa Mauri, & Petia Radeva. (2006). "Statistical Strategy for Anisotropic Adventitia Modelling in IVUS " . IEEE Transactions on Medical Imaging, 25(6), 768–778.
Abstract: Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification.
Keywords: Corners; T-junctions; Wavelets
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Saad Minhas, Aura Hernandez-Sabate, Shoaib Ehsan, & Klaus McDonald Maier. (2022). "Effects of Non-Driving Related Tasks during Self-Driving mode " . IEEE Transactions on Intelligent Transportation Systems, 23(2), 1391–1399.
Abstract: Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.
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Debora Gil, & Petia Radeva. (2004). "A Regularized Curvature Flow Designed for a Selective Shape Restoration " . IEEE Transactions on Image Processing, 13, 1444–1458.
Abstract: Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.
Keywords: Geometric flows, nonlinear filtering, shape recovery.
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David Castells, Vinh Ngo, Juan Borrego-Carazo, Marc Codina, Carles Sanchez, Debora Gil, et al. (2022). "A Survey of FPGA-Based Vision Systems for Autonomous Cars " . IEEE Access, 10, 132525–132563.
Abstract: On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.
Keywords: Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures
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Miquel Angel Piera, Jose Luis Muñoz, Debora Gil, Gonzalo Martin, & Jordi Manzano. (2022). "A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance " . IEEE Access, 10, 22330–22343.
Abstract: The future deployment of single pilot operations must be supported by new cockpit computer services. Such services require an adaptive context-aware integration of technical functionalities with the concurrent tasks that a pilot must deal with. Advanced artificial intelligence supporting services and improved communication capabilities are the key enabling technologies that will render future cockpits more integrated with the present digitalized air traffic management system. However, an issue in the integration of such technologies is the lack of socio-technical analysis in the design of these teaming mechanisms. A key factor in determining how and when a service support should be provided is the dynamic evolution of pilot workload. This paper investigates how the socio-technical model-based systems engineering approach paves the way for the design of a digital assistant framework by formalizing this workload. The model was validated in an Airbus A-320 cockpit simulator, and the results confirmed the degraded pilot behavioral model and the performance impact according to different contextual flight deck information. This study contributes to practical knowledge for designing human-machine task-sharing systems.
Keywords: Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance
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Debora Gil, Aura Hernandez-Sabate, Julien Enconniere, Saryani Asmayawati, Pau Folch, Juan Borrego-Carazo, et al. (2022). "E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights " . IEEE Access, 10, 7489–7503.
Abstract: More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.
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Jaume Garcia, Debora Gil, Sandra Pujades, & Francesc Carreras. (2008). "A Variational Framework for Assessment of the Left Ventricle Motion " . International Journal Mathematical Modelling of Natural Phenomena, 3(6), 76–100.
Abstract: Impairment of left ventricular contractility due to cardiovascular diseases is reflected in left ventricle (LV) motion patterns. An abnormal change of torsion or long axis shortening LV values can help with the diagnosis and follow-up of LV dysfunction. Tagged Magnetic Resonance (TMR) is a widely spread medical imaging modality that allows estimation of the myocardial tissue local deformation. In this work, we introduce a novel variational framework for extracting the left ventricle dynamics from TMR sequences. A bi-dimensional representation space of TMR images given by Gabor filter banks is defined. Tracking of the phases of the Gabor response is combined using a variational framework which regularizes the deformation field just at areas where the Gabor amplitude drops, while restoring the underlying motion otherwise. The clinical applicability of the proposed method is illustrated by extracting normality models of the ventricular torsion from 19 healthy subjects.
Keywords: Key words: Left Ventricle Dynamics, Ventricular Torsion, Tagged Magnetic Resonance, Motion Tracking, Variational Framework, Gabor Transform.
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Debora Gil, & Petia Radeva. (2004). "Shape Restoration via a Regularized Curvature Flow " . Journal of Mathematical Imaging and Vision, 21(3), 205–223.
Abstract: Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes.
In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications.
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Ernest Valveny, & Enric Marti. (2003). "A model for image generation and symbol recognition through the deformation of lineal shapes " . Pattern Recognition Letters, 24(15), 2857–2867.
Abstract: We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.
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