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Xavier Carrillo, E Fernandez-Nofrerias, Francesco Ciompi, Oriol Rodriguez-Leor, Petia Radeva, Neus Salvatella, et al. (2011). Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention. JOIC - Journal of Invasive Cardiology, 23(10), 401–404.
Abstract: OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil.
BACKGROUND:
Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI).
METHODS:
This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences.
RESULTS:
There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69).
CONCLUSIONS:
Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements.
Keywords: radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis
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Ikechukwu Ofodile, Ahmed Helmi, Albert Clapes, Egils Avots, Kerttu Maria Peensoo, Sandhra Mirella Valdma, et al. (2019). Action recognition using single-pixel time-of-flight detection. ENTROPY - Entropy, 21(4), 414.
Abstract: Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent
neural network.
Keywords: single pixel single photon image acquisition; time-of-flight; action recognition
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Oriol Pujol, & Petia Radeva. (2004). Texture Segmentation by Statistical Deformable Models. IJIG - International Journal of Image and Graphics, 433–452.
Abstract: Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.
Keywords: Texture segmentation, parametric active contours, statistic snakes
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Frederic Sampedro, & Sergio Escalera. (2015). Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation. IETCV - IET Computer Vision, 9(3), 439–446.
Abstract: In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
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Sergio Escalera, David Masip, Eloi Puertas, Petia Radeva, & Oriol Pujol. (2011). Online Error-Correcting Output Codes. PRL - Pattern Recognition Letters, 32(3), 458–467.
Abstract: IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.
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