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Tadashi Araki, Sumit K. Banchhor, Narendra D. Londhe, Nobutaka Ikeda, Petia Radeva, Devarshi Shukla, et al. (2016). Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos. JMS - Journal of Medical Systems, 40(3), 51:1–51:20.
Abstract: Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically.
Keywords: Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy
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M. Oliver, G. Haro, Mariella Dimiccoli, B. Mazin, & C. Ballester. (2016). A Computational Model for Amodal Completion. JMIV - Journal of Mathematical Imaging and Vision, 56(3), 511–534.
Abstract: This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
Keywords: Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica
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Mariella Dimiccoli, Benoît Girard, Alain Berthoz, & Daniel Bennequin. (2013). Striola Magica: a functional explanation of otolith organs. JCN - Journal of Computational Neuroscience, 35(2), 125–154.
Abstract: Otolith end organs of vertebrates sense linear accelerations of the head and gravitation. The hair cells on their epithelia are responsible for transduction. In mammals, the striola, parallel to the line where hair cells reverse their polarization, is a narrow region centered on a curve with curvature and torsion. It has been shown that the striolar region is functionally different from the rest, being involved in a phasic vestibular pathway. We propose a mathematical and computational model that explains the necessity of this amazing geometry for the striola to be able to carry out its function. Our hypothesis, related to the biophysics of the hair cells and to the physiology of their afferent neurons, is that striolar afferents collect information from several type I hair cells to detect the jerk in a large domain of acceleration directions. This predicts a mean number of two calyces for afferent neurons, as measured in rodents. The domain of acceleration directions sensed by our striolar model is compatible with the experimental results obtained on monkeys considering all afferents. Therefore, the main result of our study is that phasic and tonic vestibular afferents cover the same geometrical fields, but at different dynamical and frequency domains.
Keywords: Otolith organs ;Striola; Vestibular pathway
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G.Blasco, Simone Balocco, J.Puig, J.Sanchez-Gonzalez, W.Ricart, J.Daunis-I-Estadella, et al. (2015). Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome. ICJI - International Journal of Cardiovascular Imaging, 31(3), 603–612.
Abstract: Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness.
Keywords: Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2015). Combining Local and Global Learners in the Pairwise Multiclass Classification. PAA - Pattern Analysis and Applications, 18(4), 845–860.
Abstract: Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
Keywords: Multiclass classification; Pairwise approach; One-versus-one
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