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Mariella Dimiccoli. (2016). Figure-ground segregation: A fully nonlocal approach. VR - Vision Research, 126, 308–317.
Abstract: We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas.
Keywords: Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion
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Michal Drozdzal, Santiago Segui, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, & Jordi Vitria. (2015). Motility bar: a new tool for motility analysis of endoluminal videos. CBM - Computers in Biology and Medicine, 65, 320–330.
Abstract: Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information.
Keywords: Small intestine; Motility; WCE; Computer vision; Image classification
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Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey, Sayan Chakraborty, Luca Saba, Dinesh Kumar, et al. (2015). A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. CMPB - Computer Methods and Programs in Biomedicine, 118(2), 158–172.
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Andres Traumann, Gholamreza Anbarjafari, & Sergio Escalera. (2015). Accurate 3D Measurement Using Optical Depth Information. EL - Electronic Letters, 51(18), 1420–1422.
Abstract: A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II.
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Carolina Malagelada, Michal Drozdzal, Santiago Segui, Sara Mendez, Jordi Vitria, Petia Radeva, et al. (2015). Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis. AJPGI - American Journal of Physiology-Gastrointestinal and Liver Physiology, 309(6), G413–G419.
Abstract: We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function.
Keywords: capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning
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