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Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
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Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis |
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Journal Article |
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2015 |
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American Journal of Physiology-Gastrointestinal and Liver Physiology |
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AJPGI |
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309 |
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6 |
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G413--G419 |
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capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning |
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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. |
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American Physiological Society |
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MILAB; OR;MV |
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Admin @ si @ MDS2015 |
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2666 |
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Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors |
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2012 |
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Sensors |
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SENS |
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12 |
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10 |
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13333-13348 |
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In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection. |
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Admin @ si @ ISR2012b |
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2173 |
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D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom |
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Automated classification of renal interstitium and tubules by local texture analysis and a neural network |
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1996 |
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Analytical and Quantitative Cytology and Histology |
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18 |
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5 |
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410-9, PMID: 8908314 |
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BCNPCL @ bcnpcl @ SMG1996 |
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76 |
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David Guillamet; Jordi Vitria |
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Evaluation of distance metrics for recognition based on non-negative matrix factorization |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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9-10 |
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1599 –1605 |
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IF: 0.809 |
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BCNPCL @ bcnpcl @ GuV2003b |
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380 |
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David Guillamet; Jordi Vitria; B. Shiele |
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Introducing a weighted non-negative matrix factorization for image classification |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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14 |
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2447–2454 |
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IF: 0.809 |
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BCNPCL @ bcnpcl @ GVS2003 |
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382 |
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