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Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
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Journal Article |
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2010 |
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IEEE Transactions on Medical Imaging |
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TMI |
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29 |
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2 |
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246-259 |
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Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
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0278-0062 |
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800 |
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MILAB;MV;OR;SIAI |
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BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
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1281 |
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Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Title ![sorted by Title field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Intestinal motor activity, endoluminal motion and transit |
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2009 |
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Neurogastroenterology & Motility |
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NEUMOT |
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21 |
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12 |
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1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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2009 |
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Journal of Signal Processing Systems |
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55 |
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1-3 |
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35–47 |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Oriol Rodriguez-Leor; E. Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Intravascular ultrasound segmentation using local binary patterns |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna (Austria) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RFM2003a |
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407 |
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Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva |
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Title ![sorted by Title field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Introduction to the special issue: Egocentric Vision and Lifelogging |
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2018 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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55 |
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352-353 |
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MILAB; no proj |
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Admin @ si @ DGC2018 |
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3187 |
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