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Author Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva edit   pdf
doi  openurl
  Title Adaptable image cuts for motility inspection using WCE Type Journal Article
  Year 2013 Publication (down) Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 37 Issue 1 Pages 72-80  
  Keywords  
  Abstract The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE.  
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  Notes MILAB; OR; 600.046; 605.203 Approved no  
  Call Number Admin @ si @ DSM2012 Serial 2151  
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Author A. Martinez; Jordi Vitria edit  openurl
  Title A Development Plataform for Autonomous Agents. Type Journal Article
  Year 1995 Publication (down) ASI–AA–95 – Practice and Future of Autonomous Agents. Abbreviated Journal  
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  Address Monte Verita, Switzerland.  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ MaV1995b Serial 123  
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Author D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom edit  url
openurl 
  Title Automated classification of renal interstitium and tubules by local texture analysis and a neural network Type Journal Article
  Year 1996 Publication (down) Analytical and Quantitative Cytology and Histology Abbreviated Journal  
  Volume 18 Issue 5 Pages 410-9, PMID: 8908314  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ SMG1996 Serial 76  
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Author Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz edit  doi
openurl 
  Title Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis Type Journal Article
  Year 2015 Publication (down) American Journal of Physiology-Gastrointestinal and Liver Physiology Abbreviated Journal AJPGI  
  Volume 309 Issue 6 Pages G413--G419  
  Keywords capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning  
  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.  
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  Publisher American Physiological Society Place of Publication Editor  
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  Notes MILAB; OR;MV Approved no  
  Call Number Admin @ si @ MDS2015 Serial 2666  
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Author Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva edit  openurl
  Title CORKIDENT: an automatic vision system for real-time inspection of natural products. Type Journal Article
  Year 1999 Publication (down) Abbreviated Journal  
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  Address Wales  
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  Notes OR;MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RMT1999 Serial 23  
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