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Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Motility bar: a new tool for motility analysis of endoluminal videos |
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Journal Article |
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2015 |
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Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
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65 |
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320-330 |
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Small intestine; Motility; WCE; Computer vision; Image classification |
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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. |
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MILAB;MV |
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Admin @ si @ DSR2015 |
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2635 |
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Fernando Vilariño; Stephan Ameling; Gerard Lacey; Stephen Patchett; Hugh Mulcahy |
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Eye Tracking Search Patterns in Expert and Trainee Colonoscopists: A Novel Method of Assessing Endoscopic Competency? |
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2009 |
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Gastrointestinal Endoscopy |
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GI |
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69 |
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5 |
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370 |
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800 |
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MV;SIAI |
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fernando @ fernando @ |
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2420 |
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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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OR;MV |
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BCNPCL @ bcnpcl @ SMG1996 |
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76 |
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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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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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Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Online Error-Correcting Output Codes |
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Journal Article |
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2011 |
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Pattern Recognition Letters |
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PRL |
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32 |
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3 |
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458-467 |
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IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Elsevier |
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North Holland |
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0167-8655 |
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MILAB;OR;HuPBA;MV |
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Admin @ si @ EMP2011 |
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1714 |
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