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Author |
Fernando Vilariño |
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
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Book Whole |
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2006 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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 obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way. |
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CVC (UAB) |
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Ph.D. thesis |
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Petia Radeva |
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84-933652-7-0 |
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800 |
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MV;SIAI |
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Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
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738 |
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Author |
A. Richichi; O. Fors; M.T. Merino; Xavier Otazu; J. Nuñez; A. Prades; U. Thiele; D. Perez-Ramirez; F.J. Montojo |
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The Calar Alto lunar occultation program: update and new results |
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2006 |
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Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088 |
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CIC |
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CAT @ cat @ RFM2006a |
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589 |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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A data set for fuzzy colour naming |
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2006 |
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Color Research & Application, 31(1):48–56 |
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CAT @ cat @ BVB2006 |
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590 |
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Author |
Zhong Jin; Franck Davoine; Zhen Lou; Jing-Yu Yang |
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Title |
A novel PCA-based Bayes classifier and face analysis |
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2006 |
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International Conference on Advances in Biometrics (ICB’06), LNCS 3832: 144–150 |
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Hong Kong |
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Admin @ si @ JDL2006 |
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624 |
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Author |
N. Zakaria; Jean-Marc Ogier; Josep Llados |
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Title |
The Fuzzy-Spatial Descriptor for the Online Graphic Recognition: Overlapping Matrix Algorithm |
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2006 |
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7th International Workshop, Document Analysis Systems VII (DAS´06), LNCS 3872: 616–627 |
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Nelson (New Zealand) |
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DAG |
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DAG @ dag @ ZOL2006 |
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629 |
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Author |
Matthias S. Keil |
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Title |
Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk |
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2006 |
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Neural Computation |
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NEURALCOMPUT |
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18 |
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4 |
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871–903 |
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Admin @ si @ Kei2006 |
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633 |
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Author |
David Masip; Jordi Vitria |
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Title |
Boosted discriminant projections for nearest neighbor classification |
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2006 |
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Pattern Recognition, 39(2): 164–170 |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2006 |
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634 |
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Author |
Oriol Ramos Terrades; Ernest Valveny |
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Title |
A new use of the ridgelets transform for describing linear singularities in images |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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6 |
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587–596 |
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DAG |
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DAG @ dag @ RaV2006a |
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635 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data |
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Miscellaneous |
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2006 |
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International Conference on Computer Vision Theory and Applications, (2): 310–317 |
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ADAS |
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ADAS @ adas @ DoS2006a |
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636 |
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Author |
Angel Sappa |
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Title |
Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation |
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2006 |
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IEEE Transactions on Image Processing, 15(2):377–384 |
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ADAS |
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no |
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ADAS @ adas @ Sap2006a |
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637 |
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Author |
Carme Julia; Joan Serrat; Antonio Lopez; Felipe Lumbreras; Daniel Ponsa |
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Title |
Motion segmentation through factorization. Application to night driving assistance |
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Miscellaneous |
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2006 |
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International Conference on Computer Vision Theory and Applications, (2) |
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ADAS |
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ADAS @ adas @ JSL2006a |
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638 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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3 |
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201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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Author |
Joaquin Salas; Wendy Avalos; Rafael Castañeda; Mario Maya |
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A machine-vision system to measure the parameters describing the performance of a Foucault pendulum |
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2006 |
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Machine Vision and Applications |
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17 |
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2 |
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133–138 |
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Admin @ si @ SAC2006 |
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644 |
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Author |
Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes |
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2006 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ PRV2006a |
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646 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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2006 |
Publication |
Pattern Recognition Letters |
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PRL |
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27 |
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8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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MILAB;MV;SIAI |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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