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Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño edit   pdf
openurl 
  Title Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions Type Conference Article
  Year 2010 Publication 28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract One of the first usual steps in pattern recognition schemas is image segmentation, in order to reduce the dimensionality of the problem and manage smaller quantity of data. In our case as we are pursuing real-time colon cancer polyp detection, this step is crucial. In this paper we present a non-informative region estimation algorithm that will let us discard some parts of the image where we will not expect to find colon cancer polyps. The performance of our approach will be measured in terms of both non-informative areas elimination and polyps’ areas preserving. The results obtained show the importance of having correct non- informative region estimation in order to fasten the whole recognition process.  
  Address Madrid (Spain)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference CASEIB  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @ BSV2010 Serial (down) 1469  
Permanent link to this record
 
 
Author Jorge Bernal; Fernando Vilariño; F. Javier Sanchez edit   pdf
openurl 
  Title Feature Detectors and Feature Descriptors: Where We Are Now Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 154 Issue Pages  
  Keywords  
  Abstract Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @ BVS2010; IAM @ iam @ BVS2010 Serial (down) 1348  
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Author Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva edit   pdf
doi  openurl
  Title Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions Type Journal Article
  Year 2010 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI  
  Volume 29 Issue 2 Pages 246-259  
  Keywords  
  Abstract 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.  
  Address  
  Corporate Author IEEE Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0278-0062 ISBN Medium  
  Area 800 Expedition Conference  
  Notes MILAB;MV;OR;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 Serial (down) 1281  
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Author Robert Benavente; Laura Igual; Fernando Vilariño edit  isbn
openurl 
  Title Current Challenges in Computer Vision Type Book Whole
  Year 2008 Publication Proccedings of the Third Internal Workshop Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-936529-0-6 Medium  
  Area Expedition Conference CVCRD  
  Notes MILAB;CIC;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ BIV2008 Serial (down) 1110  
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Author Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; C. Malagelada; Fernando Azpiroz; Jordi Vitria edit   pdf
url  isbn
openurl 
  Title Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy Type Book Chapter
  Year 2008 Publication Computer Vision Systems. 6th International Abbreviated Journal  
  Volume 5008 Issue Pages 251–260  
  Keywords  
  Abstract Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.  
  Address Santorini (Greece)  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Berlin Heidelberg Editor A. Gasteratos, M. Vincze, and J.K. Tsotsos  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-540-79546-9 Medium  
  Area 800 Expedition Conference ICVS  
  Notes OR; MV; MILAB; SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 Serial (down) 962  
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Author Fernando Vilariño edit   pdf
openurl 
  Title A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy Type Book Whole
  Year 2006 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.		  
  Address CVC (UAB)  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Place of Publication Editor Petia Radeva  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue 84-933652-7-0 Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @ Vil2006; IAM @ iam @ Vil2006 Serial (down) 738  
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Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; C. Malagelada; Petia Radeva edit   pdf
doi  openurl
  Title A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 4225 Issue Pages 188–197  
  Keywords  
  Abstract Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.  
  Address Cancun (Mexico)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin-Heidelberg Editor J.P. Martinez–Trinidad et al  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference CIARP06  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e Serial (down) 729  
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Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; C. Malagelada; Petia Radeva edit   pdf
doi  openurl
  Title Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 4225 Issue Pages 178–187  
  Keywords  
  Abstract This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.  
  Address Cancun (Mexico)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin Heidelberg Editor .F. Mart ́ınez-Trinidad et al  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f Serial (down) 728  
Permanent link to this record
 
 
Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva edit   pdf
doi  isbn
openurl 
  Title Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy Type Conference Article
  Year 2006 Publication 18th International Conference on Pattern Recognition Abbreviated Journal  
  Volume 4 Issue Pages 719-722  
  Keywords Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization  
  Abstract Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found.  
  Address Hong Kong  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 0-7695-2521-0 Medium  
  Area 800 Expedition Conference ICPR  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g Serial (down) 727  
Permanent link to this record
 
 
Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva edit   pdf
openurl 
  Title Cascade analysis for intestinal contraction detection Type Conference Article
  Year 2006 Publication 20th International Congress and exhibition Computer Assisted Radiology and Surgery Abbreviated Journal  
  Volume Issue Pages 9-10  
  Keywords intestine video analysis, anisotropic features, support vector machine, cascade of classifiers  
  Abstract In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions.  
  Address Osaka (Japan)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference CARS  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h Serial (down) 726  
Permanent link to this record
 
 
Author Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva edit   pdf
doi  openurl
  Title Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions Type Book Chapter
  Year 2006 Publication 9th International Conference on Medical Image Computing and Computer–Assisted Intervention Abbreviated Journal  
  Volume 4191 Issue Pages 161–168  
  Keywords  
  Abstract Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.  
  Address Copenhagen (Denmark)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin Heidelberg Editor R. Larsen, M. Nielsen, and J. Sporring  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference MICCAI06  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 Serial (down) 725  
Permanent link to this record
 
 
Author Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva edit  doi
openurl 
  Title ROC curves and video analysis optimization in intestinal capsule endoscopy Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue 8 Pages 875–881  
  Keywords ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MILAB;MV;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 Serial (down) 647  
Permanent link to this record
 
 
Author Fernando Vilariño; Petia Radeva edit  openurl
  Title Cardiac Segmentation with Discriminant Active Contours Type Book Chapter
  Year 2003 Publication Abbreviated Journal  
  Volume Issue Pages 211–217  
  Keywords  
  Abstract Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.  
  Address Palma de Mallorca  
  Corporate Author Thesis  
  Publisher IOS Press Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CCIA  
  Notes MV;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ ViR2003; IAM @ iam @ VRa2003 Serial (down) 426  
Permanent link to this record
 
 
Author Fernando Vilariño; Petia Radeva edit  url
openurl 
  Title Patch-Optimized Discriminant Active Contours for Medical Image Segmentation. Type Conference Article
  Year 2002 Publication Iberoamerican Conference on Artificial Intelligence Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Sevilla, Espanya  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference IBERAMIA  
  Notes MV;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ ViR2002; IAM @ iam @ VRa2003 Serial (down) 320  
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