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| Author | Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva | ||||
| 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. | ||||
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| Area | 800 | Expedition | Conference | ||
| Notes | MILAB;MV;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 | Serial | 647 | ||
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| Author | Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| 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 | |
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| 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) | ||||
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| Publisher | Springer Verlag | Place of Publication | Berlin Heidelberg | Editor | R. Larsen, M. Nielsen, and J. Sporring |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | 800 | Expedition | Conference | MICCAI06 | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 | Serial | 725 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| 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) | ||||
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| Area | 800 | Expedition | Conference | CARS | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h | Serial | 726 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| 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 | ||||
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| 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 | 727 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva | ||||
| 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 | |
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| 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) | ||||
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| Publisher | Springer Verlag | Place of Publication | Berlin Heidelberg | Editor | .F. Mart ́ınez-Trinidad et al |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f | Serial | 728 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva | ||||
| 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 | |
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| 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) | ||||
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| Publisher | Springer Verlag | Place of Publication | Berlin-Heidelberg | Editor | J.P. Martinez–Trinidad et al |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | 800 | Expedition | Conference | CIARP06 | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e | Serial | 729 | ||
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| Author | Fernando Vilariño | ||||
| 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 | |
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| 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. |
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| Address | CVC (UAB) | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Petia Radeva | ||
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| Series Volume | Series Issue | 84-933652-7-0 | Edition |
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2006; IAM @ iam @ Vil2006 | Serial | 738 | ||
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| Author | Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
| 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 | |
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| 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) | ||||
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| Publisher | Springer-Verlag | Place of Publication | Berlin Heidelberg | Editor | A. Gasteratos, M. Vincze, and J.K. Tsotsos |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| 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 | 962 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| 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 |
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| 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. | ||||
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| Corporate Author | IEEE | Thesis | |||
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| 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 | 1281 | ||
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| Author | Jorge Bernal; Fernando Vilariño; F. Javier Sanchez | ||||
| Title | Feature Detectors and Feature Descriptors: Where We Are Now | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 154 | Issue | Pages | ||
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| 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. | ||||
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BVS2010; IAM @ iam @ BVS2010 | Serial | 1348 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| 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 | |
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| 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) | ||||
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| Area | 800 | Expedition | Conference | CASEIB | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2010 | Serial | 1469 | ||
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| Author | Jorge Bernal | ||||
| Title | Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction | Type | Report | ||
| Year | 2009 | Publication | CVC Tecnical Report | Abbreviated Journal | |
| Volume | 141 | Issue | Pages | ||
| Keywords | Projection, Back-projection, CT scan, Euclidean geometry, Radon transform | ||||
| Abstract | One of the biggest drawbacks related to the use of CT scanners is the cost (in memory and in time) associated. In this project many methods to simulate their functioning, but in a more feasible way (taking an industrial point of view), will be studied.
The main group of techniques that are being used are the one entitled as ’back-projection’. The concept behind is to simulate the X ray emission in CT scans by lines that cross with the image we want to reconstruct. In the first part of this document euclidean geometry is used to face the tasks of projec- tion and back-projection. After analysing the results achieved it has been proved that this approach does not lead to a fully perfect reconstruction (and also has some other problems related to running time and memory cost). Because of this in the second part of the document ’Filtered Back-projection’ method is introduced in order to improve the results. Filtered Back-projection methods rely on mathematical transforms (Fourier, Radon) in order to provide more accurate results that can be obtained in much less time. The main cause of this better results is the use of a filtering process before the back-projection in order to avoid high frequency-caused errors. As a result of this project two different implementations (one for each approach) had been implemented in order to compare their performance. |
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Barcelona, Spain | Editor | ||
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| Area | 800 | Expedition | Conference | ||
| Notes | MV; | Approved | no | ||
| Call Number | IAM @ iam @ Ber2009 | Serial | 1693 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach | Type | Conference Article | ||
| Year | 2011 | Publication | 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 62-71 | ||
| Keywords | Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time. | ||||
| Abstract | In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction. | ||||
| Address | Rome, Italy | ||||
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| Publisher | SciTePress | Place of Publication | Editor | Djemal, Khalifa | |
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| Area | 800 | Expedition | Conference | MIAD | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ BSV2011a | Serial | 1695 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance | Type | Conference Article | ||
| Year | 2011 | Publication | 5th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 6669 | Issue | Pages | 134-143 | |
| Keywords | Colonoscopy, Polyp Detection, Region Merging, Region Segmentation. | ||||
| Abstract | This work aims at the segmentation of colonoscopy images into a minimum number of informative regions. Our method performs in a way such, if a polyp is present in the image, it will be exclusively and totally contained in a single region. This result can be used in later stages to classify regions as polyp-containing candidates. The output of the algorithm also defines which regions can be considered as non-informative. The algorithm starts with a high number of initial regions and merges them taking into account the model of polyp appearance obtained from available data. The results show that our segmentations of polyp regions are more accurate than state-of-the-art methods. | ||||
| Address | Las Palmas de Gran Canaria, June 2011 | ||||
| Corporate Author | SpringerLink | Thesis | |||
| Publisher | Place of Publication | Editor | Vitrià, Jordi and Sanches, João and Hernández, Mario | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
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| ISSN | ISBN | 978-3-642-21256-7 | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ BSV2011c | Serial | 1696 | ||
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| Author | Jorge Bernal; Fernando Vilariño; F. Javier Sanchez | ||||
| Title | Towards Intelligent Systems for Colonoscopy | Type | Book Chapter | ||
| Year | 2011 | Publication | Colonoscopy | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 257-282 | |
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| Abstract | In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions |
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| Publisher | Intech | Place of Publication | Editor | Paul Miskovitz | |
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| ISSN | ISBN | 978-953-307-568-6 | Medium | ||
| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ BVS2011 | Serial | 1697 | ||
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