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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 |
Type |
Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
27 |
Issue |
8 |
Pages |
875–881 |
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Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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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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800 |
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Notes |
MILAB;MV;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Cascade analysis for intestinal contraction detection |
Type |
Conference Article |
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Year |
2006 |
Publication |
20th International Congress and exhibition Computer Assisted Radiology and Surgery |
Abbreviated Journal |
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Pages |
9-10 |
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Keywords |
intestine video analysis, anisotropic features, support vector machine, cascade of classifiers |
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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. |
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Osaka (Japan) |
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800 |
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Conference |
CARS |
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Notes |
MV;OR;MILAB;SIAI |
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no |
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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 |
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Title |
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy |
Type |
Conference Article |
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Year |
2006 |
Publication |
18th International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
4 |
Issue |
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Pages |
719-722 |
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Keywords |
Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization |
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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. |
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Hong Kong |
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Edition |
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ISSN |
1051-4651 |
ISBN |
0-7695-2521-0 |
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Area |
800 |
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Conference |
ICPR |
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Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g |
Serial |
727 |
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Permanent link to this record |
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Author |
Fernando Vilariño |
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
Type |
Book Whole |
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Year |
2006 |
Publication |
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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Address |
CVC (UAB) |
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Thesis |
Ph.D. thesis |
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Editor |
Petia Radeva |
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Series Issue |
84-933652-7-0 |
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Area |
800 |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
Serial |
738 |
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Permanent link to this record |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
Type |
Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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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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IEEE |
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ISSN |
0278-0062 |
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800 |
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Notes |
MILAB;MV;OR;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
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Author |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
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Title |
Feature Detectors and Feature Descriptors: Where We Are Now |
Type |
Report |
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Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
154 |
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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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800 |
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MV;SIAI |
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no |
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Admin @ si @ BVS2010; IAM @ iam @ BVS2010 |
Serial |
1348 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions |
Type |
Conference Article |
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Year |
2010 |
Publication |
28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica |
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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. |
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Madrid (Spain) |
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800 |
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CASEIB |
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MV;SIAI |
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no |
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Call Number |
Admin @ si @ BSV2010 |
Serial |
1469 |
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Author |
Jorge Bernal |
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Title |
Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction |
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Report |
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2009 |
Publication |
CVC Tecnical Report |
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141 |
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Keywords |
Projection, Back-projection, CT scan, Euclidean geometry, Radon transform |
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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 |
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Barcelona, Spain |
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800 |
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Notes |
MV; |
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no |
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Call Number |
IAM @ iam @ Ber2009 |
Serial |
1693 |
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Permanent link to this record |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance |
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Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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134-143 |
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Keywords |
Colonoscopy, Polyp Detection, Region Merging, Region Segmentation. |
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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. |
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Las Palmas de Gran Canaria, June 2011 |
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SpringerLink |
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Vitrià, Jordi and Sanches, João and Hernández, Mario |
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Lecture Notes in Computer Science |
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LNCS |
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978-3-642-21256-7 |
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800 |
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IbPRIA |
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Notes |
MV;SIAI |
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no |
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Call Number |
IAM @ iam @ BSV2011c |
Serial |
1696 |
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Permanent link to this record |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Depth of Valleys Accumulation Algorithm for Object Detection |
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Conference Article |
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Year |
2011 |
Publication |
14th Congrès Català en Intel·ligencia Artificial |
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1 |
Issue |
1 |
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71-80 |
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Object Recognition, Object Region Identification, Image Analysis, Image Processing |
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This work aims at detecting in which regions the objects in the image are by using information about the intensity of valleys, which appear to surround ob- jects in images where the source of light is in the line of direction than the camera. We present our depth of valleys accumulation method, which consists of two stages: first, the definition of the depth of valleys image which combines the output of a ridges and valleys detector with the morphological gradient to measure how deep is a point inside a valley and second, an algorithm that denotes points of the image as interior to objects those which are inside complete or incomplete boundaries in the depth of valleys image. To evaluate the performance of our method we have tested it on several application domains. Our results on object region identification are promising, specially in the field of polyp detection in colonoscopy videos, and we also show its applicability in different areas. |
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Lleida |
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978-1-60750-841-0 |
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800 |
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CCIA |
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MV;SIAI |
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no |
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Call Number |
IAM @ iam @ BSV2011b |
Serial |
1699 |
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Permanent link to this record |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Petia Radeva; Jordi Vitria; Fernando Azpiroz; Juan Malagelada |
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Title |
Method for automatic classification of in vivo images |
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Patent |
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Year |
2010 |
Publication |
US 2010/0046816 |
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A method for automatically detecting a post-duodenal boundary in an image stream of the gastrointestinal (GI) tract. The image stream is sampled to obtain a reduced set of images for processing. The reduced set of images is filtered to remove non-valid frames or non-valid portions of frames, thereby generating a filtered set of valid images. A polar representation of the valid images is generated. Textural features of the polar representation are processed to detect the post-duodenal boundary of the GI tract. |
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800 |
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MV;OR;MILAB;SIAI |
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no |
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IAM @ iam @ VSR2010 |
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1702 |
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Author |
Gerard Lacey; Fernando Vilariño |
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Title |
Endoscopy system with motion sensors |
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Patent |
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Year |
2011 |
Publication |
US 2011/0032347 A1 |
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An endoscopy system (1) comprises an endoscope (2) with a camera (3) at its tip. The endoscope extends through an endoscope guide (4) for guiding movement of the endoscope and for measurement of its movement as it enters the body. The guide (4) comprises a generally conical body (5) having a through passage (105) through which the endoscope (2) extends. A motion sensor comprises an optical transmitter (7) and a detector (8) mounted alongside the passage (105) to measure the insertion-withdrawal linear motion and also rotation of the endoscope by the endoscopist's hand. The system (1) also comprises a flexure controller (10) having wheels operated by the endoscopist. The camera (3), the motion sensor (7/8), and the flexure controller (10) are all connected to a processor (11) which feeds a display. |
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Jacobson Holman PPLC; 400 Seventh Street, N.W. Suite 600; Whashington DC 20004 DC |
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USPTO |
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800 |
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MV;SIAI |
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no |
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IAM @ iam @ LaV2011 |
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1703 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Petia Radeva; Jordi Vitria; Fernando Azpiroz; Juan Malagelada |
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Title |
Device, system and method for measurement and analysis of contractile activity |
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Patent |
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Year |
2009 |
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US 2009/0202117 A1 |
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A method and system for determining intestinal dysfunction condition are provided by classifying and analyzing image frames captured in-vivo. The method and system also relate to the detection of contractile activity in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including contractile activity, and more particularly to measurement and analysis of contractile activity of the GI tract based on image intensity of in vivo image data. |
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Pearl Cohen Zedek Latzer |
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800 |
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MV;OR;MILAB;SIAI |
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IAM @ iam @ VSR2009 |
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1704 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames |
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Conference Article |
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Year |
2013 |
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35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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7350 - 7354 |
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In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results. |
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Osaka; Japan; July 2013 |
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1557-170X |
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800 |
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EMBC |
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MV; 600.047; 600.060;SIAI |
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Admin @ si @ BSV2013 |
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2286 |
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Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
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Journal Article |
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Year |
2012 |
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IEEE Transactions on Information Technology in Biomedicine |
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TITB |
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16 |
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6 |
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1341-1352 |
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Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
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1089-7771 |
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800 |
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MILAB; MV; OR;SIAI |
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no |
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Admin @ si @ SDV2012 |
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2124 |
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