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Author |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
Type |
Book Chapter |
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Year |
2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
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Volume |
4191 |
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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. |
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Copenhagen (Denmark) |
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Springer Verlag |
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Berlin Heidelberg |
Editor |
R. Larsen, M. Nielsen, and J. Sporring |
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800 |
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Conference |
MICCAI06 |
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Notes |
MV;OR;MILAB;SIAI |
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no |
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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 |
![download PDF file pdf](img/file_PDF.gif)
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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 |
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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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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 |
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726 |
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Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva |
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Title |
Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions |
Type |
Book Chapter |
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Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition |
Abbreviated Journal |
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Volume |
4225 |
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Pages |
178–187 |
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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. |
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Cancun (Mexico) |
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Springer Verlag |
Place of Publication |
Berlin Heidelberg |
Editor |
.F. Mart ́ınez-Trinidad et al |
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800 |
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MV;OR;MILAB;SIAI |
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no |
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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 |
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Title |
A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment |
Type |
Book Chapter |
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Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition |
Abbreviated Journal |
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Volume |
4225 |
Issue |
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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. |
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Cancun (Mexico) |
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Springer Verlag |
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Berlin-Heidelberg |
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J.P. Martinez–Trinidad et al |
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LNCS |
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800 |
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Conference |
CIARP06 |
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Notes |
MV;OR;MILAB;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e |
Serial |
729 |
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Permanent link to this record |
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Author |
Francisco Javier Orozco; Pau Baiget; Jordi Gonzalez; Xavier Roca |
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Title |
Eyelids and Face Tracking in Real-Time |
Type |
Miscellaneous |
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Year |
2006 |
Publication |
6th IASTED International Conference Visualization, Imaging, and Image Processing (VIIP´06), 165–170 |
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Palma de Mallorca (Spain) |
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ISE |
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ISE @ ise @ OBG2006 |
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730 |
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Author |
Fadi Dornaika; Franck Davoine |
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Title |
On appearance based face and facial action tracking |
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Journal |
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2006 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology, 16(9): 1838–1853 |
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Admin @ si @ DoD2006b |
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732 |
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Author |
Fadi Dornaika; J. Ahlberg |
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Title |
Fitting 3D face models for tracking and active appearance model training |
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Journal |
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2006 |
Publication |
Image and Vision Computing, 24(9): 1010–1024 |
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Admin @ si @ DoA2006 |
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733 |
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Author |
Fadi Dornaika; Franck Davoine |
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Title |
Facial expression recognition using auto-regressive models |
Type |
Miscellaneous |
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2006 |
Publication |
18th International Conference on Pattern Recognition (ICPR´06), ISBN: 0–7695–2521–0, 4: 520–523 |
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Hong Kong |
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Admin @ si @ DoD2006a |
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734 |
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Author |
R. Herault; Franck Davoine; Fadi Dornaika; Y. Grandvalet |
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Title |
Simultaneous and robust face and facial action tracking |
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Miscellaneous |
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2006 |
Publication |
15eme Congres Francophone AFRIF–AFIA de Reconnaissance des Formes et Intelligence Artificielle (RFIA´06) |
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Tours (France) |
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Admin @ si @ HDD2006 |
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735 |
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Author |
David Aldavert |
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Title |
Visual Simultaneous Localization and Mapping |
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Report |
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2006 |
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CVC Technical Report #98 |
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CVC (UAB) |
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ADAS |
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no |
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Admin @ si @ Ald2006 |
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736 |
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Author |
David Geronimo |
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Title |
Model Features and Horizon Line Estimation for Pedestrian Detection in Advanced Driver Assistance Systems |
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Report |
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2006 |
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CVC Technical Report #98 |
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CVC (UAB) |
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Master's thesis |
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ADAS |
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no |
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ADAS @ adas @ Ger2006 |
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737 |
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Author |
Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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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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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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CVC (UAB) |
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Ph.D. thesis |
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Editor |
Petia Radeva |
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84-933652-7-0 |
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MV;SIAI |
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no |
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Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
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738 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios |
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Conference Article |
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2013 |
Publication |
24th British Machine Vision Conference |
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SLAM |
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Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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Bristol; UK; September 2013 |
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BMVC |
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ADAS |
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no |
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Admin @ si @ RGS2013b; ADAS @ adas @ |
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2274 |
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Author |
Karla Lizbeth Caballero; Joel Barajas; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Using Radio Frequency Reconstructed IVUS Images in Tissue Classification |
Type |
Miscellaneous |
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2006 |
Publication |
Computers in Cardiology (CiC´06), 33: 533–536 |
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Valencia (Spain) |
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BCNPCL @ bcnpcl @ CBP2006a |
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761 |
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Author |
David Rotger; Petia Radeva; Oriol Rodriguez |
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Title |
Vessel Tortuosity Extraction from IVUS Images |
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Miscellaneous |
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2006 |
Publication |
Computers in Cardiology (CiC´06), 33: 689–692 |
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Valencia (Spain) |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRR2006 |
Serial |
762 |
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Permanent link to this record |