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Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Title |
Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images |
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Journal Article |
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Year |
2014 |
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IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
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Volume |
18 |
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6 |
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1831-1838 |
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Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task. |
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OR; MILAB; 600.046;MV |
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no |
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Admin @ si @ SDZ2014 |
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2385 |
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Author |
Michal Drozdzal; Laura Igual; Petia Radeva; Jordi Vitria; Carolina Malagelada; Fernando Azpiroz |
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Title |
Aligning Endoluminal Scene Sequences in Wireless Capsule Endoscopy |
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Conference Article |
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Year |
2010 |
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IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis |
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117–124 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences will help to find regions of similar intestinal activity and in this way will provide a valuable information on intestinal motility problems. This work, for first time, addresses the problem of aligning endoluminal sequences taking into account motion and structure of the intestine. To describe motility in the sequence, we propose different descriptors based on the Sift Flow algorithm, namely: (1) Histograms of Sift Flow Directions to describe the flow course, (2) Sift Descriptors to represent image intestine structure and (3) Sift Flow Magnitude to quantify intestine deformation. We show that the merge of all three descriptors provides robust information on sequence description in terms of motility. Moreover, we develop a novel methodology to rank the intestinal sequences based on the expert feedback about relevance of the results. The experimental results show that the selected descriptors are useful in the alignment and similarity description and the proposed method allows the analysis of the WCE. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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MMBIA |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DIR2010 |
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1316 |
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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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2006 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way. |
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CVC (UAB) |
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Ph.D. thesis |
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Petia Radeva |
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84-933652-7-0 |
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800 |
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MV;SIAI |
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no |
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Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
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738 |
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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 |
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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 |
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246-259 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
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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0278-0062 |
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800 |
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MILAB;MV;OR;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
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1281 |
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Author |
Francesco Ciompi; Oriol Pujol; Oriol Rodriguez-Leor; Carlo Gatta; Angel Serrano; Petia Radeva |
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Title |
Enhancing In-Vitro IVUS Data for Tissue Characterization |
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Conference Article |
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2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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241–248 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ CPR2009a |
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1162 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
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Title |
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging |
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Conference Article |
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Year |
2005 |
Publication |
Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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67-74 |
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classification; vessel border modelling; IVUS |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. |
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IOS Press |
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Amsterdam, The Netherlands |
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IAM;MILAB |
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no |
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IAM @ iam @ HGR2005c |
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1549 |
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Author |
Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva |
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Title |
Learning the Lumen Border using a Convolutional Neural Networks classifier |
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Conference Article |
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2016 |
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19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows. |
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Athens; Greece; October 2016 |
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MICCAIW |
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MILAB; |
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no |
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Admin @ si @ MBB2016 |
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2822 |
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Author |
Misael Rosales; Petia Radeva; Oriol Rodriguez; Debora Gil |
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Title |
Suppression of IVUS Image Rotation. A Kinematic Approach |
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Book Chapter |
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Year |
2005 |
Publication |
Functional Imaging and Modeling of the Heart |
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LNCS |
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3504 |
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889-892 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
IntraVascular Ultrasound (IVUS) is an exploratory technique used in interventional procedures that shows cross section images of arteries and provides qualitative information about the causes and severity of the arterial lumen narrowing. Cross section analysis as well as visualization of plaque extension in a vessel segment during the catheter imaging pullback are the technique main advantages. However, IVUS sequence exhibits a periodic rotation artifact that makes difficult the longitudinal lesion inspection and hinders any segmentation algorithm. In this paper we propose a new kinematic method to estimate and remove the image rotation of IVUS images sequences. Results on several IVUS sequences show good results and prompt some of the clinical applications to vessel dynamics study, and relation to vessel pathology. |
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Springer Berlin / Heidelberg |
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Frangi, Alejandro and Radeva, Petia and Santos, Andres and Hernandez, Monica |
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Lecture Notes in Computer Science |
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LNCS |
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3504 |
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IAM;MILAB |
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no |
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IAM @ iam @ RRR2005 |
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1645 |
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Carlo Gatta; Simone Balocco; Francesco Ciompi; R. Hemetsberger; Oriol Rodriguez-Leor; Petia Radeva |
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Title |
Real-time gating of IVUS sequences based on motion blur analysis: Method and quantitative validation |
Type |
Conference Article |
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2010 |
Publication |
13th international conference on Medical image computing and computer-assisted intervention |
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II |
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59-67 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intravascular Ultrasound (IVUS) is an image-guiding technique for cardiovascular diagnostic, providing cross-sectional images of vessels. During the acquisition, the catheter is pulled back (pullback) at a constant speed in order to acquire spatially subsequent images of the artery. However, during this procedure, the heart twist produces a swinging fluctuation of the probe position along the vessel axis. In this paper we propose a real-time gating algorithm based on the analysis of motion blur variations during the IVUS sequence. Quantitative tests performed on an in-vitro ground truth data base shown that our method is superior to state of the art algorithms both in computational speed and accuracy. |
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Springer-Verlag Berlin |
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MICCAI |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ GBC2010 |
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1447 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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Journal Article |
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2009 |
Publication |
Journal of Signal Processing Systems |
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55 |
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1-3 |
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35–47 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
IVUS Tissue Characterization with Sub-class Error-correcting Output Codes |
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Conference Article |
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2008 |
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Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008. |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets. |
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CVPR |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPM2008 |
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1041 |
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Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva |
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Title |
Fast Rigid Registration of Vascular Structures in IVUS Sequences |
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Journal Article |
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2009 |
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IEEE Transactions on Information Technology in Biomedicine |
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13 |
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6 |
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106-1011 |
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Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation. |
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1089-7771 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ GPL2009 |
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1250 |
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Author |
Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Segmentation of Artery Wall in Coronary IVUS Images: A Probabilistic Approach |
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Conference Article |
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Year |
2000 |
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International Conference on Pattern Recognition |
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4 |
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352-355 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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IAM;MILAB |
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IAM @ iam @ GRS2000a |
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1537 |
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Author |
Debora Gil; Petia Radeva; Jordi Saludes; J. Mauri |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Segmentation of Artery Wall in Coronary IVUS Images: a Probabilistic Approach |
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Conference Article |
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2000 |
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Proceedings of CIC’2000 |
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Intravascular ultrasound images represent a unique tool to analyze the morphology of arteries and vessels (plaques, restenosis, etc). The poor quality of these images makes unsupervised segmentation based on traditional segmentation algorithms (such as edge or ridge/valley detection) fail to achieve the expected results. In this paper we present a probabilistic flexible template to separate different regions in the image. In particular, we use elliptic templates to model and detect the shape of the vessel inner wall in IVUS images. We present the results of successful segmentation obtained from patients undergoing stent treatment. A physician team has validated these results. |
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Cambridge, Massachussets |
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CIC |
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IAM;MILAB |
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IAM @ iam @ GRS2000 |
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1538 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
The Photometry of Intrinsic Images |
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Conference Article |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CVPR |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
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2506 |
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