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Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
Title | Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy | Type | Conference Article | ||
Year | 2011 | Publication | In MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging | Abbreviated Journal | |
Volume | 6668 | Issue | Pages | 76-83 | |
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Abstract ![]() |
This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed method consists of defining, for each point, a series of radial sectors around it and then accumulates the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming other approaches that also integrate depth of valleys information. | ||||
Address | Toronto, Canada | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Link | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | ||
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ISSN | ISBN | Medium | |||
Area | 800 | Expedition | Conference | ABI | |
Notes | MV;SIAI | Approved | no | ||
Call Number | IAM @ iam @ BSV2011d | Serial | 1698 | ||
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Author | Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure | ||||
Title | Slanted Stixels: A way to represent steep streets | Type | Journal Article | ||
Year | 2019 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 127 | Issue | Pages | 1643–1658 | |
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Abstract ![]() |
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. | ||||
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Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ HSC2019 | Serial | 3304 | ||
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Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
Title | CLOTH3D: Clothed 3D Humans | Type | Conference Article | ||
Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract ![]() |
This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | ||||
Address | Virtual; August 2020 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BME2020 | Serial | 3519 | ||
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Author | Ricard Borras; Agata Lapedriza; Laura Igual | ||||
Title | Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition | Type | Conference Article | ||
Year | 2012 | Publication | 9th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
Volume | 7325 | Issue | II | Pages | 98-105 |
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Abstract ![]() |
This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems. | ||||
Address | Aveiro, Portugal | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-31297-7 | Medium | |
Area | Expedition | Conference | ICIAR | ||
Notes | OR; MILAB;MV | Approved | no | ||
Call Number | Admin @ si @ BLI2012 | Serial | 2009 | ||
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Author | Javier Marin; Sergio Escalera | ||||
Title | SSSGAN: Satellite Style and Structure Generative Adversarial Networks | Type | Journal Article | ||
Year | 2021 | Publication | Remote Sensing | Abbreviated Journal | |
Volume | 13 | Issue | 19 | Pages | 3984 |
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Abstract ![]() |
This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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Area | Expedition | Conference | |||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MaE2021 | Serial | 3651 | ||
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Author | Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition | Type | Journal Article | ||
Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 78 | Issue | 18 | Pages | 25259–25271 |
Keywords | Fully convolutional networks; FusionNet; Thermal imaging; Face recognition | ||||
Abstract ![]() |
This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network. | ||||
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Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ LNE2019 | Serial | 3318 | ||
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Author | Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales | ||||
Title | Deep Learning Based Shrimp Classification | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 13598 | Issue | Pages | 36–45 | |
Keywords | Pigmentation; Color space; Light weight network | ||||
Abstract ![]() |
This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISVC | ||
Notes | MSIAU; no proj | Approved | no | ||
Call Number | Admin @ si @ SAC2022 | Serial | 3772 | ||
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Author | Rafael E. Rivadeneira; Henry Velesaca; Angel Sappa | ||||
Title | Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach | Type | Conference Article | ||
Year | 2023 | Publication | 17th International Conference on Signal-Image Technology & Internet-Based Systems | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract ![]() |
This work proposes a novel approach that integrates super-resolution techniques with off-the-shelf object detection methods to tackle the problem of handling very low-resolution thermal images. The suggested approach begins by enhancing the low-resolution (LR) thermal images through a guided super-resolution strategy, leveraging a high-resolution (HR) visible spectrum image. Subsequently, object detection is performed on the high-resolution thermal image. The experimental results demonstrate tremendous improvements in comparison with both scenarios: when object detection is performed on the LR thermal image alone, as well as when object detection is conducted on the up-sampled LR thermal image. Moreover, the proposed approach proves highly valuable in camouflaged scenarios where objects might remain undetected in visible spectrum images. | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SITIS | ||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ RVS2023 | Serial | 4010 | ||
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Author | Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera | ||||
Title | Person Perception Biases Exposed: Revisiting the First Impressions Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 13-21 | ||
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Abstract ![]() |
This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Address | Virtual; January 2021 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ JLP2021 | Serial | 3533 | ||
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Author | Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera | ||||
Title | FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition | Type | Conference Article | ||
Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
Volume | 12540 | Issue | Pages | 463-481 | |
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Abstract ![]() |
This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. | ||||
Address | Virtual; August 2020 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ SJB2020 | Serial | 3499 | ||
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Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; C. 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) | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Verlag | Place of Publication | Berlin Heidelberg | Editor | .F. Mart ́ınez-Trinidad et al |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | 800 | Expedition | Conference | ||
Notes | MV;OR;MILAB;SIAI | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f | Serial | 728 | ||
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Author | Shiqi Yang | ||||
Title | Towards Source-Free Domain Adaption of Neural Networks in an Open World | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract ![]() |
Though they achieve great success, deep neural networks typically require a huge
amount of labeled data for training. However, collecting labeled data is often laborious and expensive. It would, therefore, be ideal if the knowledge obtained from label-rich datasets could be transferred to unlabeled data. However, deep networks are weak at generalizing to unseen domains, even when the differences are only subtle between the datasets. In real-world situations, a typical factor impairing the model generalization ability is the distribution shift between data from different domains, which is a long-standing problem usually termed as (unsupervised) domain adaptation. A crucial requirement in the methodology of these domain adaptation methods is that they require access to source domain data during the adaptation process to the target domain. Accessibility to the source data of a trained source model is often impossible in real-world applications, for example, when deploying domain adaptation algorithms on mobile devices where the computational capacity is limited or in situations where data privacy rules limit access to the source domain data. Without access to the source domain data, existing methods suffer from inferior performance. Thus, in this thesis, we investigate domain adaptation without source data (termed as source-free domain adaptation) in multiple different scenarios that focus on image classification tasks. We first study the source-free domain adaptation problem in a closed-set setting, where the label space of different domains is identical. Only accessing the pretrained source model, we propose to address source-free domain adaptation from the perspective of unsupervised clustering. We achieve this based on nearest neighborhood clustering. In this way, we can transfer the challenging source-free domain adaptation task to a type of clustering problem. The final optimization objective is an upper bound containing only two simple terms, which can be explained as discriminability and diversity. We show that this allows us to relate several other methods in domain adaptation, unsupervised clustering and contrastive learning via the perspective of discriminability and diversity. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Joost | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-126409-3-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Yan2023 | Serial | 3963 | ||
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Author | Aura Hernandez-Sabate; Petia Radeva; Antonio Tovar; Debora Gil | ||||
Title | Vessel structures alignment by spectral analysis of ivus sequences | Type | Conference Article | ||
Year | 2006 | Publication | Proc. of CVII, MICCAI Workshop | Abbreviated Journal | |
Volume | Issue | Pages | 39-36 | ||
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Abstract ![]() |
Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS. | ||||
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Publisher | Place of Publication | Copenhaguen (Denmark), | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | 1st International Wokshop on Computer Vision for Intravascular and Intracardiac Imaging (CVII’06) | Abbreviated Series Title | ||
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Notes | IAM; MILAB | Approved | no | ||
Call Number | IAM @ iam @ HRT2006 | Serial | 1552 | ||
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Author | Misael Rosales; Petia Radeva;Oriol Rodriguez-Leon; Debora Gil | ||||
Title | Modelling of image-catheter motion for 3-D IVUS | Type | Journal Article | ||
Year | 2009 | Publication | Medical image analysis | Abbreviated Journal | MIA |
Volume | 13 | Issue | 1 | Pages | 91-104 |
Keywords | Intravascular ultrasound (IVUS); Motion estimation; Motion decomposition; Fourier | ||||
Abstract ![]() |
Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS. | ||||
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Notes | IAM;MILAB | Approved | no | ||
Call Number | IAM @ iam @ RRR2009 | Serial | 1646 | ||
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Author | Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti | ||||
Title | Approaching Artery Rigid Dynamics in IVUS | Type | Journal Article | ||
Year | 2009 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
Volume | 28 | Issue | 11 | Pages | 1670-1680 |
Keywords | Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation. | ||||
Abstract ![]() |
Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases. | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 0278-0062 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | IAM; MILAB | Approved | no | ||
Call Number | IAM @ iam @ HGF2009 | Serial | 1545 | ||
Permanent link to this record |