| Records |
| Author |
Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente |
| Title |
Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy |
Type |
Journal Article |
| Year |
2014 |
Publication |
Journal of Biomedical Optics |
Abbreviated Journal  |
JBiO |
| Volume |
19 |
Issue |
12 |
Pages |
126004-1 - 126004-9 |
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| Abstract |
The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. |
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| Notes |
ADAS; 600.076 |
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no |
| Call Number |
Admin @ si @ BLS2014 |
Serial |
2563 |
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| Author |
Carlos Martin-Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir |
| Title |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Journal of Biomedical and Health Informatics |
Abbreviated Journal |
JBHI |
| Volume |
27 |
Issue |
7 |
Pages |
3302-3313 |
| Keywords |
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| Abstract |
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. |
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HUPBA |
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no |
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Admin @ si @ MCI2023 |
Serial |
3880 |
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| Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
| Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Archaeological Science |
Abbreviated Journal |
JArchSci |
| Volume |
148 |
Issue |
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Pages |
105654 |
| Keywords |
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| Abstract |
This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
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December 2022 |
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| Notes |
MSIAU; MACO; 600.167 |
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no |
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Admin @ si @ BOL2022 |
Serial |
3753 |
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| Author |
Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli |
| Title |
Computer Vision for Ambient Intelligence |
Type |
Journal Article |
| Year |
2011 |
Publication |
Journal of Ambient Intelligence and Smart Environments |
Abbreviated Journal |
JAISE |
| Volume |
3 |
Issue |
3 |
Pages |
187-191 |
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ISE |
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no |
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Admin @ si @ SGS2011a |
Serial |
1725 |
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| Author |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
| Title |
Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization |
Type |
Journal Article |
| Year |
2012 |
Publication |
Journal of Ambient Intelligence and Smart Environments |
Abbreviated Journal |
JAISE |
| Volume |
4 |
Issue |
6 |
Pages |
535-546 |
| Keywords |
Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation |
| Abstract |
We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
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Edition |
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1876-1364 |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ HZM2012a |
Serial |
2006 |
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| Author |
Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; H. Tizon; Xavier Carrillo; J. Mauri; Petia Radeva |
| Title |
Radial Artery Volume Changes After Administration Of Two Different Intra-arterial Drug Regimens. Assessment by Intravascular Ultrasound |
Type |
Journal Article |
| Year |
2010 |
Publication |
Journal of the American College of Cardiology |
Abbreviated Journal |
JACC |
| Volume |
56 |
Issue |
13s1 |
Pages |
B119 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ SFC2010b |
Serial |
1364 |
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| Author |
Ferran Poveda; Enric Marti; Debora Gil; Francesc Carreras; Manel Ballester |
| Title |
Helical Structure of Ventricular Anatomy by Diffusion Tensor Cardiac MR Tractography |
Type |
Journal Article |
| Year |
2012 |
Publication |
Journal of American College of Cardiology |
Abbreviated Journal |
JACC |
| Volume |
5 |
Issue |
7 |
Pages |
754-755 |
| Keywords |
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| Abstract |
It is widely accepted that myocardial fiber architecture plays a critical role in myocardial contractility and relaxation (1). However, there is a lack of consensus about the distribution of the myocardial fibers and their spatial arrangement in the left and right ventricles. An understanding of the cardiac architecture should benefit the ventricular functional assessment, left ventricular reconstructive surgery planning, or resynchronization therapy in heart failure. Researchers have proposed several conceptual models to describe the architecture of the heart, ranging from gross dissection to histological presentation. The cardiac mesh model (2) proposes that the myocytes are arranged longitudinally and radially change their angulation along the myocardial depth. By contrast, the helical ventricular myocardial model states that the ventricular myocardium is a continuous anatomical helical layout of myocardial fibers (1 |
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1936-878X |
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IAM |
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no |
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IAM @ iam @ PMG2012 |
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1985 |
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| Author |
Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund |
| Title |
A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Journal of Selected Topics in Signal Processing |
Abbreviated Journal |
J-STSP |
| Volume |
6 |
Issue |
5 |
Pages |
553-565 |
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| Abstract |
In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach. |
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1932-4553 |
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ISE |
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no |
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Admin @ si @ HCG2012 |
Serial |
1994 |
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| Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
| Title |
Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation |
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Journal Article |
| Year |
2012 |
Publication |
IEEE Journal of Selected Topics in Signal Processing |
Abbreviated Journal |
J-STSP |
| Volume |
6 |
Issue |
5 |
Pages |
437-446 |
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This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain. |
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1932-4553 |
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ADAS |
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no |
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Admin @ si @ BLS2012b |
Serial |
2155 |
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Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria del Mar Vila; Laura Igual; Daniel L. Rubin; Elvira Fernandez-Giraldez; Petia Radeva; Sandy Napel |
| Title |
A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound |
Type |
Journal Article |
| Year |
2017 |
Publication |
IEEE Journal Biomedical and Health Informatics |
Abbreviated Journal |
J-BHI |
| Volume |
21 |
Issue |
1 |
Pages |
48-55 |
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| Abstract |
Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound. |
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| Notes |
MILAB; no menciona |
Approved |
no |
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Admin @ si @ LGB2017 |
Serial |
2931 |
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| Author |
Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva |
| Title |
Hierarchical approach to classify food scenes in egocentric photo-streams |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Journal of Biomedical and Health Informatics |
Abbreviated Journal |
J-BHI |
| Volume |
24 |
Issue |
3 |
Pages |
866 - 877 |
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| Abstract |
Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods. |
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MILAB; no proj |
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no |
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Admin @ si @ TLM2020 |
Serial |
3380 |
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| Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
| Title |
Camera pose estimation in multi-view environments: From virtual scenarios to the real world |
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Journal Article |
| Year |
2021 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IVC |
| Volume |
110 |
Issue |
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Pages |
104182 |
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This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. |
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MSIAU; 600.130; 600.122 |
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no |
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Admin @ si @ CSV2021 |
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3577 |
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Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
| Title |
Semantic Monocular Depth Estimation Based on Artificial Intelligence |
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Journal Article |
| Year |
2020 |
Publication |
IEEE Intelligent Transportation Systems Magazine |
Abbreviated Journal |
ITSM |
| Volume |
13 |
Issue |
4 |
Pages |
99-103 |
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| Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation. |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ GUH2019 |
Serial |
3306 |
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Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen |
| Title |
Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification |
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Journal Article |
| Year |
2018 |
Publication |
ISPRS Journal of Photogrammetry and Remote Sensing |
Abbreviated Journal |
ISPRS J |
| Volume |
138 |
Issue |
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Pages |
74-85 |
| Keywords |
Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis |
| Abstract |
Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene |
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LAMP; 600.109; 600.106; 600.120 |
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Admin @ si @ RKW2018 |
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3158 |
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| Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
| Title |
On-line Semantic Perception Using Uncertainty |
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Conference Article |
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2012 |
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International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Semantic Segmentation |
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Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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ADAS |
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ADAS @ adas @ NRR2012 |
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2378 |
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