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Author Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva edit  doi
openurl 
  Title SRBF: Speckle Reducing Bilateral Filtering Type Journal Article
  Year 2010 Publication Ultrasound in Medicine and Biology Abbreviated Journal UMB  
  Volume 36 Issue 8 Pages 1353-1363  
  Keywords  
  Abstract Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US).  
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  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ BGP2010 Serial 1314  
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio edit  doi
openurl 
  Title Computing quantitative indicators of structural renal damage in pediatric DMSA scans Type Journal Article
  Year 2017 Publication Revista Española de Medicina Nuclear e Imagen Molecular Abbreviated Journal REMNIM  
  Volume 36 Issue 2 Pages 72-77  
  Keywords  
  Abstract OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.
 
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  Notes HuPBA;MILAB; no menciona Approved no  
  Call Number Admin @ si @ SDE2017 Serial 2842  
Permanent link to this record
 

 
Author Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon edit  doi
openurl 
  Title Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Type Journal Article
  Year 2022 Publication Patterns Abbreviated Journal PATTERNS  
  Volume 3 Issue 7 Pages 100543  
  Keywords Machine learning; data science; benchmark platform; reproducibility; competitions  
  Abstract Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.  
  Address June 24, 2022  
  Corporate Author Thesis  
  Publisher Science Direct Place of Publication Editor  
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  Notes HuPBA Approved no  
  Call Number Admin @ si @ XEP2022 Serial 3764  
Permanent link to this record
 

 
Author Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro edit  doi
openurl 
  Title Non-Verbal Communication Analysis in Victim-Offender Mediations Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 67 Issue 1 Pages 19-27  
  Keywords Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning  
  Abstract We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals.  
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  Notes HuPBA;MV Approved no  
  Call Number Admin @ si @ PEP2015 Serial 2583  
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Author Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras edit  doi
openurl 
  Title Multi-part body segmentation based on depth maps for soft biometry analysis Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 56 Issue Pages 14-21  
  Keywords 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis  
  Abstract This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.  
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  Notes HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB Approved no  
  Call Number Admin @ si @ MEG2015 Serial 2588  
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