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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad | ||||
| Title | Locality Regularized Group Sparse Coding for Action Recognition | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 158 | Issue | Pages | 106-114 | |
| Keywords | Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition | ||||
| Abstract | Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. | ||||
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HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BGE2017 | Serial | 3014 | ||
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| Author | Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera | ||||
| Title | Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 122 | Issue | 2 | Pages | 212–227 |
| Keywords | Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation | ||||
| Abstract | This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures. | ||||
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HuPBA;MILAB; 303.100 | Approved | no | ||
| Call Number | Admin @ si @ PEB2017 | Serial | 2765 | ||
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| Author | Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio | ||||
| 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 |
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| 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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HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SDE2017 | Serial | 2842 | ||
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| Author | Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla | ||||
| Title | Editorial: Special Issue on Computational Intelligence for Vision and Robotics | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 853–854 |
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HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ GGE2017 | Serial | 2845 | ||
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| Author | Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza | ||||
| Title | Evolving weighting schemes for the Bag of Visual Words | Type | Journal Article | ||
| Year | 2017 | Publication | Neural Computing and Applications | Abbreviated Journal | Neural Computing and Applications |
| Volume | 28 | Issue | 5 | Pages | 925–939 |
| Keywords | Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision | ||||
| Abstract | The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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| Publisher | Place of Publication | Editor | Springer | ||
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HUPBA;MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EPE2017 | Serial | 2743 | ||
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| Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell | ||||
| Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
| Year | 2017 | Publication | European Respiratory Journal | Abbreviated Journal | ERJ |
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IAM | Approved | no | ||
| Call Number | Admin @ si @ DGC2017b | Serial | 3632 | ||
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| Author | Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester | ||||
| Title | Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities | Type | Journal Article | ||
| Year | 2017 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
| Volume | 35 | Issue | Pages | 390-402 | |
| Keywords | Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points | ||||
| Abstract | Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a con dent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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IAM; 600.060; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GVB2017 | Serial | 2775 | ||
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| Author | Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina | ||||
| Title | CYBERH: Cyber-Physical Systems in Health for Personalized Assistance | Type | Conference Article | ||
| Year | 2017 | Publication | International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
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| Abstract | Assistance systems for e-Health applications have some specific requirements that demand of new methods for data gathering, analysis and modeling able to deal with SmallData:
1) systems should dynamically collect data from, both, the environment and the user to issue personalized recommendations; 2) data analysis should be able to tackle a limited number of samples prone to include non-informative data and possibly evolving in time due to changes in patient condition; 3) algorithms should run in real time with possibly limited computational resources and fluctuant internet access. Electronic medical devices (and CyberPhysical devices in general) can enhance the process of data gathering and analysis in several ways: (i) acquiring simultaneously multiple sensors data instead of single magnitudes (ii) filtering data; (iii) providing real-time implementations condition by isolating tasks in individual processors of multiprocessors Systems-on-chip (MPSoC) platforms and (iv) combining information through sensor fusion techniques. Our approach focus on both aspects of the complementary role of CyberPhysical devices and analysis of SmallData in the process of personalized models building for e-Health applications. In particular, we will address the design of Cyber-Physical Systems in Health for Personalized Assistance (CyberHealth) in two specific application cases: 1) A Smart Assisted Driving System (SADs) for dynamical assessment of the driving capabilities of Mild Cognitive Impaired (MCI) people; 2) An Intelligent Operating Room (iOR) for improving the yield of bronchoscopic interventions for in-vivo lung cancer diagnosis. |
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| Address | Timisoara; Rumania; September 2017 | ||||
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| Area | Expedition | Conference | SYNASC | ||
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IAM; 600.085; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GHC2017 | Serial | 3045 | ||
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| Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso | ||||
| Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Thoracic Oncology | Abbreviated Journal | JTO |
| Volume | 12 | Issue | 1S | Pages | S596-S597 |
| Keywords | Thorax CT; diagnosis; Peripheral Pulmonary Nodule | ||||
| Abstract | A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). | ||||
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IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ DGC2017a | Serial | 2883 | ||
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| Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil | ||||
| Title | Medial structure generation for registration of anatomical structures | Type | Book Chapter | ||
| Year | 2017 | Publication | Skeletonization, Theory, Methods and Applications | Abbreviated Journal | |
| Volume | 11 | Issue | Pages | ||
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IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ MFV2017a | Serial | 2935 | ||
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| Author | Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Towards a Videobronchoscopy Localization System from Airway Centre Tracking | Type | Conference Article | ||
| Year | 2017 | Publication | 12th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 352-359 | ||
| Keywords | Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation | ||||
| Abstract | Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations. |
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| Address | Porto; Portugal; February 2017 | ||||
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| Area | Expedition | Conference | VISAPP | ||
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IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SEB2017 | Serial | 2943 | ||
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| Author | Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell | ||||
| Title | Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
| Year | 2017 | Publication | 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging | Abbreviated Journal | |
| Volume | 10550 | Issue | Pages | 151-159 | |
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| Abstract | Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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| Address | Quebec; Canada; September 2017 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CLIP | ||
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IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GRM2017 | Serial | 2957 | ||
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| Author | Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos | ||||
| Title | Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
| Year | 2017 | Publication | 18th World Conference on Lung Cancer | Abbreviated Journal | |
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| Abstract | Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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| Address | Yokohama; Japan; October 2017 | ||||
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| Area | Expedition | Conference | IASLC WCLC | ||
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IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ OGM2017 | Serial | 3044 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate | ||||
| Title | Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study | Type | Conference Article | ||
| Year | 2017 | Publication | 11th European CytoGenesis Conference | Abbreviated Journal | |
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| Address | Florencia; Italia; July 2017 | ||||
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| Area | Expedition | Conference | ECA | ||
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IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SBG2017a | Serial | 2936 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title | Is there a pattern of Chromosome territoriality along mice spermatogenesis? | Type | Conference Article | ||
| Year | 2017 | Publication | 3rd Spanish MeioNet Meeting Abstract Book | Abbreviated Journal | |
| Volume | Issue | Pages | 55-56 | ||
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| Address | Miraflores de la Sierra; Madrid; June 2017 | ||||
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| Area | Expedition | Conference | MEIONET | ||
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IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2958 | ||
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