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Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo edit  doi
openurl 
  Title Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Computer Graphics Forum Abbreviated Journal (up) CGF  
  Volume 30 Issue 7 Pages 2107-2115  
  Keywords  
  Abstract IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.
 
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  Notes MILAB; HuPBA Approved no  
  Call Number Admin @ si @ EPA2011 Serial 1881  
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Author Simone Balocco; Carlo Gatta; Francesco Ciompi; A. Wahle; Petia Radeva; S. Carlier; G. Unal; E. Sanidas; F. Mauri; X. Carillo; T. Kovarnik; C. Wang; H. Chen; T. P. Exarchos; D. I. Fotiadis; F. Destrempes; G. Cloutier; Oriol Pujol; Marina Alberti; E. G. Mendizabal-Ruiz; M. Rivera; T. Aksoy; R. W. Downe; I. A. Kakadiaris edit   pdf
doi  openurl
  Title Standardized evaluation methodology and reference database for evaluating IVUS image segmentation Type Journal Article
  Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal (up) CMIG  
  Volume 38 Issue 2 Pages 70-90  
  Keywords IVUS (intravascular ultrasound); Evaluation framework; Algorithm comparison; Image segmentation  
  Abstract This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.
We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have
been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be
solved.
 
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  Area Expedition Conference  
  Notes MILAB; LAMP; HuPBA; 600.046; 600.063; 600.079 Approved no  
  Call Number Admin @ si @ BGC2013 Serial 2314  
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Author Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva edit   pdf
url  doi
openurl 
  Title Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder Type Journal Article
  Year 2012 Publication Computerized Medical Imaging and Graphics Abbreviated Journal (up) CMIG  
  Volume 36 Issue 8 Pages 591-600  
  Keywords Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles  
  Abstract We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.  
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  Notes OR; HuPBA; MILAB Approved no  
  Call Number Admin @ si @ ISE2012 Serial 2143  
Permanent link to this record
 

 
Author Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera edit   pdf
url  doi
openurl 
  Title Automatic Digital Biometry Analysis based on Depth Maps Type Journal Article
  Year 2013 Publication Computers in Industry Abbreviated Journal (up) COMPUTIND  
  Volume 64 Issue 9 Pages 1316-1325  
  Keywords Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis  
  Abstract World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments.  
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  Publisher Elsevier Place of Publication Editor  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ RCR2013 Serial 2252  
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Author Juanjo Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger edit  url
doi  openurl
  Title Multi-class structural damage segmentation using fully convolutional networks Type Journal Article
  Year 2019 Publication Computers in Industry Abbreviated Journal (up) COMPUTIND  
  Volume 112 Issue Pages 103121  
  Keywords Bridge damage detection; Deep learning; Semantic segmentation  
  Abstract Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%.  
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  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ RKL2019 Serial 3315  
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