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Author Francesco Ciompi; Oriol Pujol; Carlo Gatta; Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva edit  url
doi  openurl
  Title HoliMab: A Holistic Approach for Media-Adventitia Border Detection in Intravascular Ultrasound Type Journal Article
  Year 2012 Publication Medical Image Analysis Abbreviated Journal MIA  
  Volume 16 Issue (up) 6 Pages 1085-1100  
  Keywords Media–Adventitia border detection; Intravascular ultrasound; Multi-Scale Stacked Sequential Learning; Error-correcting output codes; Holistic segmentation  
  Abstract We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed.  
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  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ CPG2012 Serial 1995  
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Author Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera edit   pdf
doi  openurl
  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 (up) 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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  ISSN 1876-1364 ISBN Medium  
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  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ HZM2012a Serial 2006  
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Author Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin-Yuste; Manel Sabate; Petia Radeva edit   pdf
doi  openurl
  Title Accurate coronary centerline extraction, caliber estimation and catheter detection in angiographies Type Journal Article
  Year 2012 Publication IEEE Transactions on Information Technology in Biomedicine Abbreviated Journal TITB  
  Volume 16 Issue (up) 6 Pages 1332-1340  
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  Abstract Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%.  
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  Series Volume Series Issue Edition  
  ISSN 1089-7771 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ HGE2012 Serial 2141  
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue (up) 6 Pages 2193-2204  
  Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models  
  Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.  
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  Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no  
  Call Number Admin @ si @ CPR2014b Serial 2470  
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera edit  url
doi  openurl
  Title Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans Type Journal Article
  Year 2014 Publication Journal of Medical Imaging and Health Informatics Abbreviated Journal JMIHI  
  Volume 4 Issue (up) 6 Pages 825-831  
  Keywords CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION  
  Abstract In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans.  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ SDE2014b Serial 2548  
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