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Author Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio edit  doi
openurl 
  Title A computational framework for cancer response assessment based on oncological PET-CT scans Type Journal Article
  Year 2014 Publication Computers in Biology and Medicine Abbreviated Journal CBM  
  Volume 55 Issue (up) Pages 92–99  
  Keywords Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis  
  Abstract In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ SED2014 Serial 2606  
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Author Mariella Dimiccoli edit   pdf
doi  openurl
  Title Figure-ground segregation: A fully nonlocal approach Type Journal Article
  Year 2016 Publication Vision Research Abbreviated Journal VR  
  Volume 126 Issue (up) Pages 308-317  
  Keywords Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion  
  Abstract We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; Approved no  
  Call Number Admin @ si @ Dim2016b Serial 2623  
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Author Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria edit  doi
openurl 
  Title Motility bar: a new tool for motility analysis of endoluminal videos Type Journal Article
  Year 2015 Publication Computers in Biology and Medicine Abbreviated Journal CBM  
  Volume 65 Issue (up) Pages 320-330  
  Keywords Small intestine; Motility; WCE; Computer vision; Image classification  
  Abstract Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;MV Approved no  
  Call Number Admin @ si @ DSR2015 Serial 2635  
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Author Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva edit   pdf
url  doi
openurl 
  Title SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation Type Journal Article
  Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 155 Issue (up) Pages 55-69  
  Keywords  
  Abstract While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods.  
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  Area Expedition Conference  
  Notes MILAB; 601.235 Approved no  
  Call Number Admin @ si @ DBT2017 Serial 2714  
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Author Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan edit   pdf
url  openurl
  Title Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach Type Journal Article
  Year 2016 Publication Journal of Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 27 Issue (up) Pages 511-527  
  Keywords particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging  
  Abstract In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; Approved no  
  Call Number Admin @ si @ DJM2016 Serial 2735  
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