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Author |
Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre |
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Title ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Continuous Generalized Procrustes Analysis |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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2 |
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659–671 |
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Procrustes analysis; 2D shape model; Continuous approach |
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PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the
standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.
To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).
CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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OR; HuPBA; 605.203; 600.046;MILAB |
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Admin @ si @ IPE2014 |
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2352 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS;MILAB |
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ADAS @ adas @ ASR2007b |
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922 |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Context-aware features and robust image representations |
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2014 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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25 |
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2 |
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339-348 |
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Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. |
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LAMP; 600.079;MILAB |
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Admin @ si @ MCG2014 |
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2467 |
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Author |
Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio |
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Title ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Computing quantitative indicators of structural renal damage in pediatric DMSA scans |
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2017 |
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Revista Española de Medicina Nuclear e Imagen Molecular |
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REMNIM |
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36 |
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2 |
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72-77 |
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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 |
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Admin @ si @ SDE2017 |
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2842 |
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Author |
Francesco Ciompi; Simone Balocco; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences |
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Journal Article |
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2016 |
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Medical Physics |
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MP |
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43 |
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10 |
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Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identication of struts location and the denition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape.
Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classication. The output of the classication 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.
Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts.
Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions. |
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MILAB |
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Admin @ si @ CBR2016 |
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2819 |
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