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C. Butakoff; Simone Balocco; F.M. Sukno; C. Hoogendoorn; C. Tobon-Gomez; G. Avegliano; A.F. Frangi |
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Left-ventricular Epi- and Endocardium Extraction from 3D Ultrasound Images Using an Automatically Constructed 3D ASM |
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2016 |
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Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization |
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CMBBE |
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4 |
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5 |
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265-280 |
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ASM; cardiac segmentation; statistical model; shape model; 3D ultrasound; cardiac segmentation |
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In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach. |
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2168-1163 |
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MILAB |
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Admin @ si @ BBS2016 |
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2449 |
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Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images |
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Year |
2014 |
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IEEE Transactions on Information Technology in Biomedicine |
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TITB |
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18 |
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6 |
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1831-1838 |
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Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality |
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Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task. |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2014 |
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2385 |
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Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
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Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
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2009 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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10 |
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1 |
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113–126 |
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The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BEV2008 |
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1116 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
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2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
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3 |
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285–297 |
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Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2009a |
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1153 |
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Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
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Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
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Journal Article |
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2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
Issue |
15 |
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1424–1433 |
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Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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HuPBA; DAG; MILAB |
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BCNPCL @ bcnpcl @ EFP2009a |
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1180 |
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