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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 30 | Issue | 3 | Pages | 285–297 |
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| Abstract | 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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Notes  |
MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2009a | Serial | 1153 | ||
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| Author | Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva | ||||
| Title | Fast Rigid Registration of Vascular Structures in IVUS Sequences | Type | Journal Article | ||
| Year | 2009 | Publication | IEEE Transactions on Information Technology in Biomedicine | Abbreviated Journal | |
| Volume | 13 | Issue | 6 | Pages | 106-1011 |
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| Abstract | Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation. | ||||
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| ISSN | 1089-7771 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GPL2009 | Serial | 1250 | ||
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| Author | Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva | ||||
| Title | Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2009 | Publication | Journal of Signal Processing Systems | Abbreviated Journal | |
| Volume | 55 | Issue | 1-3 | Pages | 35–47 |
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| Abstract | Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. | ||||
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| ISSN | 1939-8018 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPM2009 | Serial | 1258 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Traffic sign recognition system with β -correction | Type | Journal Article | ||
| Year | 2010 | Publication | Machine Vision and Applications | Abbreviated Journal | MVA |
| Volume | 21 | Issue | 2 | Pages | 99–111 |
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| Abstract | Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0932-8092 | ISBN | Medium | ||
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| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010a | Serial | 1276 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | On the Decoding Process in Ternary Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2010 | Publication | IEEE on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 32 | Issue | 1 | Pages | 120–134 |
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| Abstract | A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010b | Serial | 1277 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Error-Correcting Output Codes Library | Type | Journal Article | ||
| Year | 2010 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
| Volume | 11 | Issue | Pages | 661-664 | |
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| Abstract | (Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1532-4435 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010c | Serial | 1286 | ||
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| Author | Francesco Ciompi; Oriol Pujol; Carlo Gatta; Oriol Rodriguez-Leor; J. Mauri; Petia Radeva | ||||
| Title | Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal of Cardiovascular Imaging | Abbreviated Journal | IJCI |
| Volume | 26 | Issue | 7 | Pages | 763–779 |
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| Abstract | Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1569-5794 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ CPG2010 | Serial | 1305 | ||
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| Author | Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva | ||||
| Title | SRBF: Speckle Reducing Bilateral Filtering | Type | Journal Article | ||
| Year | 2010 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
| Volume | 36 | Issue | 8 | Pages | 1353-1363 |
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| Abstract | Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). | ||||
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| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BGP2010 | Serial | 1314 | ||
| Permanent link to this record | |||||
| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Re-coding ECOCs without retraining | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 7 | Pages | 555–562 |
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| Abstract | A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Notes |
MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Personalization and User Verification in Wearable Systems using Biometric Walking Patterns | Type | Journal Article | ||
| Year | 2012 | Publication | Personal and Ubiquitous Computing | Abbreviated Journal | PUC |
| Volume | 16 | Issue | 5 | Pages | 563-580 |
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| Abstract | In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1617-4909 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPR2012 | Serial | 1706 | ||
| Permanent link to this record | |||||
| Author | Jose Seabra; Francesco Ciompi; Oriol Pujol; J. Mauri; Petia Radeva; Joao Sanchez | ||||
| Title | Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Biomedical Engineering | Abbreviated Journal | TBME |
| Volume | 58 | Issue | 5 | Pages | 1314-1324 |
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| Abstract | Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology. | ||||
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| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ SCP2011 | Serial | 1712 | ||
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| Author | Xavier Carrillo; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Petia Radeva; Neus Salvatella; Oriol Pujol; J. Mauri; A. Bayes | ||||
| Title | Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Invasive Cardiology | Abbreviated Journal | JOIC |
| Volume | 23 | Issue | 10 | Pages | 401-404 |
| Keywords | radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis | ||||
| Abstract | OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil. BACKGROUND: Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI). METHODS: This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences. RESULTS: There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69). CONCLUSIONS: Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements. |
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| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CFC2011 | Serial | 1797 | ||
| Permanent link to this record | |||||
| Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
| Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 44 | Issue | 10-11 | Pages | 2414-2416 |
| Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
| Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
| Permanent link to this record | |||||
| Author | Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva | ||||
| Title | A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder | Type | Journal Article | ||
| Year | 2011 | Publication | BioMedical Engineering Online | Abbreviated Journal | BEO |
| Volume | 10 | Issue | 105 | Pages | 1-23 |
| Keywords | Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework | ||||
| Abstract | Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1475-925X | ISBN | Medium | ||
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| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ISH2011 | Serial | 1882 | ||
| Permanent link to this record | |||||
| Author | Francesco Ciompi; Oriol Pujol; Carlo Gatta; Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| 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 | 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 | ||
| Permanent link to this record | |||||