| |
Records |
Links |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1089-7771 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes  |
MILAB;HuPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ GPL2009 |
Serial |
1250 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1939-8018 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HuPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oriol Pujol; Eloi Puertas; Carlo Gatta |
|
| |
Title |
Multi-scale Stacked Sequential Learning |
Type |
Conference Article |
| |
Year |
2009 |
Publication |
8th International Workshop of Multiple Classifier Systems |
Abbreviated Journal |
|
|
| |
Volume |
5519 |
Issue |
|
Pages |
262–271 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
Reykjavik, Iceland |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0302-9743 |
ISBN |
978-3-642-02325-5 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
MCS |
|
| |
Notes |
MILAB;HuPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ PPG2009 |
Serial |
1260 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
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 |
0932-8092 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0162-8828 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPR2010b |
Serial |
1277 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
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 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
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 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
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). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
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 |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPR2010e |
Serial |
1338 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antonio Hernandez; Miguel Reyes; Sergio Escalera; Petia Radeva |
|
| |
Title |
Spatio-Temporal GrabCut human segmentation for face and pose recovery |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
33–40 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology. |
|
| |
Address |
San Francisco; CA; USA; June 2010 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2160-7508 |
ISBN |
978-1-4244-7029-7 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
AMFG |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ HRE2010 |
Serial |
1362 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Francesco Ciompi; Oriol Pujol; Petia Radeva |
|
| |
Title |
A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
710–713 |
|
| |
Keywords |
|
|
| |
Abstract |
We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems. |
|
| |
Address |
Istanbul;Turkey |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ CPR2010a |
Serial |
1365 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
|
| |
Title |
Conditional Random Fields for image segmentation in Intravascular Ultrasound |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
13–14 |
|
| |
Keywords |
|
|
| |
Abstract |
We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved. |
|
| |
Address |
Girona |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
MICCAT |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ CPF2010 |
Serial |
1453 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
|
| |
Title |
Embedding Random Projections in Regularized Gradient Boosting Machines |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
44–53 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Barcelona (Spain) |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
SUEMA |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ CPR2010c |
Serial |
1466 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
|
| |
Title |
Classyfing Agitation in Sedated ICU Patients |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
19–20 |
|
| |
Keywords |
|
|
| |
Abstract |
Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances. |
|
| |
Address |
Girona |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
MICCAT |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ COR2010 |
Serial |
1467 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antonio Hernandez; Carlo Gatta; Petia Radeva; Laura Igual; R. Letaz; Sergio Escalera |
|
| |
Title |
Automatic Vessel Segmentation For Angiography and CT Registration |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1–2 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Girona |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
MICCAT |
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ HGR2010 |
Serial |
1474 |
|
| Permanent link to this record |
