| Records |
| 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 |
| Keywords |
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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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| Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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Notes  |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2009a |
Serial |
1153 |
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| Author |
Francesco Ciompi; Oriol Pujol; Oriol Rodriguez-Leor; Carlo Gatta; Angel Serrano; Petia Radeva |
| Title |
Enhancing In-Vitro IVUS Data for Tissue Characterization |
Type |
Conference Article |
| Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
5524 |
Issue |
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Pages |
241–248 |
| Keywords |
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| Abstract |
Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%. |
| Address |
Póvoa de Varzim, Portugal |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02171-8 |
Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ CPR2009a |
Serial |
1162 |
| Permanent link to this record |
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| Author |
Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol |
| Title |
Multimodal laughter recognition in video conversations |
Type |
Conference Article |
| Year |
2009 |
Publication |
2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
110–115 |
| Keywords |
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| Abstract |
Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier. |
| Address |
Miami (USA) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2160-7508 |
ISBN |
978-1-4244-3994-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2009c |
Serial |
1188 |
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| Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
| Title |
Recoding Error-Correcting Output Codes |
Type |
Conference Article |
| Year |
2009 |
Publication |
8th International Workshop of Multiple Classifier Systems |
Abbreviated Journal |
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| Volume |
5519 |
Issue |
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Pages |
11–21 |
| Keywords |
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| Abstract |
One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design. |
| Address |
Reykjavik (Iceland) |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02325-5 |
Medium |
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| Area |
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Expedition |
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Conference |
MCS |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2009d |
Serial |
1190 |
| Permanent link to this record |
| |
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| |
| Author |
Francesco Ciompi; Oriol Pujol; Oriol Rodriguez-Leor; Angel Serrano; J. Mauri; Petia Radeva |
| Title |
On in-vitro and in-vivo IVUS data fusion |
Type |
Conference Article |
| Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
|
| Volume |
202 |
Issue |
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Pages |
147-156 |
| Keywords |
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| Abstract |
The design and the validation of an automatic plaque characterization technique based on Intravascular Ultrasound (IVUS) usually requires a data ground-truth. The histological analysis of post-mortem coronary arteries is commonly assumed as the state-of-the-art process for the extraction of a reliable data-set of atherosclerotic plaques. Unfortunately, the amount of data provided by this technique is usually few, due to the difficulties in collecting post-mortem cases and phenomena of tissue spoiling during histological analysis. In this paper we tackle the process of fusing in-vivo and in-vitro IVUS data starting with the analysis of recently proposed approaches for the creation of an enhanced IVUS data-set; furthermore, we propose a new approach, named pLDS, based on semi-supervised learning with a data selection criterion. The enhanced data-set obtained by each one of the analyzed approaches is used to train a classifier for tissue characterization purposes. Finally, the discriminative power of each classifier is quantitatively assessed and compared by classifying a data-set of validated in-vitro IVUS data. |
| Address |
Cardona (Spain) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-60750-061-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CCIA |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ CPR2009d |
Serial |
1204 |
| Permanent link to this record |
| |
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| |
| Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
| Title |
Face-to-face social activity detection using data collected with a wearable device |
Type |
Conference Article |
| Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
5524 |
Issue |
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Pages |
56–63 |
| Keywords |
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| Abstract |
In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level |
| Address |
Póvoa de Varzim, Portugal |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02171-8 |
Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ CPR2009b |
Serial |
1206 |
| Permanent link to this record |
| |
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| |
| Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
| Title |
ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences |
Type |
Conference Article |
| Year |
2009 |
Publication |
12th International Conference on Medical Image and Computer Assisted Intervention |
Abbreviated Journal |
|
| Volume |
5762 |
Issue |
II |
Pages |
|
| Keywords |
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| Abstract |
The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers. |
| Address |
London, UK |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-04270-6 |
Medium |
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| Area |
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Expedition |
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Conference |
MICCAI |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ CPF2009 |
Serial |
1228 |
| Permanent link to this record |
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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 |
| Keywords |
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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. |
| Address |
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| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1089-7771 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ GPL2009 |
Serial |
1250 |
| Permanent link to this record |
| |
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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 |
| Keywords |
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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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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1939-8018 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
| Permanent link to this record |
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| 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 |
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| Volume |
5519 |
Issue |
|
Pages |
262–271 |
| Keywords |
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| 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 |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02325-5 |
Medium |
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| Area |
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Expedition |
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Conference |
MCS |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PPG2009 |
Serial |
1260 |
| Permanent link to this record |
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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 |
| Keywords |
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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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Thesis |
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| Publisher |
Springer-Verlag |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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| ISSN |
0932-8092 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
| Permanent link to this record |
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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 |
| Keywords |
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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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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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| ISSN |
0162-8828 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2010b |
Serial |
1277 |
| Permanent link to this record |
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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 |
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Pages |
661-664 |
| Keywords |
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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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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1532-4435 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EPR2010c |
Serial |
1286 |
| Permanent link to this record |
| |
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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 |
| 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. |
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1569-5794 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ CPG2010 |
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1305 |
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| Author |
Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva |
| Title |
SRBF: Speckle Reducing Bilateral Filtering |
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Journal Article |
| Year |
2010 |
Publication |
Ultrasound in Medicine and Biology |
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UMB |
| Volume |
36 |
Issue |
8 |
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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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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ BGP2010 |
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1314 |
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