| |
Records |
Links |
|
Author |
Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
|
| |
Title |
Automatic Digital Biometry Analysis based on Depth Maps |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
|
| |
Volume |
64 |
Issue |
9 |
Pages  |
1316-1325 |
|
| |
Keywords |
Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
|
| |
Abstract |
World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
|
| |
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 |
HuPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RCR2013 |
Serial |
2252 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin-Yuste; Manel Sabate; Petia Radeva |
|
| |
Title |
Accurate coronary centerline extraction, caliber estimation and catheter detection in angiographies |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
|
| |
Volume |
16 |
Issue |
6 |
Pages |
1332-1340 |
|
| |
Keywords |
|
|
| |
Abstract |
Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%. |
|
| |
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 |
Admin @ si @ HGE2012 |
Serial |
2141 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
|
| |
Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
|
| |
Volume |
16 |
Issue |
6 |
Pages |
1341-1352 |
|
| |
Keywords |
|
|
| |
Abstract |
Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
|
| |
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 |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; MV; OR;SIAI |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SDV2012 |
Serial |
2124 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
|
| |
Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
|
| |
Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
|
| |
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 |
0196-2892 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.079;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
|
| 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 |
