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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2010). Classyfing Agitation in Sedated ICU Patients. In Medical Image Computing in Catalunya: Graduate Student Workshop (19–20).
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.
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Angel Sappa (Ed.). (2010). Computer Graphics and Imaging.
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2010). Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions. In 28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica.
Abstract: One of the first usual steps in pattern recognition schemas is image segmentation, in order to reduce the dimensionality of the problem and manage smaller quantity of data. In our case as we are pursuing real-time colon cancer polyp detection, this step is crucial. In this paper we present a non-informative region estimation algorithm that will let us discard some parts of the image where we will not expect to find colon cancer polyps. The performance of our approach will be measured in terms of both non-informative areas elimination and polyps’ areas preserving. The results obtained show the importance of having correct non- informative region estimation in order to fasten the whole recognition process.
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Antonio Hernandez, Carlo Gatta, Petia Radeva, Laura Igual, R. Letaz, & Sergio Escalera. (2010). Automatic Vessel Segmentation For Angiography and CT Registration. In Medical Image Computing in Catalunya: Graduate Student Workshop (1–2).
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Michal Drozdzal, Laura Igual, Jordi Vitria, Petia Radeva, Carolina Malagelada, & Fernando Azpiroz. (2010). SIFT flow-based Sequences Alignment. In Medical Image Computing in Catalunya: Graduate Student Workshop (7–8).
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Miguel Reyes, Jordi Vitria, Petia Radeva, & Sergio Escalera. (2010). Real-time Activity Monitoring of Inpatients. In Medical Image Computing in Catalunya: Graduate Student Workshop (35–36).
Abstract: In this paper, we present the development of an application capable of monitoring a set of patient vital signs in real time. The application has been designed to support the medical staff of a hospital. Preliminary results show the suitability
of the system to prevent the injury produced by the agitation of the patients.
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Santiago Segui, Michal Drozdzal, Petia Radeva, & Jordi Vitria. (2010). Severe Motility Diagnosis using WCE. In Medical Image Computing in Catalunya: Graduate Student Workshop (45–46).
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Patricia Marquez. (2010). Conditions Ensuring Accuracy of Local Optical Flow Schemes (Vol. 157). Master's thesis, , Bellaterra 08193, Barcelona, Spain.
Abstract: Accurate computation of optical flow is a key-point in many image processing fields. Detection of anomalous and unpredicted agents (such as pedestrians, bikers or cars) in urban scenes or pathology discrimination in medical imaging sequences, to mention just a two. The above kinds sequences present two main difficulties for standard optical flow techniques. On one hand, variability in acquisition conditions (illuminance, medical imaging modality, ...) force an alterantive representation for images fulfilling the britghtness constancy constrain. On the hand, current variational schemes produce oversmoothed fields unable to properly model discontinuous behaviours such as collisions or functionless pathological areas. This master project explores the abilities and limitations of local and global optical flow approaches. The master student will put especial emphasis in the theoretical grounds behind in order to design a variational framework combining the theoretical advantages of the considered techniques. In particular an optical flow based on Gabor phase tracking (developed in the group for medical imaging) will be generalized to urban scenes.
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Ferran Poveda, Jaume Garcia, Enric Marti, & Debora Gil. (2010). Validation of the myocardial architecture in DT-MRI tractography. In Medical Image Computing in Catalunya: Graduate Student Workshop (pp. 29–30). Girona (Spain).
Abstract: Deep understanding of myocardial structure may help to link form and funcion of the heart unraveling crucial knowledge for medical and surgical clinical procedures and studies. In this work we introduce two visualization techniques based on DT-MRI streamlining able to decipher interesting properties of the architectural organization of the heart.
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Sergio Vera. (2010). Finger joint modelling from hand X-ray images for assessing rheumatoid arthritis (Vol. 164). Master's thesis, , Bellaterra 01893, Barcelona, Spain.
Abstract: Rheumatoid arthritis is an autoimmune, systemic, inflammatory disorder that mainly af- fects bone joints. While there is no cure for this disease, continuous advances on palliative treatments require frequent verification of patient’s illness evolution. Such evolution is mea- sured through several available semi-quantitative methods that require evaluation of hand and foot X-ray images. Accurate assessment is a time consuming task that requires highly trained personnel. This hinders a generalized use in clinical practice for early diagnose and disease follow-up. In the context of the automatization of such evaluation methods we present a method for detection and characterization of finger joints in hand radiography images. Several measures for assessing the reduction of joint space width are proposed. We compare for the first time such measures to the Van der Heijde score, the gold standard method for rheumatoid arthritis assessment. The proposed method outperforms existing strategies with a detection rate above 95%. Our comparison to Van der Heijde index shows a promising correlation that encourages further research.
Keywords: Rheumatoid arthritis; joint detection; X-ray; Van der Heijde score
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Miguel Angel Bautista, Sergio Escalera, Xavier Baro, Oriol Pujol, Jordi Vitria, & Petia Radeva. (2010). Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications. In European Conference on Machine Learning (Vol. I, pp. 119–128).
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Carolina Malagelada, F.De Lorio, Fernando Azpiroz, Santiago Segui, Petia Radeva, Anna Accarino, et al. (2010). Intestinal Dysmotility in Patients with Functional Intestinal Disorders Demonstrated by Computer Vision Analysis of Capsule Endoscopy Images. In 18th United European Gastroenterology Week (Vol. 56, pp. A19–20).
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N. Serrano, L. Tarazon, D. Perez, Oriol Ramos Terrades, & S. Juan. (2010). The GIDOC Prototype. In 10th International Workshop on Pattern Recognition in Information Systems (pp. 82–89).
Abstract: Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions.
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Monica Piñol. (2010). Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning (Vol. 162). Master's thesis, , .
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Jean-Marc Ogier, Wenyin Liu, & Josep Llados (Eds.). (2010). Graphics Recognition: Achievements, Challenges, and Evolution (Vol. 6020). LNCS. Springer Link.
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