Catálogo de publicaciones - libros

In this paper the application of novel three-level recognition concept to processing of some structured documents (forms) in medical information systems is presented. The recognition process is decomposed into three levels: character recognition, word recognition and form contents recognition. On the word and form contents level the probabilistic lexicons are available. The decision on the word level is performed using results of character classification based on a character image analysis and probabilistic lexicon treated as a special kind of soft classifier. The novel approach to combining these both classifiers is proposed, where fusion procedure interleaves soft outcomes of both classifiers so as to obtain the best recognition quality. Similar approach is applied on the semantic level with combining soft outcomes of word classifier and probabilistic form lexicon. Proposed algorithms were experimentally applied in medical information system and results of automatic classification of laboratory test order forms obtained on the real data are described.

This paper presents a PACS solution for echocardiography laboratories, denominated as , that provides a cost-efficient digital archive, and enables the acquisition, storage, transmission and visualization of DICOM cardiovascular ultrasound sequences. The core of our approach is the implementation of a DICOM private transfer syntax designed to support any video encoder installed on the operating system. This structure provides great flexibility concerning the selection of an encoder that best suits the specifics of a particular imaging modality or working scenario. The major advantage of the proposed system stems from the high compression rate achieved by video encoding the ultrasound sequences at a proven diagnostic quality. This highly efficient encoding process ensures full online availability of the ultrasound studies and, at the same time, enables medical data transmission over low-bandwidth channels that are often encountered in long range telemedicine sessions. We herein propose an imaging solution that embeds a Web framework with a set of DICOM services for image visualization and manipulation, which, so far, have been traditionally restricted to intranet environments.

Medical Imaging implies executing complex post-processing tasks such as segmentation, rendering or registration which requires resources that exceeds the capabilities of conventional systems. The usage of Grid Technologies can be an efficient solution, increasing the production time of shared resources. However, the difficulties on the use of Grid technologies have reduced its spreading outside of the scientific arena.

This article tackles the problem of using Grid Technologies for the co-registration of a series of volumetric medical images. The co-registration of time series of images is a needed pre-processing task when analysing the evolution of the diffusion of contrast agents. This processing requires large computational resources and cannot be tackled efficiently on an individual basis. This article proposes and implements a four-level software architecture that provides a simple interface to the user and deals transparently with the complexity of Grid environment. The four layers implemented are: Grid Layer (the closest to the Grid infrastructure), the Gate-to-Grid (which transforms the user requests to Grid operations), the Web Services layer (which provides a simple, standard and ubiquitous interface to the user) and the application layer.

An application has been developed on top of this architecture to manage the execution of multi-parametric groups of co-registration actions on a large set of medical images. The execution has been performed on the EGEE Grid infrastructure. The application is platform-independent and can be used from any computer without special requirements.

New biomedical technologies need to be integrated for research on complex diseases. It is necessary to combine and analyze information coming from different sources: genetic-molecular, clinical data and environmental risks. This paper presents the work carried on by the INBIOMED research network within the field of biomedical image analysis. The overall objective is to respond to the growing demand of advanced information processing methods for: developing analysis tools, creating knowledge structure and validating them in pharmacogenetics, epidemiology, molecular and image based diagnosis research environments. All the image processing tools and data are integrated and work within a web services-based application, the so called INBIOMED platform. Finally, several biomedical research labs offered real data and validate the network tools and methods in the most prevalent pathologies: cancer, cardiovascular and neurological. This work provides a unique biomedical information processing platform, open to the incorporation of data coming from other feature disease networks.

Ontology is the talk of the day in the medical informatics community. Its relevant role in the design and implementation of information systems in health care is now widely acknowledged. In this paper we present two case studies showing ontologies “at work” in the genomic domain and in the clinical context. First we show how ontologies and genomic controlled vocabularies can be effectively applied to help in a genomic approach towards the comprehension of fundamental biological processes and complex cellular patho-physiological mechanisms, and hence in biological knowledge mining and discovery. Subsequently, as far as the clinical context is concerned, we emphasize the relevance of ontologies in order to maintain semantic consistency of patient data in a continuity of care scenario. In conclusion we advocate that a deep analysis of the structure and the concepts present at different granular level – from genes to organs – is needed in order to bridge this different domains and to unify bio-medical knowledge in a single paradigm.

A new non-invasive method for investigation of movement of selected points on the vertebral column is presented. The registration of position of points marked on patient’s body is performed by 4 infrared cameras. This experiment enables to reconstruct 3-dimensional trajectories of displacement of marked points. We introduce recurrent neural networks as formal nonlinear dynamical models of each point trajectory. These models are based only on experimental data and are set up of minimal number of parameters. Therefore they are suitable for pattern recognition problems.

Myocardial Contrast Echocardiography (MCE) is a recent technique that allows to measure regional perfusion in the cardiac wall. Segmentation of MCE sequences would allow simultaneous evaluation of perfusion and wall motion. This paper deals with the application of partial differential equations (PDE) for tracking the endocardial wall. We use a variational optical flow method which we solve numerically with a multigrid approach adapted to the MCE modality. The data sequence are first smoothed and a hierarchical-iterative procedure is implemented to correctly estimate the flow field magnitude. The method is tested on several sequences showing promising results for automatic wall tracking.

Unfolding, one of virtual endoscopy techniques, gives us a flatten image of the inner surface of an organ. It is more suitable for a diagnosis and polyp detection. Most common unfolding methods use radial ray casting along with pre-computed central path. However, it may produce false images deformed and lost some information because adjacent ray planes cross when the organ’s curvature is relatively high. To solve it, several methods have been presented. However, these have severe computational overhead. We propose an efficient crossing-free ray casting for unfolding. It computes ray-cones according to curvature of the path. Then in order to avoid intersection between ray-cones, it adjusts direction of ray-cones detected while testing intersection. Lastly, it determines direction of all rays fired from sample points between control points by simple linear interpolation. Experimental results show that it produces accurate images of a virtually dissected colon and takes not much time.

The huge amount of information coming from genomics and proteomics research is expected to give rise to a new clinical practice, where diagnosis and treatments will be supported by information at the molecular level. However, navigating through bioinformatics databases can be a too complex and unproductive task.

In this paper we present an information retrieval engine that is being used to gather and join information about rare diseases, from the phenotype to the genotype, in a public web portal – diseasecard.org.

Biological sources integration has been addressed in several frameworks, considering both information sources incompatibilities and data representation heterogeneities. Most of these frameworks are mainly focused on coping with interoperability constraints among distributed databases that contain diverse types of biological data. In this paper, we propose an XML-based architecture that extends integration efforts from the distributed data sources domain to heterogeneous Bioinformatics tools of similar functionalities (“vertical integration”). The proposed architecture is based on the mediator/wrapper integration paradigm and a set of prescribed definitions that associates the capabilities and functional constraints of each analysis tool. The resulting XML-formatted information is further exploited by a visualization module that generates comparative views of the analysis outcome and a query mechanism that handles multiple information sources. The applicability of the proposed integration architecture and the information handling mechanisms was tested and substantiated on widely-known ab-initio gene finders that are publicly accessible through Web interfaces.