Andrés Tello

An automatic method for the enrichment of DICOM metadata using biomedical ontologies

This work is a novel contribution for enriching medical images using semantic annotations with a strategy for unifying different ontologies and instances of DICOM medical files. We present the L-MOM library (Library for Mapping of Ontological Metadata) as a tool for making an automatic mapping between instances of DICOM medical files and different medical ontologies (e.g., FMA, RadLex, MeSH). The main contributions are: i) the domain independent L-MOM library which is able to integrate DICOM metadata with ontologies from different domains; ii) a strategy to automatically annotate DICOM data with universally accepted medical ontologies, and provide values of similarity between ontologies and DICOM metadata; and iii) a framework to traverse ontological concepts that characterized clinical studies of patients registered in the framework catalog.

RDF-ization of DICOM medical images towards linked health data cloud

This paper proposes a novel strategy for semantifying DICOM medical images (RDF-ization) automatically. We define an architecture that involves processes for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML. These processes allow for semantically enriching and sharing the metadata of DICOM medical files through the Linked Health Data cloud. Thereby providing enhanced query capabilities with respect to the ones offered by current PACS environments, while exploiting all advantages of the Linking Open Data (LOD) cloud and SemanticWeb technologies.

Detecting Similar Areas of Knowledge Using Semantic and Data Mining Technologies

Searching for scientific publications online is an essential task for researchers working on a certain topic. However, the extremely large amount of scientific publications found in the web turns the process of finding a publication into a very difficult task whereas, locating peers interested in collaborating on a specific topic or reviewing literature is even more challenging. In this paper, we propose a novel architecture to join multiple bibliographic sources, with the aim of identifying common research areas and potential collaboration networks, through a combination of ontologies, vocabularies, and Linked Data technologies for enriching a base data model. Furthermore, we implement a prototype to provide a centralized repository with bibliographic sources and to find similar knowledge areas using data mining techniques in the domain of Ecuadorian researchers community.

WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.

TV program recommender using user authentication on middleware Ginga

The system proposed in this article aims to identify and recognize television users with the objective of offering personalized television programming. In this setting, the authentication and recommendation mechanisms used require to collect the necessary information in an implicit manner as much as possible, such that the leisure and entertainment objectives this broadcasting medium brings are not interrupted. The design proposed for the implementation of the interactive application uses an authentication process based on facial recognition and a recommendation algorithm based on contextual information, which is mainly implicitly captured. Experimental obtained results show that the system offers more accurate recommendations when the user exhibits a habitual behavior; e.g. watching TV programs of a same category in a specific channel and schedule.