Wilson Pérez

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.

i * in practice: identifying frequent problems in its application

Several notations have been proposed in the last decades to support information system architecting, design and implementation. Although some of them have been widely adopted, their practical application remains cumbersome. Reasons are manifold: ambiguous semantics, confusing graphical representation, lack of safe guidelines, etc. In this paper, we explored the use of the i* framework in industry for modeling organizational context. We review the models resulting from 36 industrial collaborations conducted in the last five years, where i* has been intensively used by novice modellers, without previous exposure to i*, acting as junior consultants in the organizations. We identify and categorize the main problems that they faced and as a result, we propose a set of guidelines to improve the adoption and practical application of the framework.

Grid platform for medical federated queries supporting semantic and visual annotations

Grid computing has been successfully applied on teleradiology, leading to the creation of important platforms such as MEDICUS, VirtualPACS and mantisGRID, among others. These platforms are studied on the basis of their available documentation in order to compare and discuss differences and similarities, advantages and disadvantages between them. Then, a grid platform architecture is proposed, based on the best features of the surveyed platforms with an additional emphasis on general federated queries involving CBIR (Content-Based Image Retrieval) and Semantic Annotations.

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.

A Catalogue of Reusable Context Model Elements Based on the i* Framework

The definition of the context of a system is one of the most relevant activities in the early phases of information systems engineering. It allows system engineers to narrow the system scope, by defining well established system boundaries. In practice, outlining a system context model is complex and cumbersome. In order to support context modeling, in this paper we propose a catalogue of context model elements expressed in i*, which can be reused as building blocks in the construction of context models for new systems. We describe the process used for the identification of a set of actors and dependencies recurrently appearing in several academic and industrial cases, and the process to store them into a catalogue of reusable i* context dependencies.

Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure

Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.