Identificación de términos a partir de enumeraciones sintagmáticas nominales: una aplicación al dominio médico

Partiendo de la hipótesis de que las enumeraciones sintagmáticas nominales (ESN) que se encuentran en los textos médicos se componen de términos específicos del dominio, presentamos un método de reconocimiento de dichas enumeraciones con el objetivo de contribuir a la extracción automática. La metodología se conforma de tres etapas: (i) reconocimiento de enumeraciones sintagmáticas nominales, aquí se utiliza exclusivamente información lingüística, a partir de la cual se elaboran reglas de análisis sintáctico; (ii) extracción automática de los candidatos a términos que se correspondían con unigramas y bigramas, y (iii) evaluación de los candidatos extraídos con el asesoramiento de expertos del área médica. Los experimentos fueron realizados en el corpus IULA, conformado por textos médicos en español. Los resultados obtenidos fueron alentadores, ya que se logró un 67% y 68% de precisión en las enumeraciones detectadas para unigramas y bigramas respectivamente.Sociedad Argentina de Informática e Investigación Operativ

Fusion de multi-modalités et réduction par sémantique latente Application à la recherche de documents multimédia et à l'annotation automatique d'images

International audienceCe papier étudie la "sémantique latente" entre des éléments visuels et textuels d'une collection multimédia, appliquée à deux tâches : (1) la Recherche de Document Multimédia (RDM) contenant des images et du texte ; et (2) l'Annotation Automatique d'Images (AAI). La sémantique latente, habituellement utilisée dans l'indexation textuelle, est mise à profit ici pour faire apparaître des liens entre les descriptions textuelles et visuelles des images. Nous avons ainsi deux contributions principales. Il s'agit d'une part, de la première étude sur l'influence de la sémantique latente entre termes textuels et visuels, sur une grande collection de documents. En effet, cette méthode est testée sur une collection de 20000 images touristiques. D'autre part, nous démontrons que la fusion des différents modalités d'image (i.e. termes visuels vs textuels, et différentes méthode de représentations d'image) améliore le résultat d'une annotation au- tomatique des images par du texte. Nos collections de test sont la base d'images annotées de COREL et la base d'Image CLEF 2006

Medical Image Retrieval based on Knowledge-Assisted Text and Image Indexing

International audienceVoluminous medical images are generated daily. They are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image databases, both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework based on Support Vector Machines to facilitate modular design and learning of medical semantics from images. We present two complementary visual indexing approaches within this framework: a global indexing to access image modality, and a local indexing to access semantic local features. Two fusion approaches are developed to improve textual retrieval using the UMLS-based image indexing. First, a simple fusion of the textual and visual retrieval approaches is proposed, improving significantly the retrieval results of both text and image retrieval. Second, a visual modality filtering is designed to remove visually aberrant images according to the query modality concept(s). Using the ImageCLEFmed database, we demonstrate the effectiveness of our framework that is superior when compared to the automatic runs evaluated in 2005 on the same Medical Image Retrieval task

A graph-based approach for the retrieval of multi-modality medical images

Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images

Semantic Assisted, Multiresolution Image Retrieval in 3D Brain MR Volumes

Content Based Image Retrieval (CBIR) is an important research area in the field of multimedia information retrieval. The application of CBIR in the medical domain has been attempted before, however the use of CBIR in medical diagnostics is a daunting task. The goal of diagnostic medical image retrieval is to provide diagnostic support by displaying relevant past cases, along with proven pathologies as ground truths. Moreover, medical image retrieval can be extremely useful as a training tool for medical students and residents, follow-up studies, and for research purposes. Despite the presence of an impressive amount of research in the area of CBIR, its acceptance for mainstream and practical applications is quite limited. The research in CBIR has mostly been conducted as an academic pursuit, rather than for providing the solution to a need. For example, many researchers proposed CBIR systems where the image database consists of images belonging to a heterogeneous mixture of man-made objects and natural scenes while ignoring the practical uses of such systems. Furthermore, the intended use of CBIR systems is important in addressing the problem of "Semantic Gap". Indeed, the requirements for the semantics in an image retrieval system for pathological applications are quite different from those intended for training and education. Moreover, many researchers have underestimated the level of accuracy required for a useful and practical image retrieval system. The human eye is extremely dexterous and efficient in visual information processing; consequently, CBIR systems should be highly precise in image retrieval so as to be useful to human users. Unsurprisingly, due to these and other reasons, most of the proposed systems have not found useful real world applications. In this dissertation, an attempt is made to address the challenging problem of developing a retrieval system for medical diagnostics applications. More specifically, a system for semantic retrieval of Magnetic Resonance (MR) images in 3D brain volumes is proposed. The proposed retrieval system has a potential to be useful for clinical experts where the human eye may fail. Previously proposed systems used imprecise segmentation and feature extraction techniques, which are not suitable for precise matching requirements of the image retrieval in this application domain. This dissertation uses multiscale representation for image retrieval, which is robust against noise and MR inhomogeneity. In order to achieve a higher degree of accuracy in the presence of misalignments, an image registration based retrieval framework is developed. Additionally, to speed-up the retrieval system, a fast discrete wavelet based feature space is proposed. Further improvement in speed is achieved by semantically classifying of the human brain into various "Semantic Regions", using an SVM based machine learning approach. A novel and fast identification system is proposed for identifying a 3D volume given a 2D image slice. To this end, we used SVM output probabilities for ranking and identification of patient volumes. The proposed retrieval systems are tested not only for noise conditions but also for healthy and abnormal cases, resulting in promising retrieval performance with respect to multi-modality, accuracy, speed and robustness. This dissertation furnishes medical practitioners with a valuable set of tools for semantic retrieval of 2D images, where the human eye may fail. Specifically, the proposed retrieval algorithms provide medical practitioners with the ability to retrieve 2D MR brain images accurately and monitor the disease progression in various lobes of the human brain, with the capability to monitor the disease progression in multiple patients simultaneously. Additionally, the proposed semantic classification scheme can be extremely useful for semantic based categorization, clustering and annotation of images in MR brain databases. This research framework may evolve in a natural progression towards developing more powerful and robust retrieval systems. It also provides a foundation to researchers in semantic based retrieval systems on how to expand existing toolsets for solving retrieval problems