This paper investigates a framework for Secure Retrieval and Dissemination of Information (text and image) in Distributed and Wireless Environments (SECRET_DIDWE). Our research focuses on the evaluation and integration of Information Retrieval techniques. Text Retrieval and Content Based Image Retrieval techniques are mainly studied for use in medical environment. The framework is based on a wi...

Medical information retrieval systems help support health care experts in diagnostic and treatment decisions through the management of large amounts of clinical data. However, the heterogeneity and the ever growing amount of data produced in medical environments poses several challenges. In this paper, we propose a multimodal search interface for medical articles to provide better analysis tool...

Content-based image retrieval is used for retrieving various medical images from huge database of patients. Query for retrieval of image could be done in form of text or in form of image itself. In this paper four different methods for image retrieval are analyzed. The comparison of all these techniques is done on basis of various parameters e.g. recall, precision, sensitivity and specificity. ...

This paper presents the results of the State University of New York at Buffalo in the cross-language medical image retrieval task at CLEF 2004. Our work in image retrieval explores the combination of image and text retrieval using automatic query expansion. The system uses pseudo relevance feedback on the case descriptions associated with the top 10 images to improve ranking of images retrieved...

In the past few years, immense improvement was obtained in the field of content-based image retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted...

Content-Based Image Retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large medical image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practi...

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content–based retrieval methods for cross–language image retrieval. Four tasks were offered in the ImageCLEF track: a ad–hoc retrieval from an historic photographic collection, ad–hoc retrieval from a medical col...

This paper describes the results after using an automatic classification method to help improve the retrieval of medical images. Using a large dataset of medical images, we established links between low-level features from medical images and high-level features from textual codes of Image Retrieval for Medical Application (IRMA). This paper also explains the process and methods used to automati...

This paper discusses results and methods used to automatically classify medical images for Content Based Image Retrieval (CBIR) systems. Using a supervised learning approach, we automatically classified over 3,000 medical images according to the four facets of IRMA classification code (that's image modality, body orientation, biological system, and anatomical part). Our best results were obtain...

In this age of computers, virtually all spheres of human life including commerce, government, academics, hospitals, crime prevention, surveillance, engineering, architecture, journalism, fashion and graphic design, and historical research use images for efficient services. In the medical profession, X-rays and scanned image database are kept for diagnosis, monitoring, and research purposes. In ...