In this paper we design a medical image retrieval system that conations variety of type images for clinical student to learning or patient to understand his health condition. The image contains variety of type image, thus we consider global and local image features expect to describe variety of type images. We proposed a relative vector method for medical image content retrieval. The similarity...

This article describes the participation of the Image and Text Integration (ITI) group from the U.S. National Library of Medicine (NLM) in the ImageCLEF 2010 medical retrieval track. Our methods encompass a variety of techniques relating to document summarization and textand content-based image retrieval. Our text-based approaches utilize the Unified Medical Language System (UMLS) synonymy to i...

This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ kern...

OBJECTIVES To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. METHODS Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, sche...

We describe our experiments for the Image CLEF medical retrieval task. Our efforts were focused on the initial visual search. A content based approach was followed using global features that would perform well with the medical image collection. We used structure, texture, localisation and colour features that have been proven by previous experiments. The retrieval results showed that this simpl...

Voluminous 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, we propose a structured framework for designing and learning vocabularies of meaningful medical terms associated with visual appearance from image samples. These VisMed terms span a new feature spa...

This paper evaluates a system, UBMedTIRS, for retrieval of medical images. The system uses a combination of image and text features as well as mapping of free text to UMLS concepts. UBMedTIRS combines three publicly available tools: a content-based image retrieval system (GIFT), a text retrieval system (SMART), and a tool for mapping free text to UMLS concepts (MetaMap). The system is evaluated...

The purpose of this paper is to describe medical information retrieval in mobile device to access peer-reviewed information based on textual search and content based image retrieval (CBIR). CBIR consists of retrieving the most visually similar images to a given query image contained in a database of images. In medical diagnosis, the visual characteristics of an image carry diagnostic informatio...

Content based image retrieval technology has been proposed to benefit not only management of increasingly large image collection, but also to aid clinical care, biomedical research and education. In this paper, lifting scheme is proposed for content based retrieval method for diagnosis aid in medical field. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in ass...