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...

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...

In this paper, we use Support Vector Machine (SVM) to learn image feature characteristics for assisting the task of image classification. The ImageCLEF 2005 evaluation offers a superior test bed for medical image content retrieval. Several image visual features (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 1,000 tes...

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...

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representi...

This research work is to develop an efficient and powerful medical search engine to classify and search the radiographic medical images. It focuses on bag of visual words image representation and a similarity matching technique to represent match and retrieve the similar images. This work addresses the issues in content based image retrieval for medical images. In this system can handles differ...

This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image retrieval track. Our system uses Lemur toolkit for text retrieval. The main objective is to carry out retrieving medical image depending on associated image text. We experimented with different text features such as article title, image caption ...

In medical field, digital images are produced in ever increasing quantities and used for diagnostics and therapy. The swift expansion of digital medical images has enforced the requirement of efficient Content-based image retrieval system for retrieving medical images that are visually similar to query image. Such systems provide great assistance to doctors in clinical care and research. In thi...