Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be u...

Many clinical tasks depend upon the proper interpretation of medical images and will benefit from access and reference to similar, relevant images. In this paper, we present CBIR approaches to two clinical tasks and discuss their specific challenges. The first CBIR system retrieves anatomical regions in Computed Tomography (CT) studies of the chest and abdomen. The system can be used to provide...

In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. Imaging has occupied a huge role in the management of patients, whether hospitalized or not. This gave birth of the annotation of medical image process. The annotation is intended to image analysis and solve the problem of semantic gap. Physicians and rad...

In this paper, we provide an overview of the first workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR– CDS), which was held in conjunction with the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in 2009 in London, UK. The goal of the workshop was to bring together researchers from diverse communities including medical image analyses, ...

In the last decade, the application of content-based image retrieval (CBIR) system has raised in the medical field such as medical imaging, crime prevention. The first content-based image retrieval system was introduced in 1990s. The content-based image retrieval (CBIR) system tried to overcome the problem of retrieving images automatically from a large database based on the different features ...

An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. So effective and fast retrieval system becomes important and the need for efficient content-based image retrieval has increased tremendously. The benefits emanating from the application of content-based approaches to medical image retrieval range from clinical de...

in this paper we propose novel algorithms for retrieving dental images from databases by their contents. based on special information of dental images, for better content based dental image retrieval and representation, the image attributes are used. we propose disr (dental image segmentation and retrieval), a content-based image retrieval method which is robust to translation and scaling of th...

The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) has been to improve understanding and system capability in search for medical images. This has been done by developing a test collection that allows system-oriented evaluation of medical image retrieval systems. From 2005-2007, test collections were developed and used for ImageCLEFmed. This paper describes our recent work con...

Content Based Image Retrieval (CBIR) is an evolving topic under image processing. Retrieval on medical images plays a vital role in saving mankind. Medical Content Based Image Retrieval (MCBIR) does not stop its work in displaying similar images; it also goes one step further for diagnosis to decide the method of therapy by comparing the query image and database image. Hence we believe that, ou...

The digital image is probably the most important tools in the medicine since this is used for the diagnosis of the diseases, for drug treatment responses and also manages the diseases of patients, having very few side effects and an effective cost-effective relationship. With the wide growth of medical images database, the statistical analysis of medical images is becoming a big challenge. CBIR...