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 ...

In this paper, we propose a web-based learning tool for assisting and enhancing radiology education. Central to the learning tool is our recently developed content-based image retrieval (CBIR) system for medical image databases. In contrast to the leaming resources available through traditional radiology curricula, our leaming tool represents a more flexible, instantly updateable, and a richer ...

Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

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...

content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. the challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. hence, in this paper, an image retrieval system is introduced that provided two kind of qu...

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...

Images provide a powerful means to represent data, and many applications have as fundamental components the acquisition, processing and storing of huge amount of images. A typical example of such application is medical imaging. In hospitals working with medical images, large amounts of image data are received daily for processing, analysis and archiving. This arises the necessity of constructin...

Several approaches are proposed for retrieving images. Each of them describes image according to application domain requirements. No global approach exists to resolve retrieving image in complex domains (as medical one), in which content is multifaceted. A framework to retrieve medical images is presented. In this paper, we expose our three-dimensional approach applied to medical domain, and re...