In the process of image coding, external noises impact a lot in processing efficiency. In the application of medical image processing, this effect is more, important due to its finer content details. It is required to minimize the noise effect with preserving the image content information, without losing the image generality. Towards the objective of image denoising, in this work, a dynamic blo...

Medical imaging is a precious and essential tool in healthcare systems, helps the physicians to emanate good quality of treatment. The advancement in medical Technology has resulted in a huge number of medical images which are stored in a database for future purpose. It is very imperative to build an effective retrieval system which browse through entire database in diagnosing the various disea...

We present the methods we applied in the four different tasks of the ImageCLEF 2007 content-based image retrieval evaluation. We participated in all four tasks using a variety of methods. Global and local image descriptors are applied using nearest neighbour search for the medical and photo retrieval tasks and discriminative models for the object retrieval and the medical automatic annotation t...

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...

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vec...

MedGIFT is a medical imaging research group of the Geneva University Hospitals and the University of Geneva, Switzerland. Since 2004, the medGIFT group has participated in the ImageCLEF benchmark each year, focusing mainly on the medical imaging tasks. For the medical image retrieval task, two existing retrieval engines were used: the GNU Image Finding Tool (GIFT) as image retrieval engine and ...

Nowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, t...

We present a mobile image matching application for Android mobile phone called Virtual Telescope to assist users in viewing distant buildings. The application automatically augments photo's field of view by retrieving replacement images and satellite images from the Internet. The application adopts content based image retrieval method and allow user two choices of feature descriptor, namely Sca...

Compressed medical images are widely used in clinical teaching and diagnosis. To save computing cost and storage spaces, research on compressed medical image retrieval is meaningful. This paper proposes a novel medical image retrieval scheme in DCT (Discrete Cosine Transformation) compressed domain. We firstly obtain the multi resolution image by reorganizing the DCT coefficients, then, segment...