In this paper, we present a content-based image retrieval system designed to retrieve mammographies from large medical image databases. The system is developed based on breast density and lesion, according to the categories defined by the American College of Radiology, and is integrated to the database of the Image Retrieval in Medical Applications (IRMA) project, that provides images with clas...

In this article, a fabulous method for database retrieval is proposed.  The multi-resolution modified wavelet transform for each of image is computed and the standard deviation and average are utilized as the textural features. Then, the proposed modified bit-based color histogram and edge detectors were utilized to define the high level features. A feedback-based dynamic weighting of shap...

Automatic medical image classification refers to assigning an medical image into a class, among a number of image categories. Due to computational complexity, it is an important task in the content based image retrieval (CBIR). In this paper, we propose a method for classification and retrieval of medical images using multi-feature extraction method. Here, edge and patch based methods are used ...

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical pr...

In medical imaging DICOM (Digital Imaging and Communications in Medicine) format is the most commonly used format. Various medical imaging sources generate images in this format, which are collected in large database repository [1]. Various modalities of medical images such as CT scan, XRay, Ultrasound, Pathology, MRI, Microscopy, etc [2] are used to collect these images. From the analysis of t...

introduction: for many years, ontologies have been available in many different forms, for example: classification schema, thesauruses, authorized vocabularies, terminologies and dictionaries. terminology and knowledge resources are essential components of interoperability among disparate systems. so, this paper considered the ontology of umls (which was prepared by national library medicine of ...

The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image retrieval method for a special medical image retrieval problem where the images in the retrieval database can be annotated into one of the pre-defined labels. Even more, a user may query the database with an image that is close to but not exactly wha...

Large efforts have been made for general applications of content-based image retrieval (CBIR). Established CBIRsystems globally evaluate color, texture, and also shape for retrieval. In medical imaging, local image characteristics are fundamental for image interpretation, which is based on a large amount of a-priori knowledge. Therefore, CBIR is rather seldom applied to medical images. Successf...

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods. key words: C...