With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF a...

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems....

More number of techniques about medical image retrieval is used in medical domain. In our Content Based Medical Image Retrieval System, high level semantics of an image is very important. Regarding with this issue, we proposed a method using co-occurrences matrix to extract texture features and Canny Edge Detection is to extract shape features. Then K-means clustering algorithm and Euclidean di...

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 ...

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...

This paper presents the results of the State University of New York at Buffalo in the Cross Language Evaluation Forum 2005 (CLEF 2005). We participated in monolingual Portuguese, bilingual EnglishPortuguese and in the medical image retrieval tasks. We used the SMART retrieval system for text retrieval in the mono and bilingual retrieval tasks on Portuguese documents. The main goal of this part ...