We present a probabilistic approach to the medical retrieval task. We experimented with the Westerveld method [1] to obtain our results for ImageCLEF. In addition to these results we describe our findings of involving a medical expert in our research. The expert helped us identifying useful image retrieval applications and reflected upon the setup of ImageCLEF’s medical task. Finally we describ...

For its first participation in the ImageCLEF campaign, the LIC2M participated in both the ad hoc task and the medical task. Using our cross-language information retrieval system and content-based image retrieval system, our goal was to perform some first experiments on merging the results of the two systems. The results show that the performance of each system highly depends on the corpus and t...

An increasing number of clinicians, researchers, educators and patients routinely search for medical information on the Internet as well as in image archives. However, image retrieval is far less understood and developed than text–based search. The ImageCLEF medical image retrieval task is an international benchmark that enables researchers to assess and compare techniques for medical image ret...

OBJECTIVE We explored two strategies for query expansion utilizing medical subject headings (MeSH) ontology to improve the effectiveness of medical image retrieval systems. In order to achieve greater effectiveness in the expansion, the search text was analyzed to identify which terms were most amenable to being expanded. DESIGN To perform the expansions we utilized the hierarchical structure...

This paper aims at finding images that are similar to a medical image example query. We propose several image features based on wavelet coefficients, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar medical images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechani...

Among the applications of computer science in the field of medicine, the processing of medical image data is playing an increasingly important role. With medical imaging techniques such as X-Ray, computer tomography, magnetic resonance imaging, and ultrasound, the amount of digital images that are produced in hospitals is increasing incredibly fast. Thus the need for systems that can provide ef...

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is “Radon barcodes” that employ binarized Rado...

Abstract: For this year our team participated in the medical image retrieval task. Most of our effort was invested in processing the collection using Metamap to assign Unified Medical Language System (UMLS) concepts to each of the images that included some associated text. This process generated metadata that was added to each image and included the UMLS concept number as well as the primary te...

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extra...

several semantic image search schemes have been recently proposed to retrieve images from the web. however, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. in this paper, we make use of context to further confine the outcome of the semantic search engines. for this purpose, we propose a hybrid search engine which uti...