Evaluation is crucial for the success of most research domains, and image retrieval is no exception to this. Recently, several benchmarks have been developed for visual information retrieval such as TRECVID, ImageCLEF, and ImagEval to create frameworks for evaluating image retrieval research. An important part of evaluation is the creation of a ground truth or gold standard to evaluate systems ...

The field of medicine is often cited as an area for which content-based visual retrieval holds considerable promise. To date, very few visual image retrieval systems have been used in clinical practice; the first applications of image retrieval systems in medicine are currently being developed to complement conventional text-based searches. An image retrieval system was developed and integrated...

Oregon Health & Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual ind...

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown th...

Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that c...

The richness of health-information available on-line requires the development of efficient information retrieval methods. The CISMeF heath-catalogue provides indexing and searching capabilities for healthresources. Medical images are representing a significant part of on-line medical knowledge and a valuable component of diagnosis and teaching. In this context, a combined text and image extract...

Frequently, medical queries are labeled with the best suitable image retrieval model. This manual classification is done by domain experts. In this paper, we propose a new approach to automatically learn how to classify medical queries into a set of retrieval models. First, we generate a set of class association rules which combine query features with image retrieval models. Then, we select the...

A novel scheme for efficient content based medical image retrieval, formalized according to the PANDA (Patterns for Next generation Database systems) framework. The proposed scheme involves low-level feature extraction from image regions followed by clustering of the feature space to form higher-level patterns. The component of each pattern include a cluster representation and a measure of qual...

In this article, we present the algorithms and results of our participation in the medical image annotation and retrieval tasks of ImageCLEFmed 2006. We exploit both global features and local features to describe medical images in the annotation task. We examine different kinds global features and extract the most descriptive ones, which effectively capture the intensity, texture and shape char...

age data is rapidly expanding in quantity and heterogeneity, and there is an increasing trend towards the formation of archives adequate to support diagnostics and preventive medicine. Exploration, exploitation, and consolidation of the immense image collections require tools to access structurally different data for research, diagnostics and teaching. Currently, image data is linked to textual...