Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Overview of the ImageCLEF 2007 Object Retrieval Task
Advances in Multilingual and Multimodal Information Retrieval
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
MedFMI-SiR: a powerful DBMS solution for large-scale medical image retrieval
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Predicting modality from text queries for medical image retrieval
MMAR '11 Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval
Building implicit dictionaries based on extreme random clustering for modality recognition
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Role of domain knowledge in developing user-centered medical-image indexing
Journal of the American Society for Information Science and Technology
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Effective medical image retrieval can be useful in the clinical care of patients, education and research. Traditionally, image retrieval systems have been text-based, relying on the annotations or captions associated with the images. Although text-based information retrieval methods are mature and well researched, they are limited by the quality and availability of the annotations associated with the images. Advances in computer vision have led to methods for using the image itself as the search entity. However, the success of purely content-based techniques, when applied to a diverse set of clinical images, has been somewhat limited and these systems have not had much success in the medical domain. On the other hand, as demonstrated in recent years, a combination of text-based and content-based image retrieval techniques can achieve improved retrieval performance if combined effectively. There are many approaches to multimodal retrieval including early and late fusion of weighed results from the different search engines. In this work, we use automatic annotation based on visual attributes to label images as part of the indexing process and the subsequently use these labels to filter or reorder the results during the retrieval process. Labels for medical images can be categorized along three dimensions - imaging modality, anatomical location and image finding or pathology. Our previous research has indicated that the imaging modality is most easily identified using visual techniques whereas the caption or textual annotation frequently contains the finding or pathological information about the image. Thus, it is best to use visual methods to filter the modality and occasionally, anatomy while it is better to use the textual annotation to find the finding of interest. We have created a modality classifier for the weakly labeled images in our collection using a novel approach that combines affinity propagation for the selection of class exemplars, textons and patch-based descriptors as visual features and a NaiveBayes Nearest Neighbor technique for the classification of modality using visual features. We demonstrate significant improvement in precision attained using this technique for the ImageCLEF medical retrieval task 2009 using both our textual runs as well as runs from all participants in 2009