Another look at automatic text-retrieval systems
Communications of the ACM
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Machine Learning
Computer and Robot Vision
Using dual cascading learning frameworks for image indexing
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Ownership protection of shape datasets with geodesic distance preservation
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Automatic medical image annotation and retrieval
Neurocomputing
Combining intra-image and inter-class semantics for consumer image retrieval
Pattern Recognition
Mammogram retrieval on similar mass lesions
Computer Methods and Programs in Biomedicine
Applying spatial distribution analysis techniques to classification of 3D medical images
Artificial Intelligence in Medicine
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Content-based image retrieval (CBIR) refers to the ability to retrieve images on the basis of image content. Given a query image, the goal of a CBIR system is to search the database and return the n most visually similar images to the query image. In'this paper, we describe an approach to CBIR for medical databases that relies on human input, machine learning and computer vision. Specifically, we apply expert-level human interaction for solving that aspect of the problem which cannot yet be automated, we use computer vision for only those aspects of the problem to which it lends itself best - image characterization - and we employ machine learning algorithms to allow the system to be adapted to new clinical domains. We present empirical results for the domain of high resolution computed tomography (HRCT) of the lung. Our results illustrate the efficacy of a human-in-the-loop approach to image characterization and the ability of our approach to adapt the retrieval process to a particular clinical domain through the application of machine learning algorithms.