Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Modern Information Retrieval
Perceptual Metrics for Image Database Navigation
Perceptual Metrics for Image Database Navigation
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Content-Based Image Database Retrieval Using Variances of Gray Level Spatial Dependencies
MINAR '98 Proceedings of the IAPR International Workshop on Multimedia Information Analysis and Retrieval
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
EUROMED-the creation of a telemedical information society
CBMS '97 Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems (CBMS '97)
Comparing images with distance functions based on attribute interaction
Proceedings of the 2006 ACM symposium on Applied computing
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Proceedings of the 2008 ACM symposium on Applied computing
Seamlessly integrating similarity queries in SQL
Software—Practice & Experience
Content based mammography images retrieval using Ripley's K function
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Medical image retrieval based on complexity analysis
Machine Vision and Applications
Local structure-based region-of-interest retrieval in brain MR images
IEEE Transactions on Information Technology in Biomedicine
An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure
Journal of Medical Systems
Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
Journal of Medical Systems
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This work aims at supporting the retrieval and indexing of medical images by extracting and organizing intrinsic features of them, more specifically texture attributes from images. A tool for obtaining the relevant textures was implemented. This tool retrieves and classifies images using the extracted values, and allows the user to issue similarity queries. The application of the proposed method on images has given encouraging results that motivate to apply the method as a basis to more experiments, at diversified contexts. The accuracy degree obtained from the precision and recall plots was always over 90% for queries asking for similar images for up to 20% of the database.