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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the CLEF 2009 medical image annotation track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Automatic annotation of X-ray images: a study on attribute selection
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Overview of the ImageCLEFmed 2006 medical retrieval and medical annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
| Hi-index | 0.00 |
Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Challenge, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation challenge. We use a direct and two ensemble classification schemes that employ local binary patterns as image descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed ensemble schemes divide the classification task into sub-problems. The first ensemble scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that ensemble annotation by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme.