A Knowledge-Based Approach for Retrieving Images by Content
IEEE Transactions on Knowledge and Data Engineering
Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Color Histograms for Content-Based Image Retrieval
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Registration Using Natural Features for Augmented Reality Systems
IEEE Transactions on Visualization and Computer Graphics
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
MultiWaveMed: a system for medical image retrieval through wavelets transformations
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Retrieval by content of medical images using texture for tissue identification
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
IEEE Transactions on Information Technology in Biomedicine
Hierarchical spatial matching for medical image retrieval
MMAR '11 Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval
Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
Journal of Medical Systems
A novel model for medical image similarity retrieval
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.