Local structure-based region-of-interest retrieval in brain MR images

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 improving the effectiveness in single patient data, such as finding a brain lesion and computing its size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received as much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain magnetic resonance (MR) images. We make several contributions to the domains of brain MR image analysis, and search and retrieval: 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 and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline...

• Type: Journal
• Year: 2010
• Where: TITB
• Authors: Devrim Unay, Ahmet Ekin, Radu S. Jasinschi
• Keywords: Education, Local Binary Patterns, Medical Expert, Spatial Context, TITB 2010