MCBIR: Content-Based Image Retrieval algorithm for Medical image databases

We propose a novel algorithm for the retrieval of images from medical image databases by content. For the best result and efficient representation and retrieval of medical images, the attention is focused on the methodology and the content of medical images is represented by the regions and relationships between such objects or regions Image Attributes (IA) of objects. In this paper, we propose MCBIR (Content-Based Image Retrieval algorithm for Medical image databases), a novel content-based retrieval algorithm that is robust to scaling and translation of objects within an image. MCBIR employs a new model in which each image is first decomposed into regions. The similarity measurement between images is developed based on scheme that integrates properties of all the regions in the images using a regional matching. The method can answer queries by example. The efficiency and performance of the presented method has been evaluated using a dataset of about 5,000 simulated, but realistic computed tomography and magnetic resonance images (MRI) which the original images are selected from three large medical image databases. The results of our experiments show more than 93 percent success that it is satisfactory.