Abstract
The objective of Content-based image retrieval (CBIR) in medical field is to permit radiologists to retrieve images of similar features that lead to similar diagnosis as the input image. This is different from other fields where the objective is to find the nearest images from the same category or match a part of an image. Therefore, such techniques cannot be directly applied in the medical field. In this study, a modified wavelet-based matching technique is introduced that is more robust to motion, noise, and brightness changes within the image. Also, we propose a description-based technique in which a semantic net is built in which each node represents a specific region and its spatial relation to other regions in the image. This semantic net can be considered as a hierarchical relationship tree with links between the nodes in the same level to describe the geographic relations between these nodes. Nodes contain region-specific features such as the moments of region boundaries in addition to local textural features. In the matching phase, the semantic net is built for the input image then used in the matching process. The matching process starts from the highest level in the hierarchical relationship tree for fast convergence.