This work is focused on the modeling and development of a CBIR (Content-based image retrieval) system applied to the recovery of digital medical images of a human body, denominated M-CBIR. This model is composed on two methodologies: features extraction techniques and metric data structures. When this set of techniques is applied to the search of different human body regions, it can retrieve the most relevant similar images to a query image. A real database of medical images composed of 772 medical studies was used to compare the robustness of the extraction techniques and evaluate the performance of the system, through four different extractors. The objective of this work will result in a digital atlas of human body for medical radiological center. Finally, analysis and conclusions are also discussed.

Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the HSV color model with Gabor wavelets.