Content based mammogram image retrieval based on the multiclass visual problem

Since expertise elicited from past resolved cases plays an important role in medical application and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists, In this article we proposed a new framework to retrieve visually similar images from a large database, in which visual relevance is regarded as much as the semantic category similarity, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM features from different resolutions then after reducing feature space we used error correcting codes in order to untwist the existing multiclass visual problem introduced in preceding parts of the article, we implemented proposed algorithm on the 1000 mammograms provided by the DDSM database which consist of 2500 studies and their annotations provided by specialists.