Hybrid Appearance Based Disease Recognition of Human Brains

The magnetic resonance imaging (MRI) is a diagnostic and treatment evaluation tool which is very widely used in various areas of medicine. MRI images provide very high quality images of the brain tissue and so can be used to study the brain conditions. This research paper proposes a productive technique to classify brain MRI images. Examining the MRI brain images manually is not only slow but is also error prone. In order to both speed up the process and maintain the quality of the classification we need a very high-quality classification system. In this research work, advanced classification techniques based on the well known SIFT and Gabor features are applied on brain images. From our analysis we observed that a hybrid feature derived with SIFT and Gabor features yielded a higher accuracy than Gabor features alone.