Feature Analysis for Medical Image Modality Classifier

Nowadays, with the increase in the dreadful diseases, huge amount of database is produced in hospitals and are exponentially increasing day by day. Utilizing these medical images after efficient classification plays a major role in case based reasoning and supports in clinical decision making. Therefore, it is important to classify these images and access them accurately. A modality classifier helps to classify the medical images based on the modality. In our study, we analysed spatial and spectral features of Magnetic Resonance (MR) images and Computer Tomography (CT) scan and also the fusion of these features is performed. It is found that these modalities have different characteristics which help in classification. Initially, these images are preprocessing using the Median filter and spatial and spectral features were extracted and feature fusion is performed. The performance is evaluated using SVM and KNN classifiers based on correctness rate or accuracy.