Ensemble learning-based classification on local patches from magnetic resonance images to detect iron depositions in the brain

Iron deposition in the brain has been observed with normal aging and is associated with neurodegenerative diseases. The automated classification of brain magnetic resonance images (MRI) based on iron deposition in basal ganglia region of the brain has not been performed, to our knowledge. It is very difficult to analyse iron regions in brain using simple MRI techniques. The MRI sequence namely susceptibility weighted imaging (SWI) helps to distinguish brain iron regions. The objective of our work is to investigate the iron regions in selected areas of basal ganglia region of brain and classify MR images. The study included a total of 60 MRI images which consists of 40 subjects with iron region and 20 subjects of healthy controls. We performed Gaussian smoothing followed by construction of 40 localised patches of each MR image based on iron and normal regions. Grey level co-occurrence matrix (GLCM) features are extracted from the patches and fed to random forest (RF) classifier for patch-based classification of iron region. Training of data patch features was done by random forest classifier and the performance of classifier in terms of accuracy was measured. The experimental results show that the proposed localised patch-based approach for classification of brain iron images using random forest classifier achieved 96.25% classification accuracy in identifying normal and iron regions from brain MR sequences.