Brain Tissue Segmentation from MRI Scans using Digital Image Processing

The brain is one of the most unexplored parts of the human body and its complex and delicate structure has scientists worldwide looking for answers about its intricacies. Also, since the advent of deep learning techniques as well as imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), analysis of the brain has become the most intriguing and researched area in healthcare, as well as deep learning sectors of artificial intelligence. The extraction of the brain from the skull forms the basis and source of study for the prediction of age-related diseases like Alzheimer’s disease. Nowadays With the increase in life expectancy and the extravagant use of technology, it is evident that neurological diseases are on the rise. Therefore, it becomes essential that such diseases can be diagnosed at an early stage of their occurrence. The proposed work presents brain extraction from the skull with the help of three basic steps, data acquisition, pre-processing, and largest connected component extraction using contours. The data acquired is using the ADNI data repository. The preprocessing step involves contrast enhancement using CLAHE, binarization of the scan using Otsu thresholding, and de-blurring so that the noise that might be there in the scans can be removed and a clear image of the brain is available for further processing and classification of Alzheimer’s disease.