Unsupervised medical image segmentation on brain MRI images using Skew Gaussian distribution

In this paper, a new medical image segmentation algorithm based on Skew Gaussian distribution is proposed. In brain images, it is necessary to classify the brain voxels into one of the 3 main tissues mainly Gray matter (GM), White matter (WM) and Cerebro Spiral fluid (CSF). Quantization of Gray & White matter is a topic of concern in neuro-degenerative disorders. Viz., Alzheimer disease and Parkinson´s diseases. Hence, it is necessary to identify the tissue more efficiently. Skew Gaussian distribution is utilized for the classification of the tissue voxels and the outputs generated are evaluated using the medical image quality metrics. Experimentation is carried out on both T₁ and T₂ weighted images.