Fast and accurate tissue extraction of human brain is an ongoing challenge. Two principal factors make this task difficult:(1) quality of the reconstructed images, (2) accuracy and availability of the segmentation masks. In this proposed method, firstly, a supervised deep learning framework is used for the reconstruction of solution image from the acquired uniformly under-sampled human brain data. Later, an unsupervised clustering approach i.e. k-means is used for the extraction of specific tissue from the reconstructed image. Experimental results show a successful extraction of cerebrospinal fluid (CSF), white matter and grey matter from the human brain image.