Neurodegenerative diseases are the progressive loss of structure or function of neurons in the nerve system, including death of neurons. Many neurodegenerative diseases are in modern society; Alzheimer's disease, Parkinson's disease etc. Neuroimaging is the process of producing images of the structure or function of the brain; it is used for the diagnosis of neurodegenerative diseases. The radiologist can't diagnosis neurodegenerative diseases by direct analysis because it is difficult to quantify systematic differences in the brain image. The proposed method presents a new fully automatic two-phase visual saliency model. In which the magnetic resonance images are processed and find out the neurodegenerative diseases based on the discriminative brain patterns. This method based upon bottom-up and top-down approaches. In bottom-up approach, information comes from a multiscale analysis of different image features. And the topdown approach includes learning and fusion. These learning processes are described using support vector machine (SVM) and neural networks (NN). And the classification accuracy, sensitivity, and specificity of SVM are greater than NN.

Visual Cryptography (VC) is a technique that is gaining its importance in the modern era for sharing secret images. In conventional visual cryptography schemes, secret shares are generated from secret image itself. The proposed work initially creates a cover image based on a key. Feature images are extracted from these cover images. Secret color image is divided into n shares. Each of these n shares is encrypted using the extracted feature image. It is then encoded into Quick Response code for additional security and send to the receiver. We can retrieve the secret image using k secret encrypted shares since we use (k,n) secret sharing algorithm. The method is most useful in sharing passwords in joint bank accounts.