There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | aravind M |
| Institution: | Student |
| Department: | IT |
| Country: | |
| Proposed Analysis: | Early detection of Alzheimer's disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. Alzheimer’s disease at prodromal stage is very important as it can prevent serious damage to the patient’s brain. The Alzheimer’s disease progresses slowly in three general stages: mild, moderate and severe. The proposed approach extracts the shape features of the Hippocampus region from the MRI scans and a Neural Network is used as Multi-Class Classifier for detection of various stages of Alzheimer’s disease. The Alzheimer detection and classification systems consist of four stages, namely, MRI pre-processing, Segmentation, Feature extraction, and Classification respectively. In the first stage, the main task is to eliminate the medical resonance images (MRI) noise which may cause due to light reflections or any inaccuracies in the imaging medium. The second stage, which is the stage where the region of interest is extracted (Alzheimer region). In the third stage, the features related to MRI images will be obtained and stored in an image vector to be ready for the classification process. And finally the fourth stages, where classifier will take place to specify the Alzheimer kind. |
| Additional Investigators |
