Computer-aided automated discrimination of Alzheimer's disease and its clinical progression in magnetic resonance images using hybrid clustering and game theory-based classification strategies

Early detection and identification of morphological differences in the brain is crucial for the pre-surgical planning of Alzheimer's disease treatment. Magnetic resonance imaging (MRI) can detect Alzheimer's disease as well as its severity levels in patients. An automatic segmentation of the grey matter, white matter, cerebrospinal fluid and hippocampus is required to obtain accurate volume of various brain matters. In this study, an effective segmentation and classification techniques are proposed to accurately distinguish the progress of Alzheimer's disease, mild cognitive impairment and normal control subjects. A hybrid segmentation technique is formulated with K-means clustering and graph-cut methods to perform segmentation. The clustered regions are assigned labels according to their features for the classification analysis. They are further classified as normal cognitive impaired, stable mild cognitive impaired, progressive mild cognitive impaired or Alzheimer's disease using the game theory classifier. The proposed method achieves an accuracy of about 85.5 %.