There　has　been　significant　interest　in　approaches　that　utilize　local　intensity　patterns　within　patches　to　derive　features　for　disease　classification.　Several　existing　methods　explore　the　patch　relationship　between　different　subjects　for　classification.　Other　methods　utilize　the　patch　relationship　within　the　same　subject　to　aid　classification.　In　this　paper,　we　proposed　a　new　approach　to　extract　different　types　of　features　by　utilizing　the　patch　relationships　within　and　between　subjects.　Specifically,　features　are　first　extracted　by　exploiting　the　patch　relationship　between　subjects.　Then,　the　relationship　among　patches　within　the　same　subject　is　modeled　as　a　network　and　features　are　derived　from　the　constructed　network.　Finally,　these　two　different　types　of　features　are　integrated　into　one　framework　for　identifying　mild　cognitive　impairment　(MCI)　subjects　who　will　progress　to　Alzheimer’s　disease.　Using　the　standardized　ADNI　database,　the　proposed　method　can　achieve　an　area　under　the　receiver　operating　characteristic　curve　(AUC)　of　81.3　%　in　discriminating　patients　with　stable　MCI　and　progressive　MCI　in　a　10-fold　cross　validation,　demonstrating　that　the　integration　of　patch　relationship　between　and　within　subjects　can　aid　the　prediction　of　MCI　to　AD　conversion.　©　Springer　International　Publishing　Switzerland　2015.