Quantitatively　assessing　the　medial　temporal　lobe　(MTL)　structures　atrophy　is　vital　for　early　diagnosis　of　Alzheimers　disease　(AD)　and　accurately　tracking　of　the　disease　progression.　Morphometry　characteristics　such　as　gray　matter　volume　(GMV)　and　cortical　thickness　have　been　proved　to　be　valuable　measurements　of　brain　atrophy.　In　this　study,　we　proposed　a　morphometric　MRI　analysis　based　method　to　explore　the　cross-sectional　differences　and　longitudinal　changes　of　GMV　and　cortical　thickness　in　patients　with　AD,　MCI　(mild　cognitive　impairment)　and　the　normal　elderly.　High　resolution　3D　MRI　data　was　obtained　from　ADNI　database.　SPM8　plus　DARTEL　was　carried　out　for　data　preprocessing.　Two　kinds　of　z-score　map　were　calculated　to,　respectively,　reflect　the　GMV　and　cortical　thickness　decline　compared　with　age-matched　normal　control　database.　A　volume　of　interest　(VOI)　covering　MTL　structures　was　defined　by　group　comparison.　Within　this　VOI,　GMV,　and　cortical　thickness　decline　indicators　were,　respectively,　defined　as　the　mean　of　the　negative　z-scores　and　the　sum　of　the　normalized　negative　z-scores　of　the　corresponding　z-score　map.　KruskalWallis　test　was　applied　to　statistically　identify　group　wise　differences　of　the　indicators.　Support　vector　machines　(SVM)　based　prediction　was　performed　with　a　leave-one-out　cross-validation　design　to　evaluate　the　predictive　accuracies　of　the　indicators.　Linear　least　squares　estimation　was　utilized　to　assess　the　changing　rate　of　the　indicators　for　the　three　groups.　Cross-sectional　comparison　of　the　baseline　decline　indicators　revealed　that　the　GMV　and　cortical　thickness　decline　were　more　serious　from　NC,　MCI　to　AD,　with　statistic　significance.　Using　a　multi-region　based　SVM　model　with　the　two　indicators,　the　discrimination　accuracy　between　AD　and　NC,　MCI　and　NC,　AD　and　MCI　was　92.7,　91.7,　and　78.4%,　respectively.　For　three-way　prediction,　the　accuracy　was　74.6%.　Furthermore,　the　proposed　two　indicators　could　also　identify　the　atrophy　rate　differences　among　the　three　groups　in　longitudinal　analysis.　The　proposed　method　could　serve　as　an　automatic　and　time-sparing　approach　for　early　diagnosis　and　tracking　the　progression　of　AD.