In　recent　years,　researchers　have　increased　attentions　to　the　morphological　brain　network,　which　is　generally　constructed　by　measuring　the　mathematical　correlation　across　regions　using　a　certain　morphometric　feature,　such　as　regional　cortical　thickness　and　voxel　intensity.　However,　cerebral　structure　can　be　characterized　by　various　factors,　such　as　regional　volume,　surface　area,　and　curvature.　Moreover,　most　of　the　morphological　brain　networks　are　population-based,　which　has　limitations　in　the　investigations　of　individual　difference　and　clinical　applications.　Hence,　we　have　extended　previous　studies　by　proposing　a　novel　method　for　realizing　the　construction　of　an　individual-based　morphological　brain　network　through　a　combination　of　multiple　morphometric　features.　In　particular,　interregional　connections　are　estimated　using　our　newly　introduced　feature　vectors,　namely,　the　Pearson　correlation　coefficient　of　the　concatenation　of　seven　morphometric　features.　Experiments　were　performed　on　a　healthy　cohort　of　55　subjects　(24　males　aged　from　20　to　29　and　31　females　aged　from　20　to　28)　each　scanned　twice,　and　reproducibility　was　evaluated　through　test-retest　reliability.　The　robustness　of　morphometric　features　was　measured　firstly　to　select　the　more　reproducible　features　to　form　the　connectomes.　Then　the　topological　properties　were　analyzed　and　compared　with　previous　reports　of　different　modalities.　Small-worldness　was　observed　in　all　the　subjects　at　the　range　of　the　entire　network　sparsity　(20-40%),　and　configurations　were　comparable　with　previous　findings　at　the　sparsity　of　23%.　The　spatial　distributions　of　the　hub　were　found　to　be　significantly　influenced　by　the　individual　variances,　and　the　hubs　obtained　by　averaging　across　subjects　and　sparsities　showed　correspondence　with　previous　reports.　The　intraclass　coefficient　of　graphic　properties　(clustering　coefficient　=　0.83,　characteristic　path　length　=　0.81,　betweenness　centrality　=　0.78)　indicates　the　robustness　of　the　present　method.　Results　demonstrate　that　the　multiple　morphometric　features　can　be　applied　to　form　a　rational　reproducible　individual-based　morphological　brain　network.