In　this　paper,　a　machine　learning　approach　was　used　for　brain　tumour　localization　on　FLAIR　scans　of　magnetic　resonance　images　(MRI).　The　multi-modal　brain　images　dataset　(BraTs　2012)　was　used,　that　is　a　skull　stripped　and　co-registered.　In　order　to　remove　the　noise,　bilateral　filtering　is　applied　and　then　texton-map　images　are　created　by　using　the　Gabor　filter　bank.　From　the　texton-map,　the　image　is　segmented　out　into　superpixel　and　then　the　low-level　features　are　extracted:　the　first　order　intensity　statistical　features　and　also　calculates　the　histogram　level　of　texton-map　at　each　superpixel　level.　There　is　a　significant　contribution　here　that　the　low　features　are　not　too　much　significant　for　the　localization　of　brain　tumour　from　MR　images,　but　we　have　to　make　them　meaningful　by　integrating　features　with　the　texton-map　images　at　the　region　level　approach.　Then　these　features　which　are　provided　later　to　classifier　for　the　prediction　of　three　classes:　background,　tumour　and　nontumour　region,　and　used　the　labels　for　computation　of　two　different　areas　(i.e.　complete　tumour　and　nontumour).　A　Leave-one-out　(LOOCV)　cross　validation　technique　is　applied　and　achieves　the　dice　overlap　score　of　88%　for　the　whole　tumour　area　localization,　which　is　similar　to　the　declared　score　in　MICCAI　BraTS　challenge.　This　brain　tumour　localization　approach　using　the　textonmap　image　based　on　superpixel　features　illustrates　the　equivalent　performance　with　other　contemporary　techniques.　Recently,　medical　hypothesis　generation　by　using　autonomous　computer　based　systems　in　disease　diagnosis　have　given　the　great　contribution　in　medical　diagnosis.