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The tortuosity assessment of vessels and nerve fibres in ophthalmic images has drawn substantial attention, for its potentials in assisting various medical diagnoses. Numerous morphological tortuosity measures have been leveraged to quantify tortuosity from various perspectives, which warrants the simultaneous use of multiple measures with an aim to produce a robust and accurate assessment. This paper proposes an approach for the automated assessment of curvilinear structures' tortuosity. Starting with the generation of clusters of tortuosity density for each individual measure, labelled fuzzy sets are then extracted that enhances the readability of subsequent operations. Finally, results from multiple measures are aggregated by a nearest neighbour guided approach where weights are generated in a data-driven manner to explain the derived aggregations. Experimental results on both ophthalmic vessel and nerve images demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.