The Multi-centre Epilepsy Lesion Detection (MELD) project presents a methodology to harmonise a large heterogenous cohort of surface-based MRI data. Structural features were extracted from T1w and FLAIR images and pre-processed to reduce systematic site, scanner, and age-specific biases. The harmonised dataset enabled the characterisation of subtle radiological markers of focal cortical dysplasia (FCD), a cortical abnormality causing drug-resistant epilepsy. Machine-learning algorithms trained on the harmonised dataset improved the classification of FCD histopathologies. With open-source protocols and code, the MELD preprocessing pipeline offers a reproducible method to prepare large heterogeneous datasets for statistical analysis and machine-learning tasks.