Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artifacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT images without affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu's method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation.