In neurosurgery, cranial incisions during craniotomy can be recovered by cranioplasty--a surgical operation using cranial implants to repair skull defects. However, surgeons often encounter difficulties when grafting prefabricated cranial plates into defective areas, since a perfect match to the cranial incision is difficult to achieve. Previous studies using mirroring technique, surface interpolation, or deformed template had limitations in skull reconstruction to match the patient's original appearance. For this study, we utilized low-resolution and high-resolution computed tomography images from the patient to repair skull defects, whilst preserving the original shape. Since the accuracy of skull reconstruction was associated with the partial volume effects in the low-resolution images and the percentage of the skull defect in the high-resolution images, the low-resolution images with intact skull were resampled and thresholded followed by active contour model to suppress partial volume artifacts. The resulting low-resolution images were registered with the high-resolution ones, which exhibited different percentages of cranial defect, to extract the incised cranial part. Finally, mesh smoothing refined the three-dimensional model of the cranial defect. Simulation results indicate that the reconstruction was 93.94% accurate for a 20% skull material removal, and 97.76% accurate for 40% skull material removal. Experimental results demonstrate that the proposed algorithm effectively creates a customized implant, which can readily be used in cranioplasty.
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