Abstract: Objective To establish a fast adaptive active contour model based on local gray difference for parotid duct image segmentation. Methods On the basis of the LBF model, we added the mean difference of the local gray scale inside and outside the contour as the energy term of the driving evolution curve, and the local gray-scale variance difference was used to replace λ1 and λ2 as the control term of the energy parameter value. Two local similarity factors of different neighborhood sizes were introduced to correct the effects of image gray unevenness and boundary blur to improve the segmentation efficiency. Results During image segmentation, this algorithm allowed for adaptive adjustment of the evolution direction, velocity and the energy weight of the internal and external regions according to the difference of gray mean and variance between the internal and external regions. This algorithm was also capable of detecting the actual boundary in a complex gradient boundary region, thus enabling the evolution curve to approach the target boundary quickly and accurately. Conclusion The proposed algorithm is superior to the existing segmentation algorithms and allows fast and accurate segmentation of the parotid duct with well-preserved image details.