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This work tests several methods for implementation of automatic segmentation of Intravascular Optical Coherence Tomography (IVOCT) images, then provides a considerable approach. This method aims to provide convenience to clinical diagnosis. This work is based on machine learning and exams performance of different classification means. Meanwhile, different sets of features extracted from particular tissues are added in with the purpose to improve the accuracy of the automatic segmentation method of IVOCT images.