The segmentation of the intracoronary optical coherence tomography (IVOCT) images is the basis of the plaque assessment. Calcified plaque is one of the main thrombus plaques. Accurate segmentation of calcified plaque is important to the plaque feature analysis, vulnerable plaque recognition and further coronary disease diagnosis. Based on the knowledge of imaging processing, the inner boundary of calcified plaques is clear, but the outer boundary is hard to identify because of the weak edge. This paper proposed an algorithm about calcified plaque segmentation for IVOCT. Taking the segmented vessel wall by using dynamic threshold as the region of interest, the location of calcified plaque was determined by K-means clustering to obtain the inner edge. The Local Binary Fitting (LBF) active contour model is used to solve the problem of weak edge to clarify the outer edge. Then the distribution of superficial calcification can be evaluated. Ten coronary images with typical plaques from 3 patients in our experiment were used to taking the segmentation. The processing results were compared with the clinician manual segmentation. It is indicated that the proposed algorithm could segment the plaque regions accurately. This work hopefully can be used for automatic processing the serials of IVOCT images to reduce subjectivity and divergence between different clinician and contribute to the diagnosis and treatment of coronary artery disease from IVOCT.