This paper presents an improved method based on sequential learning and prior shape information for detecting the external elastic membrane (EEM) in intravascular ultrasound (IVUS) images to overcome the problems of interference factors

such as calcified plaque and acoustic shadow. Multi-class multi-scale stacked sequential learning was applied to divide an IVUS image into seven tissues. Subsequently

critical points on the external elastic membrane border were selected based on the classification results and the prior shape information of the vessel. Finally

a snake model combined with gradient and phase information of the IVUS images was used to obtain the final external elastic membrane border. In the experiments

the algorithm was implemented on 153 typical IVUS images from 22 in vivo clinical IVUS sequences. Statistical results showed that the average JACC measure of EEM borders detected by the algorithm was 88.5%; thus

the algorithm could meet clinical demands

and its performance was better than those of algorithms in recent studies in China. The proposed automatic algorithm is simple and effective. Compared with existing Chinese algorithms

it has improved capability of recognizing calcified plaque

fibrous plaque

and acoustic shadow and can be applied to IVUS images with calcified plaque

fibrous plaque

or catheter eccentricity.